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Sample records for cancer model fitting

  1. Cardiorespiratory fitness and muscle strength in pancreatic cancer patients.

    Science.gov (United States)

    Clauss, Dorothea; Tjaden, Christine; Hackert, Thilo; Schneider, Lutz; Ulrich, Cornelia M; Wiskemann, Joachim; Steindorf, Karen

    2017-09-01

    Cancer patients frequently experience reduced physical fitness due to the disease itself as well as treatment-related side effects. However, studies on physical fitness in pancreatic cancer patients are missing. Therefore, we assessed cardiorespiratory fitness and muscle strength of pancreatic cancer patients. We included 65 pancreatic cancer patients, mostly after surgical resection. Cardiorespiratory fitness was assessed using cardiopulmonary exercise testing (CPET) and 6-min walk test (6MWT). Hand-held dynamometry was used to evaluate isometric muscle strength. Physical fitness values were compared to reference values of a healthy population. Associations between sociodemographic and clinical variables with patients' physical fitness were analyzed using multiple regression models. Cardiorespiratory fitness (VO 2 peak, 20.5 ± 6.9 ml/min/kg) was significantly lower (-24%) compared to healthy reference values. In the 6MWT pancreatic cancer patients nearly reached predicted values (555 vs. 562 m). Maximal voluntary isometric contraction (MVIC) of the upper (-4.3%) and lower extremities (-13.8%) were significantly lower compared to reference values. Overall differences were larger in men than those in women. Participating in regular exercise in the year before diagnosis was associated with greater VO 2 peak (p fitness with regard to both cardiorespiratory function and isometric muscle strength, already in the early treatment phase (median 95 days after surgical resection). Our findings underline the need to investigate exercise training in pancreatic cancer patients to counteract the loss of physical fitness.

  2. Extracting Fitness Relationships and Oncogenic Patterns among Driver Genes in Cancer.

    Science.gov (United States)

    Zhang, Xindong; Gao, Lin; Jia, Songwei

    2017-12-25

    Driver mutation provides fitness advantage to cancer cells, the accumulation of which increases the fitness of cancer cells and accelerates cancer progression. This work seeks to extract patterns accumulated by driver genes ("fitness relationships") in tumorigenesis. We introduce a network-based method for extracting the fitness relationships of driver genes by modeling the network properties of the "fitness" of cancer cells. Colon adenocarcinoma (COAD) and skin cutaneous malignant melanoma (SKCM) are employed as case studies. Consistent results derived from different background networks suggest the reliability of the identified fitness relationships. Additionally co-occurrence analysis and pathway analysis reveal the functional significance of the fitness relationships with signaling transduction. In addition, a subset of driver genes called the "fitness core" is recognized for each case. Further analyses indicate the functional importance of the fitness core in carcinogenesis, and provide potential therapeutic opportunities in medicinal intervention. Fitness relationships characterize the functional continuity among driver genes in carcinogenesis, and suggest new insights in understanding the oncogenic mechanisms of cancers, as well as providing guiding information for medicinal intervention.

  3. The bystander effect model of Brenner and Sachs fitted to lung cancer data in 11 cohorts of underground miners, and equivalence of fit of a linear relative risk model with adjustment for attained age and age at exposure

    International Nuclear Information System (INIS)

    Little, M P

    2004-01-01

    Bystander effects following exposure to α-particles have been observed in many experimental systems, and imply that linearly extrapolating low dose risks from high dose data might materially underestimate risk. Brenner and Sachs (2002 Int. J. Radiat. Biol. 78 593-604; 2003 Health Phys. 85 103-8) have recently proposed a model of the bystander effect which they use to explain the inverse dose rate effect observed for lung cancer in underground miners exposed to radon daughters. In this paper we fit the model of the bystander effect proposed by Brenner and Sachs to 11 cohorts of underground miners, taking account of the covariance structure of the data and the period of latency between the development of the first pre-malignant cell and clinically overt cancer. We also fitted a simple linear relative risk model, with adjustment for age at exposure and attained age. The methods that we use for fitting both models are different from those used by Brenner and Sachs, in particular taking account of the covariance structure, which they did not, and omitting certain unjustifiable adjustments to the miner data. The fit of the original model of Brenner and Sachs (with 0 y period of latency) is generally poor, although it is much improved by assuming a 5 or 6 y period of latency from the first appearance of a pre-malignant cell to cancer. The fit of this latter model is equivalent to that of a linear relative risk model with adjustment for age at exposure and attained age. In particular, both models are capable of describing the observed inverse dose rate effect in this data set

  4. The effect of cardiorespiratory fitness and obesity on cancer mortality in women and men.

    Science.gov (United States)

    Evenson, Kelly R; Stevens, June; Cai, Jianwen; Thomas, Ratna; Thomas, Olivia

    2003-02-01

    The purpose of this study was to determine the independent and combined effects of cardiorespiratory fitness and obesity on all-cause cancer mortality for women and men. Using the Lipids Research Clinics Prevalence Study, we examined the relationship of fitness and obesity on cancer mortality among 2585 women and 2890 men followed from 1972-1976 to 1998. Cardiorespiratory fitness was measured using a treadmill test and obesity was assessed using body mass index (BMI) calculated from measured height and weight. Gender-specific hazard ratios (HR) were calculated from proportional hazard models, which included covariates for age, education, smoking, alcohol intake, Keys score, and menopause (women only). Adjusted cancer mortality was significantly lower in the most fit quintile relative to the other four quintiles for men (HR = 0.47; 95% CI, 0.27-0.81) but not for women (HR = 0.84; 95% CI, 0.52-1.36). Adjusted cancer mortality was significantly higher in the highest BMI quintile relative to the other four BMI quintiles for women (HR = 1.49; 95% CI, 1.06-2.09) but not for men (HR = 1.05; 95% CI, 0.77-1.43). Further adjustment for BMI on fitness and adjustment for fitness on BMI did not meaningfully change the HR. There were no significant interactions between fitness and obesity in predicting cancer mortality for either women or men. In this study, high fitness was a stronger predictor of cancer mortality in men, whereas high BMI was a stronger predictor of cancer mortality in women.

  5. Cardiorespiratory fitness and death from cancer

    DEFF Research Database (Denmark)

    Jensen, Magnus Thorsten; Holtermann, Andreas; Bay, Hans

    2017-01-01

    OBJECTIVES: Poor cardiorespiratory fitness (CRF) is associated with death from cancer. If follow-up time is short, this association may be confounded by subclinical disease already present at the time of CRF assessment. This study investigates the association between CRF and death from cancer...

  6. Tanning Shade Gradations of Models in Mainstream Fitness and Muscle Enthusiast Magazines: Implications for Skin Cancer Prevention in Men.

    Science.gov (United States)

    Basch, Corey H; Hillyer, Grace Clarke; Ethan, Danna; Berdnik, Alyssa; Basch, Charles E

    2015-07-01

    Tanned skin has been associated with perceptions of fitness and social desirability. Portrayal of models in magazines may reflect and perpetuate these perceptions. Limited research has investigated tanning shade gradations of models in men's versus women's fitness and muscle enthusiast magazines. Such findings are relevant in light of increased incidence and prevalence of melanoma in the United States. This study evaluated and compared tanning shade gradations of adult Caucasian male and female model images in mainstream fitness and muscle enthusiast magazines. Sixty-nine U.S. magazine issues (spring and summer, 2013) were utilized. Two independent reviewers rated tanning shade gradations of adult Caucasian male and female model images on magazines' covers, advertisements, and feature articles. Shade gradations were assessed using stock photographs of Caucasian models with varying levels of tanned skin on an 8-shade scale. A total of 4,683 images were evaluated. Darkest tanning shades were found among males in muscle enthusiast magazines and lightest among females in women's mainstream fitness magazines. By gender, male model images were 54% more likely to portray a darker tanning shade. In this study, images in men's (vs. women's) fitness and muscle enthusiast magazines portrayed Caucasian models with darker skin shades. Despite these magazines' fitness-related messages, pro-tanning images may promote attitudes and behaviors associated with higher skin cancer risk. To date, this is the first study to explore tanning shades in men's magazines of these genres. Further research is necessary to identify effects of exposure to these images among male readers. © The Author(s) 2014.

  7. Association of changes in fitness and body composition with cancer mortality in men.

    Science.gov (United States)

    Zhang, Peizhen; Sui, Xuemei; Hand, Gregory A; Hébert, James R; Blair, Steven N

    2014-07-01

    Both baseline cardiorespiratory fitness and adiposity predict the risk of cancer mortality. However, the effects of changes in these two factors over time have not been evaluated thoroughly. The aim of this study was to examine the independent and joint associations of changes in cardiorespiratory fitness and body composition on cancer mortality. The cohort consisted of 13,930 men (initially cancer-free) with two or more medical examinations from 1974 to 2002. Cardiorespiratory fitness was assessed by a maximal treadmill exercise test, and body composition was expressed by body mass index (BMI) and percent body fat. Changes in cardiorespiratory fitness and body composition between the baseline and the last examination were classified into loss, stable, and gain groups. There were 386 deaths from cancer during an average of 12.5 yr of follow-up. After adjusting for possible confounders and BMI, change hazard ratios (95% confidence intervals) of cancer mortality were 0.74 (0.57-0.96) for stable fitness and 0.74 (0.56-0.98) for fitness gain. Inverse dose-response relationships were observed between changes in maximal METs and cancer mortality (P for linear trend = 0.05). Neither BMI change nor percent body fat change was associated with cancer mortality after adjusting for possible confounders and maximal METs change. In the joint analyses, men who became less fit had a higher risk of cancer mortality (P for linear trend = 0.03) compared with those who became more fit, regardless of BMI change levels. Being unfit or losing cardiorespiratory fitness over time was found to predict cancer mortality in men. Improving or maintaining adequate levels of cardiorespiratory fitness appears to be important for decreasing cancer mortality in men.

  8. Fitting PAC spectra with stochastic models: PolyPacFit

    Energy Technology Data Exchange (ETDEWEB)

    Zacate, M. O., E-mail: zacatem1@nku.edu [Northern Kentucky University, Department of Physics and Geology (United States); Evenson, W. E. [Utah Valley University, College of Science and Health (United States); Newhouse, R.; Collins, G. S. [Washington State University, Department of Physics and Astronomy (United States)

    2010-04-15

    PolyPacFit is an advanced fitting program for time-differential perturbed angular correlation (PAC) spectroscopy. It incorporates stochastic models and provides robust options for customization of fits. Notable features of the program include platform independence and support for (1) fits to stochastic models of hyperfine interactions, (2) user-defined constraints among model parameters, (3) fits to multiple spectra simultaneously, and (4) any spin nuclear probe.

  9. Relationship between physical activity, disability, and physical fitness profile in sedentary Latina breast cancer survivors.

    Science.gov (United States)

    Ortiz, Alexis; Tirado, Maribel; Hughes, Daniel C; Gonzalez, Velda; Song, JaeJoon; Mama, Scherezade K; Basen-Engquist, Karen

    2018-10-01

    To report baseline data from a physical activity (PA) intervention for Latina breast cancer survivors, and assess the relationship between PA, fitness, and disability. Eighty-nine Latina breast cancer survivors from San Juan, PR and Houston, TX (age: 55.4 ± 9.9 years; BMI: 29.87 ± 5.62 kg/m 2 ; ≥ 3 months post-treatment) participated in this study. At baseline participants completed fitness testing (six-minute walk test [6MWT], 30-second sit-stand; grip strength, lower and upper extremity and low back strength, shoulder range of motion, balance testing), and assessment of physical activity (PA) and disability. PA was assessed using the International Physical Activity Questionnaire (IPAQ). A subsample (n = 27) received an accelerometer to compare objective versus self-reported PA. Participants exhibited low PA (M = 76.5 MET·minutes/week; SD = 183.4), poor fitness (6MWT M = 436.4 meters, SD = 99.1; 30s sit-stand, M = 11.6 stands, SD = 3.1), and no detectable disability. In an adjusted model lower extremity fitness was associated with PA, with a one repetition increase in sit-to-stand associated with 49 additional minutes of self-reported PA plus walking per week. The correlation between IPAQ moderate-vigorous PA and accelerometer was 0.38 (p = 0.047). Latina breast cancer survivors have low physical activity and fitness levels that increase their risk of disability, cardiometabolic comorbidities, and potential cancer recurrence.

  10. Niche Inheritance: A Cooperative Pathway to Enhance Cancer Cell Fitness Through Ecosystem Engineering

    Science.gov (United States)

    Yang, Kimberline R; Mooney, Steven M; Zarif, Jelani C; Coffey, Donald S; Taichman, Russell S; Pienta, Kenneth J

    2014-01-01

    Cancer cells can be described as an invasive species that is able to establish itself in a new environment. The concept of niche construction can be utilized to describe the process by which cancer cells terraform their environment, thereby engineering an ecosystem that promotes the genetic fitness of the species. Ecological dispersion theory can then be utilized to describe and model the steps and barriers involved in a successful diaspora as the cancer cells leave the original host organ and migrate to new host organs to successfully establish a new metastatic community. These ecological concepts can be further utilized to define new diagnostic and therapeutic areas for lethal cancers. 115: 1478–1485, 2014. © 2014 Wiley Periodicals, Inc. PMID:24700698

  11. Impact of parental cancer on IQ, stress resilience, and physical fitness in young men

    Directory of Open Access Journals (Sweden)

    Chen R

    2018-05-01

    Full Text Available Ruoqing Chen,1 Katja Fall,1,2 Kamila Czene,1 Beatrice Kennedy,2 Unnur Valdimarsdóttir,1,3,4 Fang Fang1 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 2Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden; 3Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland; 4Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA Background: A parental cancer diagnosis is a stressful life event, potentially leading to increased risks of mental and physical problems among children. This study aimed to investigate the associations of parental cancer with IQ, stress resilience, and physical fitness of the affected men during early adulthood. Materials and methods: In this Swedish population-based study, we included 465,249 men born during 1973–1983 who underwent the military conscription examination around the age of 18 years. We identified cancer diagnoses among the parents of these men from the Cancer Register. IQ, stress resilience, and physical fitness of the men were assessed at the time of conscription and categorized into three levels: low, moderate, and high (reference category. We used multinomial logistic regression to assess the studied associations. Results: Overall, parental cancer was associated with higher risks of low stress resilience (relative risk ratio [RRR]: 1.09 [95% confidence interval (CI 1.04–1.15] and low physical fitness (RRR: 1.12 [95% CI 1.05–1.19]. Stronger associations were observed for parental cancer with a poor expected prognosis (low stress resilience: RRR: 1.59 [95% CI 1.31–1.94]; low physical fitness: RRR: 1.45 [95% CI 1.14–1.85] and for parental death after cancer diagnosis (low stress resilience: RRR: 1.29 [95% CI 1.16–1.43]; low physical fitness: RRR: 1.40 [95% CI 1.23–1.59]. Although there was no overall association between parental

  12. A Stepwise Fitting Procedure for automated fitting of Ecopath with Ecosim models

    Directory of Open Access Journals (Sweden)

    Erin Scott

    2016-01-01

    Full Text Available The Stepwise Fitting Procedure automates testing of alternative hypotheses used for fitting Ecopath with Ecosim (EwE models to observation reference data (Mackinson et al. 2009. The calibration of EwE model predictions to observed data is important to evaluate any model that will be used for ecosystem based management. Thus far, the model fitting procedure in EwE has been carried out manually: a repetitive task involving setting >1000 specific individual searches to find the statistically ‘best fit’ model. The novel fitting procedure automates the manual procedure therefore producing accurate results and lets the modeller concentrate on investigating the ‘best fit’ model for ecological accuracy.

  13. The Effect of Cardiorespiratory Fitness and Obesity on Cancer Mortality in Women and Men.

    Science.gov (United States)

    Evenson, Kelly R.; Stevens, June; Cai, Jianwen; Thomas, Ratna; Thomas, Olivia

    2003-01-01

    Investigated the independent and combined effects of cardiorespiratory fitness and obesity on all-cause cancer mortality for women and men. Data from the Lipids Research Clinics Prevalence Study indicated that higher fitness level was a stronger predictor of reduced cancer mortality among men, while high body mass index was a stronger predictor of…

  14. Cardiorespiratory fitness and physical activity in children with cancer

    NARCIS (Netherlands)

    Braam, Katja I.; van Dijk-Lokkart, Elisabeth M.; Kaspers, Gertjan J. L.; Takken, Tim; Huisman, Jaap; Bierings, Marc B.; Merks, Johannes H. M.; van de Heuvel-Eibrink, Marry M.; van Dulmen-den Broeder, Eline; Veening, Margreet A.

    2016-01-01

    This study assessed cardiorespiratory fitness (CRF), physical activity (PA), and sedentary behavior (SB), as well as factors associated with these outcomes in children during or shortly after cancer treatment. Cross-sectionally, CRF data, obtained by the cardiopulmonary exercise test, and PA and SB

  15. Bayesian Age-Period-Cohort Model of Lung Cancer Mortality

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    Bhikhari P. Tharu

    2015-09-01

    Full Text Available Background The objective of this study was to analyze the time trend for lung cancer mortality in the population of the USA by 5 years based on most recent available data namely to 2010. The knowledge of the mortality rates in the temporal trends is necessary to understand cancer burden.Methods Bayesian Age-Period-Cohort model was fitted using Poisson regression with histogram smoothing prior to decompose mortality rates based on age at death, period at death, and birth-cohort.Results Mortality rates from lung cancer increased more rapidly from age 52 years. It ended up to 325 deaths annually for 82 years on average. The mortality of younger cohorts was lower than older cohorts. The risk of lung cancer was lowered from period 1993 to recent periods.Conclusions The fitted Bayesian Age-Period-Cohort model with histogram smoothing prior is capable of explaining mortality rate of lung cancer. The reduction in carcinogens in cigarettes and increase in smoking cessation from around 1960 might led to decreasing trend of lung cancer mortality after calendar period 1993.

  16. Methods for analyzing occupational cohort data with application to lung cancer in US uranium miners: Techniques for fitting and checking exposure-time-response models

    International Nuclear Information System (INIS)

    Halpern, J.; Whittemore, A.S.

    1987-01-01

    Two methods were used to examine how lung cancer death rates vary with cumulative exposures to radiation and tobacco among uranium miners. The two methods produced similar results when death rate ratios were taken to be the product of radiation and tobacco effects. The estimates were discrepant when death rate ratios were taken to be the sum of radiation and tobacco effects. Both methods indicated better fit for the multiplicative model. It may be that cumulative exposures are inappropriate measures of the effects of radiation and tobacco on lung cancer death rates, as well as for other pollutants where the assumption of cumulative dose is the basis for risk assessments

  17. Cardiorespiratory fitness and physical function in children with cancer from diagnosis throughout treatment

    DEFF Research Database (Denmark)

    Thorsteinsson, Troels; Larsen, Hanne Bækgaard; Schmiegelow, Kjeld

    2017-01-01

    Background: Children with cancer experience severe reductions in physical fitness and functionality during and following intensive treatment. This may negatively impact their quality of life. Purpose: To describe the physical capacity and functionality of children with cancer during and after...... treatment as well as the feasibility of physical activity intervention in the Rehabilitation including Social and Physical activity and Education in Children and Teenagers with Cancer study. Patients and methods: The study included children diagnosed from January 2013 to April 2016 with paediatric cancer...... or Langerhans cell histiocytosis, all treated with chemotherapy. Seventy-five of 78 consecutively eligible children (96.2%) were included. Median age was 11 years (range 6‒18). The physical capacity and function were assessed based on testing of physical strength, balance and cardiorespiratory fitness. Children...

  18. The Alberta moving beyond breast cancer (AMBER cohort study: a prospective study of physical activity and health-related fitness in breast cancer survivors

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    Courneya Kerry S

    2012-11-01

    Full Text Available Abstract Background Limited research has examined the association between physical activity, health-related fitness, and disease outcomes in breast cancer survivors. Here, we present the rationale and design of the Alberta Moving Beyond Breast Cancer (AMBER Study, a prospective cohort study designed specifically to examine the role of physical activity and health-related fitness in breast cancer survivorship from the time of diagnosis and for the balance of life. The AMBER Study will examine the role of physical activity and health-related fitness in facilitating treatment completion, alleviating treatment side effects, hastening recovery after treatments, improving long term quality of life, and reducing the risks of disease recurrence, other chronic diseases, and premature death. Methods/Design The AMBER Study will enroll 1500 newly diagnosed, incident, stage I-IIIc breast cancer survivors in Alberta, Canada over a 5 year period. Assessments will be made at baseline (within 90 days of surgery, 1 year, and 3 years consisting of objective and self-reported measurements of physical activity, health-related fitness, blood collection, lymphedema, patient-reported outcomes, and determinants of physical activity. A final assessment at 5 years will measure patient-reported data only. The cohort members will be followed for an additional 5 years for disease outcomes. Discussion The AMBER cohort will answer key questions related to physical activity and health-related fitness in breast cancer survivors including: (1 the independent and interactive associations of physical activity and health-related fitness with disease outcomes (e.g., recurrence, breast cancer-specific mortality, overall survival, treatment completion rates, symptoms and side effects (e.g., pain, lymphedema, fatigue, neuropathy, quality of life, and psychosocial functioning (e.g., anxiety, depression, self-esteem, happiness, (2 the determinants of physical activity and

  19. Health-related physical fitness assessment in a community-based cancer rehabilitation setting.

    Science.gov (United States)

    Kirkham, Amy A; Neil-Sztramko, Sarah E; Morgan, Joanne; Hodson, Sara; Weller, Sarah; McRae, Tasha; Campbell, Kristin L

    2015-09-01

    Assessment of physical fitness is important in order to set goals, appropriately prescribe exercise, and monitor change over time. This study aimed to determine the utility of a standardized physical fitness assessment for use in cancer-specific, community-based exercise programs. Tests anticipated to be feasible and suitable for a community setting and a wide range of ages and physical function were chosen to measure body composition, aerobic fitness, strength, flexibility, and balance. Cancer Exercise Trainers/Specialists at cancer-specific, community-based exercise programs assessed new clients (n = 60) at enrollment, designed individualized exercise programs, and then performed a re-assessment 3-6 months later (n = 34). Resting heart rate, blood pressure, body mass index, waist circumference, handgrip strength, chair stands, sit-and-reach, back scratch, single-leg standing, and timed up-and-go tests were considered suitable and feasible tests/measures, as they were performed in most (≥88 %) participants. The ability to capture change was also noted for resting blood pressure (-7/-5 mmHg, p = 0.02), chair stands (+4, p exercise program setting. However, a shorter treadmill protocol and more sensitive balance and upper body flexibility tests should be investigated.

  20. Measured, modeled, and causal conceptions of fitness

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    Abrams, Marshall

    2012-01-01

    This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804

  1. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

    Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

  2. Induced subgraph searching for geometric model fitting

    Science.gov (United States)

    Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

    2017-11-01

    In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

  3. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  4. Gompertzian stochastic model with delay effect to cervical cancer growth

    International Nuclear Information System (INIS)

    Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah

    2015-01-01

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits

  5. Gompertzian stochastic model with delay effect to cervical cancer growth

    Energy Technology Data Exchange (ETDEWEB)

    Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2015-02-03

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.

  6. Analytical fitting model for rough-surface BRDF.

    Science.gov (United States)

    Renhorn, Ingmar G E; Boreman, Glenn D

    2008-08-18

    A physics-based model is developed for rough surface BRDF, taking into account angles of incidence and scattering, effective index, surface autocovariance, and correlation length. Shadowing is introduced on surface correlation length and reflectance. Separate terms are included for surface scatter, bulk scatter and retroreflection. Using the FindFit function in Mathematica, the functional form is fitted to BRDF measurements over a wide range of incident angles. The model has fourteen fitting parameters; once these are fixed, the model accurately describes scattering data over two orders of magnitude in BRDF without further adjustment. The resulting analytical model is convenient for numerical computations.

  7. Curve fitting methods for solar radiation data modeling

    Energy Technology Data Exchange (ETDEWEB)

    Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my; Singh, Balbir Singh Mahinder, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia)

    2014-10-24

    This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R{sup 2}. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.

  8. Curve fitting methods for solar radiation data modeling

    Science.gov (United States)

    Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

    2014-10-01

    This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R2. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.

  9. Curve fitting methods for solar radiation data modeling

    International Nuclear Information System (INIS)

    Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

    2014-01-01

    This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R 2 . The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods

  10. Results of the FIT-based National Colorectal Cancer Screening Program in Slovenia.

    Science.gov (United States)

    Tepeš, Bojan; Bracko, Matej; Novak Mlakar, Dominika; Stefanovic, Milan; Stabuc, Borut; Frkovic Grazio, Snjezana; Maucec Zakotnik, Jozica

    2017-07-01

    Colorectal cancer (CRC) is one of the most common malignancies in the western world. We aimed to assess the first round of fecal immunochemical test (FIT)-based National CRC screening program (NCSP). In the NCSP conducted in Slovenia, a FIT and colonoscopy for those tested positive was used. The NCSP central unit sent 536,709 invitations to Slovenian residents age 50 to 69 years old between 2009 and 2011. The adherence rate was 56.9% (303,343 participants). FIT was positive in 6.2% (15,310) of the participants (men, 7.8%; women, 5.0%; P<0.01). A total of 13,919 unsedated colonoscopies were performed with the cecal intubation rate of 97.8%. The overall adenoma detection rate was 51.3% [95% confidence interval (CI), 50.5%-52.1%] of which 61.0% (95% CI, 59.9%-62.1%) was in men, and 39.1% (95% CI, 37.8%-40.3%) in women (P<0.01). The mean number of adenoma per positive colonoscopy was 1.94 (95% CI, 1.90-1.97). Adenoma, advanced adenoma, or cancer were found in 7732 (55.5%) colonoscopies. A total of 862 (6.2%) CRC cases were found. Only 161 (18.7%) carcinomas were situated in the right colon. A total of 597 (70.2%) patients with cancer were in the early clinical stages (N, negative; 194 22.8%) of all cancers were cured with only endoscopic resection. In the NCSP, CRC was found in 6.2% of those participants attending colonoscopy, with 81.3% of carcinomas found in the left colon. A localized clinical stage was found in 70.2% participants. In 22.8% of CRC patients, cancer was cured with endoscopic resection only.

  11. Modeling Evolution on Nearly Neutral Network Fitness Landscapes

    Science.gov (United States)

    Yakushkina, Tatiana; Saakian, David B.

    2017-08-01

    To describe virus evolution, it is necessary to define a fitness landscape. In this article, we consider the microscopic models with the advanced version of neutral network fitness landscapes. In this problem setting, we suppose a fitness difference between one-point mutation neighbors to be small. We construct a modification of the Wright-Fisher model, which is related to ordinary infinite population models with nearly neutral network fitness landscape at the large population limit. From the microscopic models in the realistic sequence space, we derive two versions of nearly neutral network models: with sinks and without sinks. We claim that the suggested model describes the evolutionary dynamics of RNA viruses better than the traditional Wright-Fisher model with few sequences.

  12. Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

    Science.gov (United States)

    Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

    2017-03-01

    Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. Creative Commons Attribution License

  13. Community-Based Colorectal Cancer Screening in a Rural Population: Who Returns Fecal Immunochemical Test (FIT) Kits?

    Science.gov (United States)

    Crosby, Richard A; Stradtman, Lindsay; Collins, Tom; Vanderpool, Robin

    2017-09-01

    To determine the return rate of community-delivered fecal immunochemical test (FIT) kits in a rural population and to identify significant predictors of returning kits. Residents were recruited in 8 rural Kentucky counties to enroll in the study and receive an FIT kit. Of 345 recruited, 82.0% returned an FIT kit from the point of distribution. These participants were compared to the remainder relative to age, sex, marital status, having an annual income below $15,000, not graduating from high school, not having a regular health care provider, not having health care coverage, being a current smoker, indicating current overweight or obese status, and a scale measure of fatalism pertaining to colorectal cancer. Predictors achieving significance at the bivariate level were entered into a stepwise logistic regression model to calculate adjusted OR and 95% CI. The return rate was 82.0%. In adjusted analyses, those indicating an annual income of less than $15,000 were 2.85 times more likely to return their kits (95% CI: 1.56-5.24; P < .001). Also, those not perceiving themselves to be overweight/obese were 1.95 times more likely to return their kits (95% CI: 1.07-3.55; P = .029). An outreach-based colorectal cancer screening program in a rural population may yield high return rates. People with annual incomes below $15,000 and those not having perceptions of being overweight/obese may be particularly likely to return FIT kits. © 2016 National Rural Health Association.

  14. Fitting Hidden Markov Models to Psychological Data

    Directory of Open Access Journals (Sweden)

    Ingmar Visser

    2002-01-01

    Full Text Available Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

  15. Random-growth urban model with geographical fitness

    Science.gov (United States)

    Kii, Masanobu; Akimoto, Keigo; Doi, Kenji

    2012-12-01

    This paper formulates a random-growth urban model with a notion of geographical fitness. Using techniques of complex-network theory, we study our system as a type of preferential-attachment model with fitness, and we analyze its macro behavior to clarify the properties of the city-size distributions it predicts. First, restricting the geographical fitness to take positive values and using a continuum approach, we show that the city-size distributions predicted by our model asymptotically approach Pareto distributions with coefficients greater than unity. Then, allowing the geographical fitness to take negative values, we perform local coefficient analysis to show that the predicted city-size distributions can deviate from Pareto distributions, as is often observed in actual city-size distributions. As a result, the model we propose can generate a generic class of city-size distributions, including but not limited to Pareto distributions. For applications to city-population projections, our simple model requires randomness only when new cities are created, not during their subsequent growth. This property leads to smooth trajectories of city population growth, in contrast to other models using Gibrat’s law. In addition, a discrete form of our dynamical equations can be used to estimate past city populations based on present-day data; this fact allows quantitative assessment of the performance of our model. Further study is needed to determine appropriate formulas for the geographical fitness.

  16. The importance of regional models in assessing canine cancer incidences in Switzerland.

    Science.gov (United States)

    Boo, Gianluca; Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

    2018-01-01

    Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

  17. Contrast Gain Control Model Fits Masking Data

    Science.gov (United States)

    Watson, Andrew B.; Solomon, Joshua A.; Null, Cynthia H. (Technical Monitor)

    1994-01-01

    We studied the fit of a contrast gain control model to data of Foley (JOSA 1994), consisting of thresholds for a Gabor patch masked by gratings of various orientations, or by compounds of two orientations. Our general model includes models of Foley and Teo & Heeger (IEEE 1994). Our specific model used a bank of Gabor filters with octave bandwidths at 8 orientations. Excitatory and inhibitory nonlinearities were power functions with exponents of 2.4 and 2. Inhibitory pooling was broad in orientation, but narrow in spatial frequency and space. Minkowski pooling used an exponent of 4. All of the data for observer KMF were well fit by the model. We have developed a contrast gain control model that fits masking data. Unlike Foley's, our model accepts images as inputs. Unlike Teo & Heeger's, our model did not require multiple channels for different dynamic ranges.

  18. Radiation risk models for all solid cancers other than those types of cancer requiring individual assessments after a nuclear accident

    International Nuclear Information System (INIS)

    Walsh, Linda; Zhang, Wei

    2016-01-01

    In the assessment of health risks after nuclear accidents, some health consequences require special attention. For example, in their 2013 report on health risk assessment after the Fukushima nuclear accident, the World Health Organisation (WHO) panel of experts considered risks of breast cancer, thyroid cancer and leukaemia. For these specific cancer types, use was made of already published excess relative risk (ERR) and excess absolute risk (EAR) models for radiation-related cancer incidence fitted to the epidemiological data from the Japanese A-bomb Life Span Study (LSS). However, it was also considered important to assess all other types of solid cancer together and the WHO, in their above-mentioned report, stated ''No model to calculate the risk for all other solid cancer excluding breast and thyroid cancer risks is available from the LSS data''. Applying the LSS models for all solid cancers along with the models for the specific sites means that some cancers have an overlap in the risk evaluations. Thus, calculating the total solid cancer risk plus the breast cancer risk plus the thyroid cancer risk can overestimate the total risk by several per cent. Therefore, the purpose of this paper was to publish the required models for all other solid cancers, i.e. all solid cancers other than those types of cancer requiring special attention after a nuclear accident. The new models presented here have been fitted to the same LSS data set from which the risks provided by the WHO were derived. Although it is known already that the EAR and ERR effect modifications by sex are statistically significant for the outcome ''all solid cancer'', it is shown here that sex modification is not statistically significant for the outcome ''all solid cancer other than thyroid and breast cancer''. It is also shown here that the sex-averaged solid cancer risks with and without the sex modification are very similar once breast and thyroid cancers are factored out. Some other notable model

  19. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  20. Cardiorespiratory fitness and physical function in children with cancer from diagnosis throughout treatment

    DEFF Research Database (Denmark)

    Thorsteinsson, Troels; Larsen, Hanne Baekgaard; Schmiegelow, Kjeld

    2017-01-01

    treatment as well as the feasibility of physical activity intervention in the Rehabilitation including Social and Physical activity and Education in Children and Teenagers with Cancer study. PATIENTS AND METHODS: The study included children diagnosed from January 2013 to April 2016 with paediatric cancer...... programme with no dropouts. Strenuous physical exercise and physiological testing during paediatric cancer treatment was safe and feasible, with only five minor adverse events during the intervention. Cardiorespiratory fitness was significantly lower in children with cancer than norms for healthy age...... with cancer have significantly lower physical capacity and functionality than healthy age-matched norms. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: NCT01772862....

  1. The thyroid cancer policy model: A mathematical simulation model of papillary thyroid carcinoma in The U.S. population.

    Directory of Open Access Journals (Sweden)

    Carrie Lubitz

    Full Text Available Thyroid cancer affects over ½ million people in the U.S. and the incidence of thyroid cancer has increased worldwide at a rate higher than any other cancer, while survival has remained largely unchanged. The aim of this research was to develop, calibrate and verify a mathematical disease model to simulate the natural history of papillary thyroid cancer, which will serve as a platform to assess the effectiveness of clinical and cancer control interventions.Herein, we modeled the natural pre-clinical course of both benign and malignant thyroid nodules with biologically relevant health states from normal to detected nodule. Using established calibration techniques, optimal parameter sets for tumor growth characteristics, development rate, and detection rate were used to fit Surveillance Epidemiology and End Results (SEER incidence data and other calibration targets.Model outputs compared to calibration targets demonstrating sufficient calibration fit and model validation are presented including primary targets of SEER incidence data and size distribution at detection of malignancy. Additionally, we show the predicted underlying benign and malignant prevalence of nodules in the population, the probability of detection based on size of nodule, and estimates of growth over time in both benign and malignant nodules.This comprehensive model provides a dynamic platform employable for future comparative effectiveness research. Future model analyses will test and assess various clinical management strategies to improve patient outcomes related to thyroid cancer and optimize resource utilization for patients with thyroid nodules.

  2. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

    DEFF Research Database (Denmark)

    Bolker, B.M.; Gardner, B.; Maunder, M.

    2013-01-01

    Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. R is convenient and (relatively) easy...... to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield...

  3. Local fit evaluation of structural equation models using graphical criteria.

    Science.gov (United States)

    Thoemmes, Felix; Rosseel, Yves; Textor, Johannes

    2018-03-01

    Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  4. topicmodels: An R Package for Fitting Topic Models

    Directory of Open Access Journals (Sweden)

    Bettina Grun

    2011-05-01

    Full Text Available Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.

  5. Radiation risk models for all solid cancers other than those types of cancer requiring individual assessments after a nuclear accident

    Energy Technology Data Exchange (ETDEWEB)

    Walsh, Linda [Federal Office for Radiation Protection, Department ' ' Radiation Protection and Health' ' , Oberschleissheim (Germany); University of Zurich, Medical Physics Group, Institute of Physics, Zurich (Switzerland); Zhang, Wei [Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Oxford (United Kingdom)

    2016-03-15

    In the assessment of health risks after nuclear accidents, some health consequences require special attention. For example, in their 2013 report on health risk assessment after the Fukushima nuclear accident, the World Health Organisation (WHO) panel of experts considered risks of breast cancer, thyroid cancer and leukaemia. For these specific cancer types, use was made of already published excess relative risk (ERR) and excess absolute risk (EAR) models for radiation-related cancer incidence fitted to the epidemiological data from the Japanese A-bomb Life Span Study (LSS). However, it was also considered important to assess all other types of solid cancer together and the WHO, in their above-mentioned report, stated ''No model to calculate the risk for all other solid cancer excluding breast and thyroid cancer risks is available from the LSS data''. Applying the LSS models for all solid cancers along with the models for the specific sites means that some cancers have an overlap in the risk evaluations. Thus, calculating the total solid cancer risk plus the breast cancer risk plus the thyroid cancer risk can overestimate the total risk by several per cent. Therefore, the purpose of this paper was to publish the required models for all other solid cancers, i.e. all solid cancers other than those types of cancer requiring special attention after a nuclear accident. The new models presented here have been fitted to the same LSS data set from which the risks provided by the WHO were derived. Although it is known already that the EAR and ERR effect modifications by sex are statistically significant for the outcome ''all solid cancer'', it is shown here that sex modification is not statistically significant for the outcome ''all solid cancer other than thyroid and breast cancer''. It is also shown here that the sex-averaged solid cancer risks with and without the sex modification are very similar once breast and

  6. Morbidly obese women with and without endometrial cancer: are there differences in measured physical fitness, body composition, or hormones?

    Science.gov (United States)

    Modesitt, Susan C; Geffel, Dyanna L; Via, Jennifer; L Weltman, Arthur

    2012-03-01

    Exercise is potentially protective against cancer for obese women. The objectives were to examine differences in activity, body composition, and hormones in overweight/obese women with and without endometrial cancer. Women ≥ 50 years old with a body mass index (BMI) ≥ 25 kg/m(2) scheduled for abdominal hysterectomy were enrolled. Demographics, physical activity, and quality of life (QOL) data were collected. Body composition/fitness was evaluated using Air Displacement Plethysmography (BodPod) and a standardized treadmill. Adiponectin, androstenedione, leptin, estradiol, estrone, progesterone, sex hormone binding globulin, insulin and glucose were measured. Thirty-eight women enrolled in this pilot study; 22 had endometrial cancer. Mean age was 58.3 years, mean BMI, fat weight and percent body fat were 41.3 kg/m(2), 55 kg and 51% respectively. Fitness levels were poor; 90% of women had peak oxygen uptakes below the 10th percentile of population normals yet 80% still rated their fitness level as equivalent to other women. Women with and without cancer did not differ in age, BMI, co-morbidities, energy expenditures, body composition, hormones or QOL although glucose levels were higher in women with cancer (119.5 vs. 90.7 mg/dl; p=0.049). Cancer subjects scored worse on every fitness measurement, reaching statistical significance for VO(2 peak) (15.0 vs. 17.9 ml/kg/min; p=0.033). Current exercisers had a lower BMI (p=0.039), decreased fat weight (p=0.024), decreased waist circumference (p=0.05) and improved vitality compared to non-exercisers. Physical fitness levels were abysmal in these morbidly obese subjects and worse for cancer patients. Exercise correlated with improved body composition and vitality. Copyright © 2011. Published by Elsevier Inc.

  7. Goodness-of-Fit Assessment of Item Response Theory Models

    Science.gov (United States)

    Maydeu-Olivares, Alberto

    2013-01-01

    The article provides an overview of goodness-of-fit assessment methods for item response theory (IRT) models. It is now possible to obtain accurate "p"-values of the overall fit of the model if bivariate information statistics are used. Several alternative approaches are described. As the validity of inferences drawn on the fitted model…

  8. Lower radiation weighting factor for radon indicated in mechanistic modelling of human lung cancer

    International Nuclear Information System (INIS)

    Brugmans, M.J.P.; Leenhouts, H.P.

    2002-01-01

    A two-mutation carcinogenesis (TMC) model was fitted to the age-dependent lung cancer incidence in a cohort of Dutch Hodgkin patients treated with radiotherapy. Employing the results of previous TMC analyses of lung cancer due to smoking (by British doctors) and due to exposure to radon (for Colorado miners) a model fit was obtained with an estimate for the low LET radiation effect at the cellular level. This allows risk calculations for lung cancer from low LET radiation. The excess absolute risks are in tune with the values reported in the literature, the excess relative risks differ among the exposed groups. Comparing the cellular radiation coefficients for radon and for low LET radiation leads to an estimated radiation weighting factor for radon of 3 (0.1-6). (author)

  9. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  10. The FITS model office ergonomics program: a model for best practice.

    Science.gov (United States)

    Chim, Justine M Y

    2014-01-01

    An effective office ergonomics program can predict positive results in reducing musculoskeletal injury rates, enhancing productivity, and improving staff well-being and job satisfaction. Its objective is to provide a systematic solution to manage the potential risk of musculoskeletal disorders among computer users in an office setting. A FITS Model office ergonomics program is developed. The FITS Model Office Ergonomics Program has been developed which draws on the legislative requirements for promoting the health and safety of workers using computers for extended periods as well as previous research findings. The Model is developed according to the practical industrial knowledge in ergonomics, occupational health and safety management, and human resources management in Hong Kong and overseas. This paper proposes a comprehensive office ergonomics program, the FITS Model, which considers (1) Furniture Evaluation and Selection; (2) Individual Workstation Assessment; (3) Training and Education; (4) Stretching Exercises and Rest Break as elements of an effective program. An experienced ergonomics practitioner should be included in the program design and implementation. Through the FITS Model Office Ergonomics Program, the risk of musculoskeletal disorders among computer users can be eliminated or minimized, and workplace health and safety and employees' wellness enhanced.

  11. Reliability and Model Fit

    Science.gov (United States)

    Stanley, Leanne M.; Edwards, Michael C.

    2016-01-01

    The purpose of this article is to highlight the distinction between the reliability of test scores and the fit of psychometric measurement models, reminding readers why it is important to consider both when evaluating whether test scores are valid for a proposed interpretation and/or use. It is often the case that an investigator judges both the…

  12. Modeling human papillomavirus and cervical cancer in the United States for analyses of screening and vaccination

    Directory of Open Access Journals (Sweden)

    Ortendahl Jesse

    2007-10-01

    Full Text Available Abstract Background To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. Methods We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN, HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. Results Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69–82% and 69% (60–77%, respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter

  13. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

    Science.gov (United States)

    Bolker, Benjamin M.; Gardner, Beth; Maunder, Mark; Berg, Casper W.; Brooks, Mollie; Comita, Liza; Crone, Elizabeth; Cubaynes, Sarah; Davies, Trevor; de Valpine, Perry; Ford, Jessica; Gimenez, Olivier; Kéry, Marc; Kim, Eun Jung; Lennert-Cody, Cleridy; Magunsson, Arni; Martell, Steve; Nash, John; Nielson, Anders; Regentz, Jim; Skaug, Hans; Zipkin, Elise

    2013-01-01

    1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

  14. Evaluating the number of stages in development of squamous cell and adenocarcinomas across cancer sites using human population-based cancer modeling.

    Science.gov (United States)

    Kravchenko, Julia; Akushevich, Igor; Abernethy, Amy P; Lyerly, H Kim

    2012-01-01

    Adenocarcinomas (ACs) and squamous cell carcinomas (SCCs) differ by clinical and molecular characteristics. We evaluated the characteristics of carcinogenesis by modeling the age patterns of incidence rates of ACs and SCCs of various organs to test whether these characteristics differed between cancer subtypes. Histotype-specific incidence rates of 14 ACs and 12 SCCs from the SEER Registry (1973-2003) were analyzed by fitting several biologically motivated models to observed age patterns. A frailty model with the Weibull baseline was applied to each age pattern to provide the best fit for the majority of cancers. For each cancer, model parameters describing the underlying mechanisms of carcinogenesis including the number of stages occurring during an individual's life and leading to cancer (m-stages) were estimated. For sensitivity analysis, the age-period-cohort model was incorporated into the carcinogenesis model to test the stability of the estimates. For the majority of studied cancers, the numbers of m-stages were similar within each group (i.e., AC and SCC). When cancers of the same organs were compared (i.e., lung, esophagus, and cervix uteri), the number of m-stages were more strongly associated with the AC/SCC subtype than with the organ: 9.79±0.09, 9.93±0.19 and 8.80±0.10 for lung, esophagus, and cervical ACs, compared to 11.41±0.10, 12.86±0.34 and 12.01±0.51 for SCCs of the respective organs (p<0.05 between subtypes). Most SCCs had more than ten m-stages while ACs had fewer than ten m-stages. The sensitivity analyses of the model parameters demonstrated the stability of the obtained estimates. A model containing parameters capable of representing the number of stages of cancer development occurring during individual's life was applied to the large population data on incidence of ACs and SCCs. The model revealed that the number of m-stages differed by cancer subtype being more strongly associated with ACs/SCCs histotype than with organ/site.

  15. Are Physical Education Majors Models for Fitness?

    Science.gov (United States)

    Kamla, James; Snyder, Ben; Tanner, Lori; Wash, Pamela

    2012-01-01

    The National Association of Sport and Physical Education (NASPE) (2002) has taken a firm stance on the importance of adequate fitness levels of physical education teachers stating that they have the responsibility to model an active lifestyle and to promote fitness behaviors. Since the NASPE declaration, national initiatives like Let's Move…

  16. Skin cancer prevention coverage in popular US women's health and fitness magazines: an analysis of advertisements and articles.

    Science.gov (United States)

    Basch, Corey Hannah; Ethan, Danna; Hillyer, Grace Clarke; Berdnik, Alyssa

    2014-04-02

    The desire to be tan is a phenomenon that public health researchers have investigated, as exposure to UV radiation increases the chances of developing skin cancer.  Media messages in women's magazines have been shown to contribute to this problem. Much less is known about the prevalence of skin cancer prevention messages in these magazines. This study's aim was to identify the number and type of articles and advertised products devoted to skin health (sun protection and skin cancer prevention in particular) within five popular U.S. greater than women's health and fitness magazines. We analyzed articles and advertisements over seven months of issues of the following popular women's health and fitness magazines: Fitness, Health, Self, Shape, and Women's Health, March 2013 through September 2013. Overall, 31 issues of the five magazines with a total of 780 articles and 1,986 advertisements were analyzed. Of the 780 articles, a mere 2.9% (n=23) were devoted to skin. Of the 258 skin product advertisements, less than 20% of the products contained sun protection factor (SPF). These findings suggest that women's health and fitness magazines can improve their efforts in informing women of skin cancer risks and preventive measures to minimize these risks. The role of these magazines in building health literacy among their readers is also discussed.

  17. ITEM LEVEL DIAGNOSTICS AND MODEL - DATA FIT IN ITEM ...

    African Journals Online (AJOL)

    Global Journal

    Item response theory (IRT) is a framework for modeling and analyzing item response ... data. Though, there is an argument that the evaluation of fit in IRT modeling has been ... National Council on Measurement in Education ... model data fit should be based on three types of ... prediction should be assessed through the.

  18. Assessing fit in Bayesian models for spatial processes

    KAUST Repository

    Jun, M.; Katzfuss, M.; Hu, J.; Johnson, V. E.

    2014-01-01

    © 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.

  19. Assessing fit in Bayesian models for spatial processes

    KAUST Repository

    Jun, M.

    2014-09-16

    © 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.

  20. Automated Model Fit Method for Diesel Engine Control Development

    NARCIS (Netherlands)

    Seykens, X.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.

    2014-01-01

    This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is

  1. Automated model fit method for diesel engine control development

    NARCIS (Netherlands)

    Seykens, X.L.J.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.J.H.

    2014-01-01

    This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is

  2. A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types

    Science.gov (United States)

    Lin, Chen-Ching; Zhao, Junfei; Jia, Peilin; Li, Wen-Hsiung; Zhao, Zhongming

    2015-01-01

    Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics. PMID:26352260

  3. Cardiorespiratory fitness and death from cancer: a 42-year follow-up from the Copenhagen Male Study.

    Science.gov (United States)

    Jensen, Magnus Thorsten; Holtermann, Andreas; Bay, Hans; Gyntelberg, Finn

    2017-09-01

    Poor cardiorespiratory fitness (CRF) is associated with death from cancer. If follow-up time is short, this association may be confounded by subclinical disease already present at the time of CRF assessment. This study investigates the association between CRF and death from cancer and any cause with 42 years and 44 years of follow-up, respectively. Middle-aged, employed and cancer-free Danish men from the prospective Copenhagen Male Study , enrolled in 1970-1971, were included. CRF (maximal oxygen consumption (VO 2 max)) was estimated using a bicycle ergometer test and analysed in multivariable Cox models including conventional risk factors, social class and self-reported physical activity. Death from cancer and all-cause mortality was assessed using Danish national registers. Follow-up was 100% complete. In total, 5131 men were included, mean (SD) age 48.8 (5.4) years. During 44 years of follow-up, 4486 subjects died (87.4%), 1527 (29.8%) from cancer. In multivariable models, CRF was highly significantly inversely associated with death from cancer and all-cause mortality ((HR (95% CI)) 0.83 (0.77 to 0.90) and 0.89 (0.85 to 0.93) per 10 mL/kg/min increase in estimated VO 2 max, respectively). A similar association was seen across specific cancer groups, except death from prostate cancer (1.00 (0.82 to 1.2); p=0.97; n=231). The associations between CRF and outcomes remained essentially unchanged after excluding subjects dying within 10 years (n=377) and 20 years (n=1276) of inclusion. CRF is highly significantly inversely associated with death from cancer and all-cause mortality. The associations are robust for exclusion of subjects dying within 20 years of study inclusion, thereby suggesting a minimal influence of reverse causation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  4. An R package for fitting age, period and cohort models

    Directory of Open Access Journals (Sweden)

    Adriano Decarli

    2014-11-01

    Full Text Available In this paper we present the R implementation of a GLIM macro which fits age-period-cohort model following Osmond and Gardner. In addition to the estimates of the corresponding model, owing to the programming capability of R as an object oriented language, methods for printing, plotting and summarizing the results are provided. Furthermore, the researcher has fully access to the output of the main function (apc which returns all the models fitted within the function. It is so possible to critically evaluate the goodness of fit of the resulting model.

  5. Skin Cancer Prevention Coverage in Popular US Women’s Health and Fitness Magazines: An Analysis of Advertisements and Articles

    Science.gov (United States)

    Basch, Corey Hannah; Ethan, Danna; Hillyer, Grace Clarke; Berdnik, Alyssa

    2014-01-01

    The desire to be tan is a phenomenon that public health researchers have investigated, as exposure to UV radiation increases the chances of developing skin cancer. Media messages in women’s magazines have been shown to contribute to this problem. Much less is known about the prevalence of skin cancer prevention messages in these magazines. This study’s aim was to identify the number and type of articles and advertised products devoted to skin health (sun protection and skin cancer prevention in particular) within five popular U.S. greater than women’s health and fitness magazines. We analyzed articles and advertisements over seven months of issues of the following popular women’s health and fitness magazines: Fitness, Health, Self, Shape, and Women’s Health, March 2013 through September 2013. Overall, 31 issues of the five magazines with a total of 780 articles and 1,986 advertisements were analyzed. Of the 780 articles, a mere 2.9% (n=23) were devoted to skin. Of the 258 skin product advertisements, less than 20% of the products contained sun protection factor (SPF). These findings suggest that women’s health and fitness magazines can improve their efforts in informing women of skin cancer risks and preventive measures to minimize these risks. The role of these magazines in building health literacy among their readers is also discussed. PMID:24999136

  6. Changes in fitness are associated with changes in body composition and bone health in children after cancer.

    Science.gov (United States)

    Dubnov-Raz, Gal; Azar, Meital; Reuveny, Ronen; Katz, Uriel; Weintraub, Michael; Constantini, Naama W

    2015-10-01

    This study examined the effects of physical activity on the fitness, body composition and mental health of children after cancer or bone marrow transplantation. We focused on 22 children aged from seven to 14 years who had received chemotherapy and/or bone marrow transplantation in our medical centre. Ten children took part in a six-month exercise programme, and 12 children who did not exercise formed the control group. At baseline and at the end of the trial, we measured aerobic fitness, body composition, bone density and assessed the child's mood and quality of life. We pooled all participants together post hoc to compare changes in fitness with the various study outcomes. We found no differences between groups in changes in fitness, body composition or mental health indices. Significant correlations were found between changes in aerobic fitness and changes in lean body mass (r = 0.74, p = 0.002), bone mineral content (r = 0.57, p = 0.026) and femoral neck bone mineral density (r = 0.59, p = 0.027) in all participants. Group-based exercise training did not improve aerobic fitness in children after cancer or bone marrow transplantation. However, changes in fitness throughout the study period were associated with changes in body composition and bone health in all participants. ©2015 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  7. Are Fit Indices Biased in Favor of Bi-Factor Models in Cognitive Ability Research?: A Comparison of Fit in Correlated Factors, Higher-Order, and Bi-Factor Models via Monte Carlo Simulations

    Directory of Open Access Journals (Sweden)

    Grant B. Morgan

    2015-02-01

    Full Text Available Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

  8. A joint model of persistent human papillomavirus infection and cervical cancer risk: Implications for cervical cancer screening.

    Science.gov (United States)

    Katki, Hormuzd A; Cheung, Li C; Fetterman, Barbara; Castle, Philip E; Sundaram, Rajeshwari

    2015-10-01

    New cervical cancer screening guidelines in the US and many European countries recommend that women get tested for human papillomavirus (HPV). To inform decisions about screening intervals, we calculate the increase in precancer/cancer risk per year of continued HPV infection. However, both time to onset of precancer/cancer and time to HPV clearance are interval-censored, and onset of precancer/cancer strongly informatively censors HPV clearance. We analyze this bivariate informatively interval-censored data by developing a novel joint model for time to clearance of HPV and time to precancer/cancer using shared random-effects, where the estimated mean duration of each woman's HPV infection is a covariate in the submodel for time to precancer/cancer. The model was fit to data on 9,553 HPV-positive/Pap-negative women undergoing cervical cancer screening at Kaiser Permanente Northern California, data that were pivotal to the development of US screening guidelines. We compare the implications for screening intervals of this joint model to those from population-average marginal models of precancer/cancer risk. In particular, after 2 years the marginal population-average precancer/cancer risk was 5%, suggesting a 2-year interval to control population-average risk at 5%. In contrast, the joint model reveals that almost all women exceeding 5% individual risk in 2 years also exceeded 5% in 1 year, suggesting that a 1-year interval is better to control individual risk at 5%. The example suggests that sophisticated risk models capable of predicting individual risk may have different implications than population-average risk models that are currently used for informing medical guideline development.

  9. Evaluating the number of stages in development of squamous cell and adenocarcinomas across cancer sites using human population-based cancer modeling.

    Directory of Open Access Journals (Sweden)

    Julia Kravchenko

    Full Text Available BACKGROUND: Adenocarcinomas (ACs and squamous cell carcinomas (SCCs differ by clinical and molecular characteristics. We evaluated the characteristics of carcinogenesis by modeling the age patterns of incidence rates of ACs and SCCs of various organs to test whether these characteristics differed between cancer subtypes. METHODOLOGY/PRINCIPAL FINDINGS: Histotype-specific incidence rates of 14 ACs and 12 SCCs from the SEER Registry (1973-2003 were analyzed by fitting several biologically motivated models to observed age patterns. A frailty model with the Weibull baseline was applied to each age pattern to provide the best fit for the majority of cancers. For each cancer, model parameters describing the underlying mechanisms of carcinogenesis including the number of stages occurring during an individual's life and leading to cancer (m-stages were estimated. For sensitivity analysis, the age-period-cohort model was incorporated into the carcinogenesis model to test the stability of the estimates. For the majority of studied cancers, the numbers of m-stages were similar within each group (i.e., AC and SCC. When cancers of the same organs were compared (i.e., lung, esophagus, and cervix uteri, the number of m-stages were more strongly associated with the AC/SCC subtype than with the organ: 9.79±0.09, 9.93±0.19 and 8.80±0.10 for lung, esophagus, and cervical ACs, compared to 11.41±0.10, 12.86±0.34 and 12.01±0.51 for SCCs of the respective organs (p<0.05 between subtypes. Most SCCs had more than ten m-stages while ACs had fewer than ten m-stages. The sensitivity analyses of the model parameters demonstrated the stability of the obtained estimates. CONCLUSIONS/SIGNIFICANCE: A model containing parameters capable of representing the number of stages of cancer development occurring during individual's life was applied to the large population data on incidence of ACs and SCCs. The model revealed that the number of m-stages differed by cancer subtype

  10. Does model fit decrease the uncertainty of the data in comparison with a general non-model least squares fit?

    International Nuclear Information System (INIS)

    Pronyaev, V.G.

    2003-01-01

    The information entropy is taken as a measure of knowledge about the object and the reduced univariante variance as a common measure of uncertainty. Covariances in the model versus non-model least square fits are discussed

  11. Efficient occupancy model-fitting for extensive citizen-science data

    Science.gov (United States)

    Morgan, Byron J. T.; Freeman, Stephen N.; Ridout, Martin S.; Brereton, Tom M.; Fox, Richard; Powney, Gary D.; Roy, David B.

    2017-01-01

    Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen

  12. A Model Fit Statistic for Generalized Partial Credit Model

    Science.gov (United States)

    Liang, Tie; Wells, Craig S.

    2009-01-01

    Investigating the fit of a parametric model is an important part of the measurement process when implementing item response theory (IRT), but research examining it is limited. A general nonparametric approach for detecting model misfit, introduced by J. Douglas and A. S. Cohen (2001), has exhibited promising results for the two-parameter logistic…

  13. Unifying distance-based goodness-of-fit indicators for hydrologic model assessment

    Science.gov (United States)

    Cheng, Qinbo; Reinhardt-Imjela, Christian; Chen, Xi; Schulte, Achim

    2014-05-01

    The goodness-of-fit indicator, i.e. efficiency criterion, is very important for model calibration. However, recently the knowledge about the goodness-of-fit indicators is all empirical and lacks a theoretical support. Based on the likelihood theory, a unified distance-based goodness-of-fit indicator termed BC-GED model is proposed, which uses the Box-Cox (BC) transformation to remove the heteroscedasticity of model errors and the generalized error distribution (GED) with zero-mean to fit the distribution of model errors after BC. The BC-GED model can unify all recent distance-based goodness-of-fit indicators, and reveals the mean square error (MSE) and the mean absolute error (MAE) that are widely used goodness-of-fit indicators imply statistic assumptions that the model errors follow the Gaussian distribution and the Laplace distribution with zero-mean, respectively. The empirical knowledge about goodness-of-fit indicators can be also easily interpreted by BC-GED model, e.g. the sensitivity to high flow of the goodness-of-fit indicators with large power of model errors results from the low probability of large model error in the assumed distribution of these indicators. In order to assess the effect of the parameters (i.e. the BC transformation parameter λ and the GED kurtosis coefficient β also termed the power of model errors) of BC-GED model on hydrologic model calibration, six cases of BC-GED model were applied in Baocun watershed (East China) with SWAT-WB-VSA model. Comparison of the inferred model parameters and model simulation results among the six indicators demonstrates these indicators can be clearly separated two classes by the GED kurtosis β: β >1 and β ≤ 1. SWAT-WB-VSA calibrated by the class β >1 of distance-based goodness-of-fit indicators captures high flow very well and mimics the baseflow very badly, but it calibrated by the class β ≤ 1 mimics the baseflow very well, because first the larger value of β, the greater emphasis is put on

  14. Why Victory in the War on Cancer Remains Elusive: Biomedical Hypotheses and Mathematical Models

    Directory of Open Access Journals (Sweden)

    Leonid Hanin

    2011-01-01

    Full Text Available We discuss philosophical, methodological, and biomedical grounds for the traditional paradigm of cancer and some of its critical flaws. We also review some potentially fruitful approaches to understanding cancer and its treatment. This includes the new paradigm of cancer that was developed over the last 15 years by Michael Retsky, Michael Baum, Romano Demicheli, Isaac Gukas, William Hrushesky and their colleagues on the basis of earlier pioneering work of Bernard Fisher and Judah Folkman. Next, we highlight the unique and pivotal role of mathematical modeling in testing biomedical hypotheses about the natural history of cancer and the effects of its treatment, elaborate on model selection criteria, and mention some methodological pitfalls. Finally, we describe a specific mathematical model of cancer progression that supports all the main postulates of the new paradigm of cancer when applied to the natural history of a particular breast cancer patient and fit to the observables.

  15. A Comparison of Item Fit Statistics for Mixed IRT Models

    Science.gov (United States)

    Chon, Kyong Hee; Lee, Won-Chan; Dunbar, Stephen B.

    2010-01-01

    In this study we examined procedures for assessing model-data fit of item response theory (IRT) models for mixed format data. The model fit indices used in this study include PARSCALE's G[superscript 2], Orlando and Thissen's S-X[superscript 2] and S-G[superscript 2], and Stone's chi[superscript 2*] and G[superscript 2*]. To investigate the…

  16. Incorporating Cancer Stem Cells in Radiation Therapy Treatment Response Modeling and the Implication in Glioblastoma Multiforme Treatment Resistance

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Victoria Y.; Nguyen, Dan; Pajonk, Frank; Kupelian, Patrick; Kaprealian, Tania; Selch, Michael; Low, Daniel A.; Sheng, Ke, E-mail: ksheng@mednet.ucla.edu

    2015-03-15

    Purpose: To perform a preliminary exploration with a simplistic mathematical cancer stem cell (CSC) interaction model to determine whether the tumor-intrinsic heterogeneity and dynamic equilibrium between CSCs and differentiated cancer cells (DCCs) can better explain radiation therapy treatment response with a dual-compartment linear-quadratic (DLQ) model. Methods and Materials: The radiosensitivity parameters of CSCs and DCCs for cancer cell lines including glioblastoma multiforme (GBM), non–small cell lung cancer, melanoma, osteosarcoma, and prostate, cervical, and breast cancer were determined by performing robust least-square fitting using the DLQ model on published clonogenic survival data. Fitting performance was compared with the single-compartment LQ (SLQ) and universal survival curve models. The fitting results were then used in an ordinary differential equation describing the kinetics of DCCs and CSCs in response to 2- to 14.3-Gy fractionated treatments. The total dose to achieve tumor control and the fraction size that achieved the least normal biological equivalent dose were calculated. Results: Smaller cell survival fitting errors were observed using DLQ, with the exception of melanoma, which had a low α/β = 0.16 in SLQ. Ordinary differential equation simulation indicated lower normal tissue biological equivalent dose to achieve the same tumor control with a hypofractionated approach for 4 cell lines for the DLQ model, in contrast to SLQ, which favored 2 Gy per fraction for all cells except melanoma. The DLQ model indicated greater tumor radioresistance than SLQ, but the radioresistance was overcome by hypofractionation, other than the GBM cells, which responded poorly to all fractionations. Conclusion: The distinct radiosensitivity and dynamics between CSCs and DCCs in radiation therapy response could perhaps be one possible explanation for the heterogeneous intertumor response to hypofractionation and in some cases superior outcome from

  17. Incorporating Cancer Stem Cells in Radiation Therapy Treatment Response Modeling and the Implication in Glioblastoma Multiforme Treatment Resistance

    International Nuclear Information System (INIS)

    Yu, Victoria Y.; Nguyen, Dan; Pajonk, Frank; Kupelian, Patrick; Kaprealian, Tania; Selch, Michael; Low, Daniel A.; Sheng, Ke

    2015-01-01

    Purpose: To perform a preliminary exploration with a simplistic mathematical cancer stem cell (CSC) interaction model to determine whether the tumor-intrinsic heterogeneity and dynamic equilibrium between CSCs and differentiated cancer cells (DCCs) can better explain radiation therapy treatment response with a dual-compartment linear-quadratic (DLQ) model. Methods and Materials: The radiosensitivity parameters of CSCs and DCCs for cancer cell lines including glioblastoma multiforme (GBM), non–small cell lung cancer, melanoma, osteosarcoma, and prostate, cervical, and breast cancer were determined by performing robust least-square fitting using the DLQ model on published clonogenic survival data. Fitting performance was compared with the single-compartment LQ (SLQ) and universal survival curve models. The fitting results were then used in an ordinary differential equation describing the kinetics of DCCs and CSCs in response to 2- to 14.3-Gy fractionated treatments. The total dose to achieve tumor control and the fraction size that achieved the least normal biological equivalent dose were calculated. Results: Smaller cell survival fitting errors were observed using DLQ, with the exception of melanoma, which had a low α/β = 0.16 in SLQ. Ordinary differential equation simulation indicated lower normal tissue biological equivalent dose to achieve the same tumor control with a hypofractionated approach for 4 cell lines for the DLQ model, in contrast to SLQ, which favored 2 Gy per fraction for all cells except melanoma. The DLQ model indicated greater tumor radioresistance than SLQ, but the radioresistance was overcome by hypofractionation, other than the GBM cells, which responded poorly to all fractionations. Conclusion: The distinct radiosensitivity and dynamics between CSCs and DCCs in radiation therapy response could perhaps be one possible explanation for the heterogeneous intertumor response to hypofractionation and in some cases superior outcome from

  18. Sensitivity of Fit Indices to Misspecification in Growth Curve Models

    Science.gov (United States)

    Wu, Wei; West, Stephen G.

    2010-01-01

    This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…

  19. Observing Clonal Dynamics across Spatiotemporal Axes: A Prelude to Quantitative Fitness Models for Cancer.

    Science.gov (United States)

    McPherson, Andrew W; Chan, Fong Chun; Shah, Sohrab P

    2018-02-01

    The ability to accurately model evolutionary dynamics in cancer would allow for prediction of progression and response to therapy. As a prelude to quantitative understanding of evolutionary dynamics, researchers must gather observations of in vivo tumor evolution. High-throughput genome sequencing now provides the means to profile the mutational content of evolving tumor clones from patient biopsies. Together with the development of models of tumor evolution, reconstructing evolutionary histories of individual tumors generates hypotheses about the dynamics of evolution that produced the observed clones. In this review, we provide a brief overview of the concepts involved in predicting evolutionary histories, and provide a workflow based on bulk and targeted-genome sequencing. We then describe the application of this workflow to time series data obtained for transformed and progressed follicular lymphomas (FL), and contrast the observed evolutionary dynamics between these two subtypes. We next describe results from a spatial sampling study of high-grade serous (HGS) ovarian cancer, propose mechanisms of disease spread based on the observed clonal mixtures, and provide examples of diversification through subclonal acquisition of driver mutations and convergent evolution. Finally, we state implications of the techniques discussed in this review as a necessary but insufficient step on the path to predictive modelling of disease dynamics. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

  20. Standard error propagation in R-matrix model fitting for light elements

    International Nuclear Information System (INIS)

    Chen Zhenpeng; Zhang Rui; Sun Yeying; Liu Tingjin

    2003-01-01

    The error propagation features with R-matrix model fitting 7 Li, 11 B and 17 O systems were researched systematically. Some laws of error propagation were revealed, an empirical formula P j = U j c / U j d = K j · S-bar · √m / √N for describing standard error propagation was established, the most likely error ranges for standard cross sections of 6 Li(n,t), 10 B(n,α0) and 10 B(n,α1) were estimated. The problem that the standard error of light nuclei standard cross sections may be too small results mainly from the R-matrix model fitting, which is not perfect. Yet R-matrix model fitting is the most reliable evaluation method for such data. The error propagation features of R-matrix model fitting for compound nucleus system of 7 Li, 11 B and 17 O has been studied systematically, some laws of error propagation are revealed, and these findings are important in solving the problem mentioned above. Furthermore, these conclusions are suitable for similar model fitting in other scientific fields. (author)

  1. Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

    Directory of Open Access Journals (Sweden)

    Georgios C Manikis

    Full Text Available The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2 at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG and non-Gaussian (MNG and BNG were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE. To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC and F-ratio.All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

  2. Model-fitting approach to kinetic analysis of non-isothermal oxidation of molybdenite

    International Nuclear Information System (INIS)

    Ebrahimi Kahrizsangi, R.; Abbasi, M. H.; Saidi, A.

    2007-01-01

    The kinetics of molybdenite oxidation was studied by non-isothermal TGA-DTA with heating rate 5 d eg C .min -1 . The model-fitting kinetic approach applied to TGA data. The Coats-Redfern method used of model fitting. The popular model-fitting gives excellent fit non-isothermal data in chemically controlled regime. The apparent activation energy was determined to be about 34.2 kcalmol -1 With pre-exponential factor about 10 8 sec -1 for extent of reaction less than 0.5

  3. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

    Science.gov (United States)

    Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

    2017-07-15

    New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

  4. When the model fits the frame: the impact of regulatory fit on efficacy appraisal and persuasion in health communication.

    Science.gov (United States)

    Bosone, Lucia; Martinez, Frédéric; Kalampalikis, Nikos

    2015-04-01

    In health-promotional campaigns, positive and negative role models can be deployed to illustrate the benefits or costs of certain behaviors. The main purpose of this article is to investigate why, how, and when exposure to role models strengthens the persuasiveness of a message, according to regulatory fit theory. We argue that exposure to a positive versus a negative model activates individuals' goals toward promotion rather than prevention. By means of two experiments, we demonstrate that high levels of persuasion occur when a message advertising healthy dietary habits offers a regulatory fit between its framing and the described role model. Our data also establish that the effects of such internal regulatory fit by vicarious experience depend on individuals' perceptions of response-efficacy and self-efficacy. Our findings constitute a significant theoretical complement to previous research on regulatory fit and contain valuable practical implications for health-promotional campaigns. © 2015 by the Society for Personality and Social Psychology, Inc.

  5. Flexible competing risks regression modeling and goodness-of-fit

    DEFF Research Database (Denmark)

    Scheike, Thomas; Zhang, Mei-Jie

    2008-01-01

    In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

  6. An NCME Instructional Module on Item-Fit Statistics for Item Response Theory Models

    Science.gov (United States)

    Ames, Allison J.; Penfield, Randall D.

    2015-01-01

    Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing…

  7. Improved physical fitness of cancer survivors : A randomised controlled trial comparing physical training with physical and cognitive-behavioural training

    NARCIS (Netherlands)

    May, Anne M.; Van Weert, Ellen; Korstjens, Irene; Hoekstra-Weebers, Josette E. H. M.; Van Der Schans, Cees P.; Zonderland, Maria L.; Mesters, Ilse; Van Den Borne, Bart; Ros, Wynand J. G.

    2008-01-01

    We compared the effect of a group-based 12-week supervised exercise programme, i.e. aerobic and resistance exercise, and group sports, with that of the same programme combined with cognitive-behavioural training on physical fitness and activity of cancer survivors. One hundred and forty seven cancer

  8. Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

    Science.gov (United States)

    Zamzuri, Zamira Hasanah binti

    2015-10-01

    Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

  9. Item level diagnostics and model - data fit in item response theory ...

    African Journals Online (AJOL)

    Item response theory (IRT) is a framework for modeling and analyzing item response data. Item-level modeling gives IRT advantages over classical test theory. The fit of an item score pattern to an item response theory (IRT) models is a necessary condition that must be assessed for further use of item and models that best fit ...

  10. Fitting ARMA Time Series by Structural Equation Models.

    Science.gov (United States)

    van Buuren, Stef

    1997-01-01

    This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)

  11. Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

    Science.gov (United States)

    Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

    2015-02-01

    To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

  12. Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

    Science.gov (United States)

    Nikbakht, Roya; Bahrampour, Abbas

    2017-01-01

    Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

  13. Gfitter - Revisiting the global electroweak fit of the Standard Model and beyond

    Energy Technology Data Exchange (ETDEWEB)

    Flaecher, H.; Hoecker, A. [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Goebel, M. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)]|[Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Haller, J. [Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Moenig, K.; Stelzer, J. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)

    2008-11-15

    The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter project, and presents state-of-the-art results for the global electroweak fit in the Standard Model, and for a model with an extended Higgs sector (2HDM). Numerical and graphical results for fits with and without including the constraints from the direct Higgs searches at LEP and Tevatron are given. Perspectives for future colliders are analysed and discussed. Including the direct Higgs searches, we find M{sub H}=116.4{sup +18.3}{sub -1.3} GeV, and the 2{sigma} and 3{sigma} allowed regions [114,145] GeV and [[113,168] and [180,225

  14. SPSS macros to compare any two fitted values from a regression model.

    Science.gov (United States)

    Weaver, Bruce; Dubois, Sacha

    2012-12-01

    In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

  15. LEP asymmetries and fits of the standard model

    International Nuclear Information System (INIS)

    Pietrzyk, B.

    1994-01-01

    The lepton and quark asymmetries measured at LEP are presented. The results of the Standard Model fits to the electroweak data presented at this conference are given. The top mass obtained from the fit to the LEP data is 172 -14-20 +13+18 GeV; it is 177 -11-19 +11+18 when also the collider, ν and A LR data are included. (author). 10 refs., 3 figs., 2 tabs

  16. Cancer Modeling: From Optimal Cell Renewal to Immunotherapy

    Science.gov (United States)

    Alvarado Alvarado, Cesar Leonardo

    Cancer is a disease caused by mutations in normal cells. According to the National Cancer Institute, in 2016, an estimated 1.6 million people were diagnosed and approximately 0.5 million people died from the disease in the United States. There are many factors that shape cancer at the cellular and organismal level, including genetic, immunological, and environmental components. In this thesis, we show how mathematical modeling can be used to provide insight into some of the key mechanisms underlying cancer dynamics. First, we use mathematical modeling to investigate optimal homeostatic cell renewal in tissues such as the small intestine with an emphasis on division patterns and tissue architecture. We find that the division patterns that delay the accumulation of mutations are strictly associated with the population sizes of the tissue. In particular, patterns with long chains of differentiation delay the time to observe a second-hit mutant, which is important given that for many cancers two mutations are enough to initiate a tumor. We also investigated homeostatic cell renewal under a selective pressure and find that hierarchically organized tissues act as suppressors of selection; we find that an architecture with a small number of stem cells and larger pools of transit amplifying cells and mature differentiated cells, together with long chains of differentiation, form a robust evolutionary strategy to delay the time to observe a second-hit mutant when mutations acquire a fitness advantage or disadvantage. We also formulate a model of the immune response to cancer in the presence of costimulatory and inhibitory signals. We demonstrate that the coordination of such signals is crucial to initiate an effective immune response, and while immunotherapy has become a promising cancer treatment over the past decade, these results offer some explanations for why it can fail.

  17. Checking the Adequacy of Fit of Models from Split-Plot Designs

    DEFF Research Database (Denmark)

    Almini, A. A.; Kulahci, Murat; Montgomery, D. C.

    2009-01-01

    models. In this article, we propose the computation of two R-2, R-2-adjusted, prediction error sums of squares (PRESS), and R-2-prediction statistics to measure the adequacy of fit for the WP and the SP submodels in a split-plot design. This is complemented with the graphical analysis of the two types......One of the main features that distinguish split-plot experiments from other experiments is that they involve two types of experimental errors: the whole-plot (WP) error and the subplot (SP) error. Taking this into consideration is very important when computing measures of adequacy of fit for split-plot...... of errors to check for any violation of the underlying assumptions and the adequacy of fit of split-plot models. Using examples, we show how computing two measures of model adequacy of fit for each split-plot design model is appropriate and useful as they reveal whether the correct WP and SP effects have...

  18. Repair models of cell survival and corresponding computer program for survival curve fitting

    International Nuclear Information System (INIS)

    Shen Xun; Hu Yiwei

    1992-01-01

    Some basic concepts and formulations of two repair models of survival, the incomplete repair (IR) model and the lethal-potentially lethal (LPL) model, are introduced. An IBM-PC computer program for survival curve fitting with these models was developed and applied to fit the survivals of human melanoma cells HX118 irradiated at different dose rates. Comparison was made between the repair models and two non-repair models, the multitar get-single hit model and the linear-quadratic model, in the fitting and analysis of the survival-dose curves. It was shown that either IR model or LPL model can fit a set of survival curves of different dose rates with same parameters and provide information on the repair capacity of cells. These two mathematical models could be very useful in quantitative study on the radiosensitivity and repair capacity of cells

  19. HDFITS: Porting the FITS data model to HDF5

    Science.gov (United States)

    Price, D. C.; Barsdell, B. R.; Greenhill, L. J.

    2015-09-01

    The FITS (Flexible Image Transport System) data format has been the de facto data format for astronomy-related data products since its inception in the late 1970s. While the FITS file format is widely supported, it lacks many of the features of more modern data serialization, such as the Hierarchical Data Format (HDF5). The HDF5 file format offers considerable advantages over FITS, such as improved I/O speed and compression, but has yet to gain widespread adoption within astronomy. One of the major holdbacks is that HDF5 is not well supported by data reduction software packages and image viewers. Here, we present a comparison of FITS and HDF5 as a format for storage of astronomy datasets. We show that the underlying data model of FITS can be ported to HDF5 in a straightforward manner, and that by doing so the advantages of the HDF5 file format can be leveraged immediately. In addition, we present a software tool, fits2hdf, for converting between FITS and a new 'HDFITS' format, where data are stored in HDF5 in a FITS-like manner. We show that HDFITS allows faster reading of data (up to 100x of FITS in some use cases), and improved compression (higher compression ratios and higher throughput). Finally, we show that by only changing the import lines in Python-based FITS utilities, HDFITS formatted data can be presented transparently as an in-memory FITS equivalent.

  20. Multiscale Cancer Modeling

    Science.gov (United States)

    Macklin, Paul; Cristini, Vittorio

    2013-01-01

    Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insight on the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community. PMID:21529163

  1. Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

    Science.gov (United States)

    Clark, D Angus; Bowles, Ryan P

    2018-04-23

    In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

  2. Modelling lung cancer due to radon and smoking in WISMUT miners: Preliminary results

    International Nuclear Information System (INIS)

    Bijwaard, H.; Dekkers, F.; Van Dillen, T.

    2011-01-01

    A mechanistic two-stage carcinogenesis model has been applied to model lung-cancer mortality in the largest uranium-miner cohort available. Models with and without smoking action both fit the data well. As smoking information is largely missing from the cohort data, a method has been devised to project this information from a case-control study onto the cohort. Model calculations using 256 projections show that the method works well. Preliminary results show that if an explicit smoking action is absent in the model, this is compensated by the values of the baseline parameters. This indicates that in earlier studies performed without smoking information, the results obtained for the radiation parameters are still valid. More importantly, the inclusion of smoking-related parameters shows that these mainly influence the later stages of lung-cancer development. (authors)

  3. Modelling population dynamics model formulation, fitting and assessment using state-space methods

    CERN Document Server

    Newman, K B; Morgan, B J T; King, R; Borchers, D L; Cole, D J; Besbeas, P; Gimenez, O; Thomas, L

    2014-01-01

    This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations.  The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity,  population (within a metapopulation), or species (for multi-species models). The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models.  The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.  

  4. Revisiting the Global Electroweak Fit of the Standard Model and Beyond with Gfitter

    CERN Document Server

    Flächer, Henning; Haller, J; Höcker, A; Mönig, K; Stelzer, J

    2009-01-01

    The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter projec...

  5. Tests of fit of historically-informed models of African American Admixture.

    Science.gov (United States)

    Gross, Jessica M

    2018-02-01

    African American populations in the U.S. formed primarily by mating between Africans and Europeans over the last 500 years. To date, studies of admixture have focused on either a one-time admixture event or continuous input into the African American population from Europeans only. Our goal is to gain a better understanding of the admixture process by examining models that take into account (a) assortative mating by ancestry in the African American population, (b) continuous input from both Europeans and Africans, and (c) historically informed variation in the rate of African migration over time. We used a model-based clustering method to generate distributions of African ancestry in three samples comprised of 147 African Americans from two published sources. We used a log-likelihood method to examine the fit of four models to these distributions and used a log-likelihood ratio test to compare the relative fit of each model. The mean ancestry estimates for our datasets of 77% African/23% European to 83% African/17% European ancestry are consistent with previous studies. We find admixture models that incorporate continuous gene flow from Europeans fit significantly better than one-time event models, and that a model involving continuous gene flow from Africans and Europeans fits better than one with continuous gene flow from Europeans only for two samples. Importantly, models that involve continuous input from Africans necessitate a higher level of gene flow from Europeans than previously reported. We demonstrate that models that take into account information about the rate of African migration over the past 500 years fit observed patterns of African ancestry better than alternative models. Our approach will enrich our understanding of the admixture process in extant and past populations. © 2017 Wiley Periodicals, Inc.

  6. [How to fit and interpret multilevel models using SPSS].

    Science.gov (United States)

    Pardo, Antonio; Ruiz, Miguel A; San Martín, Rafael

    2007-05-01

    Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.

  7. Physical activity, self-efficacy and self-esteem in breast cancer survivors: a panel model.

    Science.gov (United States)

    Awick, Elizabeth A; Phillips, Siobhan M; Lloyd, Gillian R; McAuley, Edward

    2017-10-01

    Physical activity (PA) has been consistently associated with improved self-esteem in breast cancer survivors. However, this relationship is poorly understood. The purpose of this study was to examine whether changes in PA and self-efficacy influenced changes in self-esteem in breast cancer survivors across 6 months. Increases in PA were hypothesized to result in increases in self-efficacy, which were hypothesized to influence increases in physical self-worth (PSW) and global self-esteem. Breast cancer survivors (n = 370; M age  = 56.04) wore accelerometers to measure PA and completed measures of self-efficacy (e.g., exercise and barriers self-efficacy), PSW, and global self-esteem at baseline and 6 months. The hypothesized model provided a good fit to the data (χ 2  = 67.56, df = 26, p self-efficacy. In turn, more efficacious women reported significantly higher PSW (β = 0.26, 0.16). Finally, higher PSW was significantly associated with greater global self-esteem (β = 0.47). Relationships were similar among changes in model constructs over 6 months. After controlling for covariates, the hypothesized model provided an excellent fit to the data (χ 2  = 59.93, df = 33, p = 0.003; comparative fit index = 0.99; standardized root mean residual = 0.03). Our findings provide support for the role played by PA and self-efficacy in positive self-esteem, a key component of well-being. Highlighting successful PA mastery experiences is likely to enhance self-efficacy and improve self-esteem in this population. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  8. NUTRITION AND FITNESS (PART 1: OBESITY, THE METABOLIC SYNDROME, CARDIOVASCULAR DISEASE, AND CANCER

    Directory of Open Access Journals (Sweden)

    Artemis P. Simopoulos

    2005-12-01

    Full Text Available The proceedings of the Fifth International Conference on Nutrition and Fitness held in Athens, Greece, on June 91-2, 2004 are presented in the book as the first volume of the series. The objectives of the book are to review/discuss the latest information on nutrition and fitness by taking into consideration i genetic endowment, ii adaptation to the nutritional factors and the effect of various resources of energy on exercise and performance, iii the epidemiology of obesity, iv the relationship of nutrition and fitness to chronic diseases (cardiovascular diseases, syndrome X, obesity, osteoporosis, diabetes, cancer. The book also discusses the classification system of obesity in several countries and compares the diets used in several regions/countries. FEATURES A common, uniform strategy and evidence-based approach to organizing and interpreting the literature is used in all chapters. This textbook is composed of three parts with sub-sections in three of them. The topics of the parts are: i Obesity and Metabolic Syndrome, ii Coronary Heart Disease and iii Cancer. In each specific chapter, an epidemiological picture has been systematically developed from the data available in prospective, retrospective, case-control, and cross-sectional studies. The tables and figures are numerous, helpful and very useful. AUDIENCE This book is almost a compulsory reading for anyone interested in cardiovascular system, nutrition, metabolism, social and preventive medicine, clinical nutrition, diabetics, genetics, obesity, public health, sports medicine and for those wishing to run comprehensive research in this and relevant areas. The fact that the contributors are leading international researchers in this field makes this book more welcome. ASSESSMENT This book is almost a compulsory reading for anyone interested in pediatric injuries and for those wishing to run comprehensive research in this and relevant areas. The fact that the contributors are leading

  9. A versatile curve-fit model for linear to deeply concave rank abundance curves

    NARCIS (Netherlands)

    Neuteboom, J.H.; Struik, P.C.

    2005-01-01

    A new, flexible curve-fit model for linear to concave rank abundance curves was conceptualized and validated using observational data. The model links the geometric-series model and log-series model and can also fit deeply concave rank abundance curves. The model is based ¿ in an unconventional way

  10. Fitting Equilibrium Search Models to Labour Market Data

    DEFF Research Database (Denmark)

    Bowlus, Audra J.; Kiefer, Nicholas M.; Neumann, George R.

    1996-01-01

    Specification and estimation of a Burdett-Mortensen type equilibrium search model is considered. The estimation is nonstandard. An estimation strategy asymptotically equivalent to maximum likelihood is proposed and applied. The results indicate that specifications with a small number of productiv...... of productivity types fit the data well compared to the homogeneous model....

  11. Tumor Control Probability Modeling for Stereotactic Body Radiation Therapy of Early-Stage Lung Cancer Using Multiple Bio-physical Models

    Science.gov (United States)

    Liu, Feng; Tai, An; Lee, Percy; Biswas, Tithi; Ding, George X.; El Naqa, Isaam; Grimm, Jimm; Jackson, Andrew; Kong, Feng-Ming (Spring); LaCouture, Tamara; Loo, Billy; Miften, Moyed; Solberg, Timothy; Li, X Allen

    2017-01-01

    Purpose To analyze pooled clinical data using different radiobiological models and to understand the relationship between biologically effective dose (BED) and tumor control probability (TCP) for stereotactic body radiotherapy (SBRT) of early-stage non-small cell lung cancer (NSCLC). Method and Materials The clinical data of 1-, 2-, 3-, and 5-year actuarial or Kaplan-Meier TCP from 46 selected studies were collected for SBRT of NSCLC in the literature. The TCP data were separated for Stage T1 and T2 tumors if possible, otherwise collected for combined stages. BED was calculated at isocenters using six radiobiological models. For each model, the independent model parameters were determined from a fit to the TCP data using the least chi-square (χ2) method with either one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately. Results The fits to the clinic data yield consistent results of large α/β ratios of about 20 Gy for all models investigated. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1 Gy physical dose at isocenters per fraction (≤5 fractions) to achieve the optimal TCP when compared to the T1 tumors. Conclusion This systematic analysis of a large set of published clinical data using different radiobiological models shows that local TCP for SBRT of early-stage NSCLC has strong dependence on BED with large α/β ratios of about 20 Gy. The six models predict that a BED (calculated with α/β of 20) of 90 Gy is sufficient to achieve TCP ≥ 95%. Among the models considered, the regrowth model leads to a better fit to the clinical data. PMID:27871671

  12. Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images

    NARCIS (Netherlands)

    Tzimiropoulos, Georgios; Pantic, Maja

    2016-01-01

    Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen variations. In this paper, we look into AAM fitting algorithms and make the following orthogonal contributions: We present a simple “project-out‿

  13. Fast and exact Newton and Bidirectional fitting of Active Appearance Models.

    Science.gov (United States)

    Kossaifi, Jean; Tzimiropoulos, Yorgos; Pantic, Maja

    2016-12-21

    Active Appearance Models (AAMs) are generative models of shape and appearance that have proven very attractive for their ability to handle wide changes in illumination, pose and occlusion when trained in the wild, while not requiring large training dataset like regression-based or deep learning methods. The problem of fitting an AAM is usually formulated as a non-linear least squares one and the main way of solving it is a standard Gauss-Newton algorithm. In this paper we extend Active Appearance Models in two ways: we first extend the Gauss-Newton framework by formulating a bidirectional fitting method that deforms both the image and the template to fit a new instance. We then formulate a second order method by deriving an efficient Newton method for AAMs fitting. We derive both methods in a unified framework for two types of Active Appearance Models, holistic and part-based, and additionally show how to exploit the structure in the problem to derive fast yet exact solutions. We perform a thorough evaluation of all algorithms on three challenging and recently annotated inthe- wild datasets, and investigate fitting accuracy, convergence properties and the influence of noise in the initialisation. We compare our proposed methods to other algorithms and show that they yield state-of-the-art results, out-performing other methods while having superior convergence properties.

  14. The Meaning of Goodness-of-Fit Tests: Commentary on "Goodness-of-Fit Assessment of Item Response Theory Models"

    Science.gov (United States)

    Thissen, David

    2013-01-01

    In this commentary, David Thissen states that "Goodness-of-fit assessment for IRT models is maturing; it has come a long way from zero." Thissen then references prior works on "goodness of fit" in the index of Lord and Novick's (1968) classic text; Yen (1984); Drasgow, Levine, Tsien, Williams, and Mead (1995); Chen and…

  15. Development of a Sampling-Based Global Sensitivity Analysis Workflow for Multiscale Computational Cancer Models

    Science.gov (United States)

    Wang, Zhihui; Deisboeck, Thomas S.; Cristini, Vittorio

    2014-01-01

    There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale in silico cancer models. The first is increased computational intensity, since a multiscale cancer model generally takes longer to run than does a scale-specific model. The second problem is the lack of a best GSA method that fits all types of models, which implies that multiple methods and their sequence need to be taken into account. In this article, we therefore propose a sampling-based GSA workflow consisting of three phases – pre-analysis, analysis, and post-analysis – by integrating Monte Carlo and resampling methods with the repeated use of analysis of variance (ANOVA); we then exemplify this workflow using a two-dimensional multiscale lung cancer model. By accounting for all parameter rankings produced by multiple GSA methods, a summarized ranking is created at the end of the workflow based on the weighted mean of the rankings for each input parameter. For the cancer model investigated here, this analysis reveals that ERK, a downstream molecule of the EGFR signaling pathway, has the most important impact on regulating both the tumor volume and expansion rate in the algorithm used. PMID:25257020

  16. The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.

    Science.gov (United States)

    Tendeiro, Jorge N

    2017-01-01

    Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

  17. Fit Gap Analysis – The Role of Business Process Reference Models

    Directory of Open Access Journals (Sweden)

    Dejan Pajk

    2013-12-01

    Full Text Available Enterprise resource planning (ERP systems support solutions for standard business processes such as financial, sales, procurement and warehouse. In order to improve the understandability and efficiency of their implementation, ERP vendors have introduced reference models that describe the processes and underlying structure of an ERP system. To select and successfully implement an ERP system, the capabilities of that system have to be compared with a company’s business needs. Based on a comparison, all of the fits and gaps must be identified and further analysed. This step usually forms part of ERP implementation methodologies and is called fit gap analysis. The paper theoretically overviews methods for applying reference models and describes fit gap analysis processes in detail. The paper’s first contribution is its presentation of a fit gap analysis using standard business process modelling notation. The second contribution is the demonstration of a process-based comparison approach between a supply chain process and an ERP system process reference model. In addition to its theoretical contributions, the results can also be practically applied to projects involving the selection and implementation of ERP systems.

  18. Soil physical properties influencing the fitting parameters in Philip and Kostiakov infiltration models

    International Nuclear Information System (INIS)

    Mbagwu, J.S.C.

    1994-05-01

    Among the many models developed for monitoring the infiltration process those of Philip and Kostiakov have been studied in detail because of their simplicity and the ease of estimating their fitting parameters. The important soil physical factors influencing the fitting parameters in these infiltration models are reported in this study. The results of the study show that the single most important soil property affecting the fitting parameters in these models is the effective porosity. 36 refs, 2 figs, 5 tabs

  19. The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing.

    Directory of Open Access Journals (Sweden)

    Jaclyn K Mann

    2014-08-01

    Full Text Available Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model, generalizing our previous approach (Ising model that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = -0.74, p = 3.6×10-6 are strongly correlated, and this was further strengthened in the regularized Ising model (r = -0.83, p = 3.7×10-12. Performance of the Potts model (r = -0.73, p = 9.7×10-9 was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion

  20. Correcting Model Fit Criteria for Small Sample Latent Growth Models with Incomplete Data

    Science.gov (United States)

    McNeish, Daniel; Harring, Jeffrey R.

    2017-01-01

    To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…

  1. Supersymmetry with prejudice: Fitting the wrong model to LHC data

    Science.gov (United States)

    Allanach, B. C.; Dolan, Matthew J.

    2012-09-01

    We critically examine interpretations of hypothetical supersymmetric LHC signals, fitting to alternative wrong models of supersymmetry breaking. The signals we consider are some of the most constraining on the sparticle spectrum: invariant mass distributions with edges and endpoints from the golden decay chain q˜→qχ20(→l˜±l∓q)→χ10l+l-q. We assume a constrained minimal supersymmetric standard model (CMSSM) point to be the ‘correct’ one, but fit the signals instead with minimal gauge mediated supersymmetry breaking models (mGMSB) with a neutralino quasistable lightest supersymmetric particle, minimal anomaly mediation and large volume string compactification models. Minimal anomaly mediation and large volume scenario can be unambiguously discriminated against the CMSSM for the assumed signal and 1fb-1 of LHC data at s=14TeV. However, mGMSB would not be discriminated on the basis of the kinematic endpoints alone. The best-fit point spectra of mGMSB and CMSSM look remarkably similar, making experimental discrimination at the LHC based on the edges or Higgs properties difficult. However, using rate information for the golden chain should provide the additional separation required.

  2. Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species.

    Science.gov (United States)

    Adams, Matthew P; Collier, Catherine J; Uthicke, Sven; Ow, Yan X; Langlois, Lucas; O'Brien, Katherine R

    2017-01-04

    When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (T opt ) for maximum photosynthetic rate (P max ). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

  3. Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

    Science.gov (United States)

    Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O'Brien, Katherine R.

    2017-01-01

    When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

  4. Understanding intention to undergo colonoscopy among intermediate-risk siblings of colorectal cancer patients: a test of a mediational model.

    Science.gov (United States)

    Manne, Sharon; Markowitz, Arnold; Winawer, Sidney; Guillem, Jose; Meropol, Neal J; Haller, Daniel; Jandorf, Lina; Rakowski, William; Babb, James; Duncan, Terry

    2003-01-01

    There is a need for research to identify factors influencing intentions to undergo colorectal cancer (CRC) screening among family members at risk for CRC. This study tested a mediational model primarily guided by Ronis' elaboration of the Health Belief Model in predicting intention to have colorectal cancer screening among siblings of individuals diagnosed with colorectal cancer prior to age 56 years. Data were collected from 534 siblings of individuals diagnosed with CRC. A baseline survey was administered by telephone. Measures included perceived susceptibility, CRC severity, physician and family support for CRC screening, cancer-specific distress, the closeness of the relationship with the affected sibling, and future intention to have a colonoscopy. Participant age, gender, and number of prior colonoscopies, as well as the stage of the affected patient's cancer and time from the patient's diagnosis to the interview, were controlled for in the analyses. The proposed model was not a good fit to the data. A respecified model was fit to the data. In this model, physician support, family support, and sibling closeness were significantly associated with both perceived benefits and barriers. Perceived severity was associated with barriers. Benefits and barriers, as well as cancer-specific distress, were directly associated with colonoscopy intentions. Results were consistent with a mediational role for benefits and barriers in the associations of sibling closeness and with a mediational role for barriers in the association between perceived severity and colonoscopy intentions. Family and physician support impacted intentions both directly and indirectly through effects on benefits and barriers. Perceived risk was not associated with benefits, barriers, or colonoscopy intentions. Intervention efforts to increase colonoscopy intentions may benefit from targeting family influences, particularly the affected proband in the family, as well as physician influence, cancer

  5. A person fit test for IRT models for polytomous items

    NARCIS (Netherlands)

    Glas, Cornelis A.W.; Dagohoy, A.V.

    2007-01-01

    A person fit test based on the Lagrange multiplier test is presented for three item response theory models for polytomous items: the generalized partial credit model, the sequential model, and the graded response model. The test can also be used in the framework of multidimensional ability

  6. Compartmental modelling of the pharmacokinetics of a breast cancer resistance protein.

    Science.gov (United States)

    Grandjean, Thomas R B; Chappell, Mike J; Yates, James T W; Jones, Kevin; Wood, Gemma; Coleman, Tanya

    2011-11-01

    A mathematical model for the pharmacokinetics of Hoechst 33342 following administration into a culture medium containing a population of transfected cells (HEK293 hBCRP) with a potent breast cancer resistance protein inhibitor, Fumitremorgin C (FTC), present is described. FTC is reported to almost completely annul resistance mediated by BCRP in vitro. This non-linear compartmental model has seven macroscopic sub-units, with 14 rate parameters. It describes the relationship between the concentration of Hoechst 33342 and FTC, initially spiked in the medium, and the observed change in fluorescence due to Hoechst 33342 binding to DNA. Structural identifiability analysis has been performed using two methods, one based on the similarity transformation/exhaustive modelling approach and the other based on the differential algebra approach. The analyses demonstrated that all models derived are uniquely identifiable for the experiments/observations available. A kinetic modelling software package, namely FACSIMILE (MPCA Software, UK), was used for parameter fitting and to obtain numerical solutions for the system equations. Model fits gave very good agreement with in vitro data provided by AstraZeneca across a variety of experimental scenarios. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  7. The lz(p)* Person-Fit Statistic in an Unfolding Model Context

    NARCIS (Netherlands)

    Tendeiro, Jorge N.

    2017-01-01

    Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded

  8. Rasch models suggested the satisfactory psychometric properties of the World Health Organization Quality of Life-Brief among lung cancer patients.

    Science.gov (United States)

    Lin, Chung-Ying; Yang, Szu-Chun; Lai, Wu-Wei; Su, Wu-Chou; Wang, Jung-Der

    2017-03-01

    The study examined whether the items of the World Health Organization Quality of Life-Brief questionnaire can assess its four underlying domains (Physical, Psychological, Social, and Environment) in a sample of lung cancer patients. All patients ( n = 1150) were recruited from a medical center in Tainan, and each participant completed the World Health Organization Quality of Life-Brief. Several Rasch rating scale models were used to examine the data-model fit, and Rasch analyses corroborated that each domain of the World Health Organization Quality of Life-Brief could be unidimensional. Although three items were found to have a poor fit, all the other items fit the unidimensionality with ordered thresholds.

  9. Analysis of survival in breast cancer patients by using different parametric models

    Science.gov (United States)

    Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

    2017-09-01

    In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

  10. Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components

    KAUST Repository

    Zhang, Saijuan; Krebs-Smith, Susan M.; Midthune, Douglas; Perez, Adriana; Buckman, Dennis W.; Kipnis, Victor; Freedman, Laurence S.; Dodd, Kevin W.; Carroll, Raymond J

    2011-01-01

    There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole

  11. Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components

    KAUST Repository

    Zhang, Saijuan

    2011-01-06

    There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole

  12. Nonlinear models for fitting growth curves of Nellore cows reared in the Amazon Biome

    Directory of Open Access Journals (Sweden)

    Kedma Nayra da Silva Marinho

    2013-09-01

    Full Text Available Growth curves of Nellore cows were estimated by comparing six nonlinear models: Brody, Logistic, two alternatives by Gompertz, Richards and Von Bertalanffy. The models were fitted to weight-age data, from birth to 750 days of age of 29,221 cows, born between 1976 and 2006 in the Brazilian states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima and Tocantins. The models were fitted by the Gauss-Newton method. The goodness of fit of the models was evaluated by using mean square error, adjusted coefficient of determination, prediction error and mean absolute error. Biological interpretation of parameters was accomplished by plotting estimated weights versus the observed weight means, instantaneous growth rate, absolute maturity rate, relative instantaneous growth rate, inflection point and magnitude of the parameters A (asymptotic weight and K (maturing rate. The Brody and Von Bertalanffy models fitted the weight-age data but the other models did not. The average weight (A and growth rate (K were: 384.6±1.63 kg and 0.0022±0.00002 (Brody and 313.40±0.70 kg and 0.0045±0.00002 (Von Bertalanffy. The Brody model provides better goodness of fit than the Von Bertalanffy model.

  13. Bladder cancer mapping in Libya based on standardized morbidity ratio and log-normal model

    Science.gov (United States)

    Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

    2017-05-01

    Disease mapping contains a set of statistical techniques that detail maps of rates based on estimated mortality, morbidity, and prevalence. A traditional approach to measure the relative risk of the disease is called Standardized Morbidity Ratio (SMR). It is the ratio of an observed and expected number of accounts in an area, which has the greatest uncertainty if the disease is rare or if geographical area is small. Therefore, Bayesian models or statistical smoothing based on Log-normal model are introduced which might solve SMR problem. This study estimates the relative risk for bladder cancer incidence in Libya from 2006 to 2007 based on the SMR and log-normal model, which were fitted to data using WinBUGS software. This study starts with a brief review of these models, starting with the SMR method and followed by the log-normal model, which is then applied to bladder cancer incidence in Libya. All results are compared using maps and tables. The study concludes that the log-normal model gives better relative risk estimates compared to the classical method. The log-normal model has can overcome the SMR problem when there is no observed bladder cancer in an area.

  14. Three dimensional fuzzy influence analysis of fitting algorithms on integrated chip topographic modeling

    International Nuclear Information System (INIS)

    Liang, Zhong Wei; Wang, Yi Jun; Ye, Bang Yan; Brauwer, Richard Kars

    2012-01-01

    In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface fitting algorithms are used for constructing micro topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process

  15. Three dimensional fuzzy influence analysis of fitting algorithms on integrated chip topographic modeling

    Energy Technology Data Exchange (ETDEWEB)

    Liang, Zhong Wei; Wang, Yi Jun [Guangzhou Univ., Guangzhou (China); Ye, Bang Yan [South China Univ. of Technology, Guangzhou (China); Brauwer, Richard Kars [Indian Institute of Technology, Kanpur (India)

    2012-10-15

    In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface fitting algorithms are used for constructing micro topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process.

  16. Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

    Science.gov (United States)

    Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F

    2009-11-01

    Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.

  17. Multi-binding site model-based curve-fitting program for the computation of RIA data

    International Nuclear Information System (INIS)

    Malan, P.G.; Ekins, R.P.; Cox, M.G.; Long, E.M.R.

    1977-01-01

    In this paper, a comparison will be made of model-based and empirical curve-fitting procedures. The implementation of a multiple binding-site curve-fitting model which will successfully fit a wide range of assay data, and which can be run on a mini-computer is described. The latter sophisticated model also provides estimates of binding site concentrations and the values of the respective equilibrium constants present: the latter have been used for refining assay conditions using computer optimisation techniques. (orig./AJ) [de

  18. ACSM Fit Society Page

    Science.gov (United States)

    ... fitness topics. Expert commentary and features on exercise, nutrition, sports and health offer tips and techniques for maintaining ... Special Populations 2011 -- Behavior Change & Exercise Adherence 2011 -- ... Preparing for Fall Sports 2009 -- Cancer and Exercise 2008 -- Group Exercise 2008 -- ...

  19. Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI.

    Science.gov (United States)

    Zhang, Guangwen; Wang, Shuangshuang; Wen, Didi; Zhang, Jing; Wei, Xiaocheng; Ma, Wanling; Zhao, Weiwei; Wang, Mian; Wu, Guosheng; Zhang, Jinsong

    2016-12-09

    Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R 2  = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.

  20. Cognitive and affective influences on perceived risk of ovarian cancer.

    Science.gov (United States)

    Peipins, Lucy A; McCarty, Frances; Hawkins, Nikki A; Rodriguez, Juan L; Scholl, Lawrence E; Leadbetter, Steven

    2015-03-01

    Studies suggest that both affective and cognitive processes are involved in the perception of vulnerability to cancer and that affect has an early influence in this assessment of risk. We constructed a path model based on a conceptual framework of heuristic reasoning (affect, resemblance, and availability) coupled with cognitive processes involved in developing personal models of cancer causation. From an eligible cohort of 16 700 women in a managed care organization, we randomly selected 2524 women at high, elevated, and average risk of ovarian cancer and administered a questionnaire to test our model (response rate 76.3%). Path analysis delineated the relationships between personal and cognitive characteristics (number of relatives with cancer, age, ideas about cancer causation, perceived resemblance to an affected friend or relative, and ovarian cancer knowledge) and emotional constructs (closeness to an affected relative or friend, time spent processing the cancer experience, and cancer worry) on perceived risk of ovarian cancer. Our final model fit the data well (root mean square error of approximation (RMSEA) = 0.028, comparative fit index (CFI) = 0.99, normed fit index (NFI) = 0.98). This final model (1) demonstrated the nature and direction of relationships between cognitive characteristics and perceived risk; (2) showed that time spent processing the cancer experience was associated with cancer worry; and (3) showed that cancer worry moderately influenced perceived risk. Our results highlight the important role that family cancer experience has on cancer worry and shows how cancer experience translates into personal risk perceptions. This understanding informs the discordance between medical or objective risk assessment and personal risk assessment. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

  1. Crossing fitness canyons by a finite population

    Science.gov (United States)

    Saakian, David B.; Bratus, Alexander S.; Hu, Chin-Kun

    2017-06-01

    We consider the Wright-Fisher model of the finite population evolution on a fitness landscape defined in the sequence space by a path of nearly neutral mutations. We study a specific structure of the fitness landscape: One of the intermediate mutations on the mutation path results in either a large fitness value (climbing up a fitness hill) or a low fitness value (crossing a fitness canyon), the rest of the mutations besides the last one are neutral, and the last sequence has much higher fitness than any intermediate sequence. We derive analytical formulas for the first arrival time of the mutant with two point mutations. For the first arrival problem for the further mutants in the case of canyon crossing, we analytically deduce how the mean first arrival time scales with the population size and fitness difference. The location of the canyon on the path of sequences has a crucial role. If the canyon is at the beginning of the path, then it significantly prolongs the first arrival time; otherwise it just slightly changes it. Furthermore, the fitness hill at the beginning of the path strongly prolongs the arrival time period; however, the hill located near the end of the path shortens it. We optimize the first arrival time by applying a nonzero selection to the intermediate sequences. We extend our results and provide a scaling for the valley crossing time via the depth of the canyon and population size in the case of a fitness canyon at the first position. Our approach is useful for understanding some complex evolution systems, e.g., the evolution of cancer.

  2. Application of support vector machine model for enhancing the diagnostic value of tumor markers in gastric cancer

    International Nuclear Information System (INIS)

    Wang Hui; Huang Gang

    2010-01-01

    Objective: To evaluate the early diagnostic value of tumor markers for gastric cancer using support vector machine (SVM) model. Methods: Subjects involved in the study consisted of 262 cases with gastric cancer, 156 cases with benign gastric diseases and 149 healthy controls. From those subjects, five tumor markers, carcinoembryonic antigen (CEA), carbohydrate (CA) 125, CA19-9, alphafetoprotein (AFP) and CA50, were assayed and collected to make the datasets. To modify SVM model to fit the diagnostic classifiers, radial basis function was adopted and kernel function was optimized and validated by grid search and cross validation. For comparative study, methods of combination tests of five markers, Logistic regression, and decision tree were also used. Results: For gastric cancer, the diagnostic accuracy of the combination tests, Logistic regression, decision tree and SVM model were 46.2%, 64.5%, 63.9% and 95.1% respectively. SVM model significantly elevated the diagnostic value comparing with other three methods. Conclusion: The application of SVM model is of high value in enhancing the tumor marker for the diagnosis of gastric cancer. (authors)

  3. Radon-induced lung cancer in smokers and non-smokers: risk implications using a two-mutation carcinogenesis model

    International Nuclear Information System (INIS)

    Leenhouts, H.P.

    1999-01-01

    Three sets of data (population statistics in non-smokers, data from an investigation of the smoking habits of British doctors and a study of Colorado uranium miners) were used to analyse lung cancer in humans as a function of exposure to radon and smoking. One of the aims was to derive implications for radon risk estimates. The data were analysed using a two-mutation radiation carcinogenesis model and a stepwise determination of the model parameters. The basic model parameters for lung cancer were derived from the age dependence fit of the spontaneous lung cancer incidence in non-smokers. The effect of smoking was described by two additional parameters and, subsequently, the effect of radon by three other parameters; these five parameters define the dependence of the two mutation steps on smoking and exposure to radon. Using this approach, a consistent fit and comprehensive description of the three sets of data have been achieved, and the parameters could, at least partly, be related to cellular radiobiological data. The model results explain the different effect of radon on non-smokers and smokers as seen in epidemiological data. Although the analysis was only applied to a limited number of populations, lung cancer incidence as a result of radon exposure is estimated to be about ten times higher for people exposed at the age of about 15 than at about 50, although this effect is masked (especially for smokers) by the high lung cancer incidence from smoking. Using the model to calculate the lung cancer risks from lifetime exposure to radon, as is the case for indoor radon, higher risks were estimated than previously derived from epidemiological studies of the miners' data. The excess absolute risk per unit exposure of radon is about 1.7 times higher for smokers of 30 cigarettes per day than for non-smokers, even though, as a result of the low spontaneous tumour incidence in the non-smokers, the excess relative risk per unit exposure for the smokers is about 20 times

  4. Designing iCanFit: A Mobile-Enabled Web Application to Promote Physical Activity for Older Cancer Survivors.

    Science.gov (United States)

    Hong, Yan; Dahlke, Deborah Vollmer; Ory, Marcia; Hochhalter, Angela; Reynolds, Jana; Purcell, Ninfa Pena; Talwar, Divya; Eugene, Nola

    2013-02-14

    Most older cancer survivors (OCS) do not engage in regular physical activity (PA) despite well-known health benefits. With the increased use of mobile technologies among older adults, mobile tools may be an effective method to deliver PA promotion programs for OCS. To document the process of designing an OCS-friendly mobile-enabled Web application of PA promotion program. Mixed methods encompassing group discussions, individual interviews, and brief surveys with community leaders, OCS, cancer care providers, and software professionals were used in this formative research. The varied stakeholders welcomed the idea of developing an online tool to promote PA in OCS. Our formative research revealed several major barriers to regular PA including limited access to senior-friendly PA resources, lack of motivation and social support, and insufficient knowledge and skills on building safe and appropriate workout plans. This feedback was incorporated into the development of iCanFit, a mobile-enabled Web application, designed specifically for OCS. The iCanFit online tools allow users to locate PA resources, set and track goals for PA, network with peer OCS in a secure online space, and receive practical and evidence-informed healthy tips. Our mixed-method formative research led to the design of iCanFit protocol to promote PA and well-being of OCS. The involvement of stakeholders is critical in the planning and design of the mobile application in order to enhance program relevance, appeal, and match with the needs of target users.

  5. Using the Flipchem Photochemistry Model When Fitting Incoherent Scatter Radar Data

    Science.gov (United States)

    Reimer, A. S.; Varney, R. H.

    2017-12-01

    The North face Resolute Bay Incoherent Scatter Radar (RISR-N) routinely images the dynamics of the polar ionosphere, providing measurements of the plasma density, electron temperature, ion temperature, and line of sight velocity with seconds to minutes time resolution. RISR-N does not directly measure ionospheric parameters, but backscattered signals, recording them as voltage samples. Using signal processing techniques, radar autocorrelation functions (ACF) are estimated from the voltage samples. A model of the signal ACF is then fitted to the ACF using non-linear least-squares techniques to obtain the best-fit ionospheric parameters. The signal model, and therefore the fitted parameters, depend on the ionospheric ion composition that is used [e.g. Zettergren et. al. (2010), Zou et. al. (2017)].The software used to process RISR-N ACF data includes the "flipchem" model, which is an ion photochemistry model developed by Richards [2011] that was adapted from the Field LineInterhemispheric Plasma (FLIP) model. Flipchem requires neutral densities, neutral temperatures, electron density, ion temperature, electron temperature, solar zenith angle, and F10.7 as inputs to compute ion densities, which are input to the signal model. A description of how the flipchem model is used in RISR-N fitting software will be presented. Additionally, a statistical comparison of the fitted electron density, ion temperature, electron temperature, and velocity obtained using a flipchem ionosphere, a pure O+ ionosphere, and a Chapman O+ ionosphere will be presented. The comparison covers nearly two years of RISR-N data (April 2015 - December 2016). Richards, P. G. (2011), Reexamination of ionospheric photochemistry, J. Geophys. Res., 116, A08307, doi:10.1029/2011JA016613.Zettergren, M., Semeter, J., Burnett, B., Oliver, W., Heinselman, C., Blelly, P.-L., and Diaz, M.: Dynamic variability in F-region ionospheric composition at auroral arc boundaries, Ann. Geophys., 28, 651-664, https

  6. Fitting Latent Cluster Models for Networks with latentnet

    Directory of Open Access Journals (Sweden)

    Pavel N. Krivitsky

    2007-12-01

    Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

  7. Trend and forecasting rate of cancer deaths at a public university hospital using univariate modeling

    Science.gov (United States)

    Ismail, A.; Hassan, Noor I.

    2013-09-01

    Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.

  8. Human Cancer Models Initiative | Office of Cancer Genomics

    Science.gov (United States)

    The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models, which are annotated with genomic and clinical data. In an effort to advance cancer research and more fully understand how in vitro findings are related to clinical biology, HCMI-developed models and related data will be available as a community resource for cancer research.

  9. Twitter classification model: the ABC of two million fitness tweets.

    Science.gov (United States)

    Vickey, Theodore A; Ginis, Kathleen Martin; Dabrowski, Maciej

    2013-09-01

    The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.

  10. Model Checking of a Diabetes-Cancer Model

    Science.gov (United States)

    Gong, Haijun; Zuliani, Paolo; Clarke, Edmund M.

    2011-06-01

    Accumulating evidence suggests that cancer incidence might be associated with diabetes mellitus, especially Type II diabetes which is characterized by hyperinsulinaemia, hyperglycaemia, obesity, and overexpression of multiple WNT pathway components. These diabetes risk factors can activate a number of signaling pathways that are important in the development of different cancers. To systematically understand the signaling components that link diabetes and cancer risk, we have constructed a single-cell, Boolean network model by integrating the signaling pathways that are influenced by these risk factors to study insulin resistance, cancer cell proliferation and apoptosis. Then, we introduce and apply the Symbolic Model Verifier (SMV), a formal verification tool, to qualitatively study some temporal logic properties of our diabetes-cancer model. The verification results show that the diabetes risk factors might not increase cancer risk in normal cells, but they will promote cell proliferation if the cell is in a precancerous or cancerous stage characterized by losses of the tumor-suppressor proteins ARF and INK4a.

  11. Spherical Cancer Models in Tumor Biology

    Directory of Open Access Journals (Sweden)

    Louis-Bastien Weiswald

    2015-01-01

    Full Text Available Three-dimensional (3D in vitro models have been used in cancer research as an intermediate model between in vitro cancer cell line cultures and in vivo tumor. Spherical cancer models represent major 3D in vitro models that have been described over the past 4 decades. These models have gained popularity in cancer stem cell research using tumorospheres. Thus, it is crucial to define and clarify the different spherical cancer models thus far described. Here, we focus on in vitro multicellular spheres used in cancer research. All these spherelike structures are characterized by their well-rounded shape, the presence of cancer cells, and their capacity to be maintained as free-floating cultures. We propose a rational classification of the four most commonly used spherical cancer models in cancer research based on culture methods for obtaining them and on subsequent differences in sphere biology: the multicellular tumor spheroid model, first described in the early 70s and obtained by culture of cancer cell lines under nonadherent conditions; tumorospheres, a model of cancer stem cell expansion established in a serum-free medium supplemented with growth factors; tissue-derived tumor spheres and organotypic multicellular spheroids, obtained by tumor tissue mechanical dissociation and cutting. In addition, we describe their applications to and interest in cancer research; in particular, we describe their contribution to chemoresistance, radioresistance, tumorigenicity, and invasion and migration studies. Although these models share a common 3D conformation, each displays its own intrinsic properties. Therefore, the most relevant spherical cancer model must be carefully selected, as a function of the study aim and cancer type.

  12. Description of cervical cancer mortality in Belgium using Bayesian age-period-cohort models

    Science.gov (United States)

    2009-01-01

    Objective To correct cervical cancer mortality rates for death cause certification problems in Belgium and to describe the corrected trends (1954-1997) using Bayesian models. Method Cervical cancer (cervix uteri (CVX), corpus uteri (CRP), not otherwise specified (NOS) uterus cancer and other very rare uterus cancer (OTH) mortality data were extracted from the WHO mortality database together with population data for Belgium and the Netherlands. Different ICD (International Classification of Diseases) were used over time for death cause certification. In the Netherlands, the proportion of not-otherwise specified uterine cancer deaths was small over large periods and therefore internal reallocation could be used to estimate the corrected rates cervical cancer mortality. In Belgium, the proportion of improperly defined uterus deaths was high. Therefore, the age-specific proportions of uterus cancer deaths that are probably of cervical origin for the Netherlands was applied to Belgian uterus cancer deaths to estimate the corrected number of cervix cancer deaths (corCVX). A Bayesian loglinear Poisson-regression model was performed to disentangle the separate effects of age, period and cohort. Results The corrected age standardized mortality rate (ASMR) decreased regularly from 9.2/100 000 in the mid 1950s to 2.5/100,000 in the late 1990s. Inclusion of age, period and cohort into the models were required to obtain an adequate fit. Cervical cancer mortality increases with age, declines over calendar period and varied irregularly by cohort. Conclusion Mortality increased with ageing and declined over time in most age-groups, but varied irregularly by birth cohort. In global, with some discrete exceptions, mortality decreased for successive generations up to the cohorts born in the 1930s. This decline stopped for cohorts born in the 1940s and thereafter. For the youngest cohorts, even a tendency of increasing risk of dying from cervical cancer could be observed, reflecting

  13. Simulation of parametric model towards the fixed covariate of right censored lung cancer data

    Science.gov (United States)

    Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

    2017-09-01

    In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

  14. Mouse Models of Gastric Cancer

    Science.gov (United States)

    Hayakawa, Yoku; Fox, James G.; Gonda, Tamas; Worthley, Daniel L.; Muthupalani, Sureshkumar; Wang, Timothy C.

    2013-01-01

    Animal models have greatly enriched our understanding of the molecular mechanisms of numerous types of cancers. Gastric cancer is one of the most common cancers worldwide, with a poor prognosis and high incidence of drug-resistance. However, most inbred strains of mice have proven resistant to gastric carcinogenesis. To establish useful models which mimic human gastric cancer phenotypes, investigators have utilized animals infected with Helicobacter species and treated with carcinogens. In addition, by exploiting genetic engineering, a variety of transgenic and knockout mouse models of gastric cancer have emerged, such as INS-GAS mice and TFF1 knockout mice. Investigators have used the combination of carcinogens and gene alteration to accelerate gastric cancer development, but rarely do mouse models show an aggressive and metastatic gastric cancer phenotype that could be relevant to preclinical studies, which may require more specific targeting of gastric progenitor cells. Here, we review current gastric carcinogenesis mouse models and provide our future perspectives on this field. PMID:24216700

  15. Brief communication: human cranial variation fits iterative founder effect model with African origin.

    Science.gov (United States)

    von Cramon-Taubadel, Noreen; Lycett, Stephen J

    2008-05-01

    Recent studies comparing craniometric and neutral genetic affinity matrices have concluded that, on average, human cranial variation fits a model of neutral expectation. While human craniometric and genetic data fit a model of isolation by geographic distance, it is not yet clear whether this is due to geographically mediated gene flow or human dispersal events. Recently, human genetic data have been shown to fit an iterative founder effect model of dispersal with an African origin, in line with the out-of-Africa replacement model for modern human origins, and Manica et al. (Nature 448 (2007) 346-349) have demonstrated that human craniometric data also fit this model. However, in contrast with the neutral model of cranial evolution suggested by previous studies, Manica et al. (2007) made the a priori assumption that cranial form has been subject to climatically driven natural selection and therefore correct for climate prior to conducting their analyses. Here we employ a modified theoretical and methodological approach to test whether human cranial variability fits the iterative founder effect model. In contrast with Manica et al. (2007) we employ size-adjusted craniometric variables, since climatic factors such as temperature have been shown to correlate with aspects of cranial size. Despite these differences, we obtain similar results to those of Manica et al. (2007), with up to 26% of global within-population craniometric variation being explained by geographic distance from sub-Saharan Africa. Comparative analyses using non-African origins do not yield significant results. The implications of these results are discussed in the light of the modern human origins debate. (c) 2007 Wiley-Liss, Inc.

  16. Rapid world modeling: Fitting range data to geometric primitives

    International Nuclear Information System (INIS)

    Feddema, J.; Little, C.

    1996-01-01

    For the past seven years, Sandia National Laboratories has been active in the development of robotic systems to help remediate DOE's waste sites and decommissioned facilities. Some of these facilities have high levels of radioactivity which prevent manual clean-up. Tele-operated and autonomous robotic systems have been envisioned as the only suitable means of removing the radioactive elements. World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data

  17. PET-based compartmental modeling of {sup 124}I-A33 antibody: quantitative characterization of patient-specific tumor targeting in colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Zanzonico, Pat; O' Donoghue, Joseph A.; Humm, John L. [Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, NY (United States); Carrasquillo, Jorge A.; Pandit-Taskar, Neeta; Ruan, Shutian; Larson, Steven M. [Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY (United States); Smith-Jones, Peter [Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY (United States); Stony Brook School of Medicine, Departments of Psychiatry and Radiology, Stony Brook, NY (United States); Divgi, Chaitanya [Columbia University Medical Center, New York, NY (United States); Scott, Andrew M. [La Trobe University, Olivia Newton-John Cancer Research Institute, Melbourne (Australia); Kemeny, Nancy E.; Wong, Douglas; Scheinberg, David [Memorial Sloan Kettering Cancer Center, Department of Medicine, New York, NY (United States); Fong, Yuman [Memorial Sloan Kettering Cancer Center, Department of Surgery, New York, NY (United States); City of Hope, Department of Surgery, Duarte, CA (United States); Ritter, Gerd; Jungbluth, Achem; Old, Lloyd J. [Memorial Sloan Kettering Cancer Center, Ludwig Institute for Cancer Research, New York, NY (United States)

    2015-10-15

    The molecular specificity of monoclonal antibodies (mAbs) directed against tumor antigens has proven effective for targeted therapy of human cancers, as shown by a growing list of successful antibody-based drug products. We describe a novel, nonlinear compartmental model using PET-derived data to determine the ''best-fit'' parameters and model-derived quantities for optimizing biodistribution of intravenously injected {sup 124}I-labeled antitumor antibodies. As an example of this paradigm, quantitative image and kinetic analyses of anti-A33 humanized mAb (also known as ''A33'') were performed in 11 colorectal cancer patients. Serial whole-body PET scans of {sup 124}I-labeled A33 and blood samples were acquired and the resulting tissue time-activity data for each patient were fit to a nonlinear compartmental model using the SAAM II computer code. Excellent agreement was observed between fitted and measured parameters of tumor uptake, ''off-target'' uptake in bowel mucosa, blood clearance, tumor antigen levels, and percent antigen occupancy. This approach should be generally applicable to antibody-antigen systems in human tumors for which the masses of antigen-expressing tumor and of normal tissues can be estimated and for which antibody kinetics can be measured with PET. Ultimately, based on each patient's resulting ''best-fit'' nonlinear model, a patient-specific optimum mAb dose (in micromoles, for example) may be derived. (orig.)

  18. Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.

    Science.gov (United States)

    Irvine, Michael A; Hollingsworth, T Déirdre

    2018-05-26

    Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  19. Model-independent partial wave analysis using a massively-parallel fitting framework

    Science.gov (United States)

    Sun, L.; Aoude, R.; dos Reis, A. C.; Sokoloff, M.

    2017-10-01

    The functionality of GooFit, a GPU-friendly framework for doing maximum-likelihood fits, has been extended to extract model-independent {\\mathscr{S}}-wave amplitudes in three-body decays such as D + → h + h + h -. A full amplitude analysis is done where the magnitudes and phases of the {\\mathscr{S}}-wave amplitudes are anchored at a finite number of m 2(h + h -) control points, and a cubic spline is used to interpolate between these points. The amplitudes for {\\mathscr{P}}-wave and {\\mathscr{D}}-wave intermediate states are modeled as spin-dependent Breit-Wigner resonances. GooFit uses the Thrust library, with a CUDA backend for NVIDIA GPUs and an OpenMP backend for threads with conventional CPUs. Performance on a variety of platforms is compared. Executing on systems with GPUs is typically a few hundred times faster than executing the same algorithm on a single CPU.

  20. Prostate cancer detection from model-free T1-weighted time series and diffusion imaging

    Science.gov (United States)

    Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

    2015-03-01

    The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

  1. Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy

    International Nuclear Information System (INIS)

    Tsougos, Ioannis; Mavroidis, Panayiotis; Theodorou, Kyriaki; Rajala, J; Pitkaenen, M A; Holli, K; Ojala, A T; Hyoedynmaa, S; Jaervenpaeae, Ritva; Lind, Bengt K; Kappas, Constantin

    2006-01-01

    The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving D-bar-bar vertical bar EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived. (letter to the editor)

  2. The issue of statistical power for overall model fit in evaluating structural equation models

    Directory of Open Access Journals (Sweden)

    Richard HERMIDA

    2015-06-01

    Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.

  3. Fitting and comparing competing models of the species abundance distribution: assessment and prospect

    Directory of Open Access Journals (Sweden)

    Thomas J Matthews

    2014-06-01

    Full Text Available A species abundance distribution (SAD characterises patterns in the commonness and rarity of all species within an ecological community. As such, the SAD provides the theoretical foundation for a number of other biogeographical and macroecological patterns, such as the species–area relationship, as well as being an interesting pattern in its own right. While there has been resurgence in the study of SADs in the last decade, less focus has been placed on methodology in SAD research, and few attempts have been made to synthesise the vast array of methods which have been employed in SAD model evaluation. As such, our review has two aims. First, we provide a general overview of SADs, including descriptions of the commonly used distributions, plotting methods and issues with evaluating SAD models. Second, we review a number of recent advances in SAD model fitting and comparison. We conclude by providing a list of recommendations for fitting and evaluating SAD models. We argue that it is time for SAD studies to move away from many of the traditional methods available for fitting and evaluating models, such as sole reliance on the visual examination of plots, and embrace statistically rigorous techniques. In particular, we recommend the use of both goodness-of-fit tests and model-comparison analyses because each provides unique information which one can use to draw inferences.

  4. Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure.

    Science.gov (United States)

    Yung, Yiu-Fai; Browne, Michael W; Zhang, Wei

    2015-02-01

    This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple-group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy-to-use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large. © 2014 The British Psychological Society.

  5. The influence of high-intensity compared with moderate-intensity exercise training on cardiorespiratory fitness and body composition in colorectal cancer survivors: a randomised controlled trial.

    Science.gov (United States)

    Devin, James L; Sax, Andrew T; Hughes, Gareth I; Jenkins, David G; Aitken, Joanne F; Chambers, Suzanne K; Dunn, Jeffrey C; Bolam, Kate A; Skinner, Tina L

    2016-06-01

    Following colorectal cancer diagnosis and anti-cancer therapy, declines in cardiorespiratory fitness and body composition lead to significant increases in morbidity and mortality. There is increasing interest within the field of exercise oncology surrounding potential strategies to remediate these adverse outcomes. This study compared 4 weeks of moderate-intensity exercise (MIE) and high-intensity exercise (HIE) training on peak oxygen consumption (V̇O2peak) and body composition in colorectal cancer survivors. Forty seven post-treatment colorectal cancer survivors (HIE = 27 months post-treatment; MIE = 38 months post-treatment) were randomised to either HIE [85-95 % peak heart rate (HRpeak)] or MIE (70 % HRpeak) in equivalence with current physical activity guidelines and completed 12 training sessions over 4 weeks. HIE was superior to MIE in improving absolute (p = 0.016) and relative (p = 0.021) V̇O2peak. Absolute (+0.28 L.min(-1), p body composition for colorectal cancer survivors. HIE appears to offer superior improvements in cardiorespiratory fitness and body composition in comparison to current physical activity recommendations for colorectal cancer survivors and therefore may be an effective clinical utility following treatment.

  6. A goodness-of-fit test for occupancy models with correlated within-season revisits

    Science.gov (United States)

    Wright, Wilson; Irvine, Kathryn M.; Rodhouse, Thomas J.

    2016-01-01

    Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and

  7. Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

    OpenAIRE

    Matthew P. Adams; Catherine J. Collier; Sven Uthicke; Yan X. Ow; Lucas Langlois; Katherine R. O’Brien

    2017-01-01

    When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluat...

  8. Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.

    Science.gov (United States)

    Tøndel, Kristin; Niederer, Steven A; Land, Sander; Smith, Nicolas P

    2014-05-20

    Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input-output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on

  9. Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level.

    Science.gov (United States)

    Onega, Tracy; Beaber, Elisabeth F; Sprague, Brian L; Barlow, William E; Haas, Jennifer S; Tosteson, Anna N A; D Schnall, Mitchell; Armstrong, Katrina; Schapira, Marilyn M; Geller, Berta; Weaver, Donald L; Conant, Emily F

    2014-10-01

    Breast cancer screening holds a prominent place in public health, health care delivery, policy, and women's health care decisions. Several factors are driving shifts in how population-based breast cancer screening is approached, including advanced imaging technologies, health system performance measures, health care reform, concern for "overdiagnosis," and improved understanding of risk. Maximizing benefits while minimizing the harms of screening requires moving from a "1-size-fits-all" guideline paradigm to more personalized strategies. A refined conceptual model for breast cancer screening is needed to align women's risks and preferences with screening regimens. A conceptual model of personalized breast cancer screening is presented herein that emphasizes key domains and transitions throughout the screening process, as well as multilevel perspectives. The key domains of screening awareness, detection, diagnosis, and treatment and survivorship are conceptualized to function at the level of the patient, provider, facility, health care system, and population/policy arena. Personalized breast cancer screening can be assessed across these domains with both process and outcome measures. Identifying, evaluating, and monitoring process measures in screening is a focus of a National Cancer Institute initiative entitled PROSPR (Population-based Research Optimizing Screening through Personalized Regimens), which will provide generalizable evidence for a risk-based model of breast cancer screening, The model presented builds on prior breast cancer screening models and may serve to identify new measures to optimize benefits-to-harms tradeoffs in population-based screening, which is a timely goal in the era of health care reform. © 2014 American Cancer Society.

  10. Efficient parallel implementation of active appearance model fitting algorithm on GPU.

    Science.gov (United States)

    Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

    2014-01-01

    The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

  11. ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction

    International Nuclear Information System (INIS)

    Toledo, Maria Luíza Guerra de; Freitas, Marta A.; Colosimo, Enrico A.; Gilardoni, Gustavo L.

    2015-01-01

    An appropriate maintenance policy is essential to reduce expenses and risks related to equipment failures. A fundamental aspect to be considered when specifying such policies is to be able to predict the reliability of the systems under study, based on a well fitted model. In this paper, the classes of models Arithmetic Reduction of Age and Arithmetic Reduction of Intensity are explored. Likelihood functions for such models are derived, and a graphical method is proposed for model selection. A real data set involving failures in trucks used by a Brazilian mining is analyzed considering models with different memories. Parameters, namely, shape and scale for Power Law Process, and the efficiency of repair were estimated for the best fitted model. Estimation of model parameters allowed us to derive reliability estimators to predict the behavior of the failure process. These results are a valuable information for the mining company and can be used to support decision making regarding preventive maintenance policy. - Highlights: • Likelihood functions for imperfect repair models are derived. • A goodness-of-fit technique is proposed as a tool for model selection. • Failures in trucks owned by a Brazilian mining are modeled. • Estimation allowed deriving reliability predictors to forecast the future failure process of the trucks

  12. Person-fit to the Five Factor Model of personality

    Czech Academy of Sciences Publication Activity Database

    Allik, J.; Realo, A.; Mõttus, R.; Borkenau, P.; Kuppens, P.; Hřebíčková, Martina

    2012-01-01

    Roč. 71, č. 1 (2012), s. 35-45 ISSN 1421-0185 R&D Projects: GA ČR GAP407/10/2394 Institutional research plan: CEZ:AV0Z70250504 Keywords : Five Factor Model * cross - cultural comparison * person-fit Subject RIV: AN - Psychology Impact factor: 0.638, year: 2012

  13. Study on fitness functions of genetic algorithm for dynamically correcting nuclide atmospheric diffusion model

    International Nuclear Information System (INIS)

    Ji Zhilong; Ma Yuanwei; Wang Dezhong

    2014-01-01

    Background: In radioactive nuclides atmospheric diffusion models, the empirical dispersion coefficients were deduced under certain experiment conditions, whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide's actual dispersion process could be done by correcting dispersion coefficients with observation data, and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions' influence on the correction procedure and the forecast ability of diffusion model. Methods: GA, coupled with Lagrange dispersion model, was used in a numerical simulation to compare 4 fitness functions' impact on the correction result. Results: In the numerical simulation, the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data, a significant boost was observed in the diffusion model's forecast ability. Conclusion: As the result shows, in order to improve dispersion models' forecast ability using GA, observation data should be given different weight in the fitness function corresponding to their error. (authors)

  14. Model Fitting for Predicted Precipitation in Darwin: Some Issues with Model Choice

    Science.gov (United States)

    Farmer, Jim

    2010-01-01

    In Volume 23(2) of the "Australian Senior Mathematics Journal," Boncek and Harden present an exercise in fitting a Markov chain model to rainfall data for Darwin Airport (Boncek & Harden, 2009). Days are subdivided into those with precipitation and precipitation-free days. The author abbreviates these labels to wet days and dry days.…

  15. Mandatory communication skills training for cancer and palliative care staff: does one size fit all?

    Science.gov (United States)

    Turner, Mary; Payne, Sheila; O'Brien, Terri

    2011-12-01

    There is increasing recognition of the importance of good communication between healthcare professionals and patients facing cancer or end of life. In England, a new national 3-day training programme called 'Connected' has been developed and is now mandatory for all cancer and palliative care professionals. This study aimed to explore the attitudes of staff in one region to undertaking this training. A survey questionnaire was developed through a series of discussions with experts and semi-structured interviews with five healthcare professionals. The questionnaire was distributed to 200 cancer and palliative care staff; 109 were completed and returned. There were significant differences between doctors' and nurses' attitudes to communication skills training, with doctors demonstrating more negative attitudes. More nurses than doctors felt that communication skills training should be mandatory for cancer and palliative care professionals (p ≤ 0.001), whilst more doctors felt that these staff should already be skilled communicators and not require further training (p ≤ 0.001). Nurses also self-rated their communication skills more highly than doctors. The current 'one size fits all' approach being taken nationally to advanced communication skills training does not meet the training preferences of all healthcare professionals, and it is recommended that tailoring courses to individuals' needs should be considered. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.

    Science.gov (United States)

    Klijn, Sven L; Weijenberg, Matty P; Lemmens, Paul; van den Brandt, Piet A; Lima Passos, Valéria

    2017-10-01

    Background and objective Group-based trajectory modelling is a model-based clustering technique applied for the identification of latent patterns of temporal changes. Despite its manifold applications in clinical and health sciences, potential problems of the model selection procedure are often overlooked. The choice of the number of latent trajectories (class-enumeration), for instance, is to a large degree based on statistical criteria that are not fail-safe. Moreover, the process as a whole is not transparent. To facilitate class enumeration, we introduce a graphical summary display of several fit and model adequacy criteria, the fit-criteria assessment plot. Methods An R-code that accepts universal data input is presented. The programme condenses relevant group-based trajectory modelling output information of model fit indices in automated graphical displays. Examples based on real and simulated data are provided to illustrate, assess and validate fit-criteria assessment plot's utility. Results Fit-criteria assessment plot provides an overview of fit criteria on a single page, placing users in an informed position to make a decision. Fit-criteria assessment plot does not automatically select the most appropriate model but eases the model assessment procedure. Conclusions Fit-criteria assessment plot is an exploratory, visualisation tool that can be employed to assist decisions in the initial and decisive phase of group-based trajectory modelling analysis. Considering group-based trajectory modelling's widespread resonance in medical and epidemiological sciences, a more comprehensive, easily interpretable and transparent display of the iterative process of class enumeration may foster group-based trajectory modelling's adequate use.

  17. Modeling qRT-PCR dynamics with application to cancer biomarker quantification.

    Science.gov (United States)

    Chervoneva, Inna; Freydin, Boris; Hyslop, Terry; Waldman, Scott A

    2017-01-01

    Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used for molecular diagnostics and evaluating prognosis in cancer. The utility of mRNA expression biomarkers relies heavily on the accuracy and precision of quantification, which is still challenging for low abundance transcripts. The critical step for quantification is accurate estimation of efficiency needed for computing a relative qRT-PCR expression. We propose a new approach to estimating qRT-PCR efficiency based on modeling dynamics of polymerase chain reaction amplification. In contrast, only models for fluorescence intensity as a function of polymerase chain reaction cycle have been used so far for quantification. The dynamics of qRT-PCR efficiency is modeled using an ordinary differential equation model, and the fitted ordinary differential equation model is used to obtain effective polymerase chain reaction efficiency estimates needed for efficiency-adjusted quantification. The proposed new qRT-PCR efficiency estimates were used to quantify GUCY2C (Guanylate Cyclase 2C) mRNA expression in the blood of colorectal cancer patients. Time to recurrence and GUCY2C expression ratios were analyzed in a joint model for survival and longitudinal outcomes. The joint model with GUCY2C quantified using the proposed polymerase chain reaction efficiency estimates provided clinically meaningful results for association between time to recurrence and longitudinal trends in GUCY2C expression.

  18. Esophageal Cancer: Insights from Mouse Models

    Directory of Open Access Journals (Sweden)

    Marie-Pier Tétreault

    2015-01-01

    Full Text Available Esophageal cancer is the eighth leading cause of cancer and the sixth most common cause of cancer-related death worldwide. Despite recent advances in the development of surgical techniques in combination with the use of radiotherapy and chemotherapy, the prognosis for esophageal cancer remains poor. The cellular and molecular mechanisms that drive the pathogenesis of esophageal cancer are still poorly understood. Hence, understanding these mechanisms is crucial to improving outcomes for patients with esophageal cancer. Mouse models constitute valuable tools for modeling human cancers and for the preclinical testing of therapeutic strategies in a manner not possible in human subjects. Mice are excellent models for studying human cancers because they are similar to humans at the physiological and molecular levels and because they have a shorter gestation time and life cycle. Moreover, a wide range of well-developed technologies for introducing genetic modifications into mice are currently available. In this review, we describe how different mouse models are used to study esophageal cancer.

  19. Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

    Directory of Open Access Journals (Sweden)

    Jinwei Wang

    2014-01-01

    Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

  20. Experience with a two-tier reflex gFOBT/FIT strategy in a national bowel screening programme.

    Science.gov (United States)

    Fraser, Callum G; Digby, Jayne; McDonald, Paula J; Strachan, Judith A; Carey, Francis A; Steele, Robert J C

    2012-03-01

    To evaluate a two-tier reflex guaiac-based faecal occult blood test (gFOBT)/faecal immunochemical test (FIT) algorithm in screening for colorectal cancer. Fourth screening round in NHS Tayside (Scotland). gFOBT were sent to 50-74-year-olds. Participants with five or six windows positive were offered colonoscopy. Participants with one to four windows positive were sent a FIT and, if positive, were offered colonoscopy. Participants providing an untestable gFOBT were sent a FIT and, if positive, were offered colonoscopy. Outcomes following positive results, cancer stages and key performance indicators were assessed. Of 131,885 invited, 73,315 (55.6%) responded. There were 66,957 (91.3%) negative, 241 (0.3%) strong positive, 5230 (7.1%) weak positive and 887 (1.2%) untestable results. The 241 participants who had five or six windows positive had more cancers than those positive by other routes: only 3 of the 30 cancers (9.7%) were Dukes' A. Among the 983 positive results from the weak positive gFOBT then positive FIT route, there were fewer cancers and more normal colonoscopies, but more adenomas than in the group with a strong positive gFOBT. In those with an untestable gFOBT, 77 had a positive FIT result, with fewer true and more false positive results than in the other groups. Fewer males had cancer and stages were earlier than in females, but more had adenoma. The detection rate for cancer was 0.18% and the PPV for cancer and all adenomas was 41.3%. The algorithm and FIT following a weak positive gFOBT have advantages. FIT following an untestable gFOBT warrants review.

  1. Local and omnibus goodness-of-fit tests in classical measurement error models

    KAUST Repository

    Ma, Yanyuan

    2010-09-14

    We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.

  2. Relation between cancer incidence or mortality and external natural background radiation in Japan

    International Nuclear Information System (INIS)

    Ujeno, Y.

    1983-01-01

    Analysis was performed on the relationships between the organ dose-equivalent rate due to natural background radiation (mSv/a) and three parameters of cancer risk: the age-adjusted cancer incidence (patients x 10 5 persons x a -1 ) in 13 large areas, the standardized mortality ratio of cancers in 46 large areas, and the cancer mortality in the population aged more than 40 years old (cancer deaths x 10 5 persons x a -1 ) in 649 small areas. The age-adjusted liver cancer incidence in males fitted the exponential model significantly (p<0.01) and the relationship of stomach cancer mortality of aged males in small areas fitted the linear model significantly (p<0.05). No relationship was observed with regard to female cancer in either case. The relationships between the three parameters and various other cancers of both sexes were not statistically significant. (author)

  3. Universal Rate Model Selector: A Method to Quickly Find the Best-Fit Kinetic Rate Model for an Experimental Rate Profile

    Science.gov (United States)

    2017-08-01

    k2 – k1) 3.3 Universal Kinetic Rate Platform Development Kinetic rate models range from pure chemical reactions to mass transfer...14 8. The rate model that best fits the experimental data is a first-order or homogeneous catalytic reaction ...Avrami (7), and intraparticle diffusion (6) rate equations to name a few. A single fitting algorithm (kinetic rate model ) for a reaction does not

  4. The global electroweak Standard Model fit after the Higgs discovery

    CERN Document Server

    Baak, Max

    2013-01-01

    We present an update of the global Standard Model (SM) fit to electroweak precision data under the assumption that the new particle discovered at the LHC is the SM Higgs boson. In this scenario all parameters entering the calculations of electroweak precision observalbes are known, allowing, for the first time, to over-constrain the SM at the electroweak scale and assert its validity. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted from the global fit. The results are compatible with, and exceed in precision, the direct measurements. An updated determination of the S, T and U parameters, which parametrize the oblique vacuum corrections, is given. The obtained values show good consistency with the SM expectation and no direct signs of new physics are seen. We conclude with an outlook to the global electroweak fit for a future e+e- collider.

  5. Resilience model for parents of children with cancer in mainland China-An exploratory study.

    Science.gov (United States)

    Ye, Zeng Jie; Qiu, Hong Zhong; Li, Peng Fei; Liang, Mu Zi; Wang, Shu Ni; Quan, Xiao Ming

    2017-04-01

    Parents have psychosocial functions that are critical for the entire family. Therefore, when their child is diagnosed with cancer, it is important that they exhibit resilience, which is the ability to preserve their emotional and physical well-being in the face of stress. The Resilience Model for Parents of Children with Cancer (RMP-CC) was developed to increase our understanding of how resilience is positively and negatively affected by protective and risk factors, respectively, in Chinese parents with children diagnosed with cancer. To evaluate the RMP-CC, the latent psychosocial variables and demographics of 229 parents were evaluated using exploratory structural equation modeling (SEM) and logistic regression. The majority of goodness-of-fit indices indicate that the SEM of RMP-CC was a good model with a high level of variance in resilience (58%). Logistic regression revealed that two demographics, educational level and clinical classification of cancer, accounted for 12% of this variance. Our results indicate that RMP-CC is an effective structure by which to develop mainland Chinese parent-focused interventions that are grounded in the experiences of the parents as caregivers of children who have been diagnosed with cancer. RMP-CC allows for a better understanding of what these parents experience while their children undergo treatment. Further studies will be needed to confirm the efficiency of the current structure, and would assist in further refinement of its clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Monitoring breast cancer treatment using a Fourier transform infrared spectroscopy-based computational model.

    Science.gov (United States)

    Depciuch, J; Kaznowska, E; Golowski, S; Koziorowska, A; Zawlik, I; Cholewa, M; Szmuc, K; Cebulski, J

    2017-09-05

    Breast cancer affects one in four women, therefore, the search for new diagnostic technologies and therapeutic approaches is of critical importance. This involves the development of diagnostic tools to facilitate the detection of cancer cells, which is useful for assessing the efficacy of cancer therapies. One of the major challenges for chemotherapy is the lack of tools to monitor efficacy during the course of treatment. Vibrational spectroscopy appears to be a promising tool for such a purpose, as it yields Fourier transformation infrared (FTIR) spectra which can be used to provide information on the chemical composition of the tissue. Previous research by our group has demonstrated significant differences between the infrared spectra of healthy, cancerous and post-chemotherapy breast tissue. Furthermore, the results obtained for three extreme patient cases revealed that the infrared spectra of post-chemotherapy breast tissue closely resembles that of healthy breast tissue when chemotherapy is effective (i.e., a good therapeutic response is achieved), or that of cancerous breast tissue when chemotherapy is ineffective. In the current study, we compared the infrared spectra of healthy, cancerous and post-chemotherapy breast tissue. Characteristic parameters were designated for the obtained spectra, spreading the function of absorbance using the Kramers-Kronig transformation and the best fit procedure to obtain Lorentz functions, which represent components of the bands. The Lorentz function parameters were used to develop a physics-based computational model to verify the efficacy of a given chemotherapy protocol in a given case. The results obtained using this model reflected the actual patient data retrieved from medical records (health improvement or no improvement). Therefore, we propose this model as a useful tool for monitoring the efficacy of chemotherapy in patients with breast cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Residuals and the Residual-Based Statistic for Testing Goodness of Fit of Structural Equation Models

    Science.gov (United States)

    Foldnes, Njal; Foss, Tron; Olsson, Ulf Henning

    2012-01-01

    The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result of oblique projections. This sheds light on the…

  8. Carcinogenesis model analysis for breast cancer incidence among atomic bomb survivors and the implications for cancer risk estimate for radiological protection

    International Nuclear Information System (INIS)

    Kai, Michiaki; Kusama, Tomoko

    2000-01-01

    Breast cancer incidence is the highest risk due to radiation among atomic bomb survivors. The excess relative risk of the early-onset breast cancer seems to be remarkably high for the youngest age-at-exposure groups. The cancer risk estimate of breast cancer is a current issue in radiological protection. We used a two-stage stochastic model for carcinogenesis to analyze the breast cancer incidence among atomic bomb survivors (Kai, et al. Radiat. Res. 1997). Our purpose is to examine the dependence of radiation risk on age at exposure using the two-stage model and how to transfer it to other populations for radiological protection. We fitted the model assuming that radiation acts as an initiator and that the rate of radiation-induced mutation and background initiation mutation leading to baseline cancer are additive. We took two age-dependence, not attained age but age at exposure, of the spontaneous process into account. First, age-dependence of spontaneous initiation was expressed by a linear model. We also modeled the age-dependence of spontaneous net growth rate of initiated cells by a linear function. As far as radiation-induced initiation is concerned, we took a stepwise function other than a liner function into account. The analysis did not show that the radiation mutation for the youngest age-at-exposure groups below age 10 was higher than for the older groups. Furthermore, the incidence of female breast cancer in Japan is increasing and the birth cohort effect can be observed in atomic bomb survivors. Our model assumed that an acute exposure to atomic radiation can only initiate cancers and do not influence other stages of carcinogenesis, whereas spontaneous initiation and promotion are age-dependent to consider birth cohort effects. When these cohort effects are properly accounted for, the shape of the age-specific incidence curve in Japan is remarkably similar to the age-specific incidence in western populations (shown in figure). Recently Little and

  9. Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models.

    Science.gov (United States)

    DeCarlo, Lawrence T

    2003-02-01

    The recent addition of aprocedure in SPSS for the analysis of ordinal regression models offers a simple means for researchers to fit the unequal variance normal signal detection model and other extended signal detection models. The present article shows how to implement the analysis and how to interpret the SPSS output. Examples of fitting the unequal variance normal model and other generalized signal detection models are given. The approach offers a convenient means for applying signal detection theory to a variety of research.

  10. Validity of physical activity and cardiorespiratory fitness in the Danish cohort 'Diet, Cancer and Health - Next Generations'

    DEFF Research Database (Denmark)

    Lerche, Lene; Olsen, Anja; Petersen, Kristina Elin Nielsen

    2017-01-01

    ). When validating the questionnaire-derived measures of PA, leisure time physical activity was not correlated with VO2 max. Positive correlations were found for sports overall, but these were only significant for men: total hours per week of sports (r=0.26), MET-hours per week of sports (r=0......Valid assessments of physical activity (PA) and cardiorespiratory fitness (CRF) is essential in epidemiological studies to define dose-response relationship for e.g. formulating thorough recommendations of an appropriate pattern of PA to maintain good health. The aim of this study was to validate...... the Danish step test, the physical activity questionnaire Active-Q and self-rated fitness against directly measured maximal oxygen uptake (VO2 max). A population based subsample (n=125) was included from the 'Diet, Cancer and Health - Next Generations' (DCH-NG) cohort which is under establishment. Validity...

  11. The German cervical cancer screening model: development and validation of a decision-analytic model for cervical cancer screening in Germany.

    Science.gov (United States)

    Siebert, Uwe; Sroczynski, Gaby; Hillemanns, Peter; Engel, Jutta; Stabenow, Roland; Stegmaier, Christa; Voigt, Kerstin; Gibis, Bernhard; Hölzel, Dieter; Goldie, Sue J

    2006-04-01

    We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.

  12. Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT

    Directory of Open Access Journals (Sweden)

    Dylan Molenaar

    2015-08-01

    Full Text Available In the psychometric literature, item response theory models have been proposed that explicitly take the decision process underlying the responses of subjects to psychometric test items into account. Application of these models is however hampered by the absence of general and flexible software to fit these models. In this paper, we present diffIRT, an R package that can be used to fit item response theory models that are based on a diffusion process. We discuss parameter estimation and model fit assessment, show the viability of the package in a simulation study, and illustrate the use of the package with two datasets pertaining to extraversion and mental rotation. In addition, we illustrate how the package can be used to fit the traditional diffusion model (as it has been originally developed in experimental psychology to data.

  13. Sustained fitness gains and variability in fitness trajectories in the long-term evolution experiment with Escherichia coli

    Science.gov (United States)

    Lenski, Richard E.; Wiser, Michael J.; Ribeck, Noah; Blount, Zachary D.; Nahum, Joshua R.; Morris, J. Jeffrey; Zaman, Luis; Turner, Caroline B.; Wade, Brian D.; Maddamsetti, Rohan; Burmeister, Alita R.; Baird, Elizabeth J.; Bundy, Jay; Grant, Nkrumah A.; Card, Kyle J.; Rowles, Maia; Weatherspoon, Kiyana; Papoulis, Spiridon E.; Sullivan, Rachel; Clark, Colleen; Mulka, Joseph S.; Hajela, Neerja

    2015-01-01

    Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population's mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment. PMID:26674951

  14. A flexible, interactive software tool for fitting the parameters of neuronal models.

    Science.gov (United States)

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

  15. A flexible, interactive software tool for fitting the parameters of neuronal models

    Directory of Open Access Journals (Sweden)

    Péter eFriedrich

    2014-07-01

    Full Text Available The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problem of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting

  16. Free Base Lysine Increases Survival and Reduces Metastasis in Prostate Cancer Model.

    Science.gov (United States)

    Ibrahim-Hashim, Arig; Wojtkowiak, Jonathan W; de Lourdes Coelho Ribeiro, Maria; Estrella, Veronica; Bailey, Kate M; Cornnell, Heather H; Gatenby, Robert A; Gillies, Robert J

    2011-11-19

    Malignant tumor cells typically metabolize glucose anaerobically to lactic acid even under normal oxygen tension, a phenomenon called aerobic glycolysis or the Warburg effect. This results in increased acid production and the acidification of the extracellular microenvironment in solid tumors. H + ions tend to flow along concentration gradients into peritumoral normal tissue causing extracellular matrix degradation and increased tumor cell motility thus promoting invasion and metastasis. We have shown that reducing this acidity with sodium bicarbonate buffer decreases the metastatic fitness of circulating tumor cells in prostate cancer and other cancer models. Mathematical models of the tumor-host dynamics predicted that buffers with a pka around 7 will be more effective in reducing intra- and peri-tumoral acidosis and, thus, and possibly more effective in inhibiting tumor metastasis than sodium bicarbonate which has a pKa around 6. Here we test this prediction the efficacy of free base lysine; a non-bicarbonate/non-volatile buffer with a higher pKa (~10), on prostate tumor metastases model. Oxygen consumption and acid production rate of PC3M prostate cancer cells and normal prostate cells were determined using the Seahorse Extracellular Flux (XF-96) analyzer. In vivo effect of 200 mM lysine started four days prior to inoculation on inhibition of metastasis was examined in PC3M-LUC-C6 prostate cancer model using SCID mice. Metastases were followed by bioluminescence imaging. PC3M prostate cancer cells are highly acidic in comparison to a normal prostate cell line indicating that reduction of intra- and perit-tumoral acidosis should inhibit metastases formation. In vivo administration of 200 mM free base lysine increased survival and reduced metastasis. PC3M prostate cancer cells are highly glycolytic and produce large amounts of acid when compared to normal prostate cells. Administration of non-volatile buffer decreased growth of metastases and improved survival

  17. Analysing model fit of psychometric process models: An overview, a new test and an application to the diffusion model.

    Science.gov (United States)

    Ranger, Jochen; Kuhn, Jörg-Tobias; Szardenings, Carsten

    2017-05-01

    Cognitive psychometric models embed cognitive process models into a latent trait framework in order to allow for individual differences. Due to their close relationship to the response process the models allow for profound conclusions about the test takers. However, before such a model can be used its fit has to be checked carefully. In this manuscript we give an overview over existing tests of model fit and show their relation to the generalized moment test of Newey (Econometrica, 53, 1985, 1047) and Tauchen (J. Econometrics, 30, 1985, 415). We also present a new test, the Hausman test of misspecification (Hausman, Econometrica, 46, 1978, 1251). The Hausman test consists of a comparison of two estimates of the same item parameters which should be similar if the model holds. The performance of the Hausman test is evaluated in a simulation study. In this study we illustrate its application to two popular models in cognitive psychometrics, the Q-diffusion model and the D-diffusion model (van der Maas, Molenaar, Maris, Kievit, & Boorsboom, Psychol Rev., 118, 2011, 339; Molenaar, Tuerlinckx, & van der Maas, J. Stat. Softw., 66, 2015, 1). We also compare the performance of the test to four alternative tests of model fit, namely the M 2 test (Molenaar et al., J. Stat. Softw., 66, 2015, 1), the moment test (Ranger et al., Br. J. Math. Stat. Psychol., 2016) and the test for binned time (Ranger & Kuhn, Psychol. Test. Asess. , 56, 2014b, 370). The simulation study indicates that the Hausman test is superior to the latter tests. The test closely adheres to the nominal Type I error rate and has higher power in most simulation conditions. © 2017 The British Psychological Society.

  18. Mathematical modeling of cancer metabolism.

    Science.gov (United States)

    Medina, Miguel Ángel

    2018-04-01

    Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland

    Directory of Open Access Journals (Sweden)

    Verena Jürgens

    2013-05-01

    Full Text Available Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age- and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.

  20. Application of accelerated failure time models for breast cancer patients' survival in Kurdistan Province of Iran.

    Science.gov (United States)

    Karimi, Asrin; Delpisheh, Ali; Sayehmiri, Kourosh

    2016-01-01

    Breast cancer is the most common cancer and the second common cause of cancer-induced mortalities in Iranian women. There has been a rapid development in hazard models and survival analysis in the last decade. The aim of this study was to evaluate the prognostic factors of overall survival (OS) in breast cancer patients using accelerated failure time models (AFT). This was a retrospective-analytic cohort study. About 313 women with a pathologically proven diagnosis of breast cancer who had been treated during a 7-year period (since January 2006 until March 2014) in Sanandaj City, Kurdistan Province of Iran were recruited. Performance among AFT was assessed using the goodness of fit methods. Discrimination among the exponential, Weibull, generalized gamma, log-logistic, and log-normal distributions was done using Akaik information criteria and maximum likelihood. The 5 years OS was 75% (95% CI = 74.57-75.43). The main results in terms of survival were found for the different categories of the clinical stage covariate, tumor metastasis, and relapse of cancer. Survival time in breast cancer patients without tumor metastasis and relapse were 4, 2-fold longer than other patients with metastasis and relapse, respectively. One of the most important undermining prognostic factors in breast cancer is metastasis; hence, knowledge of the mechanisms of metastasis is necessary to prevent it so occurrence and treatment of metastatic breast cancer and ultimately extend the lifetime of patients.

  1. Mouse Models for Studying Oral Cancer: Impact in the Era of Cancer Immunotherapy.

    Science.gov (United States)

    Luo, J J; Young, C D; Zhou, H M; Wang, X J

    2018-04-01

    Model systems for oral cancer research have progressed from tumor epithelial cell cultures to in vivo systems that mimic oral cancer genetics, pathological characteristics, and tumor-stroma interactions of oral cancer patients. In the era of cancer immunotherapy, it is imperative to use model systems to test oral cancer prevention and therapeutic interventions in the presence of an immune system and to discover mechanisms of stromal contributions to oral cancer carcinogenesis. Here, we review in vivo mouse model systems commonly used for studying oral cancer and discuss the impact these models are having in advancing basic mechanisms, chemoprevention, and therapeutic intervention of oral cancer while highlighting recent discoveries concerning the role of immune cells in oral cancer. Improvements to in vivo model systems that highly recapitulate human oral cancer hold the key to identifying features of oral cancer initiation, progression, and invasion as well as molecular and cellular targets for prevention, therapeutic response, and immunotherapy development.

  2. The universal Higgs fit

    DEFF Research Database (Denmark)

    Giardino, P. P.; Kannike, K.; Masina, I.

    2014-01-01

    We perform a state-of-the-art global fit to all Higgs data. We synthesise them into a 'universal' form, which allows to easily test any desired model. We apply the proposed methodology to extract from data the Higgs branching ratios, production cross sections, couplings and to analyse composite...... Higgs models, models with extra Higgs doublets, supersymmetry, extra particles in the loops, anomalous top couplings, and invisible Higgs decays into Dark Matter. Best fit regions lie around the Standard Model predictions and are well approximated by our 'universal' fit. Latest data exclude the dilaton...... as an alternative to the Higgs, and disfavour fits with negative Yukawa couplings. We derive for the first time the SM Higgs boson mass from the measured rates, rather than from the peak positions, obtaining M-h = 124.4 +/- 1.6 GeV....

  3. Kinetic modeling and fitting software for interconnected reaction schemes: VisKin.

    Science.gov (United States)

    Zhang, Xuan; Andrews, Jared N; Pedersen, Steen E

    2007-02-15

    Reaction kinetics for complex, highly interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. To determine rate constants from experimental data, fitting algorithms that adjust rate constants to fit the model to imported data were implemented using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno methods. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.

  4. Interdependence in Women with Breast Cancer and Their Partners: An Interindividual Model of Distress

    Science.gov (United States)

    Dorros, Sam M.; Card, Noel A.; Segrin, Chris; Badger, Terry A.

    2010-01-01

    Objective: The aim of this investigation was to test whether interdependence in dyads living with breast cancer could account for person-partner crossover effects in distress outcomes. Method: The sample consisted of 95 dyads with early-stage breast cancer. By using reciprocal dyadic data from women with breast cancer and their partners, we fit a…

  5. Estrogen Metabolism and Risk of Postmenopausal Endometrial and Ovarian Cancer: the B ∼ FIT Cohort.

    Science.gov (United States)

    Dallal, Cher M; Lacey, James V; Pfeiffer, Ruth M; Bauer, Douglas C; Falk, Roni T; Buist, Diana S M; Cauley, Jane A; Hue, Trisha F; LaCroix, Andrea Z; Tice, Jeffrey A; Veenstra, Timothy D; Xu, Xia; Brinton, Louise A

    2016-02-01

    Estrogen metabolites may have different genotoxic and mitogenic properties yet their relationship with endometrial and ovarian cancer risk remains unclear. Within the Breast and Bone Follow-up to the Fracture Intervention Trial (B ∼ FIT, n = 15,595), we conducted a case-cohort study to evaluate 15 pre-diagnostic serum estrogens and estrogen metabolites with risk of incident endometrial and ovarian cancer among postmenopausal women not on hormone therapy. Participants included 66 endometrial and 67 ovarian cancer cases diagnosed during follow-up (∼ 10 years) and subcohorts of 346 and 416 women, respectively, after relevant exclusions. Serum concentrations were measured by liquid chromatography-tandem mass spectrometry. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazard regression. Exposures were categorized in tertiles (T) and analyzed individually, as metabolic pathways (C-2, -4, or -16) and as ratios to parent estrogens (estradiol, estrone). Estradiol was significantly associated with increased endometrial cancer risk (BMI-adjusted HRT3vsT1 = 4.09, 95% CI 1.70, 9.85; p trend = 0.003). 2-Hydroxyestrone and 16α-hydroxyestrone were not associated with endometrial risk after estradiol adjustment (2-OHE1:HRT3vsT1 = 1.97, 95% CI 0.78, 4.94; 16-OHE1:HRT3vsT1 = 1.50, 95% CI 0.65, 3.46; p trend = 0.16 and 0.36, respectively). Ratios of 2- and 4-pathway catechol-to-methylated estrogens remained positively associated with endometrial cancer after BMI or estradiol adjustment (2-pathway catechols-to-methylated: HRT3vsT1 = 4.02, 95% CI 1.60, 10.1; 4-pathway catechols-to-methylated: HRT3vsT1 = 4.59, 95% CI 1.64, 12.9; p trend = 0.002 for both). Estrogens and estrogen metabolites were not associated with ovarian cancer risk; however, larger studies are needed to better evaluate these relationships. Estrogen metabolism may be important in endometrial carcinogenesis, particularly with less extensive methylation of 2- or 4

  6. Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

    Science.gov (United States)

    Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

    2016-05-01

    Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for

  7. Feature extraction through least squares fit to a simple model

    International Nuclear Information System (INIS)

    Demuth, H.B.

    1976-01-01

    The Oak Ridge National Laboratory (ORNL) presented the Los Alamos Scientific Laboratory (LASL) with 18 radiographs of fuel rod test bundles. The problem is to estimate the thickness of the gap between some cylindrical rods and a flat wall surface. The edges of the gaps are poorly defined due to finite source size, x-ray scatter, parallax, film grain noise, and other degrading effects. The radiographs were scanned and the scan-line data were averaged to reduce noise and to convert the problem to one dimension. A model of the ideal gap, convolved with an appropriate point-spread function, was fit to the averaged data with a least squares program; and the gap width was determined from the final fitted-model parameters. The least squares routine did converge and the gaps obtained are of reasonable size. The method is remarkably insensitive to noise. This report describes the problem, the techniques used to solve it, and the results and conclusions. Suggestions for future work are also given

  8. Comparison of tai chi vs. strength training for fall prevention among female cancer survivors: study protocol for the GET FIT trial

    International Nuclear Information System (INIS)

    Winters-Stone, Kerri M; Li, Fuzhong; Horak, Fay; Luoh, Shiuh-Wen; Bennett, Jill A; Nail, Lillian; Dieckmann, Nathan

    2012-01-01

    Women with cancer are significantly more likely to fall than women without cancer placing them at higher risk of fall-related fractures, other injuries and disability. Currently, no evidence-based fall prevention strategies exist that specifically target female cancer survivors. The purpose of the GET FIT (Group Exercise Training for Functional Improvement after Treatment) trial is to compare the efficacy of two distinct types of exercise, tai chi versus strength training, to prevent falls in women who have completed treatment for cancer. The specific aims of this study are to: 1) Determine and compare the efficacy of both tai chi training and strength training to reduce falls in older female cancer survivors, 2) Determine the mechanism(s) by which tai chi and strength training each reduces falls and, 3) Determine whether or not the benefits of each intervention last after structured training stops. We will conduct a three-group, single-blind, parallel design, randomized controlled trial in women, aged 50–75 years old, who have completed chemotherapy for cancer comparing 1) tai chi 2) strength training and 3) a placebo control group of seated stretching exercise. Women will participate in supervised study programs twice per week for six months and will be followed for an additional six months after formal training stops. The primary outcome in this study is falls, which will be prospectively tracked by monthly self-report. Secondary outcomes are maximal leg strength measured by isokinetic dynamometry, postural stability measured by computerized dynamic posturography and physical function measured by the Physical Performance Battery, all measured at baseline, 3, 6 and 12 months. The sample for this trial (N=429, assuming 25% attrition) will provide adequate statistical power to detect at least a 47% reduction in the fall rate over 1 year by being in either of the 2 exercise groups versus the control group. The GET FIT trial will provide important new knowledge

  9. Associations of discretionary screen time with mortality, cardiovascular disease and cancer are attenuated by strength, fitness and physical activity: findings from the UK Biobank study.

    Science.gov (United States)

    Celis-Morales, Carlos A; Lyall, Donald M; Steell, Lewis; Gray, Stuart R; Iliodromiti, Stamatina; Anderson, Jana; Mackay, Daniel F; Welsh, Paul; Yates, Thomas; Pell, Jill P; Sattar, Naveed; Gill, Jason M R

    2018-05-24

    Discretionary screen time (time spent viewing a television or computer screen during leisure time) is an important contributor to total sedentary behaviour, which is associated with increased risk of mortality and cardiovascular disease (CVD). The aim of this study was to determine whether the associations of screen time with cardiovascular disease and all-cause mortality were modified by levels of cardiorespiratory fitness, grip strength or physical activity. In total, 390,089 participants (54% women) from the UK Biobank were included in this study. All-cause mortality, CVD and cancer incidence and mortality were the main outcomes. Discretionary television (TV) viewing, personal computer (PC) screen time and overall screen time (TV + PC time) were the exposure variables. Grip strength, fitness and physical activity were treated as potential effect modifiers. Altogether, 7420 participants died, and there were 22,210 CVD events, over a median of 5.0 years follow-up (interquartile range 4.3 to 5.7; after exclusion of the first 2 years from baseline in the landmark analysis). All discretionary screen-time exposures were significantly associated with all health outcomes. The associations of overall discretionary screen time with all-cause mortality and incidence of CVD and cancer were strongest amongst participants in the lowest tertile for grip strength (all-cause mortality hazard ratio per 2-h increase in screen time (1.31 [95% confidence interval: 1.22-1.43], p fitness (lowest fitness tertile: all-cause mortality 1.23 [1.13-1.34], p = 0.002 and CVD 1.10 [1.02-1.22], p = 0.010; highest fitness tertile: all-cause mortality 1.12 [0.96-1.28], p = 0.848 and CVD 1.01 [0.96-1.07], p = 0.570). Similar findings were found for physical activity for all-cause mortality and cancer incidence. The associations between discretionary screen time and adverse health outcomes were strongest in those with low grip strength, fitness and physical activity and

  10. Assessing item fit for unidimensional item response theory models using residuals from estimated item response functions.

    Science.gov (United States)

    Haberman, Shelby J; Sinharay, Sandip; Chon, Kyong Hee

    2013-07-01

    Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.

  11. Patentability aspects of computational cancer models

    Science.gov (United States)

    Lishchuk, Iryna

    2017-07-01

    Multiscale cancer models, implemented in silico, simulate tumor progression at various spatial and temporal scales. Having the innovative substance and possessing the potential of being applied as decision support tools in clinical practice, patenting and obtaining patent rights in cancer models seems prima facie possible. What legal hurdles the cancer models need to overcome for being patented we inquire from this paper.

  12. Fitness cost

    DEFF Research Database (Denmark)

    Nielsen, Karen L.; Pedersen, Thomas M.; Udekwu, Klas I.

    2012-01-01

    phage types, predominantly only penicillin resistant. We investigated whether isolates of this epidemic were associated with a fitness cost, and we employed a mathematical model to ask whether these fitness costs could have led to the observed reduction in frequency. Bacteraemia isolates of S. aureus...... from Denmark have been stored since 1957. We chose 40 S. aureus isolates belonging to phage complex 83A, clonal complex 8 based on spa type, ranging in time of isolation from 1957 to 1980 and with varyous antibiograms, including both methicillin-resistant and -susceptible isolates. The relative fitness...... of each isolate was determined in a growth competition assay with a reference isolate. Significant fitness costs of 215 were determined for the MRSA isolates studied. There was a significant negative correlation between number of antibiotic resistances and relative fitness. Multiple regression analysis...

  13. GOODNESS-OF-FIT TEST FOR THE ACCELERATED FAILURE TIME MODEL BASED ON MARTINGALE RESIDUALS

    Czech Academy of Sciences Publication Activity Database

    Novák, Petr

    2013-01-01

    Roč. 49, č. 1 (2013), s. 40-59 ISSN 0023-5954 R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:GA MŠk(CZ) SVV 261315/2011 Keywords : accelerated failure time model * survival analysis * goodness-of-fit Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2013/SI/novak-goodness-of-fit test for the aft model based on martingale residuals.pdf

  14. Modeling the Aneuploidy Control of Cancer

    Directory of Open Access Journals (Sweden)

    Wang Zhong

    2010-07-01

    Full Text Available Abstract Background Aneuploidy has long been recognized to be associated with cancer. A growing body of evidence suggests that tumorigenesis, the formation of new tumors, can be attributed to some extent to errors occurring at the mitotic checkpoint, a major cell cycle control mechanism that acts to prevent chromosome missegregation. However, so far no statistical model has been available quantify the role aneuploidy plays in determining cancer. Methods We develop a statistical model for testing the association between aneuploidy loci and cancer risk in a genome-wide association study. The model incorporates quantitative genetic principles into a mixture-model framework in which various genetic effects, including additive, dominant, imprinting, and their interactions, are estimated by implementing the EM algorithm. Results Under the new model, a series of hypotheses tests are formulated to explain the pattern of the genetic control of cancer through aneuploid loci. Simulation studies were performed to investigate the statistical behavior of the model. Conclusions The model will provide a tool for estimating the effects of genetic loci on aneuploidy abnormality in genome-wide studies of cancer cells.

  15. Building a better model of cancer

    Directory of Open Access Journals (Sweden)

    DeGregori James

    2006-10-01

    Full Text Available Abstract The 2006 Cold Spring Harbor Laboratory meeting on the Mechanisms and Models of Cancer was held August 16–20. The meeting featured several hundred presentations of many short talks (mostly selected from the abstracts and posters, with the airing of a number of exciting new discoveries. We will focus this meeting review on models of cancer (primarily mouse models, highlighting recent advances in new mouse models that better recapitulate sporadic tumorigenesis, demonstrations of tumor addiction to tumor suppressor inactivation, new insight into senescence as a tumor barrier, improved understanding of the evolutionary paths of cancer development, and environmental/immunological influences on cancer.

  16. Assessing model fit in latent class analysis when asymptotics do not hold

    NARCIS (Netherlands)

    van Kollenburg, Geert H.; Mulder, Joris; Vermunt, Jeroen K.

    2015-01-01

    The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values

  17. The effect of neoadjuvant chemoradiotherapy on whole-body physical fitness and skeletal muscle mitochondrial oxidative phosphorylation in vivo in locally advanced rectal cancer patients--an observational pilot study.

    Directory of Open Access Journals (Sweden)

    Malcolm A West

    Full Text Available In the United Kingdom, patients with locally advanced rectal cancer routinely receive neoadjuvant chemoradiotherapy. However, the effects of this on physical fitness are unclear. This pilot study is aimed to investigate the effect of neoadjuvant chemoradiotherapy on objectively measured in vivo muscle mitochondrial function and whole-body physical fitness.We prospectively studied 12 patients with rectal cancer who completed standardized neoadjuvant chemoradiotherapy, recruited from a large tertiary cancer centre, between October 2012 and July 2013. All patients underwent a cardiopulmonary exercise test and a phosphorus magnetic resonance spectroscopy quadriceps muscle exercise-recovery study before and after neoadjuvant chemoradiotherapy. Data were analysed and reported blind to patient identity and clinical course. Primary variables of interest were the two physical fitness measures; oxygen uptake at estimated anaerobic threshold and oxygen uptake at Peak exercise (ml.kg-1.min-1, and the post-exercise phosphocreatine recovery rate constant (min-1, a measure of muscle mitochondrial capacity in vivo.Median age was 67 years (IQR 64-75. Differences (95%CI in all three primary variables were significantly negative post-NACRT: Oxygen uptake at estimated anaerobic threshold -2.4 ml.kg-1.min-1 (-3.8, -0.9, p = 0.004; Oxygen uptake at Peak -4.0 ml.kg-1.min-1 (-6.8, -1.1, p = 0.011; and post-exercise phosphocreatine recovery rate constant -0.34 min-1 (-0.51, -0.17, p<0.001.The significant decrease in both whole-body physical fitness and in vivo muscle mitochondrial function raises the possibility that muscle mitochondrial mechanisms, no doubt multifactorial, may be important in deterioration of physical fitness following neoadjuvant chemoradiotherapy. This may have implications for targeted interventions to improve physical fitness pre-surgery.Clinicaltrials.gov registration NCT01859442.

  18. The value of models in informing resource allocation in colorectal cancer screening – 1 the case of the Netherlands

    Science.gov (United States)

    van Hees, Frank; Zauber, Ann G.; van Veldhuizen, Harriët; Heijnen, Marie-Louise A.; Penning, Corine; de Koning, Harry J.; van Ballegooijen, Marjolein; Lansdorp-Vogelaar, Iris

    2015-01-01

    In May 2011, the Dutch government decided to implement a national programme for colorectal cancer (CRC) screening using biennial faecal immunochemical test (FIT) screening between ages 55 and 75.[1] Decision modelling played an important role in informing this decision, as well as in the planning and implementation of the programme afterwards. In this overview, we illustrate the value of models in informing resource allocation in CRC screening, using the role that decision modelling has played in the Dutch CRC screening programme as an example. PMID:26063755

  19. Model of care for adolescents and young adults with cancer: the Youth Project in Milan

    Directory of Open Access Journals (Sweden)

    Chiara Magni

    2016-08-01

    Full Text Available Adolescents and young adults (AYA with cancer form a particular group of patients with unique characteristics, who inhabit a so-called no man’s land between pediatric and adult services. In the last ten years, the scientific oncology community has started to pay attention to these patients, implementing dedicated programs. A standardized model of care directed towards patients in this age range has yet to be developed and neither the pediatric nor the adult oncologic systems perfectly fit these patients’ needs. The Youth Project of the Istituto Nazionale Tumori in Milan, dedicated to adolescents and young adults with pediatric-type solid tumors, can be seen as a model of care for AYA patients, with its heterogeneous multidisciplinary staff and close cooperation with adult medical oncologists and surgeons. Further progress in the care of AYA cancer patients is still needed to improve their outcomes.

  20. [Development of the lung cancer diagnostic system].

    Science.gov (United States)

    Lv, You-Jiang; Yu, Shou-Yi

    2009-07-01

    To develop a lung cancer diagnosis system. A retrospective analysis was conducted in 1883 patients with primary lung cancer or benign pulmonary diseases (pneumonia, tuberculosis, or pneumonia pseudotumor). SPSS11.5 software was used for data processing. For the relevant factors, a non-factor Logistic regression analysis was used followed by establishment of the regression model. Microsoft Visual Studio 2005 system development platform and VB.Net corresponding language were used to develop the lung cancer diagnosis system. The non-factor multi-factor regression model showed a goodness-of-fit (R2) of the model of 0.806, with a diagnostic accuracy for benign lung diseases of 92.8%, a diagnostic accuracy for lung cancer of 89.0%, and an overall accuracy of 90.8%. The model system for early clinical diagnosis of lung cancer has been established.

  1. The effect of measurement quality on targeted structural model fit indices: A comment on Lance, Beck, Fan, and Carter (2016).

    Science.gov (United States)

    McNeish, Daniel; Hancock, Gregory R

    2018-03-01

    Lance, Beck, Fan, and Carter (2016) recently advanced 6 new fit indices and associated cutoff values for assessing data-model fit in the structural portion of traditional latent variable path models. The authors appropriately argued that, although most researchers' theoretical interest rests with the latent structure, they still rely on indices of global model fit that simultaneously assess both the measurement and structural portions of the model. As such, Lance et al. proposed indices intended to assess the structural portion of the model in isolation of the measurement model. Unfortunately, although these strategies separate the assessment of the structure from the fit of the measurement model, they do not isolate the structure's assessment from the quality of the measurement model. That is, even with a perfectly fitting measurement model, poorer quality (i.e., less reliable) measurements will yield a more favorable verdict regarding structural fit, whereas better quality (i.e., more reliable) measurements will yield a less favorable structural assessment. This phenomenon, referred to by Hancock and Mueller (2011) as the reliability paradox, affects not only traditional global fit indices but also those structural indices proposed by Lance et al. as well. Fortunately, as this comment will clarify, indices proposed by Hancock and Mueller help to mitigate this problem and allow the structural portion of the model to be assessed independently of both the fit of the measurement model as well as the quality of indicator variables contained therein. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  2. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

    Science.gov (United States)

    Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

    2018-04-11

    The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes

  3. An investigation into the psychometric properties of the Hospital Anxiety and Depression Scale in patients with breast cancer

    Science.gov (United States)

    Rodgers, Jacqui; Martin, Colin R; Morse, Rachel C; Kendell, Kate; Verrill, Mark

    2005-01-01

    Background To determine the psychometric properties of the Hospital Anxiety and Depression Scale (HADS) in patients with breast cancer and determine the suitability of the instrument for use with this clinical group. Methods A cross-sectional design was used. The study used a pooled data set from three breast cancer clinical groups. The dependent variables were HADS anxiety and depression sub-scale scores. Exploratory and confirmatory factor analyses were conducted on the HADS to determine its psychometric properties in 110 patients with breast cancer. Seven models were tested to determine model fit to the data. Results Both factor analysis methods indicated that three-factor models provided a better fit to the data compared to two-factor (anxiety and depression) models for breast cancer patients. Clark and Watson's three factor tripartite and three factor hierarchical models provided the best fit. Conclusion The underlying factor structure of the HADS in breast cancer patients comprises three distinct, but correlated factors, negative affectivity, autonomic anxiety and anhedonic depression. The clinical utility of the HADS in screening for anxiety and depression in breast cancer patients may be enhanced by using a modified scoring procedure based on a three-factor model of psychological distress. This proposed alternate scoring method involving regressing autonomic anxiety and anhedonic depression factors onto the third factor (negative affectivity) requires further investigation in order to establish its efficacy. PMID:16018801

  4. An investigation into the psychometric properties of the Hospital Anxiety and Depression Scale in patients with breast cancer

    Directory of Open Access Journals (Sweden)

    Kendell Kate

    2005-07-01

    Full Text Available Abstract Background To determine the psychometric properties of the Hospital Anxiety and Depression Scale (HADS in patients with breast cancer and determine the suitability of the instrument for use with this clinical group. Methods A cross-sectional design was used. The study used a pooled data set from three breast cancer clinical groups. The dependent variables were HADS anxiety and depression sub-scale scores. Exploratory and confirmatory factor analyses were conducted on the HADS to determine its psychometric properties in 110 patients with breast cancer. Seven models were tested to determine model fit to the data. Results Both factor analysis methods indicated that three-factor models provided a better fit to the data compared to two-factor (anxiety and depression models for breast cancer patients. Clark and Watson's three factor tripartite and three factor hierarchical models provided the best fit. Conclusion The underlying factor structure of the HADS in breast cancer patients comprises three distinct, but correlated factors, negative affectivity, autonomic anxiety and anhedonic depression. The clinical utility of the HADS in screening for anxiety and depression in breast cancer patients may be enhanced by using a modified scoring procedure based on a three-factor model of psychological distress. This proposed alternate scoring method involving regressing autonomic anxiety and anhedonic depression factors onto the third factor (negative affectivity requires further investigation in order to establish its efficacy.

  5. Canadian Cancer Risk Management Model: evaluation of cancer control.

    Science.gov (United States)

    Evans, William K; Wolfson, Michael C; Flanagan, William M; Shin, Janey; Goffin, John; Miller, Anthony B; Asakawa, Keiko; Earle, Craig; Mittmann, Nicole; Fairclough, Lee; Oderkirk, Jillian; Finès, Philippe; Gribble, Stephen; Hoch, Jeffrey; Hicks, Chantal; Omariba, D Walter R; Ng, Edward

    2013-04-01

    The aim of this study was to develop a decision support tool to assess the potential benefits and costs of new healthcare interventions. The Canadian Partnership Against Cancer (CPAC) commissioned the development of a Cancer Risk Management Model (CRMM)--a computer microsimulation model that simulates individual lives one at a time, from birth to death, taking account of Canadian demographic and labor force characteristics, risk factor exposures, and health histories. Information from all the simulated lives is combined to produce aggregate measures of health outcomes for the population or for particular subpopulations. The CRMM can project the population health and economic impacts of cancer control programs in Canada and the impacts of major risk factors, cancer prevention, and screening programs and new cancer treatments on population health and costs to the healthcare system. It estimates both the direct costs of medical care, as well as lost earnings and impacts on tax revenues. The lung and colorectal modules are available through the CPAC Web site (www.cancerview.ca/cancerrriskmanagement) to registered users where structured scenarios can be explored for their projected impacts. Advanced users will be able to specify new scenarios or change existing modules by varying input parameters or by accessing open source code. Model development is now being extended to cervical and breast cancers.

  6. Using multistage models to describe radiation-induced leukaemia

    International Nuclear Information System (INIS)

    Little, M.P.; Muirhead, C.R.; Boice, J.D. Jr.; Kleinerman, R.A.

    1995-01-01

    The Armitage-Doll model of carcinogenesis is fitted to data on leukaemia mortality among the Japanese atomic bomb survivors with the DS86 dosimetry and on leukaemia incidence in the International Radiation Study of Cervical Cancer patients. Two different forms of model are fitted: the first postulates up to two radiation-affected stages and the second additionally allows for the presence at birth of a non-trivial population of cells which have already accumulated the first of the mutations leading to malignancy. Among models of the first form, a model with two adjacent radiation-affected stages appears to fit the data better than other models of the first form, including both models with two affected stages in any order and models with only one affected stage. The best fitting model predicts a linear-quadratic dose-response and reductions of relative risk with increasing time after exposure and age at exposure, in agreement with what has previously been observed in the Japanese and cervical cancer data. However, on the whole it does not provide an adequate fit to either dataset. The second form of model appears to provide a rather better fit, but the optimal models have biologically implausible parameters (the number of initiated cells at birth is negative) so that this model must also be regarded as providing an unsatisfactory description of the data. (author)

  7. FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

    Science.gov (United States)

    De Geyter, G.; Baes, M.; Fritz, J.; Camps, P.

    2013-02-01

    We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi- dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

  8. A scaled Lagrangian method for performing a least squares fit of a model to plant data

    International Nuclear Information System (INIS)

    Crisp, K.E.

    1988-01-01

    Due to measurement errors, even a perfect mathematical model will not be able to match all the corresponding plant measurements simultaneously. A further discrepancy may be introduced if an un-modelled change in conditions occurs within the plant which should have required a corresponding change in model parameters - e.g. a gradual deterioration in the performance of some component(s). Taking both these factors into account, what is required is that the overall discrepancy between the model predictions and the plant data is kept to a minimum. This process is known as 'model fitting', A method is presented for minimising any function which consists of the sum of squared terms, subject to any constraints. Its most obvious application is in the process of model fitting, where a weighted sum of squares of the differences between model predictions and plant data is the function to be minimised. When implemented within existing Central Electricity Generating Board computer models, it will perform a least squares fit of a model to plant data within a single job submission. (author)

  9. Validity of physical activity and cardiorespiratory fitness in the Danish cohort "Diet, Cancer and Health-Next Generations".

    Science.gov (United States)

    Lerche, L; Olsen, A; Petersen, K E N; Rostgaard-Hansen, A L; Dragsted, L O; Nordsborg, N B; Tjønneland, A; Halkjaer, J

    2017-12-01

    Valid assessments of physical activity (PA) and cardiorespiratory fitness (CRF) are essential in epidemiological studies to define dose-response relationship for formulating thorough recommendations of an appropriate pattern of PA to maintain good health. The aim of this study was to validate the Danish step test, the physical activity questionnaire Active-Q, and self-rated fitness against directly measured maximal oxygen uptake (VO 2 max). A population-based subsample (n=125) was included from the "Diet, Cancer and Health-Next Generations" (DCH-NG) cohort which is under establishment. Validity coefficients, which express the correlation between measured and "true" exposure, were calculated, and misclassification across categories was evaluated. The validity of the Danish step test was moderate (women: r=.66, and men: r=.56); however, men were systematically underestimated (43% misclassification). When validating the questionnaire-derived measures of PA, leisure-time physical activity was not correlated with VO 2 max. Positive correlations were found for sports overall, but these were only significant for men: total hours per week of sports (r=.26), MET-hours per week of sports (r=.28) and vigorous sports (0.28) alone were positively correlated with VO 2 max. Finally, the percentage of misclassification was low for self-rated fitness (women: 9% and men: 13%). Thus, self-rated fitness was found to be a superior method to the Danish step test, as well as being less cost prohibitive and more practical than the VO 2 max method. Finally, even if correlations were low, they support the potential for questionnaire outcomes, particularly sports, vigorous sports, and self-rated fitness to be used to estimate CRF. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  10. The disconnected values model improves mental well-being and fitness in an employee wellness program.

    Science.gov (United States)

    Anshel, Mark H; Brinthaupt, Thomas M; Kang, Minsoo

    2010-01-01

    This study examined the effect of a 10-week wellness program on changes in physical fitness and mental well-being. The conceptual framework for this study was the Disconnected Values Model (DVM). According to the DVM, detecting the inconsistencies between negative habits and values (e.g., health, family, faith, character) and concluding that these "disconnects" are unacceptable promotes the need for health behavior change. Participants were 164 full-time employees at a university in the southeastern U.S. The program included fitness coaching and a 90-minute orientation based on the DVM. Multivariate Mixed Model analyses indicated significantly improved scores from pre- to post-intervention on selected measures of physical fitness and mental well-being. The results suggest that the Disconnected Values Model provides an effective cognitive-behavioral approach to generating health behavior change in a 10-week workplace wellness program.

  11. GOSSIP: SED fitting code

    Science.gov (United States)

    Franzetti, Paolo; Scodeggio, Marco

    2012-10-01

    GOSSIP fits the electro-magnetic emission of an object (the SED, Spectral Energy Distribution) against synthetic models to find the simulated one that best reproduces the observed data. It builds-up the observed SED of an object (or a large sample of objects) combining magnitudes in different bands and eventually a spectrum; then it performs a chi-square minimization fitting procedure versus a set of synthetic models. The fitting results are used to estimate a number of physical parameters like the Star Formation History, absolute magnitudes, stellar mass and their Probability Distribution Functions.

  12. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

    Science.gov (United States)

    W. Hasan, W. Z.

    2018-01-01

    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. PMID:29351554

  13. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

    Science.gov (United States)

    Sabry, A H; W Hasan, W Z; Ab Kadir, M Z A; Radzi, M A M; Shafie, S

    2018-01-01

    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

  14. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

    Directory of Open Access Journals (Sweden)

    A H Sabry

    Full Text Available The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

  15. Cancer risk of low dose/low dose rate radiation: a meta-analysis of cancer data of mammals exposed to low doses of radiation

    International Nuclear Information System (INIS)

    Ogata, Hiromitsu; Magae, Junji

    2008-01-01

    Full text: Linear No Threshold (LNT) model is a basic theory for radioprotection, but the adaptability of this hypothesis to biological responses at low doses or at low dose rates is not sufficiently investigated. Simultaneous consideration of the cumulative dose and the dose rate is necessary for evaluating the risk of long-term exposure to ionizing radiation at low dose. This study intends to examine several numerical relationships between doses and dose rates in biological responses to gamma radiation. Collected datasets on the relationship between dose and the incidence of cancer in mammals exposed to low doses of radiation were analysed using meta-regression models and modified exponential (MOE) model, which we previously published, that predicts irradiation time-dependent biological response at low dose rate ionizing radiation. Minimum doses of observable risk and effective doses with a variety of dose rates were calculated using parameters estimated by fitting meta-regression models to the data and compared them with other statistical models that find values corresponding to 'threshold limits'. By fitting a weighted regression model (fixed-effects meta-regression model) to the data on risk of all cancers, it was found that the log relative risk [log(RR)] increased as the total exposure dose increased. The intersection of this regression line with the x-axis denotes the minimum dose of observable risk. These estimated minimum doses and effective doses increased with decrease of dose rate. The goodness of fits of MOE-model depended on cancer types, but the total cancer risk is reduced when dose rates are very low. The results suggest that dose response curve for cancer risk is remarkably affected by dose rate and that dose rate effect changes as a function of dose rate. For scientific discussion on the low dose exposure risk and its uncertainty, the term 'threshold' should be statistically defined, and dose rate effects should be included in the risk

  16. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

    Science.gov (United States)

    Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

    2012-04-01

    A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

  17. Modeling bladder cancer in mice: opportunities and challenges

    Science.gov (United States)

    Kobayashi, Takashi; Owczarek, Tomasz B.; McKiernan, James M.; Abate-Shen, Cory

    2015-01-01

    The prognosis and treatment of bladder cancer have hardly improved in the last 20 years. Bladder cancer remains a debilitating and often fatal disease, and among the most costly cancers to treat. The generation of informative mouse models has the potential to improve our understanding of bladder cancer progression, as well as impact its diagnosis and treatment. However, relatively few mouse models of bladder cancer have been described and particularly few that develop invasive cancer phenotypes. This review focuses on opportunities for improving the landscape of mouse models of bladder cancer. PMID:25533675

  18. Sample Size and Statistical Conclusions from Tests of Fit to the Rasch Model According to the Rasch Unidimensional Measurement Model (Rumm) Program in Health Outcome Measurement.

    Science.gov (United States)

    Hagell, Peter; Westergren, Albert

    Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).

  19. Fitting measurement models to vocational interest data: are dominance models ideal?

    Science.gov (United States)

    Tay, Louis; Drasgow, Fritz; Rounds, James; Williams, Bruce A

    2009-09-01

    In this study, the authors examined the item response process underlying 3 vocational interest inventories: the Occupational Preference Inventory (C.-P. Deng, P. I. Armstrong, & J. Rounds, 2007), the Interest Profiler (J. Rounds, T. Smith, L. Hubert, P. Lewis, & D. Rivkin, 1999; J. Rounds, C. M. Walker, et al., 1999), and the Interest Finder (J. E. Wall & H. E. Baker, 1997; J. E. Wall, L. L. Wise, & H. E. Baker, 1996). Item response theory (IRT) dominance models, such as the 2-parameter and 3-parameter logistic models, assume that item response functions (IRFs) are monotonically increasing as the latent trait increases. In contrast, IRT ideal point models, such as the generalized graded unfolding model, have IRFs that peak where the latent trait matches the item. Ideal point models are expected to fit better because vocational interest inventories ask about typical behavior, as opposed to requiring maximal performance. Results show that across all 3 interest inventories, the ideal point model provided better descriptions of the response process. The importance of specifying the correct item response model for precise measurement is discussed. In particular, scores computed by a dominance model were shown to be sometimes illogical: individuals endorsing mostly realistic or mostly social items were given similar scores, whereas scores based on an ideal point model were sensitive to which type of items respondents endorsed.

  20. How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model.

    Science.gov (United States)

    Fry, John S; Lee, Peter N; Forey, Barbara A; Coombs, Katharine J

    2013-10-01

    The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31-10.60), but varied by sex (females 7.92, males 10.71), and age (<50years 6.98, 70+years 12.99). Given that reverse causation is taken account of, the model adequately describes the decline in excess risk. However, estimates of H may be biased by factors including misclassification of smoking status. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  1. Fitted HBT radii versus space-time variances in flow-dominated models

    International Nuclear Information System (INIS)

    Lisa, Mike; Frodermann, Evan; Heinz, Ulrich

    2007-01-01

    The inability of otherwise successful dynamical models to reproduce the 'HBT radii' extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the 'RHIC HBT Puzzle'. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source which can be directly computed from the emission function, without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models some of which exhibit significant deviations from simple Gaussian behaviour. By Fourier transforming the emission function we compute the 2-particle correlation function and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and measured HBT radii remain, we show that a more 'apples-to-apples' comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data. (author)

  2. History of Depression in Lung Cancer Patients

    DEFF Research Database (Denmark)

    Iachina, M; Brønserud, M M; Jakobsen, E

    2017-01-01

    . To estimate the effect of depression on the diagnostic process and the choice of treatment in lung cancer we fitted a logistic regression model and a Cox regression model adjusting for age, gender, resection and stage. RESULTS: Depression in a patient's anamnesis had no significant effect on the delay...

  3. Modeling Human Cancers in Drosophila.

    Science.gov (United States)

    Sonoshita, M; Cagan, R L

    2017-01-01

    Cancer is a complex disease that affects multiple organs. Whole-body animal models provide important insights into oncology that can lead to clinical impact. Here, we review novel concepts that Drosophila studies have established for cancer biology, drug discovery, and patient therapy. Genetic studies using Drosophila have explored the roles of oncogenes and tumor-suppressor genes that when dysregulated promote cancer formation, making Drosophila a useful model to study multiple aspects of transformation. Not limited to mechanism analyses, Drosophila has recently been showing its value in facilitating drug development. Flies offer rapid, efficient platforms by which novel classes of drugs can be identified as candidate anticancer leads. Further, we discuss the use of Drosophila as a platform to develop therapies for individual patients by modeling the tumor's genetic complexity. Drosophila provides both a classical and a novel tool to identify new therapeutics, complementing other more traditional cancer tools. © 2017 Elsevier Inc. All rights reserved.

  4. Radiation effects on cancer mortality among A-bomb survivors, 1950-72. Comparison of some statistical models and analysis based on the additive logit model

    Energy Technology Data Exchange (ETDEWEB)

    Otake, M [Hiroshima Univ. (Japan). Faculty of Science

    1976-12-01

    Various statistical models designed to determine the effects of radiation dose on mortality of atomic bomb survivors in Hiroshima and Nagasaki from specific cancers were evaluated on the basis of a basic k(age) x c(dose) x 2 contingency table. From the aspects of application and fits of different models, analysis based on the additive logit model was applied to the mortality experience of this population during the 22year period from 1 Oct. 1950 to 31 Dec. 1972. The advantages and disadvantages of the additive logit model were demonstrated. Leukemia mortality showed a sharp rise with an increase in dose. The dose response relationship suggests a possible curvature or a log linear model, particularly if the dose estimated to be more than 600 rad were set arbitrarily at 600 rad, since the average dose in the 200+ rad group would then change from 434 to 350 rad. In the 22year period from 1950 to 1972, a high mortality risk due to radiation was observed in survivors with doses of 200 rad and over for all cancers except leukemia. On the other hand, during the latest period from 1965 to 1972 a significant risk was noted also for stomach and breast cancers. Survivors who were 9 year old or less at the time of the bomb and who were exposed to high doses of 200+ rad appeared to show a high mortality risk for all cancers except leukemia, although the number of observed deaths is yet small. A number of interesting areas are discussed from the statistical and epidemiological standpoints, i.e., the numerical comparison of risks in various models, the general evaluation of cancer mortality by the additive logit model, the dose response relationship, the relative risk in the high dose group, the time period of radiation induced cancer mortality, the difference of dose response between Hiroshima and Nagasaki and the relative biological effectiveness of neutrons.

  5. Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

    Directory of Open Access Journals (Sweden)

    Olivier Reynaud

    2017-11-01

    Full Text Available In diffusion weighted imaging (DWI, the apparent diffusion coefficient (ADC has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times/frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a in the short time regime, disentangling structural and diffusive tissue properties, and (b near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts, a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT are reviewed. Their suitability to quantify cell size, intra- and extracellular spaces (ICS and ECS and diffusivities are assessed. The proper modeling of tissue membrane permeability—hardly a newcomer in the field, but lacking applications—and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter

  6. Time-dependent diffusion MRI in cancer: tissue modeling and applications

    Science.gov (United States)

    Reynaud, Olivier

    2017-11-01

    In diffusion weighted imaging (DWI), the apparent diffusion coefficient has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD) is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times / frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a) in the short time regime, disentangling structural and diffusive tissue properties, and (b) near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts), a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT) are reviewed. Their suitability to quantify cell size, intra- and extracellular spaces (ICS and ECS) and diffusivities are assessed. The proper modeling of tissue membrane permeability – hardly a newcomer in the field, but lacking applications - and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter estimation (i

  7. A cautionary note on the use of information fit indexes in covariance structure modeling with means

    NARCIS (Netherlands)

    Wicherts, J.M.; Dolan, C.V.

    2004-01-01

    Information fit indexes such as Akaike Information Criterion, Consistent Akaike Information Criterion, Bayesian Information Criterion, and the expected cross validation index can be valuable in assessing the relative fit of structural equation models that differ regarding restrictiveness. In cases

  8. Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering.

    Directory of Open Access Journals (Sweden)

    Mónica A Silva

    Full Text Available Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF. The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km was nearly half that of LS estimates (11.6 ± 8.4 km. Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.

  9. Radiation dose responses for chemoradiation therapy of pancreatic cancer: an analysis of compiled clinical data using biophysical models.

    Science.gov (United States)

    Moraru, Ion C; Tai, An; Erickson, Beth; Li, X Allen

    2014-01-01

    We analyzed recent clinical data obtained from chemoradiation of unresectable, locally advanced pancreatic cancer (LAPC) in order to examine possible benefits from radiation therapy dose escalation. A modified linear quadratic model was used to fit clinical tumor response and survival data of chemoradiation treatments for LAPC reported from 20 institutions. Biophysical radiosensitivity parameters were extracted from the fits. Examination of the clinical data demonstrated an enhancement in tumor response with higher irradiation dose, an important clinical result for palliation and quality of life. Little indication of improvement in 1-year survival with increased radiation dose was observed. Possible dose escalation schemes are proposed based on calculations of the biologically effective dose required for a 50% tumor response rate. Based on the evaluation of tumor response data, the escalation of radiation dose presents potential clinical benefits which when combined with normal tissue complication analyses may result in improved treatment outcome for locally advanced pancreatic cancer patients. Copyright © 2014 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

  10. Genes Contributing to Porphyromonas gingivalis Fitness in Abscess and Epithelial Cell Colonization Environments

    Directory of Open Access Journals (Sweden)

    Daniel P. Miller

    2017-08-01

    Full Text Available Porphyromonas gingivalis is an important cause of serious periodontal diseases, and is emerging as a pathogen in several systemic conditions including some forms of cancer. Initial colonization by P. gingivalis involves interaction with gingival epithelial cells, and the organism can also access host tissues and spread haematogenously. To better understand the mechanisms underlying these properties, we utilized a highly saturated transposon insertion library of P. gingivalis, and assessed the fitness of mutants during epithelial cell colonization and survival in a murine abscess model by high-throughput sequencing (Tn-Seq. Transposon insertions in many genes previously suspected as contributing to virulence showed significant fitness defects in both screening assays. In addition, a number of genes not previously associated with P. gingivalis virulence were identified as important for fitness. We further examined fitness defects of four such genes by generating defined mutations. Genes encoding a carbamoyl phosphate synthetase, a replication-associated recombination protein, a nitrosative stress responsive HcpR transcription regulator, and RNase Z, a zinc phosphodiesterase, showed a fitness phenotype in epithelial cell colonization and in a competitive abscess infection. This study verifies the importance of several well-characterized putative virulence factors of P. gingivalis and identifies novel fitness determinants of the organism.

  11. Genes Contributing to Porphyromonas gingivalis Fitness in Abscess and Epithelial Cell Colonization Environments

    Science.gov (United States)

    Miller, Daniel P.; Hutcherson, Justin A.; Wang, Yan; Nowakowska, Zuzanna M.; Potempa, Jan; Yoder-Himes, Deborah R.; Scott, David A.; Whiteley, Marvin; Lamont, Richard J.

    2017-01-01

    Porphyromonas gingivalis is an important cause of serious periodontal diseases, and is emerging as a pathogen in several systemic conditions including some forms of cancer. Initial colonization by P. gingivalis involves interaction with gingival epithelial cells, and the organism can also access host tissues and spread haematogenously. To better understand the mechanisms underlying these properties, we utilized a highly saturated transposon insertion library of P. gingivalis, and assessed the fitness of mutants during epithelial cell colonization and survival in a murine abscess model by high-throughput sequencing (Tn-Seq). Transposon insertions in many genes previously suspected as contributing to virulence showed significant fitness defects in both screening assays. In addition, a number of genes not previously associated with P. gingivalis virulence were identified as important for fitness. We further examined fitness defects of four such genes by generating defined mutations. Genes encoding a carbamoyl phosphate synthetase, a replication-associated recombination protein, a nitrosative stress responsive HcpR transcription regulator, and RNase Z, a zinc phosphodiesterase, showed a fitness phenotype in epithelial cell colonization and in a competitive abscess infection. This study verifies the importance of several well-characterized putative virulence factors of P. gingivalis and identifies novel fitness determinants of the organism. PMID:28900609

  12. Genes Contributing to Porphyromonas gingivalis Fitness in Abscess and Epithelial Cell Colonization Environments.

    Science.gov (United States)

    Miller, Daniel P; Hutcherson, Justin A; Wang, Yan; Nowakowska, Zuzanna M; Potempa, Jan; Yoder-Himes, Deborah R; Scott, David A; Whiteley, Marvin; Lamont, Richard J

    2017-01-01

    Porphyromonas gingivalis is an important cause of serious periodontal diseases, and is emerging as a pathogen in several systemic conditions including some forms of cancer. Initial colonization by P. gingivalis involves interaction with gingival epithelial cells, and the organism can also access host tissues and spread haematogenously. To better understand the mechanisms underlying these properties, we utilized a highly saturated transposon insertion library of P. gingivalis , and assessed the fitness of mutants during epithelial cell colonization and survival in a murine abscess model by high-throughput sequencing (Tn-Seq). Transposon insertions in many genes previously suspected as contributing to virulence showed significant fitness defects in both screening assays. In addition, a number of genes not previously associated with P. gingivalis virulence were identified as important for fitness. We further examined fitness defects of four such genes by generating defined mutations. Genes encoding a carbamoyl phosphate synthetase, a replication-associated recombination protein, a nitrosative stress responsive HcpR transcription regulator, and RNase Z, a zinc phosphodiesterase, showed a fitness phenotype in epithelial cell colonization and in a competitive abscess infection. This study verifies the importance of several well-characterized putative virulence factors of P. gingivalis and identifies novel fitness determinants of the organism.

  13. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  14. Laboratory animal models for esophageal cancer

    Directory of Open Access Journals (Sweden)

    Dhanya Venugopalan Nair

    2016-11-01

    Full Text Available The incidence of esophageal cancer is rapidly increasing especially in developing countries. The major risk factors include unhealthy lifestyle practices such as alcohol consumption, smoking, and chewing tobacco to name a few. Diagnosis at an advanced stage and poor prognosis make esophageal cancer one of the most lethal diseases. These factors have urged further research in understanding the pathophysiology of the disease. Animal models not only aid in understanding the molecular pathogenesis of esophageal cancer but also help in developing therapeutic interventions for the disease. This review throws light on the various recent laboratory animal models for esophageal cancer.

  15. The FIT Model - Fuel-cycle Integration and Tradeoffs

    International Nuclear Information System (INIS)

    Piet, Steven J.; Soelberg, Nick R.; Bays, Samuel E.; Pereira, Candido; Pincock, Layne F.; Shaber, Eric L.; Teague, Melissa C.; Teske, Gregory M.; Vedros, Kurt G.

    2010-01-01

    All mass streams from fuel separation and fabrication are products that must meet some set of product criteria - fuel feedstock impurity limits, waste acceptance criteria (WAC), material storage (if any), or recycle material purity requirements such as zirconium for cladding or lanthanides for industrial use. These must be considered in a systematic and comprehensive way. The FIT model and the 'system losses study' team that developed it (Shropshire2009, Piet2010) are an initial step by the FCR and D program toward a global analysis that accounts for the requirements and capabilities of each component, as well as major material flows within an integrated fuel cycle. This will help the program identify near-term R and D needs and set longer-term goals. The question originally posed to the 'system losses study' was the cost of separation, fuel fabrication, waste management, etc. versus the separation efficiency. In other words, are the costs associated with marginal reductions in separations losses (or improvements in product recovery) justified by the gains in the performance of other systems? We have learned that that is the wrong question. The right question is: how does one adjust the compositions and quantities of all mass streams, given uncertain product criteria, to balance competing objectives including cost? FIT is a method to analyze different fuel cycles using common bases to determine how chemical performance changes in one part of a fuel cycle (say used fuel cooling times or separation efficiencies) affect other parts of the fuel cycle. FIT estimates impurities in fuel and waste via a rough estimate of physics and mass balance for a set of technologies. If feasibility is an issue for a set, as it is for 'minimum fuel treatment' approaches such as melt refining and AIROX, it can help to make an estimate of how performances would have to change to achieve feasibility.

  16. Animal Cancer Models of Skeletal Metastasis

    Directory of Open Access Journals (Sweden)

    Catherine Hibberd

    2013-01-01

    Full Text Available The bony skeleton is one of the most common sites of metastatic spread of cancer and is a significant source of morbidity in cancer patients, causing pain and pathologic fracture, impaired ambulatory ability, and poorer quality of life. Animal cancer models of skeletal metastases are essential for better understanding of the molecular pathways behind metastatic spread and local growth and invasion of bone, to enable analysis of host-tumor cell interactions, identify barriers to the metastatic process, and to provide platforms to develop and test novel therapies prior to clinical application in human patients. Thus, the ideal model should be clinically relevant, reproducible and representative of the human condition. This review summarizes the current in vivo animal models used in the study of cancer metastases of the skeleton.

  17. An Improved Cognitive Model of the Iowa and Soochow Gambling Tasks With Regard to Model Fitting Performance and Tests of Parameter Consistency

    Directory of Open Access Journals (Sweden)

    Junyi eDai

    2015-03-01

    Full Text Available The Iowa Gambling Task (IGT and the Soochow Gambling Task (SGT are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning model (EVL and the prospect valence learning model (PVL, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79 and 27 control participants (mean age 35; SD 10.44 completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.

  18. Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

    Science.gov (United States)

    Leite, Walter L.; Stapleton, Laura M.

    2011-01-01

    In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

  19. Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data

    Directory of Open Access Journals (Sweden)

    Ji Hoon Ryoo

    2017-08-01

    Full Text Available As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications.

  20. A mathematical model of cancer cells with phenotypic plasticity

    Directory of Open Access Journals (Sweden)

    Da Zhou

    2015-12-01

    Full Text Available Purpose: The phenotypic plasticity of cancer cells is recently becoming a cutting-edge research area in cancer, which challenges the cellular hierarchy proposed by the conventional cancer stem cell theory. In this study, we establish a mathematical model for describing the phenotypic plasticity of cancer cells, based on which we try to find some salient features that can characterize the dynamic behavior of the phenotypic plasticity especially in comparison to the hierarchical model of cancer cells. Methods: We model cancer as population dynamics composed of different phenotypes of cancer cells. In this model, not only can cancer cells divide (symmetrically and asymmetrically and die, but they can also convert into other cellular phenotypes. According to the Law of Mass Action, the cellular processes can be captured by a system of ordinary differential equations (ODEs. On one hand, we can analyze the long-term stability of the model by applying qualitative method of ODEs. On the other hand, we are also concerned about the short-term behavior of the model by studying its transient dynamics. Meanwhile, we validate our model to the cell-state dynamics in published experimental data.Results: Our results show that the phenotypic plasticity plays important roles in both stabilizing the distribution of different phenotypic mixture and maintaining the cancer stem cells proportion. In particular, the phenotypic plasticity model shows decided advantages over the hierarchical model in predicting the phenotypic equilibrium and cancer stem cells’ overshoot reported in previous biological experiments in cancer cell lines.Conclusion: Since the validity of the phenotypic plasticity paradigm and the conventional cancer stem cell theory is still debated in experimental biology, it is worthy of theoretically searching for good indicators to distinguish the two models through quantitative methods. According to our study, the phenotypic equilibrium and overshoot

  1. Source Localization with Acoustic Sensor Arrays Using Generative Model Based Fitting with Sparse Constraints

    Directory of Open Access Journals (Sweden)

    Javier Macias-Guarasa

    2012-10-01

    Full Text Available This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies.

  2. An approximation to the adaptive exponential integrate-and-fire neuron model allows fast and predictive fitting to physiological data

    Directory of Open Access Journals (Sweden)

    Loreen eHertäg

    2012-09-01

    Full Text Available For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ('in-vivo-like' input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a 'high-throughput' model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

  3. A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Huang, Can

    2018-01-01

    –slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....

  4. Development of a statistical model for cervical cancer cell death with irreversible electroporation in vitro.

    Science.gov (United States)

    Yang, Yongji; Moser, Michael A J; Zhang, Edwin; Zhang, Wenjun; Zhang, Bing

    2018-01-01

    The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric

  5. A Data-Driven Method for Selecting Optimal Models Based on Graphical Visualisation of Differences in Sequentially Fitted ROC Model Parameters

    Directory of Open Access Journals (Sweden)

    K S Mwitondi

    2013-05-01

    Full Text Available Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.

  6. Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder

    DEFF Research Database (Denmark)

    Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David

    2015-01-01

    recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argos satellite tracking data, we demonstrate that the use of continuous...... are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack....

  7. Thyroid Cancer Incidences From Selected South America Population-Based Cancer Registries: An Age-Period-Cohort Study

    Directory of Open Access Journals (Sweden)

    Anne Karin da Mota Borges

    2017-08-01

    Full Text Available Purpose: The incidence of thyroid cancer (TC has increased substantially worldwide. However, there is a lack of knowledge about age-period-cohort (APC effects on incidence rates in South American countries. This study describes the TC incidence trends and analyzes APC effects in Cali, Colombia; Costa Rica; Goiânia, Brazil; and Quito, Ecuador. Materials and Methods: Data were obtained from the Cancer Incidence in Five Continents series, and the crude and age-standardized incidence rates were calculated. Trends were assessed using the estimated annual percentage change, and APC models were estimated using Poisson regression for individuals between age 20 and 79 years. Results: An increasing trend in age-standardized incidence rates was observed among women from Goiânia (9.2%, Costa Rica (5.7%, Quito (4.0%, and Cali (3.4%, and in men from Goiânia (10.0% and Costa Rica (3.4%. The APC modeling showed that there was a period effect in all regions and for both sexes. Increasing rate ratios were observed among women over the periods. The best fit model was the APC model in women from all regions and in men from Quito, whereas the age-cohort model showed a better fit in men from Cali and Costa Rica, and the age-drift model showed a better fit among men from Goiânia. Conclusion: These findings suggest that overdiagnosis is a possible explanation for the observed increasing pattern of TC incidence. However, some environmental exposures may also have contributed to the observed increase.

  8. Log-normal frailty models fitted as Poisson generalized linear mixed models.

    Science.gov (United States)

    Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

    2016-12-01

    The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Mouse models of estrogen receptor-positive breast cancer

    Directory of Open Access Journals (Sweden)

    Shakur Mohibi

    2011-01-01

    Full Text Available Breast cancer is the most frequent malignancy and second leading cause of cancer-related deaths among women. Despite advances in genetic and biochemical analyses, the incidence of breast cancer and its associated mortality remain very high. About 60 - 70% of breast cancers are Estrogen Receptor alpha (ER-α positive and are dependent on estrogen for growth. Selective estrogen receptor modulators (SERMs have therefore provided an effective targeted therapy to treat ER-α positive breast cancer patients. Unfortunately, development of resistance to endocrine therapy is frequent and leads to cancer recurrence. Our understanding of molecular mechanisms involved in the development of ER-α positive tumors and their resistance to ER antagonists is currently limited due to lack of experimental models of ER-α positive breast cancer. In most mouse models of breast cancer, the tumors that form are typically ER-negative and independent of estrogen for their growth. However, in recent years more attention has been given to develop mouse models that develop different subtypes of breast cancers, including ER-positive tumors. In this review, we discuss the currently available mouse models that develop ER-α positive mammary tumors and their potential use to elucidate the molecular mechanisms of ER-α positive breast cancer development and endocrine resistance.

  1. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2017-01-01

    of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...

  2. Context Sensitive Modeling of Cancer Drug Sensitivity.

    Directory of Open Access Journals (Sweden)

    Bo-Juen Chen

    Full Text Available Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression, an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.

  3. Three-dimensional in vitro cancer models: a short review

    International Nuclear Information System (INIS)

    Wang, Chengyang; Sun, Wei; Tang, Zhenyu; Li, Lingsong; Zhao, Yu; Yao, Rui

    2014-01-01

    The re-creation of the tumor microenvironment including tumor–stromal interactions, cell–cell adhesion and cellular signaling is essential in cancer-related studies. Traditional two-dimensional (2D) cell culture and animal models have been proven to be valid in some areas of explaining cancerous cell behavior and interpreting hypotheses of possible mechanisms. However, a well-defined three-dimensional (3D) in vitro cancer model, which mimics tumor structures found in vivo and allows cell–cell and cell–matrix interactions, has gained strong interest for a wide variety of diagnostic and therapeutic applications. This communication attempts to provide a representative overview of applying 3D in vitro biological model systems for cancer related studies. The review compares and comments on the differences in using 2D models, animal models and 3D in vitro models for cancer research. Recent technologies to construct and develop 3D in vitro cancer models are summarized in aspects of modeling design, fabrication technique and potential application to biology, pathogenesis study and drug testing. With the help of advanced engineering techniques, the development of a novel complex 3D in vitro cancer model system will provide a better opportunity to understand crucial cancer mechanisms and to develop new clinical therapies. (topical review)

  4. Multiple organ definition in CT using a Bayesian approach for 3D model fitting

    Science.gov (United States)

    Boes, Jennifer L.; Weymouth, Terry E.; Meyer, Charles R.

    1995-08-01

    Organ definition in computed tomography (CT) is of interest for treatment planning and response monitoring. We present a method for organ definition using a priori information about shape encoded in a set of biometric organ models--specifically for the liver and kidney-- that accurately represents patient population shape information. Each model is generated by averaging surfaces from a learning set of organ shapes previously registered into a standard space defined by a small set of landmarks. The model is placed in a specific patient's data set by identifying these landmarks and using them as the basis for model deformation; this preliminary representation is then iteratively fit to the patient's data based on a Bayesian formulation of the model's priors and CT edge information, yielding a complete organ surface. We demonstrate this technique using a set of fifteen abdominal CT data sets for liver surface definition both before and after the addition of a kidney model to the fitting; we demonstrate the effectiveness of this tool for organ surface definition in this low-contrast domain.

  5. A comparative review of radiation-induced cancer risk models

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung Hee; Kim, Ju Youl [FNC Technology Co., Ltd., Yongin (Korea, Republic of); Han, Seok Jung [Risk and Environmental Safety Research Division, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2017-06-15

    With the need for a domestic level 3 probabilistic safety assessment (PSA), it is essential to develop a Korea-specific code. Health effect assessments study radiation-induced impacts; in particular, long-term health effects are evaluated in terms of cancer risk. The objective of this study was to analyze the latest cancer risk models developed by foreign organizations and to compare the methodology of how they were developed. This paper also provides suggestions regarding the development of Korean cancer risk models. A review of cancer risk models was carried out targeting the latest models: the NUREG model (1993), the BEIR VII model (2006), the UNSCEAR model (2006), the ICRP 103 model (2007), and the U.S. EPA model (2011). The methodology of how each model was developed is explained, and the cancer sites, dose and dose rate effectiveness factor (DDREF) and mathematical models are also described in the sections presenting differences among the models. The NUREG model was developed by assuming that the risk was proportional to the risk coefficient and dose, while the BEIR VII, UNSCEAR, ICRP, and U.S. EPA models were derived from epidemiological data, principally from Japanese atomic bomb survivors. The risk coefficient does not consider individual characteristics, as the values were calculated in terms of population-averaged cancer risk per unit dose. However, the models derived by epidemiological data are a function of sex, exposure age, and attained age of the exposed individual. Moreover, the methodologies can be used to apply the latest epidemiological data. Therefore, methodologies using epidemiological data should be considered first for developing a Korean cancer risk model, and the cancer sites and DDREF should also be determined based on Korea-specific studies. This review can be used as a basis for developing a Korean cancer risk model in the future.

  6. The 'fitting problem' in cosmology

    International Nuclear Information System (INIS)

    Ellis, G.F.R.; Stoeger, W.

    1987-01-01

    The paper considers the best way to fit an idealised exactly homogeneous and isotropic universe model to a realistic ('lumpy') universe; whether made explicit or not, some such approach of necessity underlies the use of the standard Robertson-Walker models as models of the real universe. Approaches based on averaging, normal coordinates and null data are presented, the latter offering the best opportunity to relate the fitting procedure to data obtainable by astronomical observations. (author)

  7. A fitting LEGACY – modelling Kepler's best stars

    Directory of Open Access Journals (Sweden)

    Aarslev Magnus J.

    2017-01-01

    Full Text Available The LEGACY sample represents the best solar-like stars observed in the Kepler mission[5, 8]. The 66 stars in the sample are all on the main sequence or only slightly more evolved. They each have more than one year's observation data in short cadence, allowing for precise extraction of individual frequencies. Here we present model fits using a modified ASTFIT procedure employing two different near-surface-effect corrections, one by Christensen-Dalsgaard[4] and a newer correction proposed by Ball & Gizon[1]. We then compare the results obtained using the different corrections. We find that using the latter correction yields lower masses and significantly lower χ2 values for a large part of the sample.

  8. Mechanisms of Cancer Cell Dormancy--Another Hallmark of Cancer?

    Science.gov (United States)

    Yeh, Albert C; Ramaswamy, Sridhar

    2015-12-01

    Disease relapse in cancer patients many years after clinical remission, often referred to as cancer dormancy, is well documented but remains an incompletely understood phenomenon on the biologic level. Recent reviews have summarized potential models that can explain this phenomenon, including angiogenic, immunologic, and cellular dormancy. We focus on mechanisms of cellular dormancy as newer biologic insights have enabled better understanding of this process. We provide a historical context, synthesize current advances in the field, and propose a mechanistic framework that treats cancer cell dormancy as a dynamic cell state conferring a fitness advantage to an evolving malignancy under stress. Cellular dormancy appears to be an active process that can be toggled through a variety of signaling mechanisms that ultimately downregulate the RAS/MAPK and PI(3)K/AKT pathways, an ability that is preserved even in cancers that constitutively depend on these pathways for their growth and survival. Just as unbridled proliferation is a key hallmark of cancer, the ability of cancer cells to become quiescent may be critical to evolving malignancies, with implications for understanding cancer initiation, progression, and treatment resistance. ©2015 American Association for Cancer Research.

  9. Fitting the CDO correlation skew: a tractable structural jump-diffusion model

    DEFF Research Database (Denmark)

    Willemann, Søren

    2007-01-01

    We extend a well-known structural jump-diffusion model for credit risk to handle both correlations through diffusion of asset values and common jumps in asset value. Through a simplifying assumption on the default timing and efficient numerical techniques, we develop a semi-analytic framework...... allowing for instantaneous calibration to heterogeneous CDS curves and fast computation of CDO tranche spreads. We calibrate the model to CDX and iTraxx data from February 2007 and achieve a satisfactory fit. To price the senior tranches for both indices, we require a risk-neutral probability of a market...

  10. Development and design of a late-model fitness test instrument based on LabView

    Science.gov (United States)

    Xie, Ying; Wu, Feiqing

    2010-12-01

    Undergraduates are pioneers of China's modernization program and undertake the historic mission of rejuvenating our nation in the 21st century, whose physical fitness is vital. A smart fitness test system can well help them understand their fitness and health conditions, thus they can choose more suitable approaches and make practical plans for exercising according to their own situation. following the future trends, a Late-model fitness test Instrument based on LabView has been designed to remedy defects of today's instruments. The system hardware consists of fives types of sensors with their peripheral circuits, an acquisition card of NI USB-6251 and a computer, while the system software, on the basis of LabView, includes modules of user register, data acquisition, data process and display, and data storage. The system, featured by modularization and an open structure, is able to be revised according to actual needs. Tests results have verified the system's stability and reliability.

  11. Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

    Science.gov (United States)

    Molitor, John

    2012-03-01

    Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

  12. “Psychosocial Interventions for Cancer Survivors, Caregivers and Family Members—One Size Does Not Fit All: My Perspective as a Young Adult Survivor, Advocate and Oncology Social Worker” a personal reflection by Mary Grace Bontempo - Office of Cancer Survivorship

    Science.gov (United States)

    “Psychosocial Interventions for Cancer Survivors, Caregivers and Family Members—One Size Does Not Fit All: My Perspective as a Young Adult Survivor, Advocate and Oncology Social Worker” a personal reflection by Mary Grace Bontempo page

  13. Mouse Models of Breast Cancer: Platforms for Discovering Precision Imaging Diagnostics and Future Cancer Medicine.

    Science.gov (United States)

    Manning, H Charles; Buck, Jason R; Cook, Rebecca S

    2016-02-01

    Representing an enormous health care and socioeconomic challenge, breast cancer is the second most common cancer in the world and the second most common cause of cancer-related death. Although many of the challenges associated with preventing, treating, and ultimately curing breast cancer are addressable in the laboratory, successful translation of groundbreaking research to clinical populations remains an important barrier. Particularly when compared with research on other types of solid tumors, breast cancer research is hampered by a lack of tractable in vivo model systems that accurately recapitulate the relevant clinical features of the disease. A primary objective of this article was to provide a generalizable overview of the types of in vivo model systems, with an emphasis primarily on murine models, that are widely deployed in preclinical breast cancer research. Major opportunities to advance precision cancer medicine facilitated by molecular imaging of preclinical breast cancer models are discussed. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

  14. Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

    Science.gov (United States)

    Duarte, Adam; Adams, Michael J.; Peterson, James T.

    2018-01-01

    Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision

  15. Decision making on fitness landscapes

    Science.gov (United States)

    Arthur, R.; Sibani, P.

    2017-04-01

    We discuss fitness landscapes and how they can be modified to account for co-evolution. We are interested in using the landscape as a way to model rational decision making in a toy economic system. We develop a model very similar to the Tangled Nature Model of Christensen et al. that we call the Tangled Decision Model. This is a natural setting for our discussion of co-evolutionary fitness landscapes. We use a Monte Carlo step to simulate decision making and investigate two different decision making procedures.

  16. Decision Making on Fitness Landscapes

    DEFF Research Database (Denmark)

    Arthur, Rudy; Sibani, Paolo

    2017-01-01

    We discuss fitness landscapes and how they can be modified to account for co-evolution. We are interested in using the landscape as a way to model rational decision making in a toy economic system. We develop a model very similar to the Tangled Nature Model of Christensen et. al. that we call...... the Tangled Decision Model. This is a natural setting for our discussion of co-evolutionary fitness landscapes. We use a Monte Carlo step to simulate decision making and investigate two different decision making procedures....

  17. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  18. Assessing a moderating effect and the global fit of a PLS model on online trading

    Directory of Open Access Journals (Sweden)

    Juan J. García-Machado

    2017-12-01

    Full Text Available This paper proposes a PLS Model for the study of Online Trading. Traditional investing has experienced a revolution due to the rise of e-trading services that enable investors to use Internet conduct secure trading. On the hand, model results show that there is a positive, direct and statistically significant relationship between personal outcome expectations, perceived relative advantage, shared vision and economy-based trust with the quality of knowledge. On the other hand, trading frequency and portfolio performance has also this relationship. After including the investor’s income and financial wealth (IFW as moderating effect, the PLS model was enhanced, and we found that the interaction term is negative and statistically significant, so, higher IFW levels entail a weaker relationship between trading frequency and portfolio performance and vice-versa. Finally, with regard to the goodness of overall model fit measures, they showed that the model is fit for SRMR and dG measures, so it is likely that the model is true.

  19. Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model.

    Science.gov (United States)

    Yuan, Shupei; Ma, Wenjuan; Kanthawala, Shaheen; Peng, Wei

    2015-09-01

    Health and fitness applications (apps) are one of the major app categories in the current mobile app market. Few studies have examined this area from the users' perspective. This study adopted the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Model to examine the predictors of the users' intention to adopt health and fitness apps. A survey (n=317) was conducted with college-aged smartphone users at a Midwestern university in the United States. Performance expectancy, hedonic motivations, price value, and habit were significant predictors of users' intention of continued usage of health and fitness apps. However, effort expectancy, social influence, and facilitating conditions were not found to predict users' intention of continued usage of health and fitness apps. This study extends the UTATU2 Model to the mobile apps domain and provides health professions, app designers, and marketers with the insights of user experience in terms of continuously using health and fitness apps.

  20. Improved numerical solutions for chaotic-cancer-model

    Directory of Open Access Journals (Sweden)

    Muhammad Yasir

    2017-01-01

    Full Text Available In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.

  1. Improved numerical solutions for chaotic-cancer-model

    Science.gov (United States)

    Yasir, Muhammad; Ahmad, Salman; Ahmed, Faizan; Aqeel, Muhammad; Akbar, Muhammad Zubair

    2017-01-01

    In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR) method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.

  2. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  3. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  4. Mouse models for gastric cancer: Matching models to biological questions

    Science.gov (United States)

    Poh, Ashleigh R; O'Donoghue, Robert J J

    2016-01-01

    Abstract Gastric cancer is the third leading cause of cancer‐related mortality worldwide. This is in part due to the asymptomatic nature of the disease, which often results in late‐stage diagnosis, at which point there are limited treatment options. Even when treated successfully, gastric cancer patients have a high risk of tumor recurrence and acquired drug resistance. It is vital to gain a better understanding of the molecular mechanisms underlying gastric cancer pathogenesis to facilitate the design of new‐targeted therapies that may improve patient survival. A number of chemically and genetically engineered mouse models of gastric cancer have provided significant insight into the contribution of genetic and environmental factors to disease onset and progression. This review outlines the strengths and limitations of current mouse models of gastric cancer and their relevance to the pre‐clinical development of new therapeutics. PMID:26809278

  5. Animal models for cancer and uses thereof

    NARCIS (Netherlands)

    Demaria, Marco; Campisi, Judith; van Deursen, Jan M.; Kirkland, James; Tchkonia, Tamara T.; Baker, Darren J.

    2017-01-01

    Non-human animal cancer models are provided herein for identifying and characterizing agents useful for therapy and prophylaxis of cancers, including agents useful for diminishing side effects related to cancer therapies and reducing metastatic disease.

  6. Phylogenetic tree reconstruction accuracy and model fit when proportions of variable sites change across the tree.

    Science.gov (United States)

    Shavit Grievink, Liat; Penny, David; Hendy, Michael D; Holland, Barbara R

    2010-05-01

    Commonly used phylogenetic models assume a homogeneous process through time in all parts of the tree. However, it is known that these models can be too simplistic as they do not account for nonhomogeneous lineage-specific properties. In particular, it is now widely recognized that as constraints on sequences evolve, the proportion and positions of variable sites can vary between lineages causing heterotachy. The extent to which this model misspecification affects tree reconstruction is still unknown. Here, we evaluate the effect of changes in the proportions and positions of variable sites on model fit and tree estimation. We consider 5 current models of nucleotide sequence evolution in a Bayesian Markov chain Monte Carlo framework as well as maximum parsimony (MP). We show that for a tree with 4 lineages where 2 nonsister taxa undergo a change in the proportion of variable sites tree reconstruction under the best-fitting model, which is chosen using a relative test, often results in the wrong tree. In this case, we found that an absolute test of model fit is a better predictor of tree estimation accuracy. We also found further evidence that MP is not immune to heterotachy. In addition, we show that increased sampling of taxa that have undergone a change in proportion and positions of variable sites is critical for accurate tree reconstruction.

  7. Measuring fit of sequence data to phylogenetic model: gain of power using marginal tests.

    Science.gov (United States)

    Waddell, Peter J; Ota, Rissa; Penny, David

    2009-10-01

    Testing fit of data to model is fundamentally important to any science, but publications in the field of phylogenetics rarely do this. Such analyses discard fundamental aspects of science as prescribed by Karl Popper. Indeed, not without cause, Popper (Unended quest: an intellectual autobiography. Fontana, London, 1976) once argued that evolutionary biology was unscientific as its hypotheses were untestable. Here we trace developments in assessing fit from Penny et al. (Nature 297:197-200, 1982) to the present. We compare the general log-likelihood ratio (the G or G (2) statistic) statistic between the evolutionary tree model and the multinomial model with that of marginalized tests applied to an alignment (using placental mammal coding sequence data). It is seen that the most general test does not reject the fit of data to model (P approximately 0.5), but the marginalized tests do. Tests on pairwise frequency (F) matrices, strongly (P < 0.001) reject the most general phylogenetic (GTR) models commonly in use. It is also clear (P < 0.01) that the sequences are not stationary in their nucleotide composition. Deviations from stationarity and homogeneity seem to be unevenly distributed amongst taxa; not necessarily those expected from examining other regions of the genome. By marginalizing the 4( t ) patterns of the i.i.d. model to observed and expected parsimony counts, that is, from constant sites, to singletons, to parsimony informative characters of a minimum possible length, then the likelihood ratio test regains power, and it too rejects the evolutionary model with P < 0.001. Given such behavior over relatively recent evolutionary time, readers in general should maintain a healthy skepticism of results, as the scale of the systematic errors in published trees may really be far larger than the analytical methods (e.g., bootstrap) report.

  8. UROX 2.0: an interactive tool for fitting atomic models into electron-microscopy reconstructions

    International Nuclear Information System (INIS)

    Siebert, Xavier; Navaza, Jorge

    2009-01-01

    UROX is software designed for the interactive fitting of atomic models into electron-microscopy reconstructions. The main features of the software are presented, along with a few examples. Electron microscopy of a macromolecular structure can lead to three-dimensional reconstructions with resolutions that are typically in the 30–10 Å range and sometimes even beyond 10 Å. Fitting atomic models of the individual components of the macromolecular structure (e.g. those obtained by X-ray crystallography or nuclear magnetic resonance) into an electron-microscopy map allows the interpretation of the latter at near-atomic resolution, providing insight into the interactions between the components. Graphical software is presented that was designed for the interactive fitting and refinement of atomic models into electron-microscopy reconstructions. Several characteristics enable it to be applied over a wide range of cases and resolutions. Firstly, calculations are performed in reciprocal space, which results in fast algorithms. This allows the entire reconstruction (or at least a sizeable portion of it) to be used by taking into account the symmetry of the reconstruction both in the calculations and in the graphical display. Secondly, atomic models can be placed graphically in the map while the correlation between the model-based electron density and the electron-microscopy reconstruction is computed and displayed in real time. The positions and orientations of the models are refined by a least-squares minimization. Thirdly, normal-mode calculations can be used to simulate conformational changes between the atomic model of an individual component and its corresponding density within a macromolecular complex determined by electron microscopy. These features are illustrated using three practical cases with different symmetries and resolutions. The software, together with examples and user instructions, is available free of charge at http://mem.ibs.fr/UROX/

  9. Fitness, Sleep-Disordered Breathing, Symptoms of Depression, and Cognition in Inactive Overweight Children: Mediation Models.

    Science.gov (United States)

    Stojek, Monika M K; Montoya, Amanda K; Drescher, Christopher F; Newberry, Andrew; Sultan, Zain; Williams, Celestine F; Pollock, Norman K; Davis, Catherine L

    We used mediation models to examine the mechanisms underlying the relationships among physical fitness, sleep-disordered breathing (SDB), symptoms of depression, and cognitive functioning. We conducted a cross-sectional secondary analysis of the cohorts involved in the 2003-2006 project PLAY (a trial of the effects of aerobic exercise on health and cognition) and the 2008-2011 SMART study (a trial of the effects of exercise on cognition). A total of 397 inactive overweight children aged 7-11 received a fitness test, standardized cognitive test (Cognitive Assessment System, yielding Planning, Attention, Simultaneous, Successive, and Full Scale scores), and depression questionnaire. Parents completed a Pediatric Sleep Questionnaire. We used bootstrapped mediation analyses to test whether SDB mediated the relationship between fitness and depression and whether SDB and depression mediated the relationship between fitness and cognition. Fitness was negatively associated with depression ( B = -0.041; 95% CI, -0.06 to -0.02) and SDB ( B = -0.005; 95% CI, -0.01 to -0.001). SDB was positively associated with depression ( B = 0.99; 95% CI, 0.32 to 1.67) after controlling for fitness. The relationship between fitness and depression was mediated by SDB (indirect effect = -0.005; 95% CI, -0.01 to -0.0004). The relationship between fitness and the attention component of cognition was independently mediated by SDB (indirect effect = 0.058; 95% CI, 0.004 to 0.13) and depression (indirect effect = -0.071; 95% CI, -0.01 to -0.17). SDB mediates the relationship between fitness and depression, and SDB and depression separately mediate the relationship between fitness and the attention component of cognition.

  10. The Predicting Model of E-commerce Site Based on the Ideas of Curve Fitting

    Science.gov (United States)

    Tao, Zhang; Li, Zhang; Dingjun, Chen

    On the basis of the idea of the second multiplication curve fitting, the number and scale of Chinese E-commerce site is analyzed. A preventing increase model is introduced in this paper, and the model parameters are solved by the software of Matlab. The validity of the preventing increase model is confirmed though the numerical experiment. The experimental results show that the precision of preventing increase model is ideal.

  11. Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients.

    Directory of Open Access Journals (Sweden)

    Mark E Sherman

    Full Text Available Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC for 37,939 invasive breast cancers (1996-2007, we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67% with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions; Breast Cancer Risk Assessment Tool (BCRAT; and BCSC 5-year risk model (BCSC-5. Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.Of 6,021 deaths, 2,993 (49.7% were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR = 0.82 (95% CI = 0.75-0.90; BCRAT: HR = 0.72 (95% CI = 0.65-0.81 and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94. Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high

  12. Mathematical Models of Breast and Ovarian Cancers

    Science.gov (United States)

    Botesteanu, Dana-Adriana; Lipkowitz, Stanley; Lee, Jung-Min; Levy, Doron

    2016-01-01

    Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, since answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible, in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions. PMID:27259061

  13. Testing the validity of stock-recruitment curve fits

    International Nuclear Information System (INIS)

    Christensen, S.W.; Goodyear, C.P.

    1988-01-01

    The utilities relied heavily on the Ricker stock-recruitment model as the basis for quantifying biological compensation in the Hudson River power case. They presented many fits of the Ricker model to data derived from striped bass catch and effort records compiled by the National Marine Fisheries Service. Based on this curve-fitting exercise, a value of 4 was chosen for the parameter alpha in the Ricker model, and this value was used to derive the utilities' estimates of the long-term impact of power plants on striped bass populations. A technique was developed and applied to address a single fundamental question: if the Ricker model were applicable to the Hudson River striped bass population, could the estimates of alpha from the curve-fitting exercise be considered reliable. The technique involved constructing a simulation model that incorporated the essential biological features of the population and simulated the characteristics of the available actual catch-per-unit-effort data through time. The ability or failure to retrieve the known parameter values underlying the simulation model via the curve-fitting exercise was a direct test of the reliability of the results of fitting stock-recruitment curves to the real data. The results demonstrated that estimates of alpha from the curve-fitting exercise were not reliable. The simulation-modeling technique provides an effective way to identify whether or not particular data are appropriate for use in fitting such models. 39 refs., 2 figs., 3 tabs

  14. Design of the Quality of Life in Motion (QLIM study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of a combined physical exercise and psychosocial training program to improve physical fitness in children with cancer

    Directory of Open Access Journals (Sweden)

    Takken Tim

    2010-11-01

    Full Text Available Abstract Background Childhood cancer and its treatment have considerable impact on a child's physical and mental wellbeing. Especially long-term administration of chemotherapy and/or radiotherapy impairs physical fitness both during and after therapy, when children often present with muscle weakness and/or low cardiorespiratory fitness. Physical exercise can improve these two elements of physical fitness, but the positive effects of physical exercise might be further increased when a child's wellbeing is simultaneously enhanced by psychosocial training. Feeling better may increase the willingness and motivation to engage in sports activities. Therefore, this multi-centre study evaluates the short and long-term changes in physical fitness of a child with a childhood malignancy, using a combined physical exercise and psychosocial intervention program, implemented during or shortly after treatment. Also examined is whether positive effects on physical fitness reduce inactivity-related adverse health problems, improve quality of life, and are cost-effective. Methods This multi-centre randomized controlled trial compares a combined physical and psychosocial intervention program for children with cancer, with care as usual (controls. Children with cancer (aged 8-18 years treated with chemotherapy and/or radiotherapy, and who are no longer than 1 year post-treatment, are eligible for participation. A total of 100 children are being recruited from the paediatric oncology/haematology departments of three Dutch university medical centres. Patients are stratified according to pubertal stage (girls: age ≤10 or >10 years; boys: ≤11 or >11 years, type of malignancy (haematological or solid tumour, and moment of inclusion into the study (during or after treatment, and are randomly assigned to the intervention or control group. Discussion Childhood cancer patients undergoing long-term cancer therapy may benefit from a combined physical exercise and

  15. Design of the Quality of Life in Motion (QLIM) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of a combined physical exercise and psychosocial training program to improve physical fitness in children with cancer

    International Nuclear Information System (INIS)

    Braam, Katja I; Huisman, Jaap; Kaspers, Gertjan JL; Dulmen-den Broeder, Eline van; Dijk, Elisabeth M van; Veening, Margreet A; Bierings, Marc B; Merks, Johannes HM; Grootenhuis, Martha A; Chinapaw, Mai JM; Sinnema, Gerben; Takken, Tim

    2010-01-01

    Childhood cancer and its treatment have considerable impact on a child's physical and mental wellbeing. Especially long-term administration of chemotherapy and/or radiotherapy impairs physical fitness both during and after therapy, when children often present with muscle weakness and/or low cardiorespiratory fitness. Physical exercise can improve these two elements of physical fitness, but the positive effects of physical exercise might be further increased when a child's wellbeing is simultaneously enhanced by psychosocial training. Feeling better may increase the willingness and motivation to engage in sports activities. Therefore, this multi-centre study evaluates the short and long-term changes in physical fitness of a child with a childhood malignancy, using a combined physical exercise and psychosocial intervention program, implemented during or shortly after treatment. Also examined is whether positive effects on physical fitness reduce inactivity-related adverse health problems, improve quality of life, and are cost-effective. This multi-centre randomized controlled trial compares a combined physical and psychosocial intervention program for children with cancer, with care as usual (controls). Children with cancer (aged 8-18 years) treated with chemotherapy and/or radiotherapy, and who are no longer than 1 year post-treatment, are eligible for participation. A total of 100 children are being recruited from the paediatric oncology/haematology departments of three Dutch university medical centres. Patients are stratified according to pubertal stage (girls: age ≤10 or >10 years; boys: ≤11 or >11 years), type of malignancy (haematological or solid tumour), and moment of inclusion into the study (during or after treatment), and are randomly assigned to the intervention or control group. Childhood cancer patients undergoing long-term cancer therapy may benefit from a combined physical exercise and psychosocial intervention program since it may

  16. Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach.

    Science.gov (United States)

    König, Laura M; Sproesser, Gudrun; Schupp, Harald T; Renner, Britta

    2018-03-13

    Although mobile technologies such as smartphone apps are promising means for motivating people to adopt a healthier lifestyle (mHealth apps), previous studies have shown low adoption and continued use rates. Developing the means to address this issue requires further understanding of mHealth app nonusers and adoption processes. This study utilized a stage model approach based on the Precaution Adoption Process Model (PAPM), which proposes that people pass through qualitatively different motivational stages when adopting a behavior. To establish a better understanding of between-stage transitions during app adoption, this study aimed to investigate the adoption process of nutrition and fitness app usage, and the sociodemographic and behavioral characteristics and decision-making style preferences of people at different adoption stages. Participants (N=1236) were recruited onsite within the cohort study Konstanz Life Study. Use of mobile devices and nutrition and fitness apps, 5 behavior adoption stages of using nutrition and fitness apps, preference for intuition and deliberation in eating decision-making (E-PID), healthy eating style, sociodemographic variables, and body mass index (BMI) were assessed. Analysis of the 5 behavior adoption stages showed that stage 1 ("unengaged") was the most prevalent motivational stage for both nutrition and fitness app use, with half of the participants stating that they had never thought about using a nutrition app (52.41%, 533/1017), whereas less than one-third stated they had never thought about using a fitness app (29.25%, 301/1029). "Unengaged" nonusers (stage 1) showed a higher preference for an intuitive decision-making style when making eating decisions, whereas those who were already "acting" (stage 4) showed a greater preference for a deliberative decision-making style (F 4,1012 =21.83, Pdigital interventions. This study highlights that new user groups might be better reached by apps designed to address a more intuitive

  17. Detection of oral early cancerous lesion by using polarization-sensitive optical coherence tomography: mice model

    Science.gov (United States)

    Lee, Hong-Yi; Chen, Ping-Hsien; Lee, Tzu-Han; Chang, Kuo-Wei; Kuo, Wen-Chuan

    2018-02-01

    Oral cancer is the 11th most common cancer worldwide, especially in a male adult. The median age of death in oral cancer was 55 years, 10-20 years earlier than other cancers. Presently, oral cancer is often found in late stage, because the lesion is often flat in early stage and is difficult to diagnose under traditional white light imaging. The only definitive method for determining cancer is an invasive biopsy and then using histology examination. How to detect precancerous lesions or early malignant lesions is an important issue for improving prognosis of oral cancer. Optical coherence tomography (OCT) is a new optical tool for diagnosing early malignant lesions in the skin or gastrointestinal tract recently. Here we report a new method for detecting precancerous or early malignant oral lesions by using swept source polarization-sensitive optical coherence tomography (PS-OCT) with center-wavelength 1310 nm, bandwidth 110 nm and 100 kHz swept rate. We used all single-mode fiber design to detect the change of birefringence information in the epithelium structure. This system has an advantage that enables measurement of backscattered intensity and birefringence simultaneously with only one A-scan per transverse location. In preliminary result, we computed the slope of the every A-scan signal in tissue part using a linear-curve fitting in backscattered intensity and birefringence on the enface. In this research, we used an oral cancer mice model for observing the change of structure and birefringence properties in different stages of oral cancer mice. We presented the parametric enface imaging that can detect the early oral malignant lesions.

  18. Modelling job support, job fit, job role and job satisfaction for school of nursing sessional academic staff.

    Science.gov (United States)

    Cowin, Leanne S; Moroney, Robyn

    2018-01-01

    Sessional academic staff are an important part of nursing education. Increases in casualisation of the academic workforce continue and satisfaction with the job role is an important bench mark for quality curricula delivery and influences recruitment and retention. This study examined relations between four job constructs - organisation fit, organisation support, staff role and job satisfaction for Sessional Academic Staff at a School of Nursing by creating two path analysis models. A cross-sectional correlational survey design was utilised. Participants who were currently working as sessional or casual teaching staff members were invited to complete an online anonymous survey. The data represents a convenience sample of Sessional Academic Staff in 2016 at a large school of Nursing and Midwifery in Australia. After psychometric evaluation of each of the job construct measures in this study we utilised Structural Equation Modelling to better understand the relations of the variables. The measures used in this study were found to be both valid and reliable for this sample. Job support and job fit are positively linked to job satisfaction. Although the hypothesised model did not meet model fit standards, a new 'nested' model made substantive sense. This small study explored a new scale for measuring academic job role, and demonstrated how it promotes the constructs of job fit and job supports. All four job constructs are important in providing job satisfaction - an outcome that in turn supports staffing stability, retention, and motivation.

  19. A differential equation for the asymptotic fitness distribution in the Bak-Sneppen model with five species.

    Science.gov (United States)

    Schlemm, Eckhard

    2015-09-01

    The Bak-Sneppen model is an abstract representation of a biological system that evolves according to the Darwinian principles of random mutation and selection. The species in the system are characterized by a numerical fitness value between zero and one. We show that in the case of five species the steady-state fitness distribution can be obtained as a solution to a linear differential equation of order five with hypergeometric coefficients. Similar representations for the asymptotic fitness distribution in larger systems may help pave the way towards a resolution of the question of whether or not, in the limit of infinitely many species, the fitness is asymptotically uniformly distributed on the interval [fc, 1] with fc ≳ 2/3. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Permutation tests for goodness-of-fit testing of mathematical models to experimental data.

    Science.gov (United States)

    Fişek, M Hamit; Barlas, Zeynep

    2013-03-01

    This paper presents statistical procedures for improving the goodness-of-fit testing of theoretical models to data obtained from laboratory experiments. We use an experimental study in the expectation states research tradition which has been carried out in the "standardized experimental situation" associated with the program to illustrate the application of our procedures. We briefly review the expectation states research program and the fundamentals of resampling statistics as we develop our procedures in the resampling context. The first procedure we develop is a modification of the chi-square test which has been the primary statistical tool for assessing goodness of fit in the EST research program, but has problems associated with its use. We discuss these problems and suggest a procedure to overcome them. The second procedure we present, the "Average Absolute Deviation" test, is a new test and is proposed as an alternative to the chi square test, as being simpler and more informative. The third and fourth procedures are permutation versions of Jonckheere's test for ordered alternatives, and Kendall's tau(b), a rank order correlation coefficient. The fifth procedure is a new rank order goodness-of-fit test, which we call the "Deviation from Ideal Ranking" index, which we believe may be more useful than other rank order tests for assessing goodness-of-fit of models to experimental data. The application of these procedures to the sample data is illustrated in detail. We then present another laboratory study from an experimental paradigm different from the expectation states paradigm - the "network exchange" paradigm, and describe how our procedures may be applied to this data set. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. Functional Time Series Models to Estimate Future Age-Specific Breast Cancer Incidence Rates for Women in Karachi, Pakistan

    Institute of Scientific and Technical Information of China (English)

    Farah Yasmeen[1; Sidra Zaheer[2

    2014-01-01

    Background: Breast cancer is the most common female cancer in Pakistan. The incidence of breast cancer in Pakistan is about 2.5 times higher than that in the neighboring countries India and Iran. In Karachi, the most populated city of Pakistan, the age-standardized rate of breast cancer was 69.1 per 100,000 women during 1998-2002, which is the highest recorded rate in Asia. The carcinoma of breast in Pakistan is an enormous public health concern. In this study, we examined the recent trends of breast cancer incidence rates among the women in Karachi. Methods: We obtained the secondary data of breast cancer incidence from various hospitals. They included Jinnah Hospital, KIRAN (Karachi Institute of Radiotherapy and Nuclear Medicine), and Civil hospital, where the data were available for the years 2004-2011. A total of 5331 new cases of female breast cancer were registered during this period. We analyzed the data in 5-year age groups 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+. Nonparametric smoothing were used to obtained age-specific incidence curves, and then the curves are decomposed using principal components analysis to fit FTS (functional time series) model. We then used exponential smoothing statspace models to estimate the forecasts of incidence curve and construct prediction intervals. Results: The breast cancer incidence rates in Karachi increased with age for all available years. The rates increased monotonically and are relatively sharp with the age from 15 years to 50 years and then they show variability after the age of 50 years. 10-year forecasts for the female breast cancer incidence rates in Karachi show that the future rates are expected to remain stable for the age-groups 15-50 years, but they will increase for the females of 50-years and over. Hence in future, the newly diagnosed breast cancer cases in the older women in Karachi are expected to increase. Conclusion: Prediction of age

  2. THE HERSCHEL ORION PROTOSTAR SURVEY: SPECTRAL ENERGY DISTRIBUTIONS AND FITS USING A GRID OF PROTOSTELLAR MODELS

    Energy Technology Data Exchange (ETDEWEB)

    Furlan, E. [Infrared Processing and Analysis Center, California Institute of Technology, 770 S. Wilson Ave., Pasadena, CA 91125 (United States); Fischer, W. J. [Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771 (United States); Ali, B. [Space Science Institute, 4750 Walnut Street, Boulder, CO 80301 (United States); Stutz, A. M. [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Stanke, T. [ESO, Karl-Schwarzschild-Strasse 2, D-85748 Garching bei München (Germany); Tobin, J. J. [National Radio Astronomy Observatory, Charlottesville, VA 22903 (United States); Megeath, S. T.; Booker, J. [Ritter Astrophysical Research Center, Department of Physics and Astronomy, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606 (United States); Osorio, M. [Instituto de Astrofísica de Andalucía, CSIC, Camino Bajo de Huétor 50, E-18008 Granada (Spain); Hartmann, L.; Calvet, N. [Department of Astronomy, University of Michigan, 500 Church Street, Ann Arbor, MI 48109 (United States); Poteet, C. A. [New York Center for Astrobiology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180 (United States); Manoj, P. [Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400005 (India); Watson, D. M. [Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627 (United States); Allen, L., E-mail: furlan@ipac.caltech.edu [National Optical Astronomy Observatory, 950 N. Cherry Avenue, Tucson, AZ 85719 (United States)

    2016-05-01

    We present key results from the Herschel Orion Protostar Survey: spectral energy distributions (SEDs) and model fits of 330 young stellar objects, predominantly protostars, in the Orion molecular clouds. This is the largest sample of protostars studied in a single, nearby star formation complex. With near-infrared photometry from 2MASS, mid- and far-infrared data from Spitzer and Herschel , and submillimeter photometry from APEX, our SEDs cover 1.2–870 μ m and sample the peak of the protostellar envelope emission at ∼100 μ m. Using mid-IR spectral indices and bolometric temperatures, we classify our sample into 92 Class 0 protostars, 125 Class I protostars, 102 flat-spectrum sources, and 11 Class II pre-main-sequence stars. We implement a simple protostellar model (including a disk in an infalling envelope with outflow cavities) to generate a grid of 30,400 model SEDs and use it to determine the best-fit model parameters for each protostar. We argue that far-IR data are essential for accurate constraints on protostellar envelope properties. We find that most protostars, and in particular the flat-spectrum sources, are well fit. The median envelope density and median inclination angle decrease from Class 0 to Class I to flat-spectrum protostars, despite the broad range in best-fit parameters in each of the three categories. We also discuss degeneracies in our model parameters. Our results confirm that the different protostellar classes generally correspond to an evolutionary sequence with a decreasing envelope infall rate, but the inclination angle also plays a role in the appearance, and thus interpretation, of the SEDs.

  3. Multiscale agent-based cancer modeling.

    Science.gov (United States)

    Zhang, Le; Wang, Zhihui; Sagotsky, Jonathan A; Deisboeck, Thomas S

    2009-04-01

    Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.

  4. Mathematical modeling for novel cancer drug discovery and development.

    Science.gov (United States)

    Zhang, Ping; Brusic, Vladimir

    2014-10-01

    Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

  5. The colorectal cancer mortality-to-incidence ratio as an indicator of global cancer screening and care.

    Science.gov (United States)

    Sunkara, Vasu; Hébert, James R

    2015-05-15

    Disparities in cancer screening, incidence, treatment, and survival are worsening globally. The mortality-to-incidence ratio (MIR) has been used previously to evaluate such disparities. The MIR for colorectal cancer is calculated for all Organisation for Economic Cooperation and Development (OECD) countries using the 2012 GLOBOCAN incidence and mortality statistics. Health system rankings were obtained from the World Health Organization. Two linear regression models were fit with the MIR as the dependent variable and health system ranking as the independent variable; one included all countries and one model had the "divergents" removed. The regression model for all countries explained 24% of the total variance in the MIR. Nine countries were found to have regression-calculated MIRs that differed from the actual MIR by >20%. Countries with lower-than-expected MIRs were found to have strong national health systems characterized by formal colorectal cancer screening programs. Conversely, countries with higher-than-expected MIRs lack screening programs. When these divergent points were removed from the data set, the recalculated regression model explained 60% of the total variance in the MIR. The MIR proved useful for identifying disparities in cancer screening and treatment internationally. It has potential as an indicator of the long-term success of cancer surveillance programs and may be extended to other cancer types for these purposes. © 2015 American Cancer Society.

  6. Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

    Science.gov (United States)

    Nabizadeh, Nooshin; John, Nigel

    2014-03-01

    Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

  7. Modeling mechanical interactions between cancerous mammary acini

    Science.gov (United States)

    Wang, Jeffrey; Liphardt, Jan; Rycroft, Chris

    2015-03-01

    The rules and mechanical forces governing cell motility and interactions with the extracellular matrix of a tissue are often critical for understanding the mechanisms by which breast cancer is able to spread through the breast tissue and eventually metastasize. Ex vivo experimentation has demonstrated the the formation of long collagen fibers through collagen gels between the cancerous mammary acini responsible for milk production, providing a fiber scaffolding along which cancer cells can disorganize. We present a minimal mechanical model that serves as a potential explanation for the formation of these collagen fibers and the resultant motion. Our working hypothesis is that cancerous cells induce this fiber formation by pulling on the gel and taking advantage of the specific mechanical properties of collagen. To model this system, we employ a new Eulerian, fixed grid simulation method to model the collagen as a nonlinear viscoelastic material subject to various forces coupled with a multi-agent model to describe individual cancer cells. We find that these phenomena can be explained two simple ideas: cells pull collagen radially inwards and move towards the tension gradient of the collagen gel, while being exposed to standard adhesive and collision forces.

  8. Models of breast cancer: quo vadis, animal modeling?

    International Nuclear Information System (INIS)

    Wagner, Kay-Uwe

    2004-01-01

    Rodent models for breast cancer have for many decades provided unparalleled insights into cellular and molecular aspects of neoplastic transformation and tumorigenesis. Despite recent improvements in the fidelity of genetically engineered mice, rodent models are still being criticized by many colleagues for not being 'authentic' enough to the human disease. Motives for this criticism are manifold and range from a very general antipathy against the rodent model system to well-founded arguments that highlight physiological variations between species. Newly proposed differences in genetic pathways that cause cancer in humans and mice invigorated the ongoing discussion about the legitimacy of the murine system to model the human disease. The present commentary intends to stimulate a debate on this subject by providing the background about new developments in animal modeling, by disputing suggested limitations of genetically engineered mice, and by discussing improvements but also ambiguous expectations on the authenticity of xenograft models to faithfully mimic the human disease

  9. Obesity-Linked Mouse Models of Liver Cancer | Center for Cancer Research

    Science.gov (United States)

    Jimmy Stauffer, Ph.D., and colleagues working with Robert  Wiltrout, Ph.D., in CCR’s Cancer and Inflammation Program, along with collaborators in the Laboratory of Human Carcinogenesis, have developed a novel mouse model that demonstrates how fat-producing phenotypes can influence the development of hepatic cancer.   The team recently reported their findings in Cancer Research.

  10. Anticipating mismatches of HIT investments: Developing a viability-fit model for e-health services.

    Science.gov (United States)

    Mettler, Tobias

    2016-01-01

    Albeit massive investments in the recent years, the impact of health information technology (HIT) has been controversial and strongly disputed by both research and practice. While many studies are concerned with the development of new or the refinement of existing measurement models for assessing the impact of HIT adoption (ex post), this study presents an initial attempt to better understand the factors affecting viability and fit of HIT and thereby underscores the importance of also having instruments for managing expectations (ex ante). We extend prior research by undertaking a more granular investigation into the theoretical assumptions of viability and fit constructs. In doing so, we use a mixed-methods approach, conducting qualitative focus group discussions and a quantitative field study to improve and validate a viability-fit measurement instrument. Our findings suggest two issues for research and practice. First, the results indicate that different stakeholders perceive HIT viability and fit of the same e-health services very unequally. Second, the analysis also demonstrates that there can be a great discrepancy between the organizational viability and individual fit of a particular e-health service. The findings of this study have a number of important implications such as for health policy making, HIT portfolios, and stakeholder communication. Copyright © 2015. Published by Elsevier Ireland Ltd.

  11. Evaluating for a geospatial relationship between radon levels and thyroid cancer in Pennsylvania.

    Science.gov (United States)

    Goyal, Neerav; Camacho, Fabian; Mangano, Joseph; Goldenberg, David

    2015-01-01

    To determine whether there is an association between radon levels and the rise in incidence of thyroid cancer in Pennsylvania. Epidemiological study of the state of Pennsylvania. We used information from the Pennsylvania Cancer Registry and the Pennsylvania Department of Energy. From the registry, information regarding thyroid incidence by county and zip code was recorded. Information regarding radon levels per county was recorded from the state. Poisson regression models were fit predicting county-level thyroid incidence and change as a function of radon/lagged radon levels. To account for measurement error in the radon levels, a Bayesian Model extending the Poisson models was fit. Geospatial clustering analysis was also performed. No association was noted between cumulative radon levels and thyroid incidence. In the Poisson modeling, no significant association was noted between county radon level and thyroid cancer incidence (P = .23). Looking for a lag between the radon level and its effect, no significant effect was seen with a lag of 0 to 6 years between exposure and effect (P = .063 to P = .59). The Bayesian models also failed to show a statistically significant association. A cluster of high thyroid cancer incidence was found in western Pennsylvania. Through a variety of models, no association was elicited between annual radon levels recorded in Pennsylvania and the rising incidence of thyroid cancer. However, a cluster of thyroid cancer incidence was found in western Pennsylvania. Further studies may be helpful in looking for other exposures or associations. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

  12. Amino acids intake and physical fitness among adolescents.

    Science.gov (United States)

    Gracia-Marco, Luis; Bel-Serrat, Silvia; Cuenca-Garcia, Magdalena; Gonzalez-Gross, Marcela; Pedrero-Chamizo, Raquel; Manios, Yannis; Marcos, Ascensión; Molnar, Denes; Widhalm, Kurt; Polito, Angela; Vanhelst, Jeremy; Hagströmer, Maria; Sjöström, Michael; Kafatos, Anthony; de Henauw, Stefaan; Gutierrez, Ángel; Castillo, Manuel J; Moreno, Luis A

    2017-06-01

    The aim was to investigate whether there was an association between amino acid (AA) intake and physical fitness and if so, to assess whether this association was independent of carbohydrates intake. European adolescents (n = 1481, 12.5-17.5 years) were measured. Intake was assessed via two non-consecutive 24-h dietary recalls. Lower and upper limbs muscular fitness was assessed by standing long jump and handgrip strength tests, respectively. Cardiorespiratory fitness was assessed by the 20-m shuttle run test. Physical activity was objectively measured. Socioeconomic status was obtained via questionnaires. Lower limbs muscular fitness seems to be positively associated with tryptophan, histidine and methionine intake in boys, regardless of centre, age, socioeconomic status, physical activity and total energy intake (model 1). However, these associations disappeared once carbohydrates intake was controlled for (model 2). In girls, only proline intake seems to be positively associated with lower limbs muscular fitness (model 2) while cardiorespiratory fitness seems to be positively associated with leucine (model 1) and proline intake (models 1 and 2). None of the observed significant associations remained significant once multiple testing was controlled for. In conclusion, we failed to detect any associations between any of the evaluated AAs and physical fitness after taking into account the effect of multiple testing.

  13. Quo natas, Danio?—Recent Progress in Modeling Cancer in Zebrafish

    Directory of Open Access Journals (Sweden)

    Stefanie Kirchberger

    2017-08-01

    Full Text Available Over the last decade, zebrafish has proven to be a powerful model in cancer research. Zebrafish form tumors that histologically and genetically resemble human cancers. The live imaging and cost-effective compound screening possible with zebrafish especially complement classic mouse cancer models. Here, we report recent progress in the field, including genetically engineered zebrafish cancer models, xenotransplantation of human cancer cells into zebrafish, promising approaches toward live investigation of the tumor microenvironment, and identification of therapeutic strategies by performing compound screens on zebrafish cancer models. Given the recent advances in genome editing, personalized zebrafish cancer models are now a realistic possibility. In addition, ongoing automation will soon allow high-throughput compound screening using zebrafish cancer models to be part of preclinical precision medicine approaches.

  14. Fitting the two-compartment model in DCE-MRI by linear inversion.

    Science.gov (United States)

    Flouri, Dimitra; Lesnic, Daniel; Sourbron, Steven P

    2016-09-01

    Model fitting of dynamic contrast-enhanced-magnetic resonance imaging-MRI data with nonlinear least squares (NLLS) methods is slow and may be biased by the choice of initial values. The aim of this study was to develop and evaluate a linear least squares (LLS) method to fit the two-compartment exchange and -filtration models. A second-order linear differential equation for the measured concentrations was derived where model parameters act as coefficients. Simulations of normal and pathological data were performed to determine calculation time, accuracy and precision under different noise levels and temporal resolutions. Performance of the LLS was evaluated by comparison against the NLLS. The LLS method is about 200 times faster, which reduces the calculation times for a 256 × 256 MR slice from 9 min to 3 s. For ideal data with low noise and high temporal resolution the LLS and NLLS were equally accurate and precise. The LLS was more accurate and precise than the NLLS at low temporal resolution, but less accurate at high noise levels. The data show that the LLS leads to a significant reduction in calculation times, and more reliable results at low noise levels. At higher noise levels the LLS becomes exceedingly inaccurate compared to the NLLS, but this may be improved using a suitable weighting strategy. Magn Reson Med 76:998-1006, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  15. The regression-calibration method for fitting generalized linear models with additive measurement error

    OpenAIRE

    James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll

    2003-01-01

    This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...

  16. A Mouse Model for Human Anal Cancer

    Science.gov (United States)

    Stelzer, Marie K.; Pitot, Henry C.; Liem, Amy; Schweizer, Johannes; Mahoney, Charles; Lambert, Paul F.

    2010-01-01

    Human anal cancers are associated with high-risk human papillomaviruses (HPVs) that cause other anogenital cancers and head and neck cancers. As with other cancers, HPV16 is the most common high-risk HPV in anal cancers. We describe the generation and characterization of a mouse model for human anal cancer. This model makes use of K14E6 and K14E7 transgenic mice in which the HPV16 E6 and E7 genes are directed in their expression to stratified squamous epithelia. HPV16 E6 and E7 possess oncogenic properties including but not limited to their capacity to inactivate the cellular tumor suppressors p53 and pRb, respectively. Both E6 and E7 were found to be functionally expressed in the anal epithelia of K14E6/K14E7 transgenic mice. To assess the susceptibility of these mice to anal cancer, mice were treated topically with dimethylbenz[a]anthracene (DMBA), a chemical carcinogen that is known to induce squamous cell carcinomas in other sites. Nearly 50% of DMBA-treated HPV16 E6/E7 transgenic mice showed overt signs of tumors; whereas, none of the like treated non-transgenic mice showed tumors. Histopathological analyses confirmed that the HPV16 transgenic mice were increased in their susceptibility to anal cancers and precancerous lesions. Biomarker analyses demonstrated that these mouse anal cancers exhibit properties that are similar to those observed in HPV-positive precursors to human anal cancer. This is the first mouse model for investigating the contributions of viral and cellular factors in anal carcinogenesis, and should provide a platform for assessing new therapeutic modalities for treating and/or preventing this type of cancer. PMID:20947489

  17. Econometric modelling of risk adverse behaviours of entrepreneurs in the provision of house fittings in China

    Directory of Open Access Journals (Sweden)

    Rita Yi Man Li

    2012-03-01

    Full Text Available Entrepreneurs have always born the risk of running their business. They reap a profit in return for their risk taking and work. Housing developers are no different. In many countries, such as Australia, the United Kingdom and the United States, they interpret the tastes of the buyers and provide the dwellings they develop with basic fittings such as floor and wall coverings, bathroom fittings and kitchen cupboards. In mainland China, however, in most of the developments, units or houses are sold without floor or wall coverings, kitchen  or bathroom fittings. What is the motive behind this choice? This paper analyses the factors affecting housing developers’ decisions to provide fittings based on 1701 housing developments in Hangzhou, Chongqing and Hangzhou using a Probit model. The results show that developers build a higher proportion of bare units in mainland China when: 1 there is shortage of housing; 2 land costs are high so that the comparative costs of providing fittings become relatively low.

  18. FIREFLY (Fitting IteRativEly For Likelihood analYsis): a full spectral fitting code

    Science.gov (United States)

    Wilkinson, David M.; Maraston, Claudia; Goddard, Daniel; Thomas, Daniel; Parikh, Taniya

    2017-12-01

    We present a new spectral fitting code, FIREFLY, for deriving the stellar population properties of stellar systems. FIREFLY is a chi-squared minimization fitting code that fits combinations of single-burst stellar population models to spectroscopic data, following an iterative best-fitting process controlled by the Bayesian information criterion. No priors are applied, rather all solutions within a statistical cut are retained with their weight. Moreover, no additive or multiplicative polynomials are employed to adjust the spectral shape. This fitting freedom is envisaged in order to map out the effect of intrinsic spectral energy distribution degeneracies, such as age, metallicity, dust reddening on galaxy properties, and to quantify the effect of varying input model components on such properties. Dust attenuation is included using a new procedure, which was tested on Integral Field Spectroscopic data in a previous paper. The fitting method is extensively tested with a comprehensive suite of mock galaxies, real galaxies from the Sloan Digital Sky Survey and Milky Way globular clusters. We also assess the robustness of the derived properties as a function of signal-to-noise ratio (S/N) and adopted wavelength range. We show that FIREFLY is able to recover age, metallicity, stellar mass, and even the star formation history remarkably well down to an S/N ∼ 5, for moderately dusty systems. Code and results are publicly available.1

  19. FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    U. S. Panday

    2012-09-01

    Full Text Available In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of – 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for

  20. Presenting an Evaluation Model for the Cancer Registry Software.

    Science.gov (United States)

    Moghaddasi, Hamid; Asadi, Farkhondeh; Rabiei, Reza; Rahimi, Farough; Shahbodaghi, Reihaneh

    2017-12-01

    As cancer is increasingly growing, cancer registry is of great importance as the main core of cancer control programs, and many different software has been designed for this purpose. Therefore, establishing a comprehensive evaluation model is essential to evaluate and compare a wide range of such software. In this study, the criteria of the cancer registry software have been determined by studying the documents and two functional software of this field. The evaluation tool was a checklist and in order to validate the model, this checklist was presented to experts in the form of a questionnaire. To analyze the results of validation, an agreed coefficient of %75 was determined in order to apply changes. Finally, when the model was approved, the final version of the evaluation model for the cancer registry software was presented. The evaluation model of this study contains tool and method of evaluation. The evaluation tool is a checklist including the general and specific criteria of the cancer registry software along with their sub-criteria. The evaluation method of this study was chosen as a criteria-based evaluation method based on the findings. The model of this study encompasses various dimensions of cancer registry software and a proper method for evaluating it. The strong point of this evaluation model is the separation between general criteria and the specific ones, while trying to fulfill the comprehensiveness of the criteria. Since this model has been validated, it can be used as a standard to evaluate the cancer registry software.

  1. Mechanisms of Cancer Cell Dormancy – Another Hallmark of Cancer?

    Science.gov (United States)

    Yeh, Albert C.; Ramaswamy, Sridhar

    2015-01-01

    Disease relapse in cancer patients many years after clinical remission, often referred to as cancer dormancy, is well documented but remains an incompletely understood phenomenon on the biological level. Recent reviews have summarized potential models that can explain this phenomenon, including angiogenic, immunologic, and cellular dormancy. We focus on mechanisms of cellular dormancy as newer biological insights have enabled better understanding of this process. We provide a historical context, synthesize current advances in the field, and propose a mechanistic framework that treats cancer cell dormancy as a dynamic cell state conferring a fitness advantage to an evolving malignancy under stress. Cellular dormancy appears to be an active process that can be toggled through a variety of signaling mechanisms that ultimately down-regulate the Ras/MAPK and PI(3)K/AKT pathways, an ability that is preserved even in cancers that constitutively depend on these pathways for their growth and survival. Just as unbridled proliferation is a key hallmark of cancer, the ability of cancer cells to become quiescent may be critical to evolving malignancies, with implications for understanding cancer initiation, progression, and treatment resistance. PMID:26354021

  2. Fitting the Probability Distribution Functions to Model Particulate Matter Concentrations

    International Nuclear Information System (INIS)

    El-Shanshoury, Gh.I.

    2017-01-01

    The main objective of this study is to identify the best probability distribution and the plotting position formula for modeling the concentrations of Total Suspended Particles (TSP) as well as the Particulate Matter with an aerodynamic diameter<10 μm (PM 10 ). The best distribution provides the estimated probabilities that exceed the threshold limit given by the Egyptian Air Quality Limit value (EAQLV) as well the number of exceedance days is estimated. The standard limits of the EAQLV for TSP and PM 10 concentrations are 24-h average of 230 μg/m 3 and 70 μg/m 3 , respectively. Five frequency distribution functions with seven formula of plotting positions (empirical cumulative distribution functions) are compared to fit the average of daily TSP and PM 10 concentrations in year 2014 for Ain Sokhna city. The Quantile-Quantile plot (Q-Q plot) is used as a method for assessing how closely a data set fits a particular distribution. A proper probability distribution that represents the TSP and PM 10 has been chosen based on the statistical performance indicator values. The results show that Hosking and Wallis plotting position combined with Frechet distribution gave the highest fit for TSP and PM 10 concentrations. Burr distribution with the same plotting position follows Frechet distribution. The exceedance probability and days over the EAQLV are predicted using Frechet distribution. In 2014, the exceedance probability and days for TSP concentrations are 0.052 and 19 days, respectively. Furthermore, the PM 10 concentration is found to exceed the threshold limit by 174 days

  3. Using geometry to improve model fitting and experiment design for glacial isostasy

    Science.gov (United States)

    Kachuck, S. B.; Cathles, L. M.

    2017-12-01

    As scientists we routinely deal with models, which are geometric objects at their core - the manifestation of a set of parameters as predictions for comparison with observations. When the number of observations exceeds the number of parameters, the model is a hypersurface (the model manifold) in the space of all possible predictions. The object of parameter fitting is to find the parameters corresponding to the point on the model manifold as close to the vector of observations as possible. But the geometry of the model manifold can make this difficult. By curving, ending abruptly (where, for instance, parameters go to zero or infinity), and by stretching and compressing the parameters together in unexpected directions, it can be difficult to design algorithms that efficiently adjust the parameters. Even at the optimal point on the model manifold, parameters might not be individually resolved well enough to be applied to new contexts. In our context of glacial isostatic adjustment, models of sparse surface observations have a broad spread of sensitivity to mixtures of the earth's viscous structure and the surface distribution of ice over the last glacial cycle. This impedes precise statements about crucial geophysical processes, such as the planet's thermal history or the climates that controlled the ice age. We employ geometric methods developed in the field of systems biology to improve the efficiency of fitting (geodesic accelerated Levenberg-Marquardt) and to identify the maximally informative sources of additional data to make better predictions of sea levels and ice configurations (optimal experiment design). We demonstrate this in particular in reconstructions of the Barents Sea Ice Sheet, where we show that only certain kinds of data from the central Barents have the power to distinguish between proposed models.

  4. Modelling DW-MRI data from primary and metastatic ovarian tumours

    Energy Technology Data Exchange (ETDEWEB)

    Winfield, Jessica M. [Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Division of Radiotherapy and Imaging, Surrey (United Kingdom); Royal Marsden NHS Foundation Trust, Surrey (United Kingdom); Institute of Cancer Research and Royal Marsden Hospital, MRI Unit, Surrey (United Kingdom); DeSouza, Nandita M.; Collins, David J. [Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Division of Radiotherapy and Imaging, Surrey (United Kingdom); Royal Marsden NHS Foundation Trust, Surrey (United Kingdom); Priest, Andrew N.; Hodgkin, Charlotte; Freeman, Susan [University of Cambridge, Department of Radiology, Addenbrooke' s Hospital, Cambridge (United Kingdom); Wakefield, Jennifer C.; Orton, Matthew R. [Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Division of Radiotherapy and Imaging, Surrey (United Kingdom)

    2015-07-15

    To assess goodness-of-fit and repeatability of mono-exponential, stretched exponential and bi-exponential models of diffusion-weighted MRI (DW-MRI) data in primary and metastatic ovarian cancer. Thirty-nine primary and metastatic lesions from thirty-one patients with stage III or IV ovarian cancer were examined before and after chemotherapy using DW-MRI with ten diffusion-weightings. The data were fitted with (a) a mono-exponential model to give the apparent diffusion coefficient (ADC), (b) a stretched exponential model to give the distributed diffusion coefficient (DDC) and stretching parameter (α), and (c) a bi-exponential model to give the diffusion coefficient (D), perfusion fraction (f) and pseudodiffusion coefficient (D*). Coefficients of variation, established from repeated baseline measurements, were: ADC 3.1 %, DDC 4.3 %, α 7.0 %, D 13.2 %, f 44.0 %, D* 165.1 %. The bi-exponential model was unsuitable in these data owing to poor repeatability. After excluding the bi-exponential model, analysis using Akaike Information Criteria showed that the stretched exponential model provided the better fit to the majority of pixels in 64 % of lesions. The stretched exponential model provides the optimal fit to DW-MRI data from ovarian, omental and peritoneal lesions and lymph nodes in pre-treatment and post-treatment measurements with good repeatability. (orig.)

  5. vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments

    Directory of Open Access Journals (Sweden)

    Demeter Lisa

    2010-05-01

    Full Text Available Abstract Background The replication rate (or fitness between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV. HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness. Results Based on a mathematical model and several statistical methods (least-squares approach and measurement error models, a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1. Conclusions Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ASP.NET, and is publicly available on the Web at http://bis.urmc.rochester.edu/vFitness/.

  6. Fitted Hanbury-Brown Twiss radii versus space-time variances in flow-dominated models

    Science.gov (United States)

    Frodermann, Evan; Heinz, Ulrich; Lisa, Michael Annan

    2006-04-01

    The inability of otherwise successful dynamical models to reproduce the Hanbury-Brown Twiss (HBT) radii extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the RHIC HBT Puzzle. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source that can be directly computed from the emission function without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models, some of which exhibit significant deviations from simple Gaussian behavior. By Fourier transforming the emission function, we compute the two-particle correlation function, and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and the measured HBT radii remain, we show that a more apples-to-apples comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data.

  7. Fitted Hanbury-Brown-Twiss radii versus space-time variances in flow-dominated models

    International Nuclear Information System (INIS)

    Frodermann, Evan; Heinz, Ulrich; Lisa, Michael Annan

    2006-01-01

    The inability of otherwise successful dynamical models to reproduce the Hanbury-Brown-Twiss (HBT) radii extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the RHIC HBT Puzzle. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source that can be directly computed from the emission function without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models, some of which exhibit significant deviations from simple Gaussian behavior. By Fourier transforming the emission function, we compute the two-particle correlation function, and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and the measured HBT radii remain, we show that a more apples-to-apples comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data

  8. Animal models and therapeutic molecular targets of cancer: utility and limitations

    Directory of Open Access Journals (Sweden)

    Cekanova M

    2014-10-01

    Full Text Available Maria Cekanova, Kusum Rathore Department of Small Animal Clinical Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA Abstract: Cancer is the term used to describe over 100 diseases that share several common hallmarks. Despite prevention, early detection, and novel therapies, cancer is still the second leading cause of death in the USA. Successful bench-to-bedside translation of basic scientific findings about cancer into therapeutic interventions for patients depends on the selection of appropriate animal experimental models. Cancer research uses animal and human cancer cell lines in vitro to study biochemical pathways in these cancer cells. In this review, we summarize the important animal models of cancer with focus on their advantages and limitations. Mouse cancer models are well known, and are frequently used for cancer research. Rodent models have revolutionized our ability to study gene and protein functions in vivo and to better understand their molecular pathways and mechanisms. Xenograft and chemically or genetically induced mouse cancers are the most commonly used rodent cancer models. Companion animals with spontaneous neoplasms are still an underexploited tool for making rapid advances in human and veterinary cancer therapies by testing new drugs and delivery systems that have shown promise in vitro and in vivo in mouse models. Companion animals have a relatively high incidence of cancers, with biological behavior, response to therapy, and response to cytotoxic agents similar to those in humans. Shorter overall lifespan and more rapid disease progression are factors contributing to the advantages of a companion animal model. In addition, the current focus is on discovering molecular targets for new therapeutic drugs to improve survival and quality of life in cancer patients. Keywords: mouse cancer model, companion animal cancer model, dogs, cats, molecular targets

  9. Worm plot to diagnose fit in quantile regression

    NARCIS (Netherlands)

    Buuren, S. van

    2007-01-01

    The worm plot is a series of detrended Q-Q plots, split by covariate levels. The worm plot is a diagnostic tool for visualizing how well a statistical model fits the data, for finding locations at which the fit can be improved, and for comparing the fit of different models. This paper shows how the

  10. Worm plot to diagnose fit in quantile regression

    NARCIS (Netherlands)

    Buuren, S. van

    2007-01-01

    The worm plot is a series of detrended Q-Q plots, split by covariate levels. The worm plot is a diagnostic tool for visualizing how well a statistical model fits the data, for finding locations at which the fit can be improved, and for comparing the fit of different models. This paper shows how

  11. Development and Analysis of Volume Multi-Sphere Method Model Generation using Electric Field Fitting

    Science.gov (United States)

    Ingram, G. J.

    Electrostatic modeling of spacecraft has wide-reaching applications such as detumbling space debris in the Geosynchronous Earth Orbit regime before docking, servicing and tugging space debris to graveyard orbits, and Lorentz augmented orbits. The viability of electrostatic actuation control applications relies on faster-than-realtime characterization of the electrostatic interaction. The Volume Multi-Sphere Method (VMSM) seeks the optimal placement and radii of a small number of equipotential spheres to accurately model the electrostatic force and torque on a conducting space object. Current VMSM models tuned using force and torque comparisons with commercially available finite element software are subject to the modeled probe size and numerical errors of the software. This work first investigates fitting of VMSM models to Surface-MSM (SMSM) generated electrical field data, removing modeling dependence on probe geometry while significantly increasing performance and speed. A proposed electric field matching cost function is compared to a force and torque cost function, the inclusion of a self-capacitance constraint is explored and 4 degree-of-freedom VMSM models generated using electric field matching are investigated. The resulting E-field based VMSM development framework is illustrated on a box-shaped hub with a single solar panel, and convergence properties of select models are qualitatively analyzed. Despite the complex non-symmetric spacecraft geometry, elegantly simple 2-sphere VMSM solutions provide force and torque fits within a few percent.

  12. A unified dose response relationship to predict high dose fractionation response in the lung cancer stereotactic body radiation therapy

    Directory of Open Access Journals (Sweden)

    Than S Kehwar

    2017-01-01

    Full Text Available Aim: This study is designed to investigate the superiority and applicability of the model among the linear-quadratic (LQ, linear-quadratic-linear (LQ-L and universal-survival-curve (USC models by fitting published radiation cell survival data of lung cancer cell lines. Materials and Method: The radiation cell survival data for small cell (SC and non-small cell (NSC lung cancer cell lines were obtained from published reports, and were used to determine the LQ and cell survival curve parameters, which ultimately were used in the curve fitting of the LQ, LQ-L and USC models. Results: The results of this study demonstrate that the LQ-L(Dt-mt model, compared with the LQ and USC models, provides best fit with smooth and gradual transition to the linear portion of the curve at transition dose Dt-mt, where the LQ model loses its validity, and the LQ-L(Dt-2α/β and USC(Dt-mt models do not transition smoothly to the linear portion of the survival curve. Conclusion: The LQ-L(Dt-mt model is able to fit wide variety of cell survival data over a very wide dose range, and retains the strength of the LQ model in the low-dose range.

  13. Prediction of Pressing Quality for Press-Fit Assembly Based on Press-Fit Curve and Maximum Press-Mounting Force

    Directory of Open Access Journals (Sweden)

    Bo You

    2015-01-01

    Full Text Available In order to predict pressing quality of precision press-fit assembly, press-fit curves and maximum press-mounting force of press-fit assemblies were investigated by finite element analysis (FEA. The analysis was based on a 3D Solidworks model using the real dimensions of the microparts and the subsequent FEA model that was built using ANSYS Workbench. The press-fit process could thus be simulated on the basis of static structure analysis. To verify the FEA results, experiments were carried out using a press-mounting apparatus. The results show that the press-fit curves obtained by FEA agree closely with the curves obtained using the experimental method. In addition, the maximum press-mounting force calculated by FEA agrees with that obtained by the experimental method, with the maximum deviation being 4.6%, a value that can be tolerated. The comparison shows that the press-fit curve and max press-mounting force calculated by FEA can be used for predicting the pressing quality during precision press-fit assembly.

  14. Cardiorespiratory Fitness and Body Composition Responses to Different Intensities and Frequencies of Exercise Training in Colorectal Cancer Survivors.

    Science.gov (United States)

    Devin, James L; Jenkins, David G; Sax, Andrew T; Hughes, Gareth I; Aitken, Joanne F; Chambers, Suzanne K; Dunn, Jeffrey C; Bolam, Kate A; Skinner, Tina L

    2018-06-01

    Deteriorations in cardiorespiratory fitness (V˙o 2peak ) and body composition are associated with poor prognosis after colorectal cancer treatment. However, the optimal intensity and frequency of aerobic exercise training to improve these outcomes in colorectal cancer survivors is unknown. This trial compared 8 weeks of moderate-intensity continuous exercise (MICE; 50 minutes; 70% peak heart rate [HR peak ]; 24 sessions), with high-intensity interval exercise (HIIE; 4 × 4 minutes; 85%-95% HR peak ) at an equivalent (HIIE; 24 sessions) and tapered frequency (HIIE-T; 16 sessions) on V˙o 2peak and on lean and fat mass, measured at baseline, 4, 8, and 12 weeks. Increases in V˙o 2peak were significantly greater after both 4 (+3.0 mL·kg -1 ·min -1 , P = .008) and 8 (+2.3 mL·kg -1 ·min -1 , P = .049) weeks of HIIE compared to MICE. After 8 weeks, there was a significantly greater reduction in fat mass after HIIE compared to MICE (-0.7 kg, P = .038). Four weeks after training, the HIIE group maintained elevated V˙o 2peak (+3.3 mL·kg -1 ·min -1 , P = .006) and reduced fat mass (-0.7 kg, P = .045) compared to the MICE group, with V˙o 2peak in the HIIE-T also being superior to the MICE group (+2.8 mL·kg -1 ·min -1 , P = .013). Compared to MICE, HIIE promotes superior improvements and short-term maintenance of V˙o 2peak and fat mass improvements. HIIE training at a reduced frequency also promotes maintainable cardiorespiratory fitness improvements. In addition to promoting accelerated and superior benefits to the current aerobic exercise guidelines, HIIE promotes clinically relevant improvements even with a substantial reduction in exercise training and for a period after withdrawal. Copyright © 2018 Elsevier Inc. All rights reserved.

  15. Direct fit of a theoretical model of phase transition in oscillatory finger motions.

    NARCIS (Netherlands)

    Newell, K.M.; Molenaar, P.C.M.

    2003-01-01

    This paper presents a general method to fit the Schoner-Haken-Kelso (SHK) model of human movement phase transitions directly to time series data. A robust variant of the extended Kalman filter technique is applied to the data of a single subject. The options of covariance resetting and iteration

  16. Computer modeling of lung cancer diagnosis-to-treatment process.

    Science.gov (United States)

    Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U; Yu, Xinhua; Faris, Nick; Li, Jingshan

    2015-08-01

    We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

  17. Cardiorespiratory fitness, fatness and incident diabetes

    DEFF Research Database (Denmark)

    Holtermann, Andreas; Gyntelberg, Finn; Bauman, Adrian

    2017-01-01

    Aims Increases in prevalence have led to a diabetes pandemic. Obesity and low cardiorespiratory fitness (CRF) are considered to be central mechanisms. We investigated if the effect of CRF on diabetes risk was equivalent across levels of fatness among healthy men. Methods In total 4988 middle-aged......: 0.76–1.23). Conclusion High CRF has a stronger protective effect on diabetes among obese than among normal weight men, supporting the recommendation of fitness-enhancing physical activity for preventing diabetes among the obese.......Aims Increases in prevalence have led to a diabetes pandemic. Obesity and low cardiorespiratory fitness (CRF) are considered to be central mechanisms. We investigated if the effect of CRF on diabetes risk was equivalent across levels of fatness among healthy men. Methods In total 4988 middle......-aged Caucasian employed men free of cardiovascular disease, diabetes and cancer were included from the Copenhagen Male Study starting in 1970–71. CRF was assessed using a sub-maximal bicycle ergometer test and body mass index (BMI) was measured by height and weight. Their interaction and stratified associations...

  18. Tumor-Volume Simulation During Radiotherapy for Head-and-Neck Cancer Using a Four-Level Cell Population Model

    International Nuclear Information System (INIS)

    Chvetsov, Alexei V.; Dong Lei; Palta, Jantinder R.; Amdur, Robert J.

    2009-01-01

    Purpose: To develop a fast computational radiobiologic model for quantitative analysis of tumor volume during fractionated radiotherapy. The tumor-volume model can be useful for optimizing image-guidance protocols and four-dimensional treatment simulations in proton therapy that is highly sensitive to physiologic changes. Methods: The analysis is performed using two approximations: (1) tumor volume is a linear function of total cell number and (2) tumor-cell population is separated into four subpopulations: oxygenated viable cells, oxygenated lethally damaged cells, hypoxic viable cells, and hypoxic lethally damaged cells. An exponential decay model is used for disintegration and removal of oxygenated lethally damaged cells from the tumor. Results: We tested our model on daily volumetric imaging data available for 14 head-and-neck cancer patients treated with an integrated computed tomography/linear accelerator system. A simulation based on the averaged values of radiobiologic parameters was able to describe eight cases during the entire treatment and four cases partially (50% of treatment time) with a maximum 20% error. The largest discrepancies between the model and clinical data were obtained for small tumors, which may be explained by larger errors in the manual tumor volume delineation procedure. Conclusions: Our results indicate that the change in gross tumor volume for head-and-neck cancer can be adequately described by a relatively simple radiobiologic model. In future research, we propose to study the variation of model parameters by fitting to clinical data for a cohort of patients with head-and-neck cancer and other tumors. The potential impact of other processes, like concurrent chemotherapy, on tumor volume should be evaluated.

  19. Complex growing networks with intrinsic vertex fitness

    International Nuclear Information System (INIS)

    Bedogne, C.; Rodgers, G. J.

    2006-01-01

    One of the major questions in complex network research is to identify the range of mechanisms by which a complex network can self organize into a scale-free state. In this paper we investigate the interplay between a fitness linking mechanism and both random and preferential attachment. In our models, each vertex is assigned a fitness x, drawn from a probability distribution ρ(x). In Model A, at each time step a vertex is added and joined to an existing vertex, selected at random, with probability p and an edge is introduced between vertices with fitnesses x and y, with a rate f(x,y), with probability 1-p. Model B differs from Model A in that, with probability p, edges are added with preferential attachment rather than randomly. The analysis of Model A shows that, for every fixed fitness x, the network's degree distribution decays exponentially. In Model B we recover instead a power-law degree distribution whose exponent depends only on p, and we show how this result can be generalized. The properties of a number of particular networks are examined

  20. Modeling familial clustered breast cancer using published data

    NARCIS (Netherlands)

    Jonker, MA; Jacobi, CE; Hoogendoorn, WE; Nagelkerke, NJD; de Bock, GH; van Houwelingen, JC

    2003-01-01

    The purpose of this research was to model the familial clustering of breast cancer and to provide an accurate risk estimate for individuals from the general population, based on their family history of breast and ovarian cancer. We constructed a genetic model as an extension of a model by Claus et

  1. The challenge of preserving cardiorespiratory fitness in physically inactive patients with colon or breast cancer during adjuvant chemotherapy: a randomised feasibility study

    DEFF Research Database (Denmark)

    Møller, Tom; Lillelund, Christian; Andersen, Christina

    2015-01-01

    Introduction Anti-neoplastic treatment is synonymous with an inactive daily life for a substantial number of patients. It remains unclear what is the optimal setting, dosage and combination of exercise and health promoting components that best facilitate patient adherence and symptom management...... in order to support cardio-respiratory fitness and lifestyle changes in an at-risk population of pre-illness physically inactive cancer patients.Methods Patients with breast or colon cancer referred to adjuvant chemotherapy and by the oncologists pre-screening verified as physically inactive were eligible...... to enter a randomised three-armed feasibility study comparing a 12-week supervised hospital-based moderate to high intensity exercise intervention or alternate an instructive home-based12-week pedometer intervention, with usual care.Results Using a recommendation based physical activity screening...

  2. Round one of the Adelaide and Meath Hospital/Trinity College Colorectal Cancer Screening Programme: programme report and analysis based on established international key performance indices.

    LENUS (Irish Health Repository)

    McNamara, D

    2012-02-01

    BACKGROUND: In Ireland, colorectal cancer (CRC) is the second most frequently diagnosed cancer in men, after prostate cancer, and the second most frequently diagnosed cancer in women, after breast cancer. By 2020, the number of new cases diagnosed annually in Ireland is projected to have increased by 79% in men and 56% in women. Organised screening for CRC is already underway or is in the process of being rolled out in several European countries, either at a regional or national level. The Adelaide and Meath Hospital\\/ Trinity College Dublin Colorectal Cancer Screening Programme (TTC-CRC-SP) is Ireland\\'s first pilot population based bowel screening programme. METHOD: Based on a biennial test model the pilot aimed to assess the accuracy of FIT and to evaluate the whole programme based on established international key performance indices. RESULTS: To date 9,993 individuals aged 50-74 years have been invited to participate in the TTC-CRC-SP with over 5,000 FIT\\'s analysed. Overall uptake was 51% and FIT positivity was 10%. The programme has undertaken over 400 screening colonoscopies and detected 154 precancerous adenomas and 38 cancerous lesions. CONCLUSIONS: The first round of The Adelaide and Meath Hospital Tallaght\\/Trinity College Dublin Colorectal Cancer Screening Programme has been highly successful and confirmed that there is an advantage for FIT based two stage bowel cancer screening programmes.

  3. Additional mailing phase for FIT after a medical offer phase: The best way to improve compliance with colorectal cancer screening in France.

    Science.gov (United States)

    Piette, Christine; Durand, Gérard; Bretagne, Jean-François; Faivre, Jean

    2017-03-01

    Compliance with colorectal cancer screening is critical to its effectiveness. The organisation of the mass screening programme in France has recently been modified with no evaluation of the consequences. To evaluate the impact of the way the screening test is delivered on compliance. During the first six months of the screening campaign (Ille-Vilaine, Brittany), general practitioners were asked to propose a faecal immunochemical test (FIT), OC-Sensor, to individuals at average risk for colorectal cancer (n=152,097). A subset of non-participants in the medical phase (n=13,071) was randomly chosen to receive a reminder that included the screening test or a simple postal reminder without the screening test. Compliance was 31% if the screening test was proposed during a medical consultation. In non-participants during the medical phase, it was 45% in those receiving both a reminder and the screening test and 28% amongst those receiving a simple reminder. An estimated overall participation rate of 54% can be expected if non-participants in the medical phase are sent a reminder together with the screening test. In France, a compliance rate above the minimum uptake rate of 45% recommended by European Union experts can be achieved if the FIT is mailed to non-participants after the medical free-offer phase. Copyright © 2016. Published by Elsevier Ltd.

  4. Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

    Science.gov (United States)

    Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

    2016-05-01

    Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

  5. Measuring Quasar Spin via X-ray Continuum Fitting

    Science.gov (United States)

    Jenkins, Matthew; Pooley, David; Rappaport, Saul; Steiner, Jack

    2018-01-01

    We have identified several quasars whose X-ray spectra appear very soft. When fit with power-law models, the best-fit indices are greater than 3. This is very suggestive of thermal disk emission, indicating that the X-ray spectrum is dominated by the disk component. Galactic black hole binaries in such states have been successfully fit with disk-blackbody models to constrain the inner radius, which also constrains the spin of the black hole. We have fit those models to XMM-Newton spectra of several of our identified soft X-ray quasars to place constraints on the spins of the supermassive black holes.

  6. RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS

    Science.gov (United States)

    Perfeito, L; Sousa, A; Bataillon, T; Gordo, I

    2014-01-01

    Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of −0.01, and an effective number of traits nine in mutS− E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models. PMID:24372601

  7. Fitting model-based psychometric functions to simultaneity and temporal-order judgment data: MATLAB and R routines.

    Science.gov (United States)

    Alcalá-Quintana, Rocío; García-Pérez, Miguel A

    2013-12-01

    Research on temporal-order perception uses temporal-order judgment (TOJ) tasks or synchrony judgment (SJ) tasks in their binary SJ2 or ternary SJ3 variants. In all cases, two stimuli are presented with some temporal delay, and observers judge the order of presentation. Arbitrary psychometric functions are typically fitted to obtain performance measures such as sensitivity or the point of subjective simultaneity, but the parameters of these functions are uninterpretable. We describe routines in MATLAB and R that fit model-based functions whose parameters are interpretable in terms of the processes underlying temporal-order and simultaneity judgments and responses. These functions arise from an independent-channels model assuming arrival latencies with exponential distributions and a trichotomous decision space. Different routines fit data separately for SJ2, SJ3, and TOJ tasks, jointly for any two tasks, or also jointly for the three tasks (for common cases in which two or even the three tasks were used with the same stimuli and participants). Additional routines provide bootstrap p-values and confidence intervals for estimated parameters. A further routine is included that obtains performance measures from the fitted functions. An R package for Windows and source code of the MATLAB and R routines are available as Supplementary Files.

  8. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  9. Fitness

    Science.gov (United States)

    ... gov home http://www.girlshealth.gov/ Home Fitness Fitness Want to look and feel your best? Physical ... are? Check out this info: What is physical fitness? top Physical fitness means you can do everyday ...

  10. Extinction models for cancer stem cell therapy

    Science.gov (United States)

    Sehl, Mary; Zhou, Hua; Sinsheimer, Janet S.; Lange, Kenneth L.

    2012-01-01

    Cells with stem cell-like properties are now viewed as initiating and sustaining many cancers. This suggests that cancer can be cured by driving these cancer stem cells to extinction. The problem with this strategy is that ordinary stem cells are apt to be killed in the process. This paper sets bounds on the killing differential (difference between death rates of cancer stem cells and normal stem cells) that must exist for the survival of an adequate number of normal stem cells. Our main tools are birth–death Markov chains in continuous time. In this framework, we investigate the extinction times of cancer stem cells and normal stem cells. Application of extreme value theory from mathematical statistics yields an accurate asymptotic distribution and corresponding moments for both extinction times. We compare these distributions for the two cell populations as a function of the killing rates. Perhaps a more telling comparison involves the number of normal stem cells NH at the extinction time of the cancer stem cells. Conditioning on the asymptotic time to extinction of the cancer stem cells allows us to calculate the asymptotic mean and variance of NH. The full distribution of NH can be retrieved by the finite Fourier transform and, in some parameter regimes, by an eigenfunction expansion. Finally, we discuss the impact of quiescence (the resting state) on stem cell dynamics. Quiescence can act as a sanctuary for cancer stem cells and imperils the proposed therapy. We approach the complication of quiescence via multitype branching process models and stochastic simulation. Improvements to the τ-leaping method of stochastic simulation make it a versatile tool in this context. We conclude that the proposed therapy must target quiescent cancer stem cells as well as actively dividing cancer stem cells. The current cancer models demonstrate the virtue of attacking the same quantitative questions from a variety of modeling, mathematical, and computational perspectives

  11. Radiosensitivity of skin fibroblasts from atomic bomb survivors with and without breast cancer

    International Nuclear Information System (INIS)

    Ban, Sadayuki; Setlow, R.B.; Bender, M.A.

    1990-11-01

    Fibroblasts were established in vitro from skin biopsies obtained from 55 women and one man with or without breast cancer and with or without exposure to radiation from the atomic bomb explosion in Hiroshima. The radiosensitivity of these cells was evaluated by clonogenic assays after exposure to X rays or to fission neutrons from a 252 Cf source. Data were fitted to a multitarget model, S/S 0 = A[1-(1-e kD ) N ], for both X-ray and neutron dose-survival curves. A single-hit model, S/S 0 = Ae kD , fits the neutron dose-survival responses as well. These was no difference in the means or variances of radiosensitivity between exposed and nonexposed groups, or between patients with or without breast cancer. Hence, although the sample is not large, it provides no support for the hypothesis that A-bomb radiation preferentially induces breast cancer in women whose cells in vitro are sensitive to cell killing by radiation. (author)

  12. The latest animal models of ovarian cancer for novel drug discovery.

    Science.gov (United States)

    Magnotti, Elizabeth; Marasco, Wayne A

    2018-03-01

    Epithelial ovarian cancer is a heterogeneous disease classified into five subtypes, each with a different molecular profile. Most cases of ovarian cancer are diagnosed after metastasis of the primary tumor and are resistant to traditional platinum-based chemotherapeutics. Mouse models of ovarian cancer have been utilized to discern ovarian cancer tumorigenesis and the tumor's response to therapeutics. Areas covered: The authors provide a review of mouse models currently employed to understand ovarian cancer. This article focuses on advances in the development of orthotopic and patient-derived tumor xenograft (PDX) mouse models of ovarian cancer and discusses current humanized mouse models of ovarian cancer. Expert opinion: The authors suggest that humanized mouse models of ovarian cancer will provide new insight into the role of the human immune system in combating and augmenting ovarian cancer and aid in the development of novel therapeutics. Development of humanized mouse models will take advantage of the NSG and NSG-SGM3 strains of mice as well as new strains that are actively being derived.

  13. Predictive model for survival in patients with gastric cancer.

    Science.gov (United States)

    Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

    2017-12-01

    Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

  14. Hamiltonian inclusive fitness: a fitter fitness concept.

    Science.gov (United States)

    Costa, James T

    2013-01-01

    In 1963-1964 W. D. Hamilton introduced the concept of inclusive fitness, the only significant elaboration of Darwinian fitness since the nineteenth century. I discuss the origin of the modern fitness concept, providing context for Hamilton's discovery of inclusive fitness in relation to the puzzle of altruism. While fitness conceptually originates with Darwin, the term itself stems from Spencer and crystallized quantitatively in the early twentieth century. Hamiltonian inclusive fitness, with Price's reformulation, provided the solution to Darwin's 'special difficulty'-the evolution of caste polymorphism and sterility in social insects. Hamilton further explored the roles of inclusive fitness and reciprocation to tackle Darwin's other difficulty, the evolution of human altruism. The heuristically powerful inclusive fitness concept ramified over the past 50 years: the number and diversity of 'offspring ideas' that it has engendered render it a fitter fitness concept, one that Darwin would have appreciated.

  15. Inferential Statistics from Black Hispanic Breast Cancer Survival Data

    Directory of Open Access Journals (Sweden)

    Hafiz M. R. Khan

    2014-01-01

    Full Text Available In this paper we test the statistical probability models for breast cancer survival data for race and ethnicity. Data was collected from breast cancer patients diagnosed in United States during the years 1973–2009. We selected a stratified random sample of Black Hispanic female patients from the Surveillance Epidemiology and End Results (SEER database to derive the statistical probability models. We used three common model building criteria which include Akaike Information Criteria (AIC, Bayesian Information Criteria (BIC, and Deviance Information Criteria (DIC to measure the goodness of fit tests and it was found that Black Hispanic female patients survival data better fit the exponentiated exponential probability model. A novel Bayesian method was used to derive the posterior density function for the model parameters as well as to derive the predictive inference for future response. We specifically focused on Black Hispanic race. Markov Chain Monte Carlo (MCMC method was used for obtaining the summary results of posterior parameters. Additionally, we reported predictive intervals for future survival times. These findings would be of great significance in treatment planning and healthcare resource allocation.

  16. Fitting the Phenomenological MSSM

    CERN Document Server

    AbdusSalam, S S; Quevedo, F; Feroz, F; Hobson, M

    2010-01-01

    We perform a global Bayesian fit of the phenomenological minimal supersymmetric standard model (pMSSM) to current indirect collider and dark matter data. The pMSSM contains the most relevant 25 weak-scale MSSM parameters, which are simultaneously fit using `nested sampling' Monte Carlo techniques in more than 15 years of CPU time. We calculate the Bayesian evidence for the pMSSM and constrain its parameters and observables in the context of two widely different, but reasonable, priors to determine which inferences are robust. We make inferences about sparticle masses, the sign of the $\\mu$ parameter, the amount of fine tuning, dark matter properties and the prospects for direct dark matter detection without assuming a restrictive high-scale supersymmetry breaking model. We find the inferred lightest CP-even Higgs boson mass as an example of an approximately prior independent observable. This analysis constitutes the first statistically convergent pMSSM global fit to all current data.

  17. CRAPONE, Optical Model Potential Fit of Neutron Scattering Data

    International Nuclear Information System (INIS)

    Fabbri, F.; Fratamico, G.; Reffo, G.

    2004-01-01

    1 - Description of problem or function: Automatic search for local and non-local optical potential parameters for neutrons. Total, elastic, differential elastic cross sections, l=0 and l=1 strength functions and scattering length can be considered. 2 - Method of solution: A fitting procedure is applied to different sets of experimental data depending on the local or non-local approximation chosen. In the non-local approximation the fitting procedure can be simultaneously performed over the whole energy range. The best fit is obtained when a set of parameters is found where CHI 2 is at its minimum. The solution of the system equations is obtained by diagonalization of the matrix according to the Jacobi method

  18. Cancer: Implications for pre-registration radiography curricula

    International Nuclear Information System (INIS)

    Paterson, Audrey

    2012-01-01

    The aim of this paper is to discuss pre-registration radiography education curricula in the context of cancer, changing healthcare delivery in the UK, and the considerable interaction of radiographers with people with cancer. The fitness for purpose of the long-standing curriculum model of alternating academic and clinical learning experiences is questioned and a view expressed that it is no longer sufficient to prepare student radiographers for practice and as professionals. A suggestion is made that curricula should be aligned with cancer (and other) care pathways although it is recognised that such a change would be difficult. It is concluded that the profession should explore what is the appropriate curriculum model given the development of the care pathway approach to healthcare delivery, and, if appropriate, make changes based on research evidence.

  19. Budget Impact Analysis of Against Colorectal Cancer In Our Neighborhoods (ACCION): A Successful Community-Based Colorectal Cancer Screening Program for a Medically Underserved Minority Population.

    Science.gov (United States)

    Kim, Bumyang; Lairson, David R; Chung, Tong Han; Kim, Junghyun; Shokar, Navkiran K

    2017-06-01

    Given the uncertain cost of delivering community-based cancer screening programs, we developed a Markov simulation model to project the budget impact of implementing a comprehensive colorectal cancer (CRC) prevention program compared with the status quo. The study modeled the impacts on the costs of clinical services, materials, and staff expenditures for recruitment, education, fecal immunochemical testing (FIT), colonoscopy, follow-up, navigation, and initial treatment. We used data from the Against Colorectal Cancer In Our Neighborhoods comprehensive CRC prevention program implemented in El Paso, Texas, since 2012. We projected the 3-year financial consequences of the presence and absence of the CRC prevention program for a hypothetical population cohort of 10,000 Hispanic medically underserved individuals. The intervention cohort experienced a 23.4% higher test completion rate for CRC prevention, 8 additional CRC diagnoses, and 84 adenomas. The incremental 3-year cost was $1.74 million compared with the status quo. The program cost per person was $261 compared with $86 for the status quo. The costs were sensitive to the proportion of high-risk participants and the frequency of colonoscopy screening and diagnostic procedures. The budget impact mainly derived from colonoscopy-related costs incurred for the high-risk group. The effectiveness of FIT to detect CRC was critically dependent on follow-up after positive FIT. Community cancer prevention programs need reliable estimates of the cost of CRC screening promotion and the added budget impact of screening with colonoscopy. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  20. State Authenticity as Fit to Environment: The Implications of Social Identity for Fit, Authenticity, and Self-Segregation.

    Science.gov (United States)

    Schmader, Toni; Sedikides, Constantine

    2017-10-01

    People seek out situations that "fit," but the concept of fit is not well understood. We introduce State Authenticity as Fit to the Environment (SAFE), a conceptual framework for understanding how social identities motivate the situations that people approach or avoid. Drawing from but expanding the authenticity literature, we first outline three types of person-environment fit: self-concept fit, goal fit, and social fit. Each type of fit, we argue, facilitates cognitive fluency, motivational fluency, and social fluency that promote state authenticity and drive approach or avoidance behaviors. Using this model, we assert that contexts subtly signal social identities in ways that implicate each type of fit, eliciting state authenticity for advantaged groups but state inauthenticity for disadvantaged groups. Given that people strive to be authentic, these processes cascade down to self-segregation among social groups, reinforcing social inequalities. We conclude by mapping out directions for research on relevant mechanisms and boundary conditions.

  1. Fit-for-purpose: species distribution model performance depends on evaluation criteria - Dutch Hoverflies as a case study.

    Science.gov (United States)

    Aguirre-Gutiérrez, Jesús; Carvalheiro, Luísa G; Polce, Chiara; van Loon, E Emiel; Raes, Niels; Reemer, Menno; Biesmeijer, Jacobus C

    2013-01-01

    Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species' data

  2. Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

    Science.gov (United States)

    Mandal, S.; Choudhury, B. U.

    2015-07-01

    Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

  3. Testing the goodness of fit of selected infiltration models on soils with different land use histories

    International Nuclear Information System (INIS)

    Mbagwu, J.S.C.

    1993-10-01

    Six infiltration models, some obtained by reformulating the fitting parameters of the classical Kostiakov (1932) and Philip (1957) equations, were investigated for their ability to describe water infiltration into highly permeable sandy soils from the Nsukka plains of SE Nigeria. The models were Kostiakov, Modified Kostiakov (A), Modified Kostiakov (B), Philip, Modified Philip (A) and Modified Philip (B). Infiltration data were obtained from double ring infiltrometers on field plots established on a Knadic Paleustult (Nkpologu series) to investigate the effects of land use on soil properties and maize yield. The treatments were; (i) tilled-mulched (TM), (ii) tilled-unmulched (TU), (iii) untilled-mulched (UM), (iv) untilled-unmulched (UU) and (v) continuous pasture (CP). Cumulative infiltration was highest on the TM and lowest on the CP plots. All estimated model parameters obtained by the best fit of measured data differed significantly among the treatments. Based on the magnitude of R 2 values, the Kostiakov, Modified Kostiakov (A), Philip and Modified Philip (A) models provided best predictions of cumulative infiltration as a function of time. Comparing experimental with model-predicted cumulative infiltration showed, however, that on all treatments the values predicted by the classical Kostiakov, Philip and Modified Philip (A) models deviated most from experimental data. The other models produced values that agreed very well with measured data. Considering the eases of determining the fitting parameters it is proposed that on soils with high infiltration rates, either Modified Kostiakov model (I = Kt a + Ict) or Modified Philip model (I St 1/2 + Ict), (where I is cumulative infiltration, K, the time coefficient, t, time elapsed, 'a' the time exponent, Ic the equilibrium infiltration rate and S, the soil water sorptivity), be used for routine characterization of the infiltration process. (author). 33 refs, 3 figs 6 tabs

  4. A bivariate contaminated binormal model for robust fitting of proper ROC curves to a pair of correlated, possibly degenerate, ROC datasets.

    Science.gov (United States)

    Zhai, Xuetong; Chakraborty, Dev P

    2017-06-01

    The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics

  5. Computational Modeling of Micrometastatic Breast Cancer Radiation Dose Response

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Daniel L.; Debeb, Bisrat G. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Thames, Howard D. [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Woodward, Wendy A., E-mail: wwoodward@mdanderson.org [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

    2016-09-01

    Purpose: Prophylactic cranial irradiation (PCI) involves giving radiation to the entire brain with the goals of reducing the incidence of brain metastasis and improving overall survival. Experimentally, we have demonstrated that PCI prevents brain metastases in a breast cancer mouse model. We developed a computational model to expand on and aid in the interpretation of our experimental results. Methods and Materials: MATLAB was used to develop a computational model of brain metastasis and PCI in mice. Model input parameters were optimized such that the model output would match the experimental number of metastases per mouse from the unirradiated group. An independent in vivo–limiting dilution experiment was performed to validate the model. The effect of whole brain irradiation at different measurement points after tumor cells were injected was evaluated in terms of the incidence, number of metastases, and tumor burden and was then compared with the corresponding experimental data. Results: In the optimized model, the correlation between the number of metastases per mouse and the experimental fits was >95. Our attempt to validate the model with a limiting dilution assay produced 99.9% correlation with respect to the incidence of metastases. The model accurately predicted the effect of whole-brain irradiation given 3 weeks after cell injection but substantially underestimated its effect when delivered 5 days after cell injection. The model further demonstrated that delaying whole-brain irradiation until the development of gross disease introduces a dose threshold that must be reached before a reduction in incidence can be realized. Conclusions: Our computational model of mouse brain metastasis and PCI correlated strongly with our experiments with unirradiated mice. The results further suggest that early treatment of subclinical disease is more effective than irradiating established disease.

  6. A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL.

    Science.gov (United States)

    O'Connell, Oisin J; Almeida, Francisco A; Simoff, Michael J; Yarmus, Lonny; Lazarus, Ray; Young, Benjamin; Chen, Yu; Semaan, Roy; Saettele, Timothy M; Cicenia, Joseph; Bedi, Harmeet; Kliment, Corrine; Li, Liang; Sethi, Sonali; Diaz-Mendoza, Javier; Feller-Kopman, David; Song, Juhee; Gildea, Thomas; Lee, Hans; Grosu, Horiana B; Machuzak, Michael; Rodriguez-Vial, Macarena; Eapen, George A; Jimenez, Carlos A; Casal, Roberto F; Ost, David E

    2017-06-15

    Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies. To develop a clinical prediction model for estimating the prN2/3. We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals. The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132). Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.

  7. Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

    OpenAIRE

    Shan, Min

    2017-01-01

    With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

  8. Different fits satisfy different needs: linking person-environment fit to employee commitment and performance using self-determination theory.

    Science.gov (United States)

    Greguras, Gary J; Diefendorff, James M

    2009-03-01

    Integrating and expanding upon the person-environment fit (PE fit) and the self-determination theory literatures, the authors hypothesized and tested a model in which the satisfaction of the psychological needs for autonomy, relatedness, and competence partially mediated the relations between different types of perceived PE fit (i.e., person-organization fit, person-group fit, and job demands-abilities fit) with employee affective organizational commitment and overall job performance. Data from 163 full-time working employees and their supervisors were collected across 3 time periods. Results indicate that different types of PE fit predicted different types of psychological need satisfaction and that psychological need satisfaction predicted affective commitment and performance. Further, person-organization fit and demands-abilities fit also evidenced direct effects on employee affective commitment. These results begin to explicate the processes through which different types of PE fit relate to employee attitudes and behaviors. (c) 2009 APA, all rights reserved.

  9. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  10. Intrapersonal and interpersonal dimensions of cancer perception: a confirmatory factor analysis of the cancer experience and efficacy scale (CEES).

    Science.gov (United States)

    Hou, Wai Kai

    2010-05-01

    Sociocultural factors influence psychological adjustment to cancer in Asian patients in two major ways: prioritization of relationships over individual orientations and belief in the efficacy of interpersonal cooperation. We derived and validated among Chinese colorectal cancer (CRC) patients an instrument assessing cancer perceptions to enable the study of the sociocultural processes. Qualitative interviews (n = 16) derived 15 items addressing interpersonal experience in Chinese CRC patients' adjustment. These 15 items and 18 corresponding self-referent items were administered to 166 Chinese CRC survivors and subjected to exploratory factor analysis (EFA) to establish the initial scale structure and reliability. The final 29 items, together with other psychometric measures, were administered to a second cohort of 215 CRC patients and subjected to confirmatory factor analysis (CFA). EFA (63.35% of the total variance) extracted six factors: personal strain, socioeconomic strain, emotional strain, personal efficacy, collective efficacy, and proxy efficacy. CFA confirmed the psychometric structure [chi (2)(df) = 702.91(368); Comparative Fit Index = 0.95; Nonnormed Fit Index = 0.94; Incremental Fit Index = 0.95; standardized root mean square residual = 0.08] of the six factors by using a model with two latent factors: experience and efficacy. All subscales were reliable (alpha = 0.76-0.92). Appropriate correlations with adjustment outcomes (symptom distress, psychological morbidity, and subjective well-being), optimistic personalities, and social relational quality indicated its convergent and divergent validity. Known group comparisons (i.e., age, active treatment, and colostomy) showed its clinical utility. The cancer experience and efficacy scale is a valid multidimensional instrument for assessing intrapersonal and interpersonal dimensions of cancer experience in Asian patients, potentiating existing patient-reported outcome measures.

  11. Fit reduced GUTS models online: From theory to practice.

    Science.gov (United States)

    Baudrot, Virgile; Veber, Philippe; Gence, Guillaume; Charles, Sandrine

    2018-05-20

    Mechanistic modeling approaches, such as the toxicokinetic-toxicodynamic (TKTD) framework, are promoted by international institutions such as the European Food Safety Authority and the Organization for Economic Cooperation and Development to assess the environmental risk of chemical products generated by human activities. TKTD models can encompass a large set of mechanisms describing the kinetics of compounds inside organisms (e.g., uptake and elimination) and their effect at the level of individuals (e.g., damage accrual, recovery, and death mechanism). Compared to classical dose-response models, TKTD approaches have many advantages, including accounting for temporal aspects of exposure and toxicity, considering data points all along the experiment and not only at the end, and making predictions for untested situations as realistic exposure scenarios. Among TKTD models, the general unified threshold model of survival (GUTS) is within the most recent and innovative framework but is still underused in practice, especially by risk assessors, because specialist programming and statistical skills are necessary to run it. Making GUTS models easier to use through a new module freely available from the web platform MOSAIC (standing for MOdeling and StAtistical tools for ecotoxIClogy) should promote GUTS operability in support of the daily work of environmental risk assessors. This paper presents the main features of MOSAIC_GUTS: uploading of the experimental data, GUTS fitting analysis, and LCx estimates with their uncertainty. These features will be exemplified from literature data. Integr Environ Assess Manag 2018;00:000-000. © 2018 SETAC. © 2018 SETAC.

  12. POLARIZATION IMAGING AND SCATTERING MODEL OF CANCEROUS LIVER TISSUES

    Directory of Open Access Journals (Sweden)

    DONGZHI LI

    2013-07-01

    Full Text Available We apply different polarization imaging techniques for cancerous liver tissues, and compare the relative contrasts for difference polarization imaging (DPI, degree of polarization imaging (DOPI and rotating linear polarization imaging (RLPI. Experimental results show that a number of polarization imaging parameters are capable of differentiating cancerous cells in isotropic liver tissues. To analyze the contrast mechanism of the cancer-sensitive polarization imaging parameters, we propose a scattering model containing two types of spherical scatterers and carry on Monte Carlo simulations based on this bi-component model. Both the experimental and Monte Carlo simulated results show that the RLPI technique can provide a good imaging contrast of cancerous tissues. The bi-component scattering model provides a useful tool to analyze the contrast mechanism of polarization imaging of cancerous tissues.

  13. Genetic instability model for cancer risk in A-bomb survivors

    International Nuclear Information System (INIS)

    Niwa, Ohtsura

    1998-01-01

    This review was written rather against Mendelsohn's reductionist model for cancer risk in A-bomb survivors in following chapters. Assumptions for carcinogenic process: mutation of a cell to the cancer cell and its proliferation. Multi-step theory for carcinogenesis and age of crisis: induction of cancer by accumulation of cancer-related gene mutations which being linear to time (age). Effect of exogenous hit in the multi-step theory: radiation as an exogenous hit to damage DNA. Dose-effect relationship for cancer risk in the survivors and the problem for the latent period: for solid tumors, dose-effect relationship is linear and shortening of the latent period is not observed. Considerations on cancer data in adulthood exposure/Indirect effect model in radiation carcinogenesis: solid cancer data supporting the indirect effect model. Possible mechanism for radiation-induced long-term increase of natural mutation frequency: genetic instability remaining in the irradiated cells which being a basis of the indirect effect model. Notes for considerations of carcinogenicity in exposed people/Difference in carcinogenic mechanisms due to age. The author concluded that the radiation-induced carcinogenesis is deeply related with the natural carcinogenesis and particularly for solid cancers, it can not be explained by the classic reductionist model. (K.H.)

  14. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Science.gov (United States)

    Shin, Aesun; Joo, Jungnam; Yang, Hye-Ryung; Bak, Jeongin; Park, Yunjin; Kim, Jeongseon; Oh, Jae Hwan; Nam, Byung-Ho

    2014-01-01

    Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  15. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Improvement of the projection models for radiogenic cancer risk

    International Nuclear Information System (INIS)

    Tong Jian

    2005-01-01

    Calculations of radiogenic cancer risk are based on the risk projection models for specific cancer sites. Improvement has been made for the parameters used in the previous models including introductions of mortality and morbidity risk coefficients, and age-/ gender-specific risk coefficients. These coefficients have been applied to calculate the radiogenic cancer risks for specific organs and radionuclides under different exposure scenarios. (authors)

  17. Rasch analysis of the Mini-Mental Adjustment to Cancer Scale (mini-MAC) among a heterogeneous sample of long-term cancer survivors: a cross-sectional study.

    Science.gov (United States)

    Zucca, Alison; Lambert, Sylvie D; Boyes, Allison W; Pallant, Julie F

    2012-05-20

    The mini-Mental Adjustment to Cancer Scale (mini-MAC) is a well-recognised, popular measure of coping in psycho-oncology and assesses five cancer-specific coping strategies. It has been suggested that these five subscales could be grouped to form the over-arching adaptive and maladptive coping subscales to facilitate the interpretation and clinical application of the scale. Despite the popularity of the mini-MAC, few studies have examined its psychometric properties among long-term cancer survivors, and further validation of the mini-MAC is needed to substantiate its use with the growing population of survivors. Therefore, this study examined the psychometric properties and dimensionality of the mini-MAC in a sample of long-term cancer survivors using Rasch analysis. RUMM 2030 was used to analyse the mini-MAC data (n=851). Separate Rasch analyses were conducted for each of the original mini-MAC subscales as well as the over-arching adaptive and maladaptive coping subscales to examine summary and individual model fit statistics, person separation index (PSI), response format, local dependency, targeting, item bias (or differential item functioning -DIF), and dimensionality. For the fighting spirit, fatalism, and helplessness-hopelessness subscales, a revised three-point response format seemed more optimal than the original four-point response. To achieve model fit, items were deleted from four of the five subscales - Anxious Preoccupation items 7, 25, and 29; Cognitive Avoidance items 11 and 17; Fighting Spirit item 18; and Helplessness-Hopelessness items 16 and 20. For those subscales with sufficient items, analyses supported unidimensionality. Combining items to form the adaptive and maladaptive subscales was partially supported. The original five subscales required item deletion and/or rescaling to improve goodness of fit to the Rasch model. While evidence was found for overarching subscales of adaptive and maladaptive coping, extensive modifications were

  18. Rasch analysis of the Mini-Mental Adjustment to Cancer Scale (mini-MAC among a heterogeneous sample of long-term cancer survivors: A cross-sectional study

    Directory of Open Access Journals (Sweden)

    Zucca Alison

    2012-05-01

    Full Text Available Abstract Background The mini-Mental Adjustment to Cancer Scale (mini-MAC is a well-recognised, popular measure of coping in psycho-oncology and assesses five cancer-specific coping strategies. It has been suggested that these five subscales could be grouped to form the over-arching adaptive and maladptive coping subscales to facilitate the interpretation and clinical application of the scale. Despite the popularity of the mini-MAC, few studies have examined its psychometric properties among long-term cancer survivors, and further validation of the mini-MAC is needed to substantiate its use with the growing population of survivors. Therefore, this study examined the psychometric properties and dimensionality of the mini-MAC in a sample of long-term cancer survivors using Rasch analysis. Methods RUMM 2030 was used to analyse the mini-MAC data (n=851. Separate Rasch analyses were conducted for each of the original mini-MAC subscales as well as the over-arching adaptive and maladaptive coping subscales to examine summary and individual model fit statistics, person separation index (PSI, response format, local dependency, targeting, item bias (or differential item functioning -DIF, and dimensionality. Results For the fighting spirit, fatalism, and helplessness-hopelessness subscales, a revised three-point response format seemed more optimal than the original four-point response. To achieve model fit, items were deleted from four of the five subscales – Anxious Preoccupation items 7, 25, and 29; Cognitive Avoidance items 11 and 17; Fighting Spirit item 18; and Helplessness-Hopelessness items 16 and 20. For those subscales with sufficient items, analyses supported unidimensionality. Combining items to form the adaptive and maladaptive subscales was partially supported. Conclusions The original five subscales required item deletion and/or rescaling to improve goodness of fit to the Rasch model. While evidence was found for overarching subscales of

  19. AMS-02 fits dark matter

    Science.gov (United States)

    Balázs, Csaba; Li, Tong

    2016-05-01

    In this work we perform a comprehensive statistical analysis of the AMS-02 electron, positron fluxes and the antiproton-to-proton ratio in the context of a simplified dark matter model. We include known, standard astrophysical sources and a dark matter component in the cosmic ray injection spectra. To predict the AMS-02 observables we use propagation parameters extracted from observed fluxes of heavier nuclei and the low energy part of the AMS-02 data. We assume that the dark matter particle is a Majorana fermion coupling to third generation fermions via a spin-0 mediator, and annihilating to multiple channels at once. The simultaneous presence of various annihilation channels provides the dark matter model with additional flexibility, and this enables us to simultaneously fit all cosmic ray spectra using a simple particle physics model and coherent astrophysical assumptions. Our results indicate that AMS-02 observations are not only consistent with the dark matter hypothesis within the uncertainties, but adding a dark matter contribution improves the fit to the data. Assuming, however, that dark matter is solely responsible for this improvement of the fit, it is difficult to evade the latest CMB limits in this model.

  20. AMS-02 fits dark matter

    Energy Technology Data Exchange (ETDEWEB)

    Balázs, Csaba; Li, Tong [ARC Centre of Excellence for Particle Physics at the Tera-scale,School of Physics and Astronomy, Monash University, Melbourne, Victoria 3800 (Australia)

    2016-05-05

    In this work we perform a comprehensive statistical analysis of the AMS-02 electron, positron fluxes and the antiproton-to-proton ratio in the context of a simplified dark matter model. We include known, standard astrophysical sources and a dark matter component in the cosmic ray injection spectra. To predict the AMS-02 observables we use propagation parameters extracted from observed fluxes of heavier nuclei and the low energy part of the AMS-02 data. We assume that the dark matter particle is a Majorana fermion coupling to third generation fermions via a spin-0 mediator, and annihilating to multiple channels at once. The simultaneous presence of various annihilation channels provides the dark matter model with additional flexibility, and this enables us to simultaneously fit all cosmic ray spectra using a simple particle physics model and coherent astrophysical assumptions. Our results indicate that AMS-02 observations are not only consistent with the dark matter hypothesis within the uncertainties, but adding a dark matter contribution improves the fit to the data. Assuming, however, that dark matter is solely responsible for this improvement of the fit, it is difficult to evade the latest CMB limits in this model.

  1. A History of Regression and Related Model-Fitting in the Earth Sciences (1636?-2000)

    International Nuclear Information System (INIS)

    Howarth, Richard J.

    2001-01-01

    The (statistical) modeling of the behavior of a dependent variate as a function of one or more predictors provides examples of model-fitting which span the development of the earth sciences from the 17th Century to the present. The historical development of these methods and their subsequent application is reviewed. Bond's predictions (c. 1636 and 1668) of change in the magnetic declination at London may be the earliest attempt to fit such models to geophysical data. Following publication of Newton's theory of gravitation in 1726, analysis of data on the length of a 1 o meridian arc, and the length of a pendulum beating seconds, as a function of sin 2 (latitude), was used to determine the ellipticity of the oblate spheroid defining the Figure of the Earth. The pioneering computational methods of Mayer in 1750, Boscovich in 1755, and Lambert in 1765, and the subsequent independent discoveries of the principle of least squares by Gauss in 1799, Legendre in 1805, and Adrain in 1808, and its later substantiation on the basis of probability theory by Gauss in 1809 were all applied to the analysis of such geodetic and geophysical data. Notable later applications include: the geomagnetic survey of Ireland by Lloyd, Sabine, and Ross in 1836, Gauss's model of the terrestrial magnetic field in 1838, and Airy's 1845 analysis of the residuals from a fit to pendulum lengths, from which he recognized the anomalous character of measurements of gravitational force which had been made on islands. In the early 20th Century applications to geological topics proliferated, but the computational burden effectively held back applications of multivariate analysis. Following World War II, the arrival of digital computers in universities in the 1950s facilitated computation, and fitting linear or polynomial models as a function of geographic coordinates, trend surface analysis, became popular during the 1950-60s. The inception of geostatistics in France at this time by Matheron had its

  2. A Sport Education Fitness Season's Impact on Students' Fitness Levels, Knowledge, and In-Class Physical Activity.

    Science.gov (United States)

    Ward, Jeffery Kurt; Hastie, Peter A; Wadsworth, Danielle D; Foote, Shelby; Brock, Sheri J; Hollett, Nikki

    2017-09-01

    The purpose of this study was to determine the extent to which a sport education season of fitness could provide students with recommended levels of in-class moderate-to-vigorous physical activity (MVPA) while also increasing students' fitness knowledge and fitness achievement. One hundred and sixty-six 5th-grade students (76 boys, 90 girls) participated in a 20-lesson season called "CrossFit Challenge" during a 4-week period. The Progressive Aerobic Cardiovascular Endurance Run, push-ups, and curl-ups tests of the FITNESSGRAM® were used to assess fitness at pretest and posttest, while fitness knowledge was assessed through a validated, grade-appropriate test of health-related fitness knowledge (HRF). Physical activity was measured with Actigraph GT3X triaxial accelerometers. Results indicated a significant time effect for all fitness tests and the knowledge test. Across the entire season, the students spent an average of 54.5% of lesson time engaged in MVPA, irrespective of the type of lesson (instruction, free practice, or competition). The results suggest that configuring the key principles of sport education within a unit of fitness is an efficient model for providing students with the opportunity to improve fitness skill and HRF knowledge while attaining recommended levels of MVPA.

  3. Re-examining the factor structure and psychometric properties of the Mini-Mental Adjustment to Cancer Scale in a sample of 364 Chinese cancer patients.

    Science.gov (United States)

    Fong, Ted C T; Ho, Rainbow T H

    2015-02-01

    The Mini-Mental Adjustment to Cancer Scale (Mini-MAC) is widely used to evaluate cancer patients' psychological responses. Validation studies of the scale have shown methodological shortcomings and inconsistency in the factor solutions. The aim of this study was to examine the factor structure and psychometric properties of the Mini-MAC. A large sample of 364 Chinese patients with breast or colorectal cancer completed the Mini-MAC and psychosocial measures (general health, perceived stress, anxiety, and depression). Exploratory factor analyses examined the relative fit of two- to six-factor models using robust weighted least square estimation and oblique target rotation. Convergent validity was evaluated via correlations between the Mini-MAC factor scores and the psychosocial outcomes. The five-factor model showed the best model fit and largely replicated the original Mini-MAC's helpless/hopeless (HH), anxious preoccupation (AP), fighting spirit (FS), fatalism (FA), and cognitive avoidance (CA) subscales. The five factors had acceptable reliability (Cronbach's α = 0.67-0.88) and 4-month test-retest reliability (r = 0.45-0.64). HH, AP, and CA were positively associated with the psychosocial outcomes (r = 0.19-0.60). Modest and negative correlations were found between the psychosocial outcomes and FS and FA. The results support the Mini-MAC's original five-factor structure with satisfactory reliability and convergent validity. The results demonstrate that the Mini-MAC is a valid measure for assessing psychological responses in cancer patients.

  4. The application of the sinusoidal model to lung cancer patient respiratory motion

    International Nuclear Information System (INIS)

    George, R.; Vedam, S.S.; Chung, T.D.; Ramakrishnan, V.; Keall, P.J.

    2005-01-01

    Accurate modeling of the respiratory cycle is important to account for the effect of organ motion on dose calculation for lung cancer patients. The aim of this study is to evaluate the accuracy of a respiratory model for lung cancer patients. Lujan et al. [Med. Phys. 26(5), 715-720 (1999)] proposed a model, which became widely used, to describe organ motion due to respiration. This model assumes that the parameters do not vary between and within breathing cycles. In this study, first, the correlation of respiratory motion traces with the model f(t) as a function of the parameter n(n=1,2,3) was undertaken for each breathing cycle from 331 four-minute respiratory traces acquired from 24 lung cancer patients using three breathing types: free breathing, audio instruction, and audio-visual biofeedback. Because cos 2 and cos 4 had similar correlation coefficients, and cos 2 and cos 1 have a trigonometric relationship, for simplicity, the cos 1 value was consequently used for further analysis in which the variations in mean position (z 0 ), amplitude of motion (b) and period (τ) with and without biofeedback or instructions were investigated. For all breathing types, the parameter values, mean position (z 0 ), amplitude of motion (b), and period (τ) exhibited significant cycle-to-cycle variations. Audio-visual biofeedback showed the least variations for all three parameters (z 0 , b, and τ). It was found that mean position (z 0 ) could be approximated with a normal distribution, and the amplitude of motion (b) and period (τ) could be approximated with log normal distributions. The overall probability density function (pdf) of f(t) for each of the three breathing types was fitted with three models: normal, bimodal, and the pdf of a simple harmonic oscillator. It was found that the normal and the bimodal models represented the overall respiratory motion pdfs with correlation values from 0.95 to 0.99, whereas the range of the simple harmonic oscillator pdf correlation

  5. The More, the Better? Curvilinear Effects of Job Autonomy on Well-Being From Vitamin Model and PE-Fit Theory Perspectives.

    Science.gov (United States)

    Stiglbauer, Barbara; Kovacs, Carrie

    2017-12-28

    In organizational psychology research, autonomy is generally seen as a job resource with a monotone positive relationship with desired occupational outcomes such as well-being. However, both Warr's vitamin model and person-environment (PE) fit theory suggest that negative outcomes may result from excesses of some job resources, including autonomy. Thus, the current studies used survey methodology to explore cross-sectional relationships between environmental autonomy, person-environment autonomy (mis)fit, and well-being. We found that autonomy and autonomy (mis)fit explained between 6% and 22% of variance in well-being, depending on type of autonomy (scheduling, method, or decision-making) and type of (mis)fit operationalization (atomistic operationalization through the separate assessment of actual and ideal autonomy levels vs. molecular operationalization through the direct assessment of perceived autonomy (mis)fit). Autonomy (mis)fit (PE-fit perspective) explained more unique variance in well-being than environmental autonomy itself (vitamin model perspective). Detrimental effects of autonomy excess on well-being were most evident for method autonomy and least consistent for decision-making autonomy. We argue that too-much-of-a-good-thing effects of job autonomy on well-being exist, but suggest that these may be dependent upon sample characteristics (range of autonomy levels), type of operationalization (molecular vs. atomistic fit), autonomy facet (method, scheduling, or decision-making), as well as individual and organizational moderators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  6. Comparison of various models on cancer rate and forecasting ...

    African Journals Online (AJOL)

    ADOWIE PERE

    model and the quadratic trend model and the results of the work compared. Data collected ... Keywords: Cancer, Tumor, Leukemia, Linear Regression, Mean Percentage Error. Cancer is a .... by a simple mathematical method. The quadratic ...

  7. Global fits of GUT-scale SUSY models with GAMBIT

    Science.gov (United States)

    Athron, Peter; Balázs, Csaba; Bringmann, Torsten; Buckley, Andy; Chrząszcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Edsjö, Joakim; Farmer, Ben; Jackson, Paul; Krislock, Abram; Kvellestad, Anders; Mahmoudi, Farvah; Martinez, Gregory D.; Putze, Antje; Raklev, Are; Rogan, Christopher; de Austri, Roberto Ruiz; Saavedra, Aldo; Savage, Christopher; Scott, Pat; Serra, Nicola; Weniger, Christoph; White, Martin

    2017-12-01

    We present the most comprehensive global fits to date of three supersymmetric models motivated by grand unification: the constrained minimal supersymmetric standard model (CMSSM), and its Non-Universal Higgs Mass generalisations NUHM1 and NUHM2. We include likelihoods from a number of direct and indirect dark matter searches, a large collection of electroweak precision and flavour observables, direct searches for supersymmetry at LEP and Runs I and II of the LHC, and constraints from Higgs observables. Our analysis improves on existing results not only in terms of the number of included observables, but also in the level of detail with which we treat them, our sampling techniques for scanning the parameter space, and our treatment of nuisance parameters. We show that stau co-annihilation is now ruled out in the CMSSM at more than 95% confidence. Stop co-annihilation turns out to be one of the most promising mechanisms for achieving an appropriate relic density of dark matter in all three models, whilst avoiding all other constraints. We find high-likelihood regions of parameter space featuring light stops and charginos, making them potentially detectable in the near future at the LHC. We also show that tonne-scale direct detection will play a largely complementary role, probing large parts of the remaining viable parameter space, including essentially all models with multi-TeV neutralinos.

  8. Global fits of GUT-scale SUSY models with GAMBIT

    Energy Technology Data Exchange (ETDEWEB)

    Athron, Peter [Monash University, School of Physics and Astronomy, Melbourne, VIC (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); Balazs, Csaba [Monash University, School of Physics and Astronomy, Melbourne, VIC (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); Bringmann, Torsten; Dal, Lars A.; Krislock, Abram; Raklev, Are [University of Oslo, Department of Physics, Oslo (Norway); Buckley, Andy [University of Glasgow, SUPA, School of Physics and Astronomy, Glasgow (United Kingdom); Chrzaszcz, Marcin [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); H. Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Krakow (Poland); Conrad, Jan; Edsjoe, Joakim; Farmer, Ben [AlbaNova University Centre, Oskar Klein Centre for Cosmoparticle Physics, Stockholm (Sweden); Stockholm University, Department of Physics, Stockholm (Sweden); Cornell, Jonathan M. [McGill University, Department of Physics, Montreal, QC (Canada); Jackson, Paul; White, Martin [Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); University of Adelaide, Department of Physics, Adelaide, SA (Australia); Kvellestad, Anders; Savage, Christopher [NORDITA, Stockholm (Sweden); Mahmoudi, Farvah [Univ Lyon, Univ Lyon 1, CNRS, ENS de Lyon, Centre de Recherche Astrophysique de Lyon UMR5574, Saint-Genis-Laval (France); Theoretical Physics Department, CERN, Geneva (Switzerland); Martinez, Gregory D. [University of California, Physics and Astronomy Department, Los Angeles, CA (United States); Putze, Antje [LAPTh, Universite de Savoie, CNRS, Annecy-le-Vieux (France); Rogan, Christopher [Harvard University, Department of Physics, Cambridge, MA (United States); Ruiz de Austri, Roberto [IFIC-UV/CSIC, Instituto de Fisica Corpuscular, Valencia (Spain); Saavedra, Aldo [Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); The University of Sydney, Faculty of Engineering and Information Technologies, Centre for Translational Data Science, School of Physics, Camperdown, NSW (Australia); Scott, Pat [Imperial College London, Department of Physics, Blackett Laboratory, London (United Kingdom); Serra, Nicola [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); Weniger, Christoph [University of Amsterdam, GRAPPA, Institute of Physics, Amsterdam (Netherlands); Collaboration: The GAMBIT Collaboration

    2017-12-15

    We present the most comprehensive global fits to date of three supersymmetric models motivated by grand unification: the constrained minimal supersymmetric standard model (CMSSM), and its Non-Universal Higgs Mass generalisations NUHM1 and NUHM2. We include likelihoods from a number of direct and indirect dark matter searches, a large collection of electroweak precision and flavour observables, direct searches for supersymmetry at LEP and Runs I and II of the LHC, and constraints from Higgs observables. Our analysis improves on existing results not only in terms of the number of included observables, but also in the level of detail with which we treat them, our sampling techniques for scanning the parameter space, and our treatment of nuisance parameters. We show that stau co-annihilation is now ruled out in the CMSSM at more than 95% confidence. Stop co-annihilation turns out to be one of the most promising mechanisms for achieving an appropriate relic density of dark matter in all three models, whilst avoiding all other constraints. We find high-likelihood regions of parameter space featuring light stops and charginos, making them potentially detectable in the near future at the LHC. We also show that tonne-scale direct detection will play a largely complementary role, probing large parts of the remaining viable parameter space, including essentially all models with multi-TeV neutralinos. (orig.)

  9. Hair length, facial attractiveness, personality attribution: A multiple fitness model of hairdressing

    OpenAIRE

    Bereczkei, Tamas; Mesko, Norbert

    2007-01-01

    Multiple Fitness Model states that attractiveness varies across multiple dimensions, with each feature representing a different aspect of mate value. In the present study, male raters judged the attractiveness of young females with neotenous and mature facial features, with various hair lengths. Results revealed that the physical appearance of long-haired women was rated high, regardless of their facial attractiveness being valued high or low. Women rated as most attractive were those whose f...

  10. SU-D-204-03: Comparison of Patient Positioning Methods Through Modeling of Acute Rectal Toxicity in Intensity Modulated Radiation Therapy for Prostate Cancer. Does Quality of Data Matter More Than the Quantity?

    Energy Technology Data Exchange (ETDEWEB)

    Liu, X; Fatyga, M; Vora, S; Wong, W; Schild, S; Schild, M [Mayo Clinic Arizona, Phoenix, AZ (United States); Herman, M [Mayo Clinic, Rochester, MN (United States); Li, J; Wu, T [Arizona State University, Tempe, AZ (United States)

    2016-06-15

    Purpose: To determine if differences in patient positioning methods have an impact on the incidence and modeling of grade >=2 acute rectal toxicity in prostate cancer patients who were treated with Intensity Modulated Radiation Therapy (IMRT). Methods: We compared two databases of patients treated with radiation therapy for prostate cancer: a database of 79 patients who were treated with 7 field IMRT and daily image guided positioning based on implanted gold markers (IGRTdb), and a database of 302 patients who were treated with 5 field IMRT and daily positioning using a trans-abdominal ultrasound system (USdb). Complete planning dosimetry was available for IGRTdb patients while limited planning dosimetry, recorded at the time of planning, was available for USdb patients. We fit Lyman-Kutcher-Burman (LKB) model to IGRTdb only, and Univariate Logistic Regression (ULR) NTCP model to both databases. We perform Receiver Operating Characteristics analysis to determine the predictive power of NTCP models. Results: The incidence of grade >= 2 acute rectal toxicity in IGRTdb was 20%, while the incidence in USdb was 54%. Fits of both LKB and ULR models yielded predictive NTCP models for IGRTdb patients with Area Under the Curve (AUC) in the 0.63 – 0.67 range. Extrapolation of the ULR model from IGRTdb to planning dosimetry in USdb predicts that the incidence of acute rectal toxicity in USdb should not exceed 40%. Fits of the ULR model to the USdb do not yield predictive NTCP models and their AUC is consistent with AUC = 0.5. Conclusion: Accuracy of a patient positioning system affects clinically observed toxicity rates and the quality of NTCP models that can be derived from toxicity data. Poor correlation between planned and clinically delivered dosimetry may lead to erroneous or poorly performing NTCP models, even if the number of patients in a database is large.

  11. A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

    Science.gov (United States)

    Saunders, Christina T; Blume, Jeffrey D

    2017-10-26

    Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

  12. Are all models created equal? A content analysis of women in advertisements of fitness versus fashion magazines.

    Science.gov (United States)

    Wasylkiw, L; Emms, A A; Meuse, R; Poirier, K F

    2009-03-01

    The current study is a content analysis of women appearing in advertisements in two types of magazines: fitness/health versus fashion/beauty chosen because of their large and predominantly female readerships. Women appearing in advertisements of the June 2007 issue of five fitness/health magazines were compared to women appearing in advertisements of the June 2007 issue of five beauty/fashion magazines. Female models appearing in advertisements of both types of magazines were primarily young, thin Caucasians; however, images of models were more likely to emphasize appearance over performance when they appeared in fashion magazines. This difference in emphasis has implications for future research.

  13. Dogs as a Model for Cancer.

    Science.gov (United States)

    Gardner, Heather L; Fenger, Joelle M; London, Cheryl A

    2016-01-01

    Spontaneous cancers in client-owned dogs closely recapitulate their human counterparts with respect to clinical presentation, histological features, molecular profiles, and response and resistance to therapy, as well as the evolution of drug-resistant metastases. In several instances the incorporation of dogs with cancer into the preclinical development path of cancer therapeutics has influenced outcome by helping to establish pharmacokinetic/pharmacodynamics relationships, dose/regimen, expected clinical toxicities, and ultimately the potential for biologic activity. As our understanding regarding the molecular drivers of canine cancers has improved, unique opportunities have emerged to leverage this spontaneous model to better guide cancer drug development so that therapies likely to fail are eliminated earlier and therapies with true potential are optimized prior to human studies. Both pets and people benefit from this approach, as it provides dogs with access to cutting-edge cancer treatments and helps to insure that people are given treatments more likely to succeed.

  14. A Monte Carlo-adjusted goodness-of-fit test for parametric models describing spatial point patterns

    KAUST Repository

    Dao, Ngocanh; Genton, Marc G.

    2014-01-01

    Assessing the goodness-of-fit (GOF) for intricate parametric spatial point process models is important for many application fields. When the probability density of the statistic of the GOF test is intractable, a commonly used procedure is the Monte

  15. Entropy, complexity, and Markov diagrams for random walk cancer models.

    Science.gov (United States)

    Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter

    2014-12-19

    The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

  16. Fitness function and nonunique solutions in x-ray reflectivity curve fitting: crosserror between surface roughness and mass density

    International Nuclear Information System (INIS)

    Tiilikainen, J; Bosund, V; Mattila, M; Hakkarainen, T; Sormunen, J; Lipsanen, H

    2007-01-01

    Nonunique solutions of the x-ray reflectivity (XRR) curve fitting problem were studied by modelling layer structures with neural networks and designing a fitness function to handle the nonidealities of measurements. Modelled atomic-layer-deposited aluminium oxide film structures were used in the simulations to calculate XRR curves based on Parratt's formalism. This approach reduced the dimensionality of the parameter space and allowed the use of fitness landscapes in the study of nonunique solutions. Fitness landscapes, where the height in a map represents the fitness value as a function of the process parameters, revealed tracks where the local fitness optima lie. The tracks were projected on the physical parameter space thus allowing the construction of the crosserror equation between weakly determined parameters, i.e. between the mass density and the surface roughness of a layer. The equation gives the minimum error for the other parameters which is a consequence of the nonuniqueness of the solution if noise is present. Furthermore, the existence of a possible unique solution in a certain parameter range was found to be dependent on the layer thickness and the signal-to-noise ratio

  17. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Directory of Open Access Journals (Sweden)

    Aesun Shin

    Full Text Available PURPOSE: Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. METHODS: Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. RESULTS: Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. CONCLUSIONS: Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  18. Incidence of late rectal bleeding in high-dose conformal radiotherapy of prostate cancer using equivalent uniform dose-based and dose-volume-based normal tissue complication probability models

    International Nuclear Information System (INIS)

    Soehn, Matthias; Yan Di; Liang Jian; Meldolesi, Elisa; Vargas, Carlos; Alber, Markus

    2007-01-01

    Purpose: Accurate modeling of rectal complications based on dose-volume histogram (DVH) data are necessary to allow safe dose escalation in radiotherapy of prostate cancer. We applied different equivalent uniform dose (EUD)-based and dose-volume-based normal tissue complication probability (NTCP) models to rectal wall DVHs and follow-up data for 319 prostate cancer patients to identify the dosimetric factors most predictive for Grade ≥ 2 rectal bleeding. Methods and Materials: Data for 319 patients treated at the William Beaumont Hospital with three-dimensional conformal radiotherapy (3D-CRT) under an adaptive radiotherapy protocol were used for this study. The following models were considered: (1) Lyman model and (2) logit-formula with DVH reduced to generalized EUD (3) serial reconstruction unit (RU) model (4) Poisson-EUD model, and (5) mean dose- and (6) cutoff dose-logistic regression model. The parameters and their confidence intervals were determined using maximum likelihood estimation. Results: Of the patients, 51 (16.0%) showed Grade 2 or higher bleeding. As assessed qualitatively and quantitatively, the Lyman- and Logit-EUD, serial RU, and Poisson-EUD model fitted the data very well. Rectal wall mean dose did not correlate to Grade 2 or higher bleeding. For the cutoff dose model, the volume receiving > 73.7 Gy showed most significant correlation to bleeding. However, this model fitted the data more poorly than the EUD-based models. Conclusions: Our study clearly confirms a volume effect for late rectal bleeding. This can be described very well by the EUD-like models, of which the serial RU- and Poisson-EUD model can describe the data with only two parameters. Dose-volume-based cutoff-dose models performed worse

  19. Methodology to predict long-term cancer survival from short-term data using Tobacco Cancer Risk and Absolute Cancer Cure models

    International Nuclear Information System (INIS)

    Mould, R F; Lederman, M; Tai, P; Wong, J K M

    2002-01-01

    Three parametric statistical models have been fully validated for cancer of the larynx for the prediction of long-term 15, 20 and 25 year cancer-specific survival fractions when short-term follow-up data was available for just 1-2 years after the end of treatment of the last patient. In all groups of cases the treatment period was only 5 years. Three disease stage groups were studied, T1N0, T2N0 and T3N0. The models are the Standard Lognormal (SLN) first proposed by Boag (1949 J. R. Stat. Soc. Series B 11 15-53) but only ever fully validated for cancer of the cervix, Mould and Boag (1975 Br. J. Cancer 32 529-50), and two new models which have been termed Tobacco Cancer Risk (TCR) and Absolute Cancer Cure (ACC). In each, the frequency distribution of survival times of defined groups of cancer deaths is lognormally distributed: larynx only (SLN), larynx and lung (TCR) and all cancers (ACC). All models each have three unknown parameters but it was possible to estimate a value for the lognormal parameter S a priori. By reduction to two unknown parameters the model stability has been improved. The material used to validate the methodology consisted of case histories of 965 patients, all treated during the period 1944-1968 by Dr Manuel Lederman of the Royal Marsden Hospital, London, with follow-up to 1988. This provided a follow-up range of 20- 44 years and enabled predicted long-term survival fractions to be compared with the actual survival fractions, calculated by the Kaplan and Meier (1958 J. Am. Stat. Assoc. 53 457-82) method. The TCR and ACC models are better than the SLN model and for a maximum short-term follow-up of 6 years, the 20 and 25 year survival fractions could be predicted. Therefore the numbers of follow-up years saved are respectively 14 years and 19 years. Clinical trial results using the TCR and ACC models can thus be analysed much earlier than currently possible. Absolute cure from cancer was also studied, using not only the prediction models which

  20. Lung cancer from radon and smoking: a multistage model for the WISMUT uranium miners

    International Nuclear Information System (INIS)

    Dillen, Teun van; Bijwaard, Harmen; Schnelzer, Maria; Kreuzer, Michaela; Grosche, Bernd

    2008-01-01

    Full text: In the world's third-largest uranium-mining province located in areas of Saxony and Thuringia in the former German Democratic Republic, the WISMUT Company conducted extensive uranium mining starting in 1946. Up to 1990, when mining activities were discontinued, most of the 400,000 employees had been exposed to uranium ore dust and radon and its progeny. It is well established that, besides smoking, such exposures are associated with an increased risk of lung cancer. From about 130,000 known miners a huge cohort of 59,000 miners has been formed and in an epidemiological analysis lung cancer risks have been evaluated (Grosche et al., 2006). We will present an alternative approach using a biologically-based multistage carcinogenesis model quantifying the lung-cancer risk related to both the exposure to radon and smoking habits. This mechanistic technique allows for extrapolation to the low exposures that are important for present-day radiation protection purposes and the transfer of risk across populations. The model is applied to a sub-cohort of about 35,000 persons who were employed at WISMUT after 1955, with known annual exposures estimated from the job-exposure matrix (Lehmann et al., 2004). Unfortunately, detailed information on smoking is missing for most miners. However, this information has been retrieved in two case-control studies, one of which was nested in the cohort while the other was not (Brueske-Hohlfeld et al., 2006). For these studies, the relevant smoking parameters are assembled in so-called smoking spectra that are next projected onto the entire cohort using a Monte-Carlo sampling method. Individual smoking habits that are randomly assigned to the cohort members, together with the information on annual exposure to radon, is used as an input for the multistage model. Model parameters related to radon and tobacco exposure are fitted with a maximum-likelihood technique. We will show results of the observed and expected lung-cancer

  1. Modeling cancer registration processes with an enhanced activity diagram.

    Science.gov (United States)

    Lyalin, D; Williams, W

    2005-01-01

    Adequate instruments are needed to reflect the complexity of routine cancer registry operations properly in a business model. The activity diagram is a key instrument of the Unified Modeling Language (UML) for the modeling of business processes. The authors aim to improve descriptions of processes in cancer registration, as well as in other public health domains, through the enhancements of an activity diagram notation within the standard semantics of UML. The authors introduced the practical approach to enhance a conventional UML activity diagram, complementing it with the following business process concepts: timeline, duration for individual activities, responsibilities for individual activities within swimlanes, and descriptive text. The authors used an enhanced activity diagram for modeling surveillance processes in the cancer registration domain. Specific example illustrates the use of an enhanced activity diagram to visualize a process of linking cancer registry records with external mortality files. Enhanced activity diagram allows for the addition of more business concepts to a single diagram and can improve descriptions of processes in cancer registration, as well as in other domains. Additional features of an enhanced activity diagram allow to advance the visualization of cancer registration processes. That, in turn, promotes the clarification of issues related to the process timeline, responsibilities for particular operations, and collaborations among process participants. Our first experiences in a cancer registry best practices development workshop setting support the usefulness of such an approach.

  2. Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain

    Science.gov (United States)

    Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises

    2015-01-01

    Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156

  3. Modelling breast cancer tumour growth for a stable disease population.

    Science.gov (United States)

    Isheden, Gabriel; Humphreys, Keith

    2017-01-01

    Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.

  4. Neural network hydrological modelling: on questions of over-fitting, over-training and over-parameterisation

    Science.gov (United States)

    Abrahart, R. J.; Dawson, C. W.; Heppenstall, A. J.; See, L. M.

    2009-04-01

    The most critical issue in developing a neural network model is generalisation: how well will the preferred solution perform when it is applied to unseen datasets? The reported experiments used far-reaching sequences of model architectures and training periods to investigate the potential damage that could result from the impact of several interrelated items: (i) over-fitting - a machine learning concept related to exceeding some optimal architectural size; (ii) over-training - a machine learning concept related to the amount of adjustment that is applied to a specific model - based on the understanding that too much fine-tuning might result in a model that had accommodated random aspects of its training dataset - items that had no causal relationship to the target function; and (iii) over-parameterisation - a statistical modelling concept that is used to restrict the number of parameters in a model so as to match the information content of its calibration dataset. The last item in this triplet stems from an understanding that excessive computational complexities might permit an absurd and false solution to be fitted to the available material. Numerous feedforward multilayered perceptrons were trialled and tested. Two different methods of model construction were also compared and contrasted: (i) traditional Backpropagation of Error; and (ii) state-of-the-art Symbiotic Adaptive Neuro-Evolution. Modelling solutions were developed using the reported experimental set ups of Gaume & Gosset (2003). The models were applied to a near-linear hydrological modelling scenario in which past upstream and past downstream discharge records were used to forecast current discharge at the downstream gauging station [CS1: River Marne]; and a non-linear hydrological modelling scenario in which past river discharge measurements and past local meteorological records (precipitation and evaporation) were used to forecast current discharge at the river gauging station [CS2: Le Sauzay].

  5. Universality Classes of Interaction Structures for NK Fitness Landscapes

    Science.gov (United States)

    Hwang, Sungmin; Schmiegelt, Benjamin; Ferretti, Luca; Krug, Joachim

    2018-02-01

    Kauffman's NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k ≤ L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

  6. Statistical topography of fitness landscapes

    OpenAIRE

    Franke, Jasper

    2011-01-01

    Fitness landscapes are generalized energy landscapes that play an important conceptual role in evolutionary biology. These landscapes provide a relation between the genetic configuration of an organism and that organism’s adaptive properties. In this work, global topographical features of these fitness landscapes are investigated using theoretical models. The resulting predictions are compared to empirical landscapes. It is shown that these landscapes allow, at least with respe...

  7. Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

    Science.gov (United States)

    Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-20

    Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

  8. Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example

    Science.gov (United States)

    Helgesson, P.; Sjöstrand, H.

    2017-11-01

    Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

  9. Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example.

    Science.gov (United States)

    Helgesson, P; Sjöstrand, H

    2017-11-01

    Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r 1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r 1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r 1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

  10. Mathematical models in cell biology and cancer chemotherapy

    CERN Document Server

    Eisen, Martin

    1979-01-01

    The purpose of this book is to show how mathematics can be applied to improve cancer chemotherapy. Unfortunately, most drugs used in treating cancer kill both normal and abnormal cells. However, more cancer cells than normal cells can be destroyed by the drug because tumor cells usually exhibit different growth kinetics than normal cells. To capitalize on this last fact, cell kinetics must be studied by formulating mathematical models of normal and abnormal cell growth. These models allow the therapeutic and harmful effects of cancer drugs to be simulated quantitatively. The combined cell and drug models can be used to study the effects of different methods of administering drugs. The least harmful method of drug administration, according to a given criterion, can be found by applying optimal control theory. The prerequisites for reading this book are an elementary knowledge of ordinary differential equations, probability, statistics, and linear algebra. In order to make this book self-contained, a chapter on...

  11. Development of A Mouse Model of Menopausal Ovarian Cancer

    Directory of Open Access Journals (Sweden)

    Elizabeth R. Smith

    2014-02-01

    Full Text Available Despite significant understanding of the genetic mutations involved in ovarian epithelial cancer and advances in genomic approaches for expression and mutation profiling of tumor tissues, several key questions in ovarian cancer biology remain enigmatic: the mechanism for the well-established impact of reproductive factors on ovarian cancer risk remains obscure; questions of the cell of origin of ovarian cancer continue to be debated; and the precursor lesion, sequence, or events in progression remain to be defined. Suitable mouse models should complement the analysis of human tumor tissues and may provide clues to these questions currently perplexing ovarian cancer biology.A potentially useful model is the germ cell-deficient Wv (white spotting variant mutant mouse line, which may be used to study the impact of menopausal physiology on the increased risk of ovarian cancer. The Wv mice harbor a point mutation in c-Kit that reduces the receptor tyrosine kinase activity to about 1-5% (it is not a null mutation. Homozygous Wv mutant females have a reduced ovarian germ cell reservoir at birth and the follicles are rapidly depleted upon reaching reproductive maturity, but other biological phenotypes are minimal and the mice have a normal life span. The loss of ovarian function precipitates changes in hormonal and metabolic activity that model features of menopause in humans. As a consequence of follicle depletion, the Wv ovaries develop ovarian tubular adenomas, a benign epithelial tumor corresponding to surface epithelial invaginations and papillomatosis that mark human ovarian aging. Ongoing work will test the possibility of converting the benign epithelial tubular adenomas into neoplastic tumors by addition of an oncogenic mutation, such as of Tp53, to model the genotype and biology of serous ovarian cancer.Model based on the Wv mice may have the potential to gain biological and etiological insights into ovarian cancer development and prevention.

  12. Colorectal cancer screening for average-risk North Americans: an economic evaluation.

    Directory of Open Access Journals (Sweden)

    Steven J Heitman

    Full Text Available BACKGROUND: Colorectal cancer (CRC fulfills the World Health Organization criteria for mass screening, but screening uptake is low in most countries. CRC screening is resource intensive, and it is unclear if an optimal strategy exists. The objective of this study was to perform an economic evaluation of CRC screening in average risk North American individuals considering all relevant screening modalities and current CRC treatment costs. METHODS AND FINDINGS: An incremental cost-utility analysis using a Markov model was performed comparing guaiac-based fecal occult blood test (FOBT or fecal immunochemical test (FIT annually, fecal DNA every 3 years, flexible sigmoidoscopy or computed tomographic colonography every 5 years, and colonoscopy every 10 years. All strategies were also compared to a no screening natural history arm. Given that different FIT assays and collection methods have been previously tested, three distinct FIT testing strategies were considered, on the basis of studies that have reported "low," "mid," and "high" test performance characteristics for detecting adenomas and CRC. Adenoma and CRC prevalence rates were based on a recent systematic review whereas screening adherence, test performance, and CRC treatment costs were based on publicly available data. The outcome measures included lifetime costs, number of cancers, cancer-related deaths, quality-adjusted life-years gained, and incremental cost-utility ratios. Sensitivity and scenario analyses were performed. Annual FIT, assuming mid-range testing characteristics, was more effective and less costly compared to all strategies (including no screening except FIT-high. Among the lifetimes of 100,000 average-risk patients, the number of cancers could be reduced from 4,857 to 1,393 [corrected] and the number of CRC deaths from 1,782 [corrected] to 457, while saving CAN$68 per person. Although screening patients with FIT became more expensive than a strategy of no screening when the

  13. Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model

    International Nuclear Information System (INIS)

    Edwards, Darrin C.; Kupinski, Matthew A.; Metz, Charles E.; Nishikawa, Robert M.

    2002-01-01

    We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well

  14. Design of the Steps to Health Study of Physical Activity in Survivors of Endometrial Cancer: Testing a Social Cognitive Theory Model.

    Science.gov (United States)

    Basen-Engquist, Karen; Carmack, Cindy L; Perkins, Heidi; Hughes, Daniel; Serice, Susan; Scruggs, Stacie; Pinto, Bernardine; Waters, Andrew

    2011-01-01

    Physical activity has been shown to benefit cancer survivors' physical functioning, emotional well-being, and symptoms. Physical activity may be of particular benefit to survivors of endometrial cancer because they are more likely to be obese and sedentary than the general population, as these are risk factors for the disease, and thus experience a number of related co-morbid health problems. However, there is little research systematically studying mechanisms of physical activity adherence in cancer survivor populations. This paper describes the design of the Steps to Health study, which applies a Social Cognitive Theory-based model of endometrial cancer survivors' adoption and maintenance of exercise in the context of an intervention to increase walking or other moderate intensity cardiovascular activity. In Steps to Health we will test the influence of self-efficacy and outcome expectations on adherence to exercise recommendations, as well as studying the determinants of self-efficacy. Endometrial cancer survivors who are at least 6 months post-treatment are provided with an intervention involving print materials and telephone counseling, and complete assessments of fitness, activity, self-efficacy and outcome expectations, and determinants of self-efficacy every two months for a six month period. In addition to testing an innovative model, the Steps to Health study employs multiple assessment methods, including ecological momentary assessment, implicit tests of cognitive variables, and ambulatory monitoring of physical activity. The study results can be used to develop more effective interventions for increasing physical activity in sedentary cancer survivors by taking into account the full complement of sources of self-efficacy information and outcome expectations.

  15. Preclinical Murine Models for Lung Cancer: Clinical Trial Applications

    Directory of Open Access Journals (Sweden)

    Amelia Kellar

    2015-01-01

    Full Text Available Murine models for the study of lung cancer have historically been the backbone of preliminary preclinical data to support early human clinical trials. However, the availability of multiple experimental systems leads to debate concerning which model, if any, is best suited for a particular therapeutic strategy. It is imperative that these models accurately predict clinical benefit of therapy. This review provides an overview of the current murine models used to study lung cancer and the advantages and limitations of each model, as well as a retrospective evaluation of the uses of each model with respect to accuracy in predicting clinical benefit of therapy. A better understanding of murine models and their uses, as well as their limitations may aid future research concerning the development and implementation of new targeted therapies and chemotherapeutic agents for lung cancer.

  16. Quantifying and Reducing Curve-Fitting Uncertainty in Isc

    Energy Technology Data Exchange (ETDEWEB)

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-06-14

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  17. Fitting monthly Peninsula Malaysian rainfall using Tweedie distribution

    Science.gov (United States)

    Yunus, R. M.; Hasan, M. M.; Zubairi, Y. Z.

    2017-09-01

    In this study, the Tweedie distribution was used to fit the monthly rainfall data from 24 monitoring stations of Peninsula Malaysia for the period from January, 2008 to April, 2015. The aim of the study is to determine whether the distributions within the Tweedie family fit well the monthly Malaysian rainfall data. Within the Tweedie family, the gamma distribution is generally used for fitting the rainfall totals, however the Poisson-gamma distribution is more useful to describe two important features of rainfall pattern, which are the occurrences (dry months) and the amount (wet months). First, the appropriate distribution of the monthly rainfall was identified within the Tweedie family for each station. Then, the Tweedie Generalised Linear Model (GLM) with no explanatory variable was used to model the monthly rainfall data. Graphical representation was used to assess model appropriateness. The QQ plots of quantile residuals show that the Tweedie models fit the monthly rainfall data better for majority of the stations in the west coast and mid land than those in the east coast of Peninsula. This significant finding suggests that the best fitted distribution depends on the geographical location of the monitoring station. In this paper, a simple model is developed for generating synthetic rainfall data for use in various areas, including agriculture and irrigation. We have showed that the data that were simulated using the Tweedie distribution have fairly similar frequency histogram to that of the actual data. Both the mean number of rainfall events and mean amount of rain for a month were estimated simultaneously for the case that the Poisson gamma distribution fits the data reasonably well. Thus, this work complements previous studies that fit the rainfall amount and the occurrence of rainfall events separately, each to a different distribution.

  18. Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project

    Directory of Open Access Journals (Sweden)

    Berman Barbara

    2011-03-01

    Full Text Available Abstract Background The Mayo Lung Project (MLP, a randomized controlled clinical trial of lung cancer screening conducted between 1971 and 1986 among male smokers aged 45 or above, demonstrated an increase in lung cancer survival since the time of diagnosis, but no reduction in lung cancer mortality. Whether this result necessarily indicates a lack of mortality benefit for screening remains controversial. A number of hypotheses have been proposed to explain the observed outcome, including over-diagnosis, screening sensitivity, and population heterogeneity (initial difference in lung cancer risks between the two trial arms. This study is intended to provide model-based testing for some of these important arguments. Method Using a micro-simulation model, the MISCAN-lung model, we explore the possible influence of screening sensitivity, systematic error, over-diagnosis and population heterogeneity. Results Calibrating screening sensitivity, systematic error, or over-diagnosis does not noticeably improve the fit of the model, whereas calibrating population heterogeneity helps the model predict lung cancer incidence better. Conclusions Our conclusion is that the hypothesized imperfection in screening sensitivity, systematic error, and over-diagnosis do not in themselves explain the observed trial results. Model fit improvement achieved by accounting for population heterogeneity suggests a higher risk of cancer incidence in the intervention group as compared with the control group.

  19. A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.

    Science.gov (United States)

    Glas, Cees A. W.; Meijer, Rob R.

    A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…

  20. GRace: a MATLAB-based application for fitting the discrimination-association model.

    Science.gov (United States)

    Stefanutti, Luca; Vianello, Michelangelo; Anselmi, Pasquale; Robusto, Egidio

    2014-10-28

    The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.

  1. Challenges in economic modeling of anticancer therapies: an example of modeling the survival benefit of olaparib maintenance therapy for patients with BRCA-mutated platinum-sensitive relapsed ovarian cancer.

    Science.gov (United States)

    Hettle, Robert; Posnett, John; Borrill, John

    2015-01-01

    The aim of this paper is to describe a four health-state, semi-Markov model structure with health states defined by initiation of subsequent treatment, designed to make best possible use of the data available from a phase 2 clinical trial. The approach is illustrated using data from a sub-group of patients enrolled in a phase 2 clinical trial of olaparib maintenance therapy in patients with platinum-sensitive relapsed ovarian cancer and a BRCA mutation (NCT00753545). A semi-Markov model was developed with four health states: progression-free survival (PFS), first subsequent treatment (FST), second subsequent treatment (SST), and death. Transition probabilities were estimated by fitting survival curves to trial data for time from randomization to FST, time from FST to SST, and time from SST to death. Survival projections generated by the model are broadly consistent with the outcomes observed in the clinical trial. However, limitations of the trial data (small sample size, immaturity of the PFS and overall survival [OS] end-points, and treatment switching) create uncertainty in estimates of survival. The model framework offers a promising approach to evaluating cost-effectiveness of a maintenance therapy for patients with cancer, which may be generalizable to other chronic diseases.

  2. Life after cancer: how does public stigma increase psychological distress of childhood cancer survivors?

    Science.gov (United States)

    Kim, Min Ah; Yi, Jaehee

    2014-12-01

    Public stigma is a major source of stress for cancer survivors. However, factors that buffer or exacerbate the negative effects of public stigma on psychological distress have not been elucidated. This study examined how perceived public stigma affects psychological distress as mediated by cancer disclosure, internalized reactions to stigma, and social support availability. Cross-sectional study. The study was conducted in South Korea. The study sample was 223 adolescent and young adult survivors of childhood cancer diagnosed before the age of 19 and currently between 15 and 39 years old. Psychological distress was assessed using the Brief Symptom Inventory-18. Structural equation modeling was used with 1000 bootstrap samples. The goodness of model fit was acceptable. Public stigma perceived by cancer survivors influenced psychological distress via cancer disclosure, internalized shame, and social support availability. Higher levels of perceived public stigma predicted higher levels of internalized shame and self-blame and lower levels of social support availability, which subsequently increased psychological distress. Higher levels of perceived public stigma predicted lower levels of disclosure about cancer history and experiences. Cancer disclosure indirectly ameliorated psychological distress by reducing internalized shame. This study offers evidence that cognitive and social factors play important roles in mediating the effects of perceived public stigma on psychological distress in Korean cancer survivors. A greater understanding of factors that influence psychological distress may help psychosocial oncology service providers to identify childhood cancer survivors in need of psychosocial services and provide them with appropriate resources and interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. A CAD System for Evaluating Footwear Fit

    Science.gov (United States)

    Savadkoohi, Bita Ture; de Amicis, Raffaele

    With the great growth in footwear demand, the footwear manufacturing industry, for achieving commercial success, must be able to provide the footwear that fulfills consumer's requirement better than it's competitors. Accurate fitting for shoes is an important factor in comfort and functionality. Footwear fitter measurement have been using manual measurement for a long time, but the development of 3D acquisition devices and the advent of powerful 3D visualization and modeling techniques, automatically analyzing, searching and interpretation of the models have now made automatic determination of different foot dimensions feasible. In this paper, we proposed an approach for finding footwear fit within the shoe last data base. We first properly aligned the 3D models using "Weighted" Principle Component Analysis (WPCA). After solving the alignment problem we used an efficient algorithm for cutting the 3D model in order to find the footwear fit from shoe last data base.

  4. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  5. New insights into survival trend analyses in cancer population-based studies: the SUDCAN methodology.

    Science.gov (United States)

    Uhry, Zoé; Bossard, Nadine; Remontet, Laurent; Iwaz, Jean; Roche, Laurent

    2017-01-01

    The main objective of the SUDCAN study was to compare, for 15 cancer sites, the trends in net survival and excess mortality rates from cancer 5 years after diagnosis between six European Latin countries (Belgium, France, Italy, Portugal, Spain and Switzerland). The data were extracted from the EUROCARE-5 database. The study period ranged from 6 (Portugal, 2000-2005) to 18 years (Switzerland, 1989-2007). Trend analyses were carried out separately for each country and cancer site; the number of cases ranged from 1500 to 104 000 cases. We developed an original flexible excess rate modelling strategy that accounts for the continuous effects of age, year of diagnosis, time since diagnosis and their interactions. Nineteen models were constructed; they differed in the modelling of the effect of the year of diagnosis in terms of linearity, proportionality and interaction with age. The final model was chosen according to the Akaike Information Criterion. The fit was assessed graphically by comparing model estimates versus nonparametric (Pohar-Perme) net survival estimates. Out of the 90 analyses carried out, the effect of the year of diagnosis on the excess mortality rate depended on age in 61 and was nonproportional in 64; it was nonlinear in 27 out of the 75 analyses where this effect was considered. The model fit was overall satisfactory. We analysed successfully 15 cancer sites in six countries. The refined methodology proved necessary for detailed trend analyses. It is hoped that three-dimensional parametric modelling will be used more widely in net survival trend studies as it has major advantages over stratified analyses.

  6. The Many Null Distributions of Person Fit Indices.

    Science.gov (United States)

    Molenaar, Ivo W.; Hoijtink, Herbert

    1990-01-01

    Statistical properties of person fit indices are reviewed as indicators of the extent to which a person's score pattern is in agreement with a measurement model. Distribution of a fit index and ability-free fit evaluation are discussed. The null distribution was simulated for a test of 20 items. (SLD)

  7. Lung Cancer Screening Participation: Developing a Conceptual Model to Guide Research.

    Science.gov (United States)

    Carter-Harris, Lisa; Davis, Lorie L; Rawl, Susan M

    2016-11-01

    To describe the development of a conceptual model to guide research focused on lung cancer screening participation from the perspective of the individual in the decision-making process. Based on a comprehensive review of empirical and theoretical literature, a conceptual model was developed linking key psychological variables (stigma, medical mistrust, fatalism, worry, and fear) to the health belief model and precaution adoption process model. Proposed model concepts have been examined in prior research of either lung or other cancer screening behavior. To date, a few studies have explored a limited number of variables that influence screening behavior in lung cancer specifically. Therefore, relationships among concepts in the model have been proposed and future research directions presented. This proposed model is an initial step to support theoretically based research. As lung cancer screening becomes more widely implemented, it is critical to theoretically guide research to understand variables that may be associated with lung cancer screening participation. Findings from future research guided by the proposed conceptual model can be used to refine the model and inform tailored intervention development.

  8. Information model design health service childhood cancer for parents and caregivers

    Science.gov (United States)

    Ramli, Syazwani; Muda, Zurina

    2015-05-01

    Most Malaysians do not realize that they are suffer from a chronic disease until the disease is confirmed to be at a critical stage. This is because lack of awareness among Malaysians about a chronic disease especially in a childhood cancer. Based on report of the National Cancer Council (MAKNA),11 million adults and children suffered with cancer and 6 million of them die in a worldwide. Lack of public exposure to this disease leads to health problems to their children. Information model design health service childhood cancer for p arents and caregivers using an android application medium can be used by a doctor to deliver an information of cancer to the parents and caregivers. The development of this information model design health service childhood cancer for parents and caregivers are using an integration of health promotion theory, spiral model and lean model to form a new model that can be used as a model design content of health service. The method using in this study are by an interview technique and questionnaires along the study was conducted. Hopefully the production of this information model design health service childhood cancer for parents and caregivers using an android apps as a medium can help parents, caregivers and public to know more about information of childhood cancer and at the same time can gain an awareness among them and this app also can be used as a medium for doctors to deliver an information to the parents and caregivers.

  9. Inverse problem theory methods for data fitting and model parameter estimation

    CERN Document Server

    Tarantola, A

    2002-01-01

    Inverse Problem Theory is written for physicists, geophysicists and all scientists facing the problem of quantitative interpretation of experimental data. Although it contains a lot of mathematics, it is not intended as a mathematical book, but rather tries to explain how a method of acquisition of information can be applied to the actual world.The book provides a comprehensive, up-to-date description of the methods to be used for fitting experimental data, or to estimate model parameters, and to unify these methods into the Inverse Problem Theory. The first part of the book deals wi

  10. Rat models of 17β-estradiol-induced mammary cancer reveal novel insights into breast cancer etiology and prevention.

    Science.gov (United States)

    Shull, James D; Dennison, Kirsten L; Chack, Aaron C; Trentham-Dietz, Amy

    2018-03-01

    Numerous laboratory and epidemiologic studies strongly implicate endogenous and exogenous estrogens in the etiology of breast cancer. Data summarized herein suggest that the ACI rat model of 17β-estradiol (E2)-induced mammary cancer is unique among rodent models in the extent to which it faithfully reflects the etiology and biology of luminal types of breast cancer, which together constitute ~70% of all breast cancers. E2 drives cancer development in this model through mechanisms that are largely dependent upon estrogen receptors and require progesterone and its receptors. Moreover, mammary cancer development appears to be associated with generation of oxidative stress and can be modified by multiple dietary factors, several of which may attenuate the actions of reactive oxygen species. Studies of susceptible ACI rats and resistant COP or BN rats provide novel insights into the genetic bases of susceptibility and the biological processes regulated by genetic determinants of susceptibility. This review summarizes research progress resulting from use of these physiologically relevant rat models to advance understanding of breast cancer etiology and prevention.

  11. ISOCT study of collagen crosslinking of collagen in cancer models (Conference Presentation)

    Science.gov (United States)

    Spicer, Graham; Young, Scott T.; Yi, Ji; Shea, Lonnie D.; Backman, Vadim

    2016-03-01

    The role of extracellular matrix modification and signaling in cancer progression is an increasingly recognized avenue for the progression of the disease. Previous study of field effect carcinogenesis with Inverse Spectroscopic Optical Coherence Tomography (ISOCT) has revealed pronounced changes in the nanoscale-sensitive mass fractal dimension D measured from field effect tissue when compared to healthy tissue. However, the origin of this difference in tissue ultrastructure in field effect carcinogenesis has remained poorly understood. Here, we present findings supporting the idea that enzymatic crosslinking of the extracellular matrix is an effect that presents at the earliest stages of carcinogenesis. We use a model of collagen gel with crosslinking induced by lysyl oxidase (LOXL4) to recapitulate the difference in D previously reported from healthy and cancerous tissue biopsies. Furthermore, STORM imaging of this collagen gel model verifies the morphologic effects of enzymatic crosslinking at length scales as small as 40 nm, close to the previously reported lower length scale sensitivity threshold of 35 nm for ISOCT. Analysis of the autocorrelation function from STORM images of collagen gels and subsequent fitting to the Whittle-Matérn correlation function shows a similar effect of LOXL4 on D from collagen measured with ISOCT and STORM. We extend this to mass spectrometric study of tissue to directly measure concentrations of collagen crosslink residues. The validation of ISOCT as a viable tool for non-invasive rapid quantification of collagen ultrastructure lends it to study other physiological phenomena involving ECM restructuring such as atherosclerotic plaque screening or cervical ripening during pregnancy.

  12. Black Versus Gray T-Shirts: Comparison of Spectrophotometric and Other Biophysical Properties of Physical Fitness Uniforms and Modeled Heat Strain and Thermal Comfort

    Science.gov (United States)

    2016-09-01

    PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT DISCLAIMER The opinions or assertions contained herein are the...SHIRTS: COMPARISON OF SPECTROPHOTOMETRIC AND OTHER BIOPHYSICAL PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT ...the impact of the environment on the wearer. To model these impacts on human thermal sensation (e.g., thermal comfort ) and thermoregulatory

  13. Virtual Suit Fit Assessment Using Body Shape Model

    Data.gov (United States)

    National Aeronautics and Space Administration — Shoulder injury is one of the most serious risks for crewmembers in long-duration spaceflight. While suboptimal suit fit and contact pressures between the shoulder...

  14. Cost-effectiveness and budget impact analyses of a colorectal cancer screening programme in a high adenoma prevalence scenario using MISCAN-Colon microsimulation model.

    Science.gov (United States)

    Arrospide, Arantzazu; Idigoras, Isabel; Mar, Javier; de Koning, Harry; van der Meulen, Miriam; Soto-Gordoa, Myriam; Martinez-Llorente, Jose Miguel; Portillo, Isabel; Arana-Arri, Eunate; Ibarrondo, Oliver; Lansdorp-Vogelaar, Iris

    2018-04-25

    The Basque Colorectal Cancer Screening Programme began in 2009 and the implementation has been complete since 2013. Faecal immunological testing was used for screening in individuals between 50 and 69 years old. Colorectal Cancer in Basque country is characterized by unusual epidemiological features given that Colorectal Cancer incidence is similar to other European countries while adenoma prevalence is higher. The object of our study was to economically evaluate the programme via cost-effectiveness and budget impact analyses with microsimulation models. We applied the Microsimulation Screening Analysis (MISCAN)-Colon model to predict trends in Colorectal Cancer incidence and mortality and to quantify the short- and long-term effects and costs of the Basque Colorectal Cancer Screening Programme. The model was calibrated to the Basque demographics in 2008 and age-specific Colorectal Cancer incidence data in the Basque Cancer Registry from 2005 to 2008 before the screening begun. The model was also calibrated to the high adenoma prevalence observed for the Basque population in a previously published study. The multi-cohort approach used in the model included all the cohorts in the programme during 30 years of implementation, with lifetime follow-up. Unit costs were obtained from the Basque Health Service and both cost-effectiveness analysis and budget impact analysis were carried out. The goodness-of-fit of the model adaptation to observed programme data was evidence of validation. In the cost-effectiveness analysis, the savings from treatment were larger than the added costs due to screening. Thus, the Basque programme was dominant compared to no screening, as life expectancy increased by 29.3 days per person. The savings in the budget analysis appeared 10 years after the complete implementation of the programme. The average annual budget was €73.4 million from year 2023 onwards. This economic evaluation showed a screening intervention with a major health gain

  15. Exposure to stress across the life course and its association with anxiety and depressive symptoms: Results from the Australian Women's Wellness After Cancer Program (WWACP).

    Science.gov (United States)

    Seib, Charrlotte; McCarthy, Alexandra; McGuire, Amanda; Porter-Steele, Janine; Balaam, Sarah; Ware, Robert S; Anderson, Debra

    2017-11-01

    Earlier life stressors can increase the risk of persistent anxiety and depressive symptoms in women after cancer, though our understanding of the underlying mechanisms is limited. In this study, we tested alternative life course models to determine which best described associations between exposure to stressors in childhood, adolescence, and adulthood, and self-reported health in women previously treated for breast, gynaecological, and blood cancer. Data were drawn from 351 Australian women within 2 years of completing active cancer treatment who were participating in the Women's Wellness After Cancer Program (WWACP) randomised controlled trial. A model-building framework compared "accumulative risk" and "sensitive period" stress exposure hypotheses with the saturated model to determine best fit. Symptoms of anxiety and depression were measured using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Zung Self-rating Anxiety Scale (SAS). Participants with the greatest number of stressful life events (SLEs) reported higher anxiety scores and more depressive symptoms. Alternative life course models for psychological distress (measured through the CES-D and SAS) and stress were compared with the saturated model (i.e., the accumulative risk). The more restrictive "sensitive period" models were the best fit for depressive symptoms though none was significantly better than another. In contrast, an "early sensitive" model provided the best fit for anxiety data. Anxiety scores were higher in women with early life stressors. This study highlights the need for whole-of-life supportive care approaches for women previously treated for cancer, which should include targeted strategies for effective management of stress, anxiety and depression. Copyright © 2017. Published by Elsevier B.V.

  16. Two-Stage Method Based on Local Polynomial Fitting for a Linear Heteroscedastic Regression Model and Its Application in Economics

    Directory of Open Access Journals (Sweden)

    Liyun Su

    2012-01-01

    Full Text Available We introduce the extension of local polynomial fitting to the linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to nonparametric technique of local polynomial estimation, we do not need to know the heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we focus on comparison of parameters and reach an optimal fitting. Besides, we verify the asymptotic normality of parameters based on numerical simulations. Finally, this approach is applied to a case of economics, and it indicates that our method is surely effective in finite-sample situations.

  17. Convolution based profile fitting

    International Nuclear Information System (INIS)

    Kern, A.; Coelho, A.A.; Cheary, R.W.

    2002-01-01

    Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based

  18. Mathematical modeling of CA125 kinetics in recurrent ovarian cancer (ROC) patients treated with chemotherapy and predictive value of early modeled kinetic parameters in CALYPSO trial: A GCIG study

    DEFF Research Database (Denmark)

    You, Benoit; Colomban, Olivier; Heywood, Mark

    2011-01-01

    Background: Although CA125 kinetic profiles may be related with relapse risk in ovarian cancer patients treated with chemotherapy, no reliable kinetic parameters have been reported. Mathematical modeling may help describe CA125 decline dynamically and determine parameters predictive of relapse....... Methods: Data from CALYPSO phase III trial data comparing 2 carboplatin-based regimens in ROC patients were analyzed. Based on population kinetic approach (Monolix software), a semi-mechanistic model was used to fit serum log (CA125) concentration-time profiles with following parameters: tumor growth rate...... the first 50 treatment days were tested regarding progression free survival (PFS) against other reported prognostic factors using Cox-models: treatment arm; platinum-free interval (PFI), metastatic site number, largest tumor size, elevated WBC and measurable disease. Results: The CA125 kinetics from 898...

  19. Molecular targets in urothelial cancer: detection, treatment, and animal models of bladder cancer

    Science.gov (United States)

    Smolensky, Dmitriy; Rathore, Kusum; Cekanova, Maria

    2016-01-01

    Bladder cancer remains one of the most expensive cancers to treat in the United States due to the length of required treatment and degree of recurrence. In order to treat bladder cancer more effectively, targeted therapies are being investigated. In order to use targeted therapy in a patient, it is important to provide a genetic background of the patient. Recent advances in genome sequencing, as well as transcriptome analysis, have identified major pathway components altered in bladder cancer. The purpose of this review is to provide a broad background on bladder cancer, including its causes, diagnosis, stages, treatments, animal models, as well as signaling pathways in bladder cancer. The major focus is given to the PI3K/AKT pathway, p53/pRb signaling pathways, and the histone modification machinery. Because several promising immunological therapies are also emerging in the treatment of bladder cancer, focus is also given on general activation of the immune system for the treatment of bladder cancer. PMID:27784990

  20. Implementation of the first wellness-fitness evaluation for the Dallas Fire-Rescue Department

    Science.gov (United States)

    Seals, Norman; Martin, JoAnn; Russell, Bryan

    2010-01-01

    More than 100 firefighters lose their lives in the line of duty each year; many of these deaths are caused by cardiovascular events and underlying coronary heart disease. In addition, firefighters are at higher-than-normal risk of developing certain types of cancer. To improve health and fitness among its firefighters, the Dallas Fire-Rescue Department developed and implemented an annual wellness-fitness program in 2008. The program detected and addressed medical issues including coronary disease, hypertension, high triglyceride levels, high cholesterol, high blood glucose levels, and hematuria. Prostate, thyroid, breast, kidney, and bladder cancers were also detected. By identifying these issues, engaging the firefighters' personal physicians, and recommending individualized treatment plans, this program may have extended lives and improved the quality of life for the firefighters. PMID:20671818

  1. NUTRITION AND FITNESS: CULTURAL, GENETIC AND METABOLIC ASPECTS

    Directory of Open Access Journals (Sweden)

    Artemis P. Simopoulos

    2008-12-01

    Full Text Available Selected Proceedings of the International Congress and Exhibition on Nutrition, Fitness and Health, Shanghai, November 30 to December 2, 2006 The book presents selected papers from the International Congress and Exhibition on Nutrition, Fitness and Health held in Shanghai, China from November 30 to December 2, 2006. PURPOSE This volume is designed to update interested parties on the nutrition and fitness issues from the cultural, genetic and metabolic point of views. FEATURES The book starts with a keynote presentation on nutrition, fitness and the concept of positive health from ancient times to the present. Subsequently papers focusing on the role of omega-3 and omega-6 fatty acids in health and disease follow. Other topics addressed are non-conventional genetic risk factors for cardiovascular disease; the impact of the APO E genotype on health, nutrition and fitness; nutrition in the prevention of chronic disease; and, the connection between exercise and obesity. The formation is concluded by the papers on nutritional risk factors for gastrointestinal cancers, Mediterranean diets as a global resource in health and disease, and the role of politics and politicians on the relevant issues. AUDIENCE Obviously; dieticians, nutritionists, geneticists and exercise physiologists will be interested in these proceedings since the book covers broadly their field. Then again; health care providers, historians, general practitioners and scientists in industry and government might benefit as well. ASSESSMENT It is safe to say that this volume represent a helpful source for anybody who is involved with Nutrition, Fitness and Health in one way or another

  2. Models of crk adaptor proteins in cancer.

    Science.gov (United States)

    Bell, Emily S; Park, Morag

    2012-05-01

    The Crk family of adaptor proteins (CrkI, CrkII, and CrkL), originally discovered as the oncogene fusion product, v-Crk, of the CT10 chicken retrovirus, lacks catalytic activity but engages with multiple signaling pathways through their SH2 and SH3 domains. Crk proteins link upstream tyrosine kinase and integrin-dependent signals to downstream effectors, acting as adaptors in diverse signaling pathways and cellular processes. Crk proteins are now recognized to play a role in the malignancy of many human cancers, stimulating renewed interest in their mechanism of action in cancer progression. The contribution of Crk signaling to malignancy has been predominantly studied in fibroblasts and in hematopoietic models and more recently in epithelial models. A mechanistic understanding of Crk proteins in cancer progression in vivo is still poorly understood in part due to the highly pleiotropic nature of Crk signaling. Recent advances in the structural organization of Crk domains, new roles in kinase regulation, and increased knowledge of the mechanisms and frequency of Crk overexpression in human cancers have provided an incentive for further study in in vivo models. An understanding of the mechanisms through which Crk proteins act as oncogenic drivers could have important implications in therapeutic targeting.

  3. Fitting outbreak models to data from many small norovirus outbreaks

    Directory of Open Access Journals (Sweden)

    Eamon B. O’Dea

    2014-03-01

    Full Text Available Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.

  4. Minimal see-saw model predicting best fit lepton mixing angles

    International Nuclear Information System (INIS)

    King, Stephen F.

    2013-01-01

    We discuss a minimal predictive see-saw model in which the right-handed neutrino mainly responsible for the atmospheric neutrino mass has couplings to (ν e ,ν μ ,ν τ ) proportional to (0,1,1) and the right-handed neutrino mainly responsible for the solar neutrino mass has couplings to (ν e ,ν μ ,ν τ ) proportional to (1,4,2), with a relative phase η=−2π/5. We show how these patterns of couplings could arise from an A 4 family symmetry model of leptons, together with Z 3 and Z 5 symmetries which fix η=−2π/5 up to a discrete phase choice. The PMNS matrix is then completely determined by one remaining parameter which is used to fix the neutrino mass ratio m 2 /m 3 . The model predicts the lepton mixing angles θ 12 ≈34 ∘ ,θ 23 ≈41 ∘ ,θ 13 ≈9.5 ∘ , which exactly coincide with the current best fit values for a normal neutrino mass hierarchy, together with the distinctive prediction for the CP violating oscillation phase δ≈106 ∘

  5. GENFIT - a generic track-fitting toolkit

    Energy Technology Data Exchange (ETDEWEB)

    Rauch, Johannes [Technische Universitaet Muenchen (Germany); Schlueter, Tobias [Ludwig-Maximilians-Universitaet Muenchen (Germany)

    2014-07-01

    GENFIT is an experiment-independent track-fitting toolkit, which combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, PANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing for efficient track merging, smoothing, residual calculation and alignment.

  6. SU-E-T-256: Radiation Dose Responses for Chemoradiation Therapy of Pancreatic Cancer: An Analysis of Compiled Clinical Data Using Biophysical Models.

    Science.gov (United States)

    Moraru, I; Tai, A; Erickson, B; Li, X

    2012-06-01

    We have analyzed recent clinical data obtained from chemoradiation of unresectable, locally advanced pancreatic cancer in order to examine possible benefits from radiotherapy (RT) dose escalation as well as to propose possible dose escalated fractionation schemes. A modified linear quadratic (LQ) model was used to fit clinical tumor response data from chemoradiation treatments using different fractionations. Biophysical radiosensitivy parameters, a and α/β, tumor potential doubling time, Td, and delay time for tumor doubling during treatment, Tk, were extracted from the fits and were used to calculate feasible fractionation schemes for dose escalations. Examination of published data from 20 institutions showed no clear indication of improved survival with raised radiation dose. However, an enhancement in tumor response was observed for higher irradiation doses, an important and promising clinical Result with respect to palliation and quality of life. The radiobiological parameter estimates obtained from the analysis are: α/β = 10 ± 3 Gy, a = 0.010 ± 0.003 Gŷ-1, Td = 56 ± 5 days and Tk = 7 ± 2 days. Possible dose escalation schemes are proposed based on the calculation of the biologically equivalent dose (BED) required for a 50% tumor response rate. From the point of view of tumor response, escalation of the administered radiation dose leads to a potential clinical benefit, which when combined with normal tissue complication analyses may Result in improved treatments for certain patients with advanced pancreatic cancer. Based on this analysis, a dose escalation trial with 2.25 Gy/fraction up to 69.75 Gy is being initiated for unresectable pancreatic cancer at our institution. Partially supported by MCW Cancer Center Meinerz Foundation. © 2012 American Association of Physicists in Medicine.

  7. Age Effects and Temporal Trends in HPV-Related and HPV-Unrelated Oral Cancer in the United States: A Multistage Carcinogenesis Modeling Analysis.

    Directory of Open Access Journals (Sweden)

    Andrew F Brouwer

    Full Text Available Differences in prognosis in HPV-positive and HPV-negative oral (oropharyngeal and oral cavity squamous cell carcinomas (OSCCs and increasing incidence of HPV-related cancers have spurred interest in demographic and temporal trends in OSCC incidence. We leverage multistage clonal expansion (MSCE models coupled with age-period-cohort (APC epidemiological models to analyze OSCC data in the SEER cancer registry (1973-2012. MSCE models are based on the initiation-promotion-malignant conversion paradigm in carcinogenesis and allow for interpretation of trends in terms of biological mechanisms. APC models seek to differentiate between the temporal effects of age, period, and birth cohort on cancer risk. Previous studies have looked at the effect of period and cohort on tumor initiation, and we extend this to compare model fits of period and cohort effects on each of tumor initiation, promotion, and malignant conversion rates. HPV-related, HPV-unrelated except oral tongue, and HPV-unrelated oral tongue sites are best described by placing period and cohort effects on the initiation rate. HPV-related and non-oral-tongue HPV-unrelated cancers have similar promotion rates, suggesting similar tumorigenesis dynamics once initiated. Estimates of promotion rates at oral tongue sites are lower, corresponding to a longer sojourn time; this finding is consistent with the hypothesis of an etiology distinct from HPV or alcohol and tobacco use. Finally, for the three subsite groups, men have higher initiation rates than women of the same race, and black people have higher promotion than white people of the same sex. These differences explain part of the racial and sex differences in OSCC incidence.

  8. New ROOT Graphical User Interfaces for fitting

    International Nuclear Information System (INIS)

    Maline, D Gonzalez; Moneta, L; Antcheva, I

    2010-01-01

    ROOT, as a scientific data analysis framework, provides extensive capabilities via Graphical User Interfaces (GUI) for performing interactive analysis and visualizing data objects like histograms and graphs. A new interface for fitting has been developed for performing, exploring and comparing fits on data point sets such as histograms, multi-dimensional graphs or trees. With this new interface, users can build interactively the fit model function, set parameter values and constraints and select fit and minimization methods with their options. Functionality for visualizing the fit results is as well provided, with the possibility of drawing residuals or confidence intervals. Furthermore, the new fit panel reacts as a standalone application and it does not prevent users from interacting with other windows. We will describe in great detail the functionality of this user interface, covering as well new capabilities provided by the new fitting and minimization tools introduced recently in the ROOT framework.

  9. Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.

    Directory of Open Access Journals (Sweden)

    Octavio Miramontes

    Full Text Available Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties--including Lévy flights--in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.

  10. Dose-response relationship for breast cancer induction at radiotherapy dose

    Directory of Open Access Journals (Sweden)

    Gruber Günther

    2011-06-01

    Full Text Available Abstract Purpose Cancer induction after radiation therapy is known as a severe side effect. It is therefore of interest to predict the probability of second cancer appearance for the patient to be treated including breast cancer. Materials and methods In this work a dose-response relationship for breast cancer is derived based on (i the analysis of breast cancer induction after Hodgkin's disease, (ii a cancer risk model developed for high doses including fractionation based on the linear quadratic model, and (iii the reconstruction of treatment plans for Hodgkin's patients treated with radiotherapy, (iv the breast cancer induction of the A-bomb survivor data. Results The fitted model parameters for an α/β = 3 Gy were α = 0.067Gy-1 and R = 0.62. The risk for breast cancer is according to this model for small doses consistent with the finding of the A-bomb survivors, has a maximum at doses of around 20 Gy and drops off only slightly at larger doses. The predicted EAR for breast cancer after radiotherapy of Hodgkin's disease is 11.7/10000PY which can be compared to the findings of several epidemiological studies where EAR for breast cancer varies between 10.5 and 29.4/10000PY. The model was used to predict the impact of the reduction of radiation volume on breast cancer risk. It was estimated that mantle field irradiation is associated with a 3.2-fold increased risk compared with mediastinal irradiation alone, which is in agreement with a published value of 2.7. It was also shown that the modelled age dependency of breast cancer risk is in satisfying agreement with published data. Conclusions The dose-response relationship obtained in this report can be used for the prediction of radiation induced secondary breast cancer of radiotherapy patients.

  11. Innovation Rather than Improvement: A Solvable High-Dimensional Model Highlights the Limitations of Scalar Fitness

    Science.gov (United States)

    Tikhonov, Mikhail; Monasson, Remi

    2018-01-01

    Much of our understanding of ecological and evolutionary mechanisms derives from analysis of low-dimensional models: with few interacting species, or few axes defining "fitness". It is not always clear to what extent the intuition derived from low-dimensional models applies to the complex, high-dimensional reality. For instance, most naturally occurring microbial communities are strikingly diverse, harboring a large number of coexisting species, each of which contributes to shaping the environment of others. Understanding the eco-evolutionary interplay in these systems is an important challenge, and an exciting new domain for statistical physics. Recent work identified a promising new platform for investigating highly diverse ecosystems, based on the classic resource competition model of MacArthur. Here, we describe how the same analytical framework can be used to study evolutionary questions. Our analysis illustrates how, at high dimension, the intuition promoted by a one-dimensional (scalar) notion of fitness can become misleading. Specifically, while the low-dimensional picture emphasizes organism cost or efficiency, we exhibit a regime where cost becomes irrelevant for survival, and link this observation to generic properties of high-dimensional geometry.

  12. A bipartite fitness model for online music streaming services

    Science.gov (United States)

    Pongnumkul, Suchit; Motohashi, Kazuyuki

    2018-01-01

    This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

  13. Three-dimensional models of cancer for pharmacology and cancer cell biology: capturing tumor complexity in vitro/ex vivo.

    Science.gov (United States)

    Hickman, John A; Graeser, Ralph; de Hoogt, Ronald; Vidic, Suzana; Brito, Catarina; Gutekunst, Matthias; van der Kuip, Heiko

    2014-09-01

    Cancers are complex and heterogeneous pathological "organs" in a dynamic interplay with their host. Models of human cancer in vitro, used in cancer biology and drug discovery, are generally highly reductionist. These cancer models do not incorporate complexity or heterogeneity. This raises the question as to whether the cancer models' biochemical circuitry (not their genome) represents, with sufficient fidelity, a tumor in situ. Around 95% of new anticancer drugs eventually fail in clinical trial, despite robust indications of activity in existing in vitro pre-clinical models. Innovative models are required that better capture tumor biology. An important feature of all tissues, and tumors, is that cells grow in three dimensions. Advances in generating and characterizing simple and complex (with added stromal components) three-dimensional in vitro models (3D models) are reviewed in this article. The application of stirred bioreactors to permit both scale-up/scale-down of these cancer models and, importantly, methods to permit controlled changes in environment (pH, nutrients, and oxygen) are also described. The challenges of generating thin tumor slices, their utility, and potential advantages and disadvantages are discussed. These in vitro/ex vivo models represent a distinct move to capture the realities of tumor biology in situ, but significant characterization work still remains to be done in order to show that their biochemical circuitry accurately reflects that of a tumor. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Improving breast cancer survival analysis through competition-based multidimensional modeling.

    Directory of Open Access Journals (Sweden)

    Erhan Bilal

    Full Text Available Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

  15. ACCELERATED FITTING OF STELLAR SPECTRA

    Energy Technology Data Exchange (ETDEWEB)

    Ting, Yuan-Sen; Conroy, Charlie [Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Rix, Hans-Walter [Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg (Germany)

    2016-07-20

    Stellar spectra are often modeled and fitted by interpolating within a rectilinear grid of synthetic spectra to derive the stars’ labels: stellar parameters and elemental abundances. However, the number of synthetic spectra needed for a rectilinear grid grows exponentially with the label space dimensions, precluding the simultaneous and self-consistent fitting of more than a few elemental abundances. Shortcuts such as fitting subsets of labels separately can introduce unknown systematics and do not produce correct error covariances in the derived labels. In this paper we present a new approach—Convex Hull Adaptive Tessellation (chat)—which includes several new ideas for inexpensively generating a sufficient stellar synthetic library, using linear algebra and the concept of an adaptive, data-driven grid. A convex hull approximates the region where the data lie in the label space. A variety of tests with mock data sets demonstrate that chat can reduce the number of required synthetic model calculations by three orders of magnitude in an eight-dimensional label space. The reduction will be even larger for higher dimensional label spaces. In chat the computational effort increases only linearly with the number of labels that are fit simultaneously. Around each of these grid points in the label space an approximate synthetic spectrum can be generated through linear expansion using a set of “gradient spectra” that represent flux derivatives at every wavelength point with respect to all labels. These techniques provide new opportunities to fit the full stellar spectra from large surveys with 15–30 labels simultaneously.

  16. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus

    2006-01-01

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice, the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution...

  17. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution function...

  18. Introduction: Occam’s Razor (SOT - Fit for Purpose workshop introduction)

    Science.gov (United States)

    Mathematical models provide important, reproducible, and transparent information for risk-based decision making. However, these models must be constructed to fit the needs of the problem to be solved. A “fit for purpose” model is an abstraction of a complicated problem that allow...

  19. Breast Cancer Screening in an Era of Personalized Regimens

    Science.gov (United States)

    Onega, Tracy; Beaber, Elisabeth F.; Sprague, Brian L.; Barlow, William E.; Haas, Jennifer S.; Tosteson, Anna N.A.; Schnall, Mitchell D.; Armstrong, Katrina; Schapira, Marilyn M.; Geller, Berta; Weaver, Donald L.; Conant, Emily F.

    2014-01-01

    Breast cancer screening holds a prominent place in public health, health care delivery, policy, and women’s health care decisions. Several factors are driving shifts in how population-based breast cancer screening is approached, including advanced imaging technologies, health system performance measures, health care reform, concern for “overdiagnosis,” and improved understanding of risk. Maximizing benefits while minimizing the harms of screening requires moving from a “1-size-fits-all” guideline paradigm to more personalized strategies. A refined conceptual model for breast cancer screening is needed to align women’s risks and preferences with screening regimens. A conceptual model of personalized breast cancer screening is presented herein that emphasizes key domains and transitions throughout the screening process, as well as multilevel perspectives. The key domains of screening awareness, detection, diagnosis, and treatment and survivorship are conceptualized to function at the level of the patient, provider, facility, health care system, and population/policy arena. Personalized breast cancer screening can be assessed across these domains with both process and outcome measures. Identifying, evaluating, and monitoring process measures in screening is a focus of a National Cancer Institute initiative entitled PROSPR (Population-based Research Optimizing Screening through Personalized Regimens), which will provide generalizable evidence for a risk-based model of breast cancer screening, The model presented builds on prior breast cancer screening models and may serve to identify new measures to optimize benefits-to-harms tradeoffs in population-based screening, which is a timely goal in the era of health care reform. PMID:24830599

  20. Metabolic cooperation between cancer and non-cancerous stromal cells is pivotal in cancer progression.

    Science.gov (United States)

    Lopes-Coelho, Filipa; Gouveia-Fernandes, Sofia; Serpa, Jacinta

    2018-02-01

    The way cancer cells adapt to microenvironment is crucial for the success of carcinogenesis, and metabolic fitness is essential for a cancer cell to survive and proliferate in a certain organ/tissue. The metabolic remodeling in a tumor niche is endured not only by cancer cells but also by non-cancerous cells that share the same microenvironment. For this reason, tumor cells and stromal cells constitute a complex network of signal and organic compound transfer that supports cellular viability and proliferation. The intensive dual-address cooperation of all components of a tumor sustains disease progression and metastasis. Herein, we will detail the role of cancer-associated fibroblasts, cancer-associated adipocytes, and inflammatory cells, mainly monocytes/macrophages (tumor-associated macrophages), in the remodeling and metabolic adaptation of tumors.

  1. VizieR Online Data Catalog: GRB prompt emission fitted with the DREAM model (Ahlgren+, 2015)

    Science.gov (United States)

    Ahlgren, B.; Larsson, J.; Nymark, T.; Ryde, F.; Pe'Er, A.

    2018-01-01

    We illustrate the application of the DREAM model by fitting it to two different, bright Fermi GRBs; GRB 090618 and GRB 100724B. While GRB 090618 is well fitted by a Band function, GRB 100724B was the first example of a burst with a significant additional BB component (Guiriec et al. 2011ApJ...727L..33G). GRB 090618 is analysed using Gamma-ray Burst Monitor (GBM) data (Meegan et al. 2009ApJ...702..791M) from the NaI and BGO detectors. For GRB 100724B, we used GBM data from the NaI and BGO detectors as well as Large Area Telescope Low Energy (LAT-LLE) data. For both bursts we selected NaI detectors seeing the GRB at an off-axis angle lower than 60° and the BGO detector as being the best aligned of the two BGO detectors. The spectra were fitted in the energy ranges 8-1000 keV (NaI), 200-40000 keV (BGO) and 30-1000 MeV (LAT-LLE). (2 data files).

  2. Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine

    International Nuclear Information System (INIS)

    Stites, Edward C

    2013-01-01

    Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients. (paper)

  3. Human X-chromosome inactivation pattern distributions fit a model of genetically influenced choice better than models of completely random choice

    Science.gov (United States)

    Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C

    2013-01-01

    In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377

  4. Comments on Ghassib's "Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?"

    Science.gov (United States)

    McCluskey, Ken W.

    2010-01-01

    This article presents the author's comments on Hisham B. Ghassib's "Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?" Ghassib's article focuses on the transformation of science from pre-modern times to the present. Ghassib (2010) notes that, unlike in an earlier era when the economy depended on static…

  5. Supersymmetric Fits after the Higgs Discovery and Implications for Model Building

    CERN Document Server

    Ellis, John

    2014-01-01

    The data from the first run of the LHC at 7 and 8 TeV, together with the information provided by other experiments such as precision electroweak measurements, flavour measurements, the cosmological density of cold dark matter and the direct search for the scattering of dark matter particles in the LUX experiment, provide important constraints on supersymmetric models. Important information is provided by the ATLAS and CMS measurements of the mass of the Higgs boson, as well as the negative results of searches at the LHC for events with missing transverse energy accompanied by jets, and the LHCb and CMS measurements off BR($B_s \\to \\mu^+ \\mu^-$). Results are presented from frequentist analyses of the parameter spaces of the CMSSM and NUHM1. The global $\\chi^2$ functions for the supersymmetric models vary slowly over most of the parameter spaces allowed by the Higgs mass and the missing transverse energy search, with best-fit values that are comparable to the $\\chi^2$ for the Standard Model. The $95\\%$ CL lower...

  6. Non-linear least squares curve fitting of a simple theoretical model to radioimmunoassay dose-response data using a mini-computer

    International Nuclear Information System (INIS)

    Wilkins, T.A.; Chadney, D.C.; Bryant, J.; Palmstroem, S.H.; Winder, R.L.

    1977-01-01

    Using the simple univalent antigen univalent-antibody equilibrium model the dose-response curve of a radioimmunoassay (RIA) may be expressed as a function of Y, X and the four physical parameters of the idealised system. A compact but powerful mini-computer program has been written in BASIC for rapid iterative non-linear least squares curve fitting and dose interpolation with this function. In its simplest form the program can be operated in an 8K byte mini-computer. The program has been extensively tested with data from 10 different assay systems (RIA and CPBA) for measurement of drugs and hormones ranging in molecular size from thyroxine to insulin. For each assay system the results have been analysed in terms of (a) curve fitting biases and (b) direct comparison with manual fitting. In all cases the quality of fitting was remarkably good in spite of the fact that the chemistry of each system departed significantly from one or more of the assumptions implicit in the model used. A mathematical analysis of departures from the model's principal assumption has provided an explanation for this somewhat unexpected observation. The essential features of this analysis are presented in this paper together with the statistical analyses of the performance of the program. From these and the results obtained to date in the routine quality control of these 10 assays, it is concluded that the method of curve fitting and dose interpolation presented in this paper is likely to be of general applicability. (orig.) [de

  7. A competing risk model for reduction in life expectancy from radiogenic cancer

    International Nuclear Information System (INIS)

    Davis, H.T.

    1978-01-01

    Latent radiogenic cancer fatalities from reactor accidents are considered to be more important than early fatalities. However, early fatalities generally result in appreciable life shortening for the affected individual whereas latent cancer fatalities generally result in limited life shortening. In this report a mathematical model is developed to express the reduction in life expectancy from radiogenic cancer as a function of dose received. The model is then used to compare the linear model of latent radiogenic cancer incidence with several nonlinear models that have appeared in the literature. (author)

  8. Fits combining hyperon semileptonic decays and magnetic moments and CVC

    International Nuclear Information System (INIS)

    Bohm, A.; Kielanowski, P.

    1982-10-01

    We have performed a test of CVC by determining the baryon charges and magnetic moments from the hyperon semileptonic data. Then CVC was applied in order to make a joint fit of all baryon semileptonic decay data and baryon magnetic moments for the spectrum generating group (SG) model as well as for the conventional (cabibbo and magnetic moments in nuclear magnetons) model. The SG model gives a very good fit with chi 2 /n/sub D/ = 25/20 approximately equals 21% C.L. whereas the conventional model gives a fit with chi 2 /n/sub D/ = 244/20

  9. Two Aspects of the Simplex Model: Goodness of Fit to Linear Growth Curve Structures and the Analysis of Mean Trends.

    Science.gov (United States)

    Mandys, Frantisek; Dolan, Conor V.; Molenaar, Peter C. M.

    1994-01-01

    Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)

  10. Consequences of screening in lung cancer: development and dimensionality of a questionnaire.

    Science.gov (United States)

    Brodersen, John; Thorsen, Hanne; Kreiner, Svend

    2010-08-01

    The objective of this study was to extend the Consequences of Screening (COS) Questionnaire for use in a lung cancer screening by testing for comprehension, content coverage, dimensionality, and reliability. In interviews, the suitability, content coverage, and relevance of the COS were tested on participants in a lung cancer screening program. The results were thematically analyzed to identify the key consequences of abnormal and false-positive screening results. Item Response Theory and Classical Test Theory were used to analyze data. Dimensionality, objectivity, and reliability were established by item analysis, examining the fit between item responses and Rasch models. Eight themes specifically relevant for participants in lung cancer screening results were identified: "self-blame,"focus on symptoms,"stigmatization,"introvert,"harm of smoking,"impulsivity,"empathy," and "regretful of still smoking." Altogether, 26 new items for part I and 16 new items for part II were generated. These themes were confirmed to fit a partial-credit Rasch model measuring different constructs including several of the new items. In conclusion, the reliability and the dimensionality of a condition-specific measure with high content validity for persons having abnormal or false-positive lung cancer screening results have been demonstrated. This new questionnaire called Consequences of Screening in Lung Cancer (COS-LC) covers in two parts the psychosocial experience in lung cancer screening. Part I: "anxiety,"behavior,"dejection,"sleep,"self-blame,"focus on airway symptoms,"stigmatization,"introvert," and "harm of smoking." Part II: "calm/relax,"social network,"existential values,"impulsivity,"empathy," and "regretful of still smoking."

  11. Determining Mission Statement Effectiveness from a Fit Perspective

    Directory of Open Access Journals (Sweden)

    Toh Seong-Yuen

    2017-08-01

    Full Text Available The purpose of this paper is to study the relationship between the organization's mission statement and its outcomes from a fit perspective in the alignment of the organization's structural and cultural elements. Based on an extension of Campbell's (1991 mission model by combination of ideas from two schools of thought in mission statement studies (structural and cultural, the authors introduce the concept of “fit” to show how it contributes towards a new mission statement model. The results show that both alignments are important to create a fit situation in order to positively impact organization outcomes. Based on Cohen (1988, the detected effect size of .322 is considered large. The managerial implication is that there should be more focus on managing organisational alignment to support a fit situation as this is instrumental to mission statement effectiveness. The originality of this study stems from the idea that while past studies develop model based on ideas from within the confine of a particular school of thought, this study is one of the first to combine ideas from both the structural and cultural schools of thought by extending Campbell's (1991 mission model using the fit perspective.

  12. The fitting parameters extraction of conversion model of the low dose rate effect in bipolar devices

    International Nuclear Information System (INIS)

    Bakerenkov, Alexander

    2011-01-01

    The Enhanced Low Dose Rate Sensitivity (ELDRS) in bipolar devices consists of in base current degradation of NPN and PNP transistors increase as the dose rate is decreased. As a result of almost 20-year studying, the some physical models of effect are developed, being described in detail. Accelerated test methods, based on these models use in standards. The conversion model of the effect, that allows to describe the inverse S-shaped excess base current dependence versus dose rate, was proposed. This paper presents the problem of conversion model fitting parameters extraction.

  13. Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-09-28

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  14. Pooled Bayesian analysis of 28 studies on radon induced lung cancers

    International Nuclear Information System (INIS)

    Fornalski, K.W.; Dobrzyński, L.

    2010-01-01

    The influence of ionizing radiation of radon-222 and its daughters on the lung cancer incidence and mortality published in 28 papers was reanalyzed, for two ranges of low annual radiation dose of below 70 mSv per year (391 Bq m -3 ) and 150 mSv per year (838 Bq m -3 ). The seven popular models of dose-effect relationship were tested. The assumption-free Bayesian statistical methods were used for all curve fittings. Also the Model Selection algorithm was used to verify the relative probability of all seven models. The results of the analysis demonstrate that in this ranges of doses (below 70 and 150 mSv/ year) the published data do not show the presence of a risk of lung cancer induction. The most probable dose-effect relationship is constant one (risk ratio, RR=1). The statistical analysis shows that there is no basis for increase the risk of lung cancer in low dose area. The final conclusion results from the fact that the model assuming no dependence of the lung cancer induction on the radiation doses is at least 100 times more likely than six other models tested, including the Linear No-Threshold (LNT) model

  15. Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients.

    Science.gov (United States)

    Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Kijun; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

    2018-03-01

    A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

  16. Polymorphism in the GALNT1 gene and epithelial ovarian cancer in non-Hispanic white women: the Ovarian Cancer Association Consortium

    DEFF Research Database (Denmark)

    Phelan, Catherine M; Tsai, Ya-Yu; Goode, Ellen L

    2010-01-01

    Aberrant glycosylation is a well-described hallmark of cancer. In a previous ovarian cancer case control study that examined polymorphisms in 26 glycosylation-associated genes, we found strong statistical evidence (P = 0.00017) that women who inherited two copies of a single-nucleotide polymorphism...... in the UDP-N-acetylgalactosamine:polypeptide N-acetylgalactosaminyltransferase, GALNT1, had decreased ovarian cancer risk. The current study attempted to replicate this observation. The GALNT1 single-nucleotide polymorphism rs17647532 was genotyped in 6,965 cases and 8,377 controls from 14 studies forming...... the Ovarian Cancer Association Consortium. The fixed effects estimate per rs17647532 allele was null (odds ratio, 0.99; 95% confidence interval, 0.92-1.07). When a recessive model was fit, the results were unchanged. Test for heterogeneity of the odds ratios revealed consistency across the 14 replication...

  17. Theoretically unprejudiced fits to proton scattering

    International Nuclear Information System (INIS)

    Kobos, A.M.; Mackintosh, R.S.

    1979-01-01

    By using a spline interpolation method applied to all components of the proton optical potential we have fitted elastic scattering from 40 Ca and from 16 O at a range of energies. The potentials are highly oscillatory and we have shown that similar oscillations are found when the spline fitting procedure is applied to pseudo-data generated from potentials of known l-dependence. Moreover, we show how to find an l-independent potential equivalent to one that is l-dependent and we find that it is oscillatory and that various characteristic features of empirical spline fit potentials can be explained. Thus, by fitting the data with model indenpendt l-independent potentials we have found support for the contention that the nucleon optical potential should be viewed as being l-dependent. This work may be regarded as an example of the kind of physical information that can be gained by pursuing exact fits to proton elastic scattering data

  18. A comparison of approaches in fitting continuum SEDs

    International Nuclear Information System (INIS)

    Liu Yao; Wang Hong-Chi; Madlener David; Wolf Sebastian

    2013-01-01

    We present a detailed comparison of two approaches, the use of a pre-calculated database and simulated annealing (SA), for fitting the continuum spectral energy distribution (SED) of astrophysical objects whose appearance is dominated by surrounding dust. While pre-calculated databases are commonly used to model SED data, only a few studies to date employed SA due to its unclear accuracy and convergence time for this specific problem. From a methodological point of view, different approaches lead to different fitting quality, demand on computational resources and calculation time. We compare the fitting quality and computational costs of these two approaches for the task of SED fitting to provide a guide to the practitioner to find a compromise between desired accuracy and available resources. To reduce uncertainties inherent to real datasets, we introduce a reference model resembling a typical circumstellar system with 10 free parameters. We derive the SED of the reference model with our code MC3 D at 78 logarithmically distributed wavelengths in the range [0.3 μm, 1.3 mm] and use this setup to simulate SEDs for the database and SA. Our result directly demonstrates the applicability of SA in the field of SED modeling, since the algorithm regularly finds better solutions to the optimization problem than a pre-calculated database. As both methods have advantages and shortcomings, a hybrid approach is preferable. While the database provides an approximate fit and overall probability distributions for all parameters deduced using Bayesian analysis, SA can be used to improve upon the results returned by the model grid.

  19. Fitness club

    CERN Multimedia

    Fitness club

    2011-01-01

    General fitness Classes Enrolments are open for general fitness classes at CERN taking place on Monday, Wednesday, and Friday lunchtimes in the Pump Hall (building 216). There are shower facilities for both men and women. It is possible to pay for 1, 2 or 3 classes per week for a minimum of 1 month and up to 6 months. Check out our rates and enrol at: http://cern.ch/club-fitness Hope to see you among us! CERN Fitness Club fitness.club@cern.ch  

  20. Communication Efficacy and Couples' Cancer Management: Applying a Dyadic Appraisal Model.

    Science.gov (United States)

    Magsamen-Conrad, Kate; Checton, Maria G; Venetis, Maria K; Greene, Kathryn

    2015-06-01

    The purpose of the present study was to apply Berg and Upchurch's (2007) developmental-conceptual model to understand better how couples cope with cancer. Specifically, we hypothesized a dyadic appraisal model in which proximal factors (relational quality), dyadic appraisal (prognosis uncertainty), and dyadic coping (communication efficacy) predicted adjustment (cancer management). The study was cross-sectional and included 83 dyads in which one partner had been diagnosed with and/or treated for cancer. For both patients and partners, multilevel analyses using the actor-partner interdependence model (APIM) indicated that proximal contextual factors predicted dyadic appraisal and dyadic coping. Dyadic appraisal predicted dyadic coping, which then predicted dyadic adjustment. Patients' confidence in their ability to talk about the cancer predicted their own cancer management. Partners' confidence predicted their own and the patient's ability to cope with cancer, which then predicted patients' perceptions of their general health. Implications and future research are discussed.