Multivariate refined composite multiscale entropy analysis

International Nuclear Information System (INIS)

Humeau-Heurtier, Anne

2016-01-01

Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

[Value of the albumin to globulin ratio in predicting severity and prognosis in myasthenia gravis patients].

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Yang, D H; Su, Z Q; Chen, Y; Chen, Z B; Ding, Z N; Weng, Y Y; Li, J; Li, X; Tong, Q L; Han, Y X; Zhang, X

2016-03-08

To assess the predictive value of the albumin to globulin ratio (AGR) in evaluation of disease severity and prognosis in myasthenia gravis patients. A total of 135 myasthenia gravis (MG) patients were enrolled between February 2009 and March 2015. The AGR was detected on the first day of hospitalization and ranked from lowest to highest, and the patients were divided into three equal tertiles according to the AGR values, which were T1 (AGR 1.53). The Kaplan-Meier curve was used to evaluate the prognostic value of AGR. Cox model analysis was used to evaluate the relevant factors. Multivariate Logistic regression analysis was used to find the predictors of myasthenia crisis during hospitalization. The median length of hospital stay for each tertile was: for the T1 21 days (15-35.5), T2 18 days (14-27.5), and T3 16 days (12-22.5) (Pmyasthenia gravis. At the multivariate Cox regression analysis, the AGR (Pmyasthenia gravis patients. Respectively, the hazard ratio (HR) were 4.655 (95% CI: 2.355-9.202) and 0.596 (95% CI: 0.492-0.723). Multivariate Logistic regression analysis showed the AGR (Pmyasthenia crisis. The AGR may represent a simple, potentially useful predictive biomarker for evaluating the disease severity and prognosis of patients with myasthenia gravis.

HER-2 positive and p53 negative breast cancers are associated with poor prognosis.

LENUS (Irish Health Repository)

2011-06-01

p53 and HER-2 coexpression in breast cancer has been controversial. These markers were tested using immunohistochemistry and HercepTest. HER-2 expression is related to reduced breast cancer survival (p = .02) . p53 expression relates to HER-2 expression (p = .029). Coexpression between p53 and HER-2 has no relation to prognosis. On univariate and multivariate analysis, combination of HER-2 positive and p53 negative expression was associated with a poor prognosis (p = .018 and p = .027, respectively), while the combination of HER-2 negative and p53 positive expression was associated with a favorable prognosis (p = .022 and p = .010, respectively). Therefore the expression of these markers should be considered collectively.

HER-2 positive and p53 negative breast cancers are associated with poor prognosis.

LENUS (Irish Health Repository)

2012-02-01

p53 and HER-2 coexpression in breast cancer has been controversial. These markers were tested using immunohistochemistry and HercepTest. HER-2 expression is related to reduced breast cancer survival (p = .02) . p53 expression relates to HER-2 expression (p = .029). Coexpression between p53 and HER-2 has no relation to prognosis. On univariate and multivariate analysis, combination of HER-2 positive and p53 negative expression was associated with a poor prognosis (p = .018 and p = .027, respectively), while the combination of HER-2 negative and p53 positive expression was associated with a favorable prognosis (p = .022 and p = .010, respectively). Therefore the expression of these markers should be considered collectively.

A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

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Elza Jurun

2012-12-01

Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

Long non-coding RNA PVT1 as a novel potential biomarker for predicting the prognosis of colorectal cancer.

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Fan, Heng; Zhu, Jian-Hua; Yao, Xue-Qing

2018-05-01

Long non-coding RNA (lncRNA) plays a very important role in the occurrence and development of various tumors, and is a potential biomarker for cancer diagnosis and prognosis. The purpose of this study was to investigate the relationship between the expression of lncRNA plasmacytoma variant translocation 1 (PVT1) and the prognostic significance in patients with colorectal cancer. The expression of PVT1 was measured by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in cancerous and adjacent tissues of 210 colorectal cancer patients. The disease-free survival and overall survival of colorectal cancer patients were evaluated by Kaplan-Meier analysis, and univariate and multivariate analysis were performed by Cox proportional-hazards model. Our results revealed that PVT1 expression in cancer tissues of colorectal cancer was significantly higher than that of adjacent tissues ( Pcolorectal cancer patients, whether at TNM I/II stage or at TNM III/IV stage. A multivariate Cox regression analysis demonstrated that high PVT1 expression was an independent predictor of poor prognosis in colorectal cancer patients. Our results suggest that high PVT1 expression might be a potential biomarker for assessing tumor recurrence and prognosis in colorectal cancer patients.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

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Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

[Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

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Lifshits, A M

1979-01-01

General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

Evaluation of functional outcome of the floating knee injury using multivariate analysis.

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Yokoyama, Kazuhiko; Tsukamoto, Tatsuro; Aoki, Shinichi; Wakita, Ryuji; Uchino, Masataka; Noumi, Takashi; Fukushima, Nobuaki; Itoman, Moritoshi

2002-11-01

The objective of this study is to evaluate significant contributing factors affecting the functional prognosis of floating knee injuries using multivariate analysis. A total of 68 floating knee injuries (67 patients) were treated at Kitasato University Hospital from 1986 to 1999. Both the femoral fractures and the tibial fractures were managed surgically by various methods. The functional results of these injuries were evaluated using the grading system of Karlström and Olerud. Follow-up periods ranged from 2 to 19 years (mean 50.2 months) after the original injury. We defined satisfactory (S) outcomes as those cases with excellent or good results and unsatisfactory (US) outcomes as those cases with acceptable or poor results. Logistic regression analysis was used as a multivariate analysis, and the dependent variables were defined as a satisfactory outcome or as an unsatisfactory outcome. The explanatory variables were predicting factors influencing the functional outcome such as age at trauma, gender, severity of soft-tissue injury in the femur and the tibia, AO fracture grade in the femur and the tibia, Fraser type (type I or type II), Injury Severity Score (ISS), and fixation time after injury (less than 1 week or more than 1 week) in the femur and the tibia. The final functional results were as follows: 25 cases had excellent results, 15 cases good results, 16 cases acceptable results, and 12 cases poor results. The predictive logistic regression equation was as follows: Log 1-p/p = 3.12-1.52 x Fraser type - 1.65 x severity of soft-tissue injury in the tibia - 1.31 x fixation time after injury in the tibia - 0.821 x AO fracture grade in the tibia + 1.025 x fixation time after injury in the femur - 0.687 x AO fracture grade in the femur ( p=0.01). Among the variables, Fraser type and the severity of soft-tissue injury in the tibia were significantly related to the final result. The multivariate analysis showed that both the involvement of the knee joint and

Multivariate analysis of 2-DE protein patterns - Practical approaches

DEFF Research Database (Denmark)

Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

2007-01-01

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

A comparison of clinicopathological features and prognosis in prostate cancer between atomic bomb survivors and control patients.

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Shoji, Koichi; Teishima, Jun; Hayashi, Tetsutaro; Shinmei, Shunsuke; Akita, Tomoyuki; Sentani, Kazuhiro; Takeshima, Yukio; Arihiro, Koji; Tanaka, Junko; Yasui, Wataru; Matsubara, Akio

2017-07-01

An atomic bomb (A-bomb) was dropped on Hiroshima on 6th August 1945. Although numerous studies have investigated cancer incidence and mortality among A-bomb survivors, only a small number have addressed urological cancer in these survivors. The aim of the present study was to investigate the clinicopathological features of prostate cancer (PCa) in A-bomb survivors. The clinicopathological features and prognosis of PCa were retrospectively reviewed in 212 survivors and 595 control patients between November 1996 and December 2010. The histopathological and clinical outcomes of surgical treatment of PCa were also evaluated in 69 survivors and 162 control patients. Despite the higher age at diagnosis compared with the control group (P=0.0031), survivors were more likely to have been diagnosed with PCa from a health check compared with the control group (Pbomb exposure was not found to be an independent predictor for prognosis by multivariate analysis (OS, P=0.7800; CS, P=0.8688). The clinicopathological features of patients who underwent a prostatectomy were similar except for the diagnosis opportunity between the two groups. Progression-free survival rates were similar between the two groups (P=0.5630). A-bomb exposure was not a significant and independent predictor for worsening of progression-free prognosis by multivariate analysis (P=0.3763). A-bomb exposure does not appear to exert deleterious effects on the biological aggressiveness of PCa and the prognosis of patients with PCa.

Impact of Secreted Protein Acidic and Rich in Cysteine (SPARC) Expression on Prognosis After Surgical Resection for Biliary Carcinoma.

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Toyota, Kazuhiro; Murakami, Yoshiaki; Kondo, Naru; Uemura, Kenichiro; Nakagawa, Naoya; Takahashi, Shinya; Sueda, Taijiro

2017-06-01

Secreted protein acidic and rich in cysteine (SPARC) is a matricellular protein that influences chemotherapy effectiveness and prognosis. The aim of this study was to investigate whether SPARC expression correlates with the postoperative survival of patients treated with surgical resection for biliary carcinoma. SPARC expression in resected biliary carcinoma specimens was investigated immunohistochemically in 175 patients. The relationship between SPARC expression and prognosis after surgery was evaluated using univariate and multivariate analyses. High SPARC expression in peritumoral stroma was found in 61 (35%) patients. In all patients, stromal SPARC expression was significantly associated with overall survival (OS) (P = 0.006). Multivariate analysis revealed that high stromal SPARC expression was an independent risk factor for poor OS (HR 1.81, P = 0.006). Moreover, high stromal SPARC expression was independently associated with poor prognosis in a subset of 118 patients treated with gemcitabine-based adjuvant chemotherapy (HR 2.04, P = 0.010) but not in the 57 patients who did not receive adjuvant chemotherapy (P = 0.21). Stromal SPARC expression correlated with the prognosis of patients with resectable biliary carcinoma, and its significance was enhanced in patients treated with adjuvant gemcitabine-based chemotherapy.

Ulcerative colitis: criteria and methods of prognosis of exacerbation

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Kashkina E.l.

2014-09-01

Full Text Available Objective: research is devoted to the development of criteria and methods for prognosis of the next recurrence of exacerbation of ulcerative colitis (UC after the patient discharged from hospital. Material and Methods: During a period of a year 38 patients with UC were supervised. The criteria used in the prognosis of recurrence included results of the evaluation of quality of life (SF-36 questionnaire, the analysis of the autonomic nervous system (coefficient Hildebrant and Kerdo index and the level of stressful load procedure Holmes-Rage. Results. It has been established that the risk factors for recurrence include low quality of life on the scale of RP, SF and MH SF-36, the coefficient Hildebrant >5.6 units, Kerdo index 314 points. Conclusion: The obtained data have been processed by multivariate mathematical statistics and the obtained analytical expression allows to prognose the time of recurrence of ulcerative colitis.

Association of Preoperative Nutritional Status with Prognosis in Patients with Esophageal Cancer Undergoing Salvage Esophagectomy.

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Sakai, Makoto; Sohda, Makoto; Miyazaki, Tatsuya; Yoshida, Tomonori; Kumakura, Yuji; Honjo, Hiroaki; Hara, Keigo; Ozawa, Daigo; Suzuki, Shigemasa; Tanaka, Naritaka; Yokobori, Takehiko; Kuwano, Hiroyuki

2018-02-01

To investigate whether malnutrition is associated with poor prognosis of patients who undergo salvage esophagectomy. We examined the association between the preoperative prognostic nutritional index (PNI) and prognosis of patients who undergo salvage esophagectomy. We conducted a single-center retrospective study and reviewed hospital patient records for tumor characteristics and patient outcomes. Univariate and multivariate survival analyses were carried out using the Cox proportional hazards regression model. Thirty-two patients with esophageal squamous cell carcinoma (ESCC) who underwent salvage esophagectomy between 1998 and 2015 at our Institute were included in this study. Univariate analysis revealed that clinical response (p=0.045), preoperative PNI (pnutritional status is associated with the prognosis of patients undergoing salvage esophagectomy. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

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Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Directory of Open Access Journals (Sweden)

Jing Zhang

2017-12-01

Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Long-term Postoperative Nutritional Status Affects Prognosis Even After Infectious Complications in Gastric Cancer.

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Kiuchi, Jun; Komatsu, Shuhei; Kosuga, Toshiyuki; Kubota, Takeshi; Okamoto, Kazuma; Konishi, Hirotaka; Shiozaki, Atsushi; Fujiwara, Hitoshi; Ichikawa, Daisuke; Otsuji, Eigo

2018-05-01

This study was designed to investigate the clinical impact of postoperative serum albumin level on severe postoperative complications (SPCs) and prognosis. Data for a total of 728 consecutive patients who underwent curative gastrectomy for gastric cancer between 2004 and 2013 were retrospectively analyzed. From these patients, a propensity score-matched analysis was performed based on 14 clinicopathological and surgical factors. Short-term decrease in postoperative serum albumin level was not associated with the occurrence of SPCs. Regarding long-term decrease in serum albumin level, a decrease of ≥0.5 g/dl at 3 months did not affect the long-term survival of patients without SPCs, but was related to a significantly poorer prognosis in patients with SPCs. By multivariate analysis, long-term decrease of serum albumin level was an independent prognostic factor in patients with SPCs. Long-term postoperative nutritional status as shown by a low level of albumin was related to prognosis in patients with SPCs. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Multivariate analysis of factors influencing the effect of radiosynovectomy

International Nuclear Information System (INIS)

Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr.; Wendler, J.; Kenn, W.; Reidemeister, C.

2002-01-01

Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [de

Effect of epidermal growth factor receptor gene polymorphisms on prognosis in glioma patients

Science.gov (United States)

Li, Jingjie; Yan, Mengdan; Xie, Zhilan; Zhu, Yuanyuan; Chen, Chao; Jin, Tianbo

2016-01-01

Previous studies suggested that single nucleotide polymorphisms (SNPs) in epidermal growth factor receptor (EGFR) are associated with risk of glioma. However, the associations between these SNPs and glioma patient prognosis have not yet been fully investigated. Therefore, the present study was aimed to evaluate the effects of EGFR polymorphisms on the glioma patient prognosis. We retrospectively evaluated 269 glioma patients and investigated associations between EGFR SNPs and patient prognosis using Cox proportional hazard models and Kaplan-Meier curves. Univariate analysis revealed that age, gross-total resection and chemotherapy were associated with the prognosis of glioma patients (p < 0.05). In addition, four EGFR SNPs (rs11506105, rs3752651, rs1468727 and rs845552) correlated with overall survival (OS) (Log-rank p = 0.011, 0.020, 0.008, and 0.009, respectively) and progression-free survival PFS (Log-rank p = 0.026, 0.024, 0.019 and 0.009, respectively). Multivariate analysis indicated that the rs11506105 G/G genotype, the rs3752651 and rs1468727 C/C genotype and the rs845552 A/A genotype correlated inversely with OS and PFS. In addition, OS among patients with the rs730437 C/C genotype (p = 0.030) was significantly lower OS than among patients with A/A genotype. These data suggest that five EGFR SNPs (rs11506105, rs3752651, rs1468727, rs845552 and rs730437) correlated with glioma patient prognosis, and should be furthered validated in studies of ethnically diverse patients. PMID:27437777

EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

Directory of Open Access Journals (Sweden)

2016-12-01

Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

Multivariate Analysis of Schools and Educational Policy.

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Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

Genetic Predictors of Poor Prognosis in Portuguese Patients with Juvenile Idiopathic Arthritis: Data from Reuma.pt

Science.gov (United States)

Mourão, Ana Filipa; Santos, Maria José; Mendonça, Sílvia; Oliveira-Ramos, Filipa; Salgado, Manuel; Estanqueiro, Paula; Melo-Gomes, José; Martins, Fernando; Lopes, Ana; Bettencourt, Bruno Filipe; Bruges-Armas, Jácome; Costa, José; Furtado, Carolina; Figueira, Ricardo; Brito, Iva; Branco, Jaime; Fonseca, João Eurico; Canhão, Helena

2015-01-01

Introduction. This study aimed to assess the genetic determinants of poor outcome in Portuguese patients with juvenile idiopathic arthritis (JIA). Methods. Our study was conducted in Reuma.pt, the Rheumatic Diseases Portuguese Register, which includes patients with JIA. We collected prospectively patient and disease characteristics and a blood sample for DNA analysis. Poor prognosis was defined as CHAQ/HAQ >0.75 at the last visit and/or the treatment with biological therapy. A selected panel of single nucleotide polymorphisms (SNPs) associated with susceptibility was studied to verify if there was association with poor prognosis. Results. Of the 812 patients with JIA registered in Reuma.pt, 267 had a blood sample and registered information used to define “poor prognosis.” In univariate analysis, we found significant associations with poor prognosis for allele A of TNFA1P3/20 rs6920220, allele G of TRAF1/C5 rs3761847, and allele G of PTPN2 rs7234029. In multivariate models, the associations with TRAF1/C5 (1.96 [1.17–3.3]) remained significant at the 5% level, while TNFA1P3/20 and PTPN2 were no longer significant. Nevertheless, none of associations found was significant after the Bonferroni correction was applied. Conclusion. Our study does not confirm the association between a panel of selected SNP and poor prognosis in Portuguese patients with JIA. PMID:26504858

Multivariate Analysis for the Processing of Signals

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Beattie J.R.

2014-01-01

Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

PIXE-quantified AXSIA: Elemental mapping by multivariate spectral analysis

International Nuclear Information System (INIS)

Doyle, B.L.; Provencio, P.P.; Kotula, P.G.; Antolak, A.J.; Ryan, C.G.; Campbell, J.L.; Barrett, K.

2006-01-01

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other

Cytokeratin 8/18 expression indicates a poor prognosis in squamous cell carcinomas of the oral cavity

International Nuclear Information System (INIS)

Fillies, Thomas; Werkmeister, Richard; Packeisen, Jens; Brandt, Burkhard; Morin, Philippe; Weingart, Dieter; Joos, Ulrich; Buerger, Horst

2006-01-01

Intermediary filaments are involved in cell motility and cancer progression. In a variety of organs, the expression of distinct intermediary filaments are associated with patient prognosis. In this study, we seeked to define the prognostic potential of cytokeratin and vimentin expression patterns in squamous cell carcinomas (SCC's) of the oral cavity. 308 patients with histologically proven and surgically treated squamous cell carcinomas of the oral cavity were investigated for the immunohistochemical expression of a variety of intermediary filaments including high- and low-molecular weight cytokeratins (Ck's), such as Ck 5/6, Ck 8/18, Ck 1, CK 10, Ck 14, Ck 19 and vimentin, using the tissue microarray technique. Correlations between clinical features and the expression of Cytokeratins and vimentin were evaluated statistically by Kaplan-Meier curves and multivariate Cox regression analysis. The expression of Ck 8/18 and Ck 19 were overall significantly correlated with a poor clinical prognosis (Ck 8/18 p = 0.04; Ck19 p < 0.01). These findings could also be reproduced for Ck 8/18 in primary nodal-negative SCC's and held true in multivariate-analysis. No significant correlation with patient prognosis could be found for the expression of the other cytokeratins and for vimentin. The expression of Ck 8/18 in SCC's of the oral cavity is an independent prognostic marker and indicates a decreased overall and progression free survival. These results provide an extended knowledge about the role of intermediary filament expression patterns in SCC's

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

Science.gov (United States)

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Anemia and Long-Term Renal Prognosis in Patients with Post-Renal Acute Kidney Injury of Nonmalignant Cause.

Science.gov (United States)

Sasaki, Sho; Kawarazaki, Hiroo; Hasegawa, Takeshi; Shima, Hideaki; Naganuma, Toshihide; Shibagaki, Yugo

2017-01-01

The renal prognosis of post-renal acute kidney injury (PoR-AKI) has not been verified so far. The objective of this study was to assess the association of baseline anemia with long-term renal prognosis in patients with PoR-AKI. We performed a multicenter retrospective cohort study. Consecutive adult patients from December 2006 to February 2010, who met the requirements as mentioned in the definition of PoR-AKI, were included. Patients without data on baseline renal function and at 6 months after PoR-AKI were excluded. We set baseline hemoglobin (Hb) level (g/dl) as the main exposure to be tested. The main outcome measure was long-term renal prognosis as determined by the difference between proximate estimated glomerular filtration rate (eGFR) at 6 months after diagnosis of PoR-AKI and baseline eGFR prior to the occurrence of the present PoR-AKI (ΔeGFR after 6 months) using the general linear model. We included 136 patients with PoR-AKI. The most frequent cause of PoR-AKI was malignancy, accounting for 39.0% (n = 53) of cases. Multivariate analysis adjusted for possible confounders showed that ΔeGFR after 6 months significantly changed by -4.28 ml/min/1.73 m2 for every 1 g/dl lower Hb at diagnosis (95% CI 1.86-6.69, p < 0.01). An additional multivariate analysis that was stratified by the presence or absence of malignancy as the cause of PoR-AKI yielded the same significant result only in the stratum of the nonmalignant cause of PoR-AKI. Patients with a nonmalignant cause of PoR-AKI who have baseline anemia may have poor long-term renal prognosis. In these cases, close observation of renal function after renal recovery may be required. © 2016 S. Karger AG, Basel.

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

Predictors and prognosis of patients with advanced stage small hepatocellular carcinoma after hepatectomy

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Wen-ping LV

2013-01-01

Full Text Available Objective 　To investigate the clinical predictors and prognosis of the patients with advanced stage small hepatocellular carcinoma (SHCC after hepatectomy. Methods 　A total of 110 patients with SHCC admitted to the General Hospital of PLA and undergone hepatectomy from Jan. 1995 to Dec. 2009 were included in present retrospective study. Survival analysis was performed by Log-rank test and Kaplan-Meier. The association of SHCC and nine routine clinical parameters was analyzed by the univariate and multivariate logistic regression analysis. Results 　Of the 110 patients with SHCC, 31 (28.2% were SHCC in advanced stage, and the 1, 3, 5 year survival rates were 78.6%, 61.6% and 38.5%, respectively, with a median survival time of 48.8 months (95% CI 29.2-68.4 months. Seventy-nine of the 110 patients (71.8% were suffering form early stage SHCC, and the 1, 3, 5 year survival rates were 98.7%, 83.8% and 74.8%, respectively, with a median survival time of 98.0 months (95%CI 73.8-122.2 months. The survival rate was obviously higher in the patients with early stage SHCC than in those with advanced stage SHCC (χ2=13.29, P=0.0003. Multivariate analysis showed that positive AFP was a potential significant predictor of SHCC in advanced stage (RR=14.45; 95%CI 4.05-51.64, P<0.001. Conclusion 　The SHCC in advanced stage signifies an ominous prognosis. Positive AFP is a potential significant predictor for advanced stage SHCC.

Particulate characterization by PIXE multivariate spectral analysis

International Nuclear Information System (INIS)

Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.

2007-01-01

Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media

Determination of wheat quality by mass spectrometry and multivariate data analysis

DEFF Research Database (Denmark)

Gottlieb, D.M.; Schultz, J.; Petersen, M.

2002-01-01

Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means...... of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis.Proteome analysis...

Does age affect prognosis in salivary gland carcinoma patients?

DEFF Research Database (Denmark)

Bjørndal, Kristine; Larsen, Stine R; Therkildsen, Marianne H

2016-01-01

in the young group were WHO performance status 0 and in disease stage I + II, and they presented with significantly more histological low grade tumors. In multivariate analysis, chronological age seemed to be of no prognostic significance to salivary gland carcinoma patients as opposed to performance status......, disease stage and histological grade. CONCLUSIONS: Salivary gland carcinoma patients over the age of 70 years have a poor prognosis compared to younger patients, which can be explained by higher disease stages, more histological high grade subtypes and a poorer performance status at the time of diagnosis.......AIM: To compare incidence, histology, treatment modalities, disease stages, and outcome in elderly patients (≥70 years) compared to younger (

Multivariate Analysis of Industrial Scale Fermentation Data

DEFF Research Database (Denmark)

Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

2015-01-01

Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

Multivariate spectral-analysis of movement-related EEG data

International Nuclear Information System (INIS)

Andrew, C. M.

1997-01-01

The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

Prognosis of the comorbid heart failure and Anemia: A systematic review and meta-analysis

Directory of Open Access Journals (Sweden)

M. Kyriakou

2016-04-01

Conclusion: The meta-analysis gives an outline profile of patients with the co-morbidity HF and anemia in terms of clinical outcomes. The results point out worse prognosis in HF patients with anemia. Nevertheless, the available data did not allow the extraction of a conclusion in which exact Hb levels anemia becomes a negative predictor of prognosis.

Essentials of multivariate data analysis

CERN Document Server

Spencer, Neil H

2013-01-01

""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Directory of Open Access Journals (Sweden)

Lijun Wang

2013-01-01

Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Multivariate Meta-Analysis Using Individual Participant Data

Science.gov (United States)

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

Science.gov (United States)

Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

CERN Document Server

Zapata Mesa, Omar Andres

2016-01-01

Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

The effects of gene polymorphisms on glioma prognosis.

Science.gov (United States)

Cui, Ying; Li, Guolin; Yan, Mengdan; Li, Jing; Jin, Tianbo; Li, Shanqu; Mu, Shijie

2017-11-01

Malignant gliomas are the most common primary brain tumors. Various genetic factors play important roles in the development and prognosis of glioma. The present study focuses on the impact of MPHOSPH6, TNIP1 and several other genes (ACYP2, NAF1, TERC, TERT, OBFC1, ZNF208 and RTEL1) on telomere length and how this affects the prognosis of glioma. Forty-three polymorphisms in nine genes from 605 glioma patients were selected. The association between genotype and survival outcome was analyzed using the Kaplan-Meier method, Cox regression analysis and the log-rank test. The 1-year overall survival (OS) rates of patients younger than 40 years of age was higher compared to those in patients older than 40 years of age. The 1-year OS rate of patients who underwent total resection was higher than that of patients whose gliomas were not completely resected. The 1-year OS rates of patients undergoing chemotherapy and of patients who did not undergo chemotherapy were 39.90% and 26.80%, respectively. Univariate analyses showed that ACYP2 rs12615793 and TERT rs2853676 loci affected progression-free survival in glioma patients; both ZNF208 rs8105767 and ACYP2 rs843720 affected the OS of patients with low-grade gliomas. Multivariate analyses suggested that MPHOSPH6 rs1056629 and rs1056654, and TERT rs2853676 loci were associated with good prognoses of patients with glioma or high-grade gliomas, whereas ZNF208 rs8105767 was associated with good prognosis of patients with low-grade glioma. Age, surgical resection and chemotherapy influenced the survival rates of glioma patients. TERT, MPHOSPH6, ACYP2 and ZNF208 genes were found to affect glioma prognosis. Copyright © 2017 John Wiley & Sons, Ltd.

Robust methods for multivariate data analysis A1

DEFF Research Database (Denmark)

Frosch, Stina; Von Frese, J.; Bro, Rasmus

2005-01-01

Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

International Nuclear Information System (INIS)

Blenk, Steffen; Engelmann, Julia C; Pinkert, Stefan; Weniger, Markus; Schultz, Jörg; Rosenwald, Andreas; Müller-Hermelink, Hans K; Müller, Tobias; Dandekar, Thomas

2008-01-01

Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data

Factors influencing prognosis in patients with marfan syndrome after aortic surgery.

Science.gov (United States)

Gao, Linggen; Zhou, Xianliang; Zhang, Lin; Wen, Dan; Chang, Qian; Wu, Yongbo; Sun, Lizhong; Hui, Rutai

2011-08-01

Aortic aneurysm formation leading eventually to aortic rupture or dissection in early adult life is a fatal outcome of Marfan syndrome (MFS). Advances in the treatment of the syndrome have improved prognosis, but the long-term reoperation rate is still high. It remains unknown which factors influence the long-term prognosis, including the reoperation and mortality rates, in surgically treated Chinese patients with MFS. The authors studied 125 such patients to investigate factors influencing prognosis after aortic surgery. A retrospective clinical investigation. An academic medical center. One hundred twenty-five Marfan patients who had undergone aortic surgery. None. The indications for aortic surgery were aortic aneurysm and/or dissection in the 125 Marfan patients. The most commonly performed procedure was the Bentall in 92 patients. Sixteen patients underwent total arch replacement combined with stented elephant trunk implantation. Ten patients underwent the David procedure. Overall in-hospital and 30-day mortality rate was 1.6%. The survival rate was 97.5%, 91.4%, and 74.2% at 1, 5, and 10 years after surgery, respectively. The reoperation rate was 2.5%, 12.9%, and 32.9% at 1, 5, and 10 years after surgery, respectively. Multivariate analysis revealed that increased systolic blood pressure (Sys BP) was the predictor of death (p < 0.05), and body mass index and smoking were significant predictors of reoperation (p < 0.05). The present findings report the factors influencing the prognosis of Chinese patients with MFS after aortic surgical procedures. Managing these risk factors may enable health care professionals to improve the prognosis of MFS patients after aortic surgical procedures. Copyright © 2011 Elsevier Inc. All rights reserved.

Multivariate Generalized Multiscale Entropy Analysis

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Anne Humeau-Heurtier

2016-11-01

Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

Loss of Bad expression confers poor prognosis in non-small cell lung cancer.

Science.gov (United States)

Huang, Yi; Liu, Dan; Chen, Bojiang; Zeng, Jing; Wang, Lei; Zhang, Shangfu; Mo, Xianming; Li, Weimin

2012-09-01

Proapoptotic BH-3-only protein Bad (Bcl-Xl/Bcl-2-associated death promoter homolog, Bad) initiates apoptosis in human cells, and contributes to tumorigenesis and chemotherapy resistant in malignancies. This study explored association between the Bad expression level and prognosis in patients with non-small cell lung cancer (NSCLC). In our study, a cohort of 88 resected primary NSCLC cases were collected and analyzed. Bad expression level was determined via immunohistochemical staining assay. The prognostic significances of Bad expression were evaluated with univariate and multivariate survival analysis. The results showed that compared with normal lung tissues, Bad expression level significantly decreased in NSCLC (P Bad expression was associated with adjuvant therapy status. Loss of Bad independently predicted poor prognosis in whole NSCLC cohort and early stage subjects (T1 + T2 and N0 + N1) (all P Bad negative phenotype in NSCLC patients with smoking history, especially lung squamous cell carcinoma (all P Bad is an independent and powerful predictor of adverse prognosis in NSCLC. Bad protein could be a new biomarker for selecting individual therapy strategies and predicting therapeutic response in subjects with NSCLC.

Multivariate calibration applied to the quantitative analysis of infrared spectra

Energy Technology Data Exchange (ETDEWEB)

Haaland, D.M.

1991-01-01

Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

Directory of Open Access Journals (Sweden)

Binod Neupane

Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when

Power Estimation in Multivariate Analysis of Variance

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Jean François Allaire

2007-09-01

Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

Polysialic Acid Neural Cell Adhesion Molecule (PSA-NCAM) is an adverse prognosis factor in glioblastoma, and regulates olig2 expression in glioma cell lines

International Nuclear Information System (INIS)

Amoureux, Marie-Claude; Coulibaly, Béma; Chinot, Olivier; Loundou, Anderson; Metellus, Philippe; Rougon, Geneviève; Figarella-Branger, Dominique

2010-01-01

Glioblastoma multiforme (GBM) is the most aggressive and frequent brain tumor, albeit without cure. Although patient survival is limited to one year on average, significant variability in outcome is observed. The assessment of biomarkers is needed to gain better knowledge of this type of tumor, help prognosis, design and evaluate therapies. The neurodevelopmental polysialic acid neural cell adhesion molecule (PSA-NCAM) protein is overexpressed in various cancers. Here, we studied its expression in GBM and evaluated its prognosis value for overall survival (OS) and disease free survival (DFS). We set up a specific and sensitive enzyme linked immunosorbent assay (ELISA) test for PSA-NCAM quantification, which correlated well with PSA-NCAM semi quantitative analysis by immunohistochemistry, and thus provides an accurate quantitative measurement of PSA-NCAM content for the 56 GBM biopsies analyzed. For statistics, the Spearman correlation coefficient was used to evaluate the consistency between the immunohistochemistry and ELISA data. Patients' survival was estimated by using the Kaplan-Meier method, and curves were compared using the log-rank test. On multivariate analysis, the effect of potential risk factors on the DFS and OS were evaluated using the cox regression proportional hazard models. The threshold for statistical significance was p = 0.05. We showed that PSA-NCAM was expressed by approximately two thirds of the GBM at variable levels. On univariate analysis, PSA-NCAM content was an adverse prognosis factor for both OS (p = 0.04) and DFS (p = 0.0017). On multivariate analysis, PSA-NCAM expression was an independent negative predictor of OS (p = 0.046) and DFS (p = 0.007). Furthermore, in glioma cell lines, PSA-NCAM level expression was correlated to the one of olig2, a transcription factor required for gliomagenesis. PSA-NCAM represents a valuable biomarker for the prognosis of GBM patients

Some developments in multivariate image analysis

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey

be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...

A comparison between multivariate and bivariate analysis used in marketing research

Directory of Open Access Journals (Sweden)

Constantin, C.

2012-01-01

Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

Multivariate Volatility Impulse Response Analysis of GFC News Events

NARCIS (Netherlands)

D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)

2015-01-01

markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the

[Mantle cell lymphoma, response to treatment and prognosis in 45 patients].

Science.gov (United States)

Sorigue, Marc; Sancho, Juan-Manuel; García, Olga; Vila, Jordi; Moreno, Miriam; Ribera, Josep-Maria

2016-07-01

Mantle cell lymphoma (MCL) is a rare lymphoproliferative disorder, with frequent relapses and a poor prognosis. This study analyzes response to treatment and prognosis in a series of MCL patients. Retrospective study of MCL patients diagnosed in a single institution between 1996 and 2013. The cohort was divided according to the treatment received. Forty-five patients were included (32 male) with a median age of 66 years old. Twenty-one received intensive chemotherapy or chemoimmunotherapy (based on high-dose cytarabine), 13 semi-intensive (without high-dose cytarabine), 8 not intensive and 3 did not require treatment. Overall response rate was 85% in the intensive and 77% in the semi-intensive treatment groups. In multivariate analysis, intensive treatment was correlated with a longer progression-free survival (hazard ratio 9.8 [95% CI 2.7-35.5], P=.001) and overall survival (4.5 [1.2-17.8], P=.03). In this retrospective series of MCL patients, intensive treatment was correlated with better outcomes than the other treatment modalities. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Multivariant design and multiple criteria analysis of building refurbishments

Energy Technology Data Exchange (ETDEWEB)

Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

2005-07-01

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

Data classification and MTBF prediction with a multivariate analysis approach

International Nuclear Information System (INIS)

Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

2012-01-01

The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

Correlation between CT findings and prognosis of patients with carcinoma of the cervix

International Nuclear Information System (INIS)

Kubota, Susumu; Ohara, Kiyoshi; Okumura, Toshiyuki; Gomi, Hiromichi; Nakano, Takashi; Arai, Tatsuo.

1986-01-01

Ninety-one patients with carcinoma of the cervix received radical radiation therapy from May 1981 through August 1983 at NIRS hospital. The correlation between eight CT findings and the prognosis was analysed in 75 patients, performed CT scan within 15 days form the admision. Among these CT findings, area of uterine cervix correlates well with recurrence, and enlargement of para-aortic lymph nodes showed the strong correlation between metastasis. We also analized these data by a multivariate analytical method (quantification method II). There exists a correlation between the prognosis and the score of quantification method II, and this score will be a good index of the prognosis of patients with carcinoma of the cervix. (author)

Expression of CD44v6 and Its Association with Prognosis in Epithelial Ovarian Carcinomas

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Dang-xia Zhou

2012-01-01

Full Text Available The aim of this study was to evaluate CD44v6 protein expression and its prognostic value of CD44v6 in ovarian carcinoma. The expression of CD44v6 was analyzed in 62 patients with ovarian carcinoma by immunohistochemical method. The data obtained were analyzed by univariate and multivariate analyses. The present study clearly demonstrates that tumor tissues from 41 (66.1% patients showed positive expression with CD44v6. The expression of CD44v6 was significantly correlated with histological type, FIGO stage and histological grade of ovarian carcinomas. Concerning the prognosis, the survival period of patients with CD44v6 positive was shorter than that of patients with CD44v6 negative (36.6% versus 66.7%, 5-year survival, P<0.05. Univariate analysis showed that CD44v6 expression, histological type, FIGO stage and histological grade were associated with 5-year survival, and CD44v6 expression was associated with histological type, FIGO stage and histological grade and 5-year survival. In multivariate analysis, using the COX-regression model, CD44v6 expression was important prognostic factor. In conclusion, these results suggest that CD44v6 may be related to histological type, FIGO stage and histological grade of ovarian carcinomas, and CD44v6 may be an important molecular marker for poor prognosis in ovarian carcinomas.

Multivariate Volatility Impulse Response Analysis of GFC News Events

NARCIS (Netherlands)

D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

2015-01-01

textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

Science.gov (United States)

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

International Nuclear Information System (INIS)

Hao, Ke; Sham, Pak C; Poon, Ronnie TP; Luk, John M; Lee, Nikki PY; Mao, Mao; Zhang, Chunsheng; Ferguson, Mark D; Lamb, John; Dai, Hongyue; Ng, Irene O

2009-01-01

Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new

Risk factors and prognosis for recurrent primary sclerosing cholangitis after liver transplantation

DEFF Research Database (Denmark)

Lindström, Lina; Jørgensen, Kristin K; Boberg, Kirsten M

2018-01-01

PSC for prognosis. MATERIALS AND METHODS: All liver transplanted PSC patients in the Nordic countries between 1984 and 2007 (n = 440), identified by the Nordic Liver Transplant Registry, were studied. Data were retrieved from patients' chart reviews. Multivariable Cox regression models were used to calculate risk...

Multivariate data analysis

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg

Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

Multivariate Analysis and Prediction of Dioxin-Furan ...

Science.gov (United States)

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Stonin 2 Overexpression is Correlated with Unfavorable Prognosis and Tumor Invasion in Epithelial Ovarian Cancer

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Xiaoying Sun

2017-07-01

Full Text Available Stonin 2 (STON2, which functions in adjusting endocytotic complexes, is probably involved in the monitoring of the internalization of dopamine D2 receptors which have an inhibitory action of dopamine on tumor progression. However, its clinical significance in tumor progression and prognosis remains unclear. We explored the association between STON2 and the clinicopathological characteristics of epithelial ovarian cancer (EOC. The STON2 levels in ovarian cancer and normal cell lines and tissues were detected by real-time PCR and Western blot analyses. STON2 protein expression was also detected by an immunohistochemical analysis. The clinical significance of STON2 expression in ovarian cancer was statistically analyzed. STON2 significantly increased in the ovarian cancer cell lines and tissues compared to the normal ones. In the 89 EOC samples tested, STON2 expression was significantly correlated with intraperitoneal metastasis, intestinal metastasis, intraperitoneal recurrence, ascites containing tumor cells, and CA153 level. Moreover, patients with STON2 protein overexpression were more likely to exhibit platinum resistance and to have undergone neoadjuvant chemotherapy. Patients with high STON2 protein expression had a tendency to have a shorter overall survival and a poor prognosis. A multivariate analysis showed that STON2 was an independent prognostic predictor for EOC patients. In conclusion, STON2 plays an important role in the progression and prognosis of ovarian carcinoma, especially in platinum resistance, intraperitoneal metastasis, and recurrence. STON2 can be a novel antitumor drug target and biomarker which predicts an unfavorable prognosis for EOC patients.

Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

International Nuclear Information System (INIS)

Garcia-Cabrejo, Oscar; Valocchi, Albert

2014-01-01

Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis

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Ningyun Lu

2012-01-01

Full Text Available Because of the interlinking of process equipments in process industry, event information may propagate through the plant and affect a lot of downstream process variables. Specifying the causality and estimating the time delays among process variables are critically important for data-driven fault prognosis. They are not only helpful to find the root cause when a plant-wide disturbance occurs, but to reveal the evolution of an abnormal event propagating through the plant. This paper concerns with the information flow directionality and time-delay estimation problems in process industry and presents an information synchronization technique to assist fault prognosis. Time-delayed mutual information (TDMI is used for both causality analysis and time-delay estimation. To represent causality structure of high-dimensional process variables, a time-delayed signed digraph (TD-SDG model is developed. Then, a general fault prognosis strategy is developed based on the TD-SDG model and principle component analysis (PCA. The proposed method is applied to an air separation unit and has achieved satisfying results in predicting the frequently occurred “nitrogen-block” fault.

Search for the top quark using multivariate analysis techniques

International Nuclear Information System (INIS)

Bhat, P.C.

1994-08-01

The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the eμ channel and neural networks to the e+jets channel

Experience with Traumatic Brain Injury: Is Early Tracheostomy Associated with Better Prognosis?

Science.gov (United States)

Khalili, Hosseinali; Paydar, Shahram; Safari, Rasool; Arasteh, Peyman; Niakan, Amin; Abolhasani Foroughi, Amin

2017-07-01

In this study we compared the effects of early tracheostomy (ET) versus late tracheostomy on traumatic brain injury (TBI)-related outcomes and prognosis. Data on 152 TBI patients with a Glasgow Coma Scale (GCS) score of ≤8, admitted to Rajaee Hospital between March 1, 2014 and August 23, 2015, were collected. Rajaee Hospital is the main referral trauma center in southern Iran and is affiliated with Shiraz University of Medical Sciences. Patients who had tracheostomy before or at the sixth day of their admission were considered as ET, and those who had tracheostomy after the sixth day of admission were considered as late tracheostomy. Patients with ET had a significantly lower hospital stay (46.4 vs. 38.6 days; P = 0.048) and intensive care unit stay (34.9 vs. 26.7 days; P = 0.003). Mortality rates were not significantly different between the 2 groups (P > 0.99). Although not statistically significant, favorable outcomes (Glasgow Outcome Scale >4) were higher and ventilator-associated pneumonia rates were lower among the ET group (P = 0.346 and P = 492, respectively). Multivariate analysis showed that ET significantly improves 6-month prognosis (Glasgow Outcome Scale >4) (odds ratio = 2.535; 95% confidence interval: 1.030-6.237). Higher age was inversely associated with favorable prognosis (odds ratio = -0.958; confidence interval: 0.936-0.981). Glasgow Coma Scale and Rotterdam score did not show any effect on 6-month prognosis. Despite previous concern regarding increased mortality rates among patients who undergo ET, performing a tracheostomy for patients with severe TBI <6 days after their hospital admission, in addition to decreasing hospital and intensive care unit stays, will improve patient prognosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Clinicopathologic characteristics and prognosis of proximal and distal gastric cancer

Directory of Open Access Journals (Sweden)

Yu X

2018-02-01

Full Text Available Xuefeng Yu,1,* Fulan Hu,2,* Chunfeng Li,1 Qiang Yao,1 Hongfeng Zhang,1 Yingwei Xue1 1Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China; 2Department of Epidemiology, Public Health College, Harbin Medical University, Harbin, China *These authors contributed equally to this work Background and objectives: The dismal prognosis of gastric cancer patients is a global problem. We aim to evaluate the clinicopathologic features and prognostic factors of proximal and distal gastric cancer.Materials and methods: Gastric cancer cases diagnosed and treated at the same surgical unit between 2007 and 2010 were reviewed. Follow-up data from all patients were collected for at least 5 years until 2015. A total of 964 patients were studied (distal gastric cancer [DG], n=777 and proximal gastric cancer [PG], n=187.Results: DG patients had a relatively higher percentage of females, more thorough therapy (R0 [D0/D1/D2], fewer combined organ resections, younger age, smaller tumors (<5 cm, shorter surgery durations, less blood loss during surgery, and a relatively lower percentage of nodal metastases and a TNM stage of 4 (p<0.05. A significantly higher 5-year survival rate was observed in DG patients compared to PG patients (DG: 51%, PG: 28%; p<0.001. A multivariate analysis demonstrated that tumor size, blood loss during surgery, surgery approach of lymph node dissection, treatment with palliative surgery, histopathologic type, TNM stage, and tumor location were independent predictors of poor outcome.Conclusion: The different characteristics and prognosis of DG and PG cases have implications for the development of guiding strategies for a surgical program and assessment of prognosis of gastric cancer patients based on tumor location. Keywords: gastric cancer, tumor location, clinicopathologic features, prognosis, distal gastric cancer, proximal gastric cancer 

User's Guide To CHEAP0 II-Economic Analysis of Stand Prognosis Model Outputs

Science.gov (United States)

Joseph E. Horn; E. Lee Medema; Ervin G. Schuster

1986-01-01

CHEAP0 II provides supplemental economic analysis capability for users of version 5.1 of the Stand Prognosis Model, including recent regeneration and insect outbreak extensions. Although patterned after the old CHEAP0 model, CHEAP0 II has more features and analytic capabilities, especially for analysis of existing and uneven-aged stands....

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

Science.gov (United States)

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1996-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H J; Bouanani, M E; Persson, L; Hult, M; Jonsson, P; Johnston, P N [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M; Zaring, C [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P N; Bubb, I F; Walker, B R; Stannard, W B [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D D; Dytlewski, N [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1997-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

Advanced event reweighting using multivariate analysis

International Nuclear Information System (INIS)

Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J

2012-01-01

Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.

Science.gov (United States)

Nguyen, Minh Nam; Choi, Tae Gyu; Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-10-13

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

Science.gov (United States)

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Radiosurgery for brain metastases: a score index for predicting prognosis

International Nuclear Information System (INIS)

Weltman, Eduardo; Salvajoli, Joao Victor; Brandt, Reynaldo Andre; Morais Hanriot, Rodrigo de; Prisco, Flavio Eduardo; Cruz, Jose Carlos; Oliveira Borges, Sandra Regina de; Wajsbrot, Dalia Ballas

2000-01-01

Purpose: To analyze a prognostic score index for patients with brain metastases submitted to stereotactic radiosurgery (the Score Index for Radiosurgery in Brain Metastases [SIR]). Methods and Materials: Actuarial survival of 65 brain metastases patients treated with radiosurgery between July 1993 and December 1997 was retrospectively analyzed. Prognostic factors included age, Karnofsky performance status (KPS), extracranial disease status, number of brain lesions, largest brain lesion volume, lesions site, and receiving or not whole brain irradiation. The SIR was obtained through summation of the previously noted first five prognostic factors. Kaplan-Meier actuarial survival curves for all prognostic factors, SIR, and recursive partitioning analysis (RPA) (RTOG prognostic score) were calculated. Survival curves of subsets were compared by log-rank test. Application of the Cox model was utilized to identify any correlation between prognostic factors, prognostic scores, and survival. Results: Median overall survival from radiosurgery was 6.8 months. Utilizing univariate analysis, extracranial disease status, KPS, number of brain lesions, largest brain lesion volume, RPA, and SIR were significantly correlated with prognosis. Median survival for the RPA classes 1, 2, and 3 was 20.19 months, 7.75 months, and 3.38 months respectively (p = 0.0131). Median survival for patients, grouped under SIR from 1 to 3, 4 to 7, and 8 to 10, was 2.91 months, 7.00 months, and 31.38 months respectively (p = 0.0001). Using the Cox model, extracranial disease status and KPS demonstrated significant correlation with prognosis (p 0.0001 and 0.0004 respectively). Multivariate analysis also demonstrated significance for SIR and RPA when tested individually (p = 0.0001 and 0.0040 respectively). Applying the Cox Model to both SIR and RPA, only SIR reached independent significance (p = 0.0004). Conclusions: Systemic disease status, KPS, SIR, and RPA are reliable prognostic factors for patients

BMI and breast cancer prognosis benefit: mammography screening reveals differences between normal weight and overweight women.

Science.gov (United States)

Crispo, Anna; Grimaldi, Maria; D'Aiuto, Massimiliano; Rinaldo, Massimo; Capasso, Immacolata; Amore, Alfonso; D'Aiuto, Giuseppe; Giudice, Aldo; Ciliberto, Gennaro; Montella, Maurizio

2015-02-01

Few studies are available on the potential impact of body weight on breast cancer prognosis in screen-detected patients. Moreover, it is not known whether body mass index (BMI) could have a different prognostic impact in screen-detected versus symptomatic breast cancer patients. To investigate these unsolved issues, we carried out a retrospective study evaluating the effect of BMI on breast cancer prognosis in screen-detected vs symptomatic breast cancer patients. We conducted a follow-up study on 448 women diagnosed with incident, histologically-confirmed breast cancer. Patients were categorized according to their BMI as normal weight, overweight and obese. Disease free survival (DFS), overall survival (OS), and BMI curves were compared according to mode of cancer detection. Among screen-detected patients, higher BMI was associated with a significant lower DFS, whereas no significant difference was observed among symptomatic patients. OS showed similar results. In the multivariate analysis adjusting for age, education, tumor size, nodal status, estrogen receptor (ER), progesterone receptor (PR) and menopausal status, the risk for high level of BMI among screen-detected patients did not reach the statistical significance for either recurrence or survival. Our study highlights the potential impact of high bodyweight in breast cancer prognosis, the findings confirm that obesity plays a role in women breast cancer prognosis independently from diagnosis mode. Copyright © 2014 Elsevier Ltd. All rights reserved.

Point defect characterization in HAADF-STEM images using multivariate statistical analysis

International Nuclear Information System (INIS)

Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

2011-01-01

Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

Multivariate analysis of risk factors for long-term urethroplasty outcome.

Science.gov (United States)

Breyer, Benjamin N; McAninch, Jack W; Whitson, Jared M; Eisenberg, Michael L; Mehdizadeh, Jennifer F; Myers, Jeremy B; Voelzke, Bryan B

2010-02-01

We studied the patient risk factors that promote urethroplasty failure. Records of patients who underwent urethroplasty at the University of California, San Francisco Medical Center between 1995 and 2004 were reviewed. Cox proportional hazards regression analysis was used to identify multivariate predictors of urethroplasty outcome. Between 1995 and 2004, 443 patients of 495 who underwent urethroplasty had complete comorbidity data and were included in analysis. Median patient age was 41 years (range 18 to 90). Median followup was 5.8 years (range 1 month to 10 years). Stricture recurred in 93 patients (21%). Primary estimated stricture-free survival at 1, 3 and 5 years was 88%, 82% and 79%. After multivariate analysis smoking (HR 1.8, 95% CI 1.0-3.1, p = 0.05), prior direct vision internal urethrotomy (HR 1.7, 95% CI 1.0-3.0, p = 0.04) and prior urethroplasty (HR 1.8, 95% CI 1.1-3.1, p = 0.03) were predictive of treatment failure. On multivariate analysis diabetes mellitus showed a trend toward prediction of urethroplasty failure (HR 2.0, 95% CI 0.8-4.9, p = 0.14). Length of urethral stricture (greater than 4 cm), prior urethroplasty and failed endoscopic therapy are predictive of failure after urethroplasty. Smoking and diabetes mellitus also may predict failure potentially secondary to microvascular damage. Copyright 2010 American Urological Association. Published by Elsevier Inc. All rights reserved.

Expression of XPG protein in the development, progression and prognosis of gastric cancer.

Directory of Open Access Journals (Sweden)

Na Deng

Full Text Available BACKGROUND: Xeroderma pigmentosum group G (XPG plays a critical role in preventing cells from oxidative DNA damage. This study aimed to investigate XPG protein expression in different gastric tissues and in patients with diverse prognoses, thus providing insights into its role in the development, progression and prognosis of gastric cancer (GC. METHODS: A total of 176 GC, 131 adjacent non-tumour tissues, 53 atrophic gastritis (AG and 49 superficial gastritis (SG samples were included. Immunohistochemical staining was used to detect XPG protein expression. RESULTS: XPG expression was significantly higher in GC tissues compared with adjacent non-tumour tissues. In the progressive disease sequence SG→AG→GC, XPG expression was significantly higher in AG and GC compared with SG. Analysis of clinicopathological parameters and survival in GC patients demonstrated a significant association between XPG expression level and depth of tumour invasion, macroscopic type, Lauren's classification, smoking, Helicobacter pylori infection and family history. Cox multivariate survival analysis indicated that patients with positive XPG expression had significantly longer overall survival (P = 0.020, HR = 0.394, 95%CI 0.179-0.866, especially in aged younger than 60 years (P = 0.027, HR = 0.361, 95%CI 0.147-0.888 and male patients (P = 0.002, HR = 0.209, 95%CI 0.077-0.571. CONCLUSIONS: This study demonstrated that XPG protein expression was related to the development, progression and prognosis of GC, and might thus serve as a potential biomarker for its diagnosis and prognosis.

Does buccal cancer have worse prognosis than other oral cavity cancers?

Science.gov (United States)

Camilon, P Ryan; Stokes, William A; Fuller, Colin W; Nguyen, Shaun A; Lentsch, Eric J

2014-06-01

To determine whether buccal squamous cell carcinoma has worse overall survival (OS) and disease-specific survival (DSS) than cancers in the rest of the oral cavity. Retrospective analysis of a large population database. We began with a Kaplan-Meier analysis of OS and DSS for buccal versus nonbuccal tumors with unmatched data, followed by an analysis of cases matched for race, age at diagnosis, stage at diagnosis, and treatment modality. This was supported by a univariate Cox regression comparing buccal cancer to nonbuccal cancer, followed by a multivariate Cox regression that included all significant variables studied. With unmatched data, buccal cancer had significantly lesser OS and DSS values than cancers in the rest of the oral cavity (P cancer versus nonbuccal oral cancer were no longer significant. Univariate Cox regression models with respect to OS and DSS showed a significant difference between buccal cancer and nonbuccal cancer. However, with multivariate analysis, buccal hazard ratios for OS and DSS were not significant. With the largest series of buccal carcinoma to date, our study concludes that the OS and DSS of buccal cancer are similar to those of cancers in other oral cavity sites once age at diagnosis, tumor stage, treatment, and race are taken into consideration. The previously perceived poor prognosis of buccal carcinoma may be due to variations in tumor presentation, such as later stage and older patient age. 2b. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Usefulness of BCOR gene mutation as a prognostic factor in acute myeloid leukemia with intermediate cytogenetic prognosis.

Science.gov (United States)

Terada, Kazuki; Yamaguchi, Hiroki; Ueki, Toshimitsu; Usuki, Kensuke; Kobayashi, Yutaka; Tajika, Kenji; Gomi, Seiji; Kurosawa, Saiko; Saito, Riho; Furuta, Yutaka; Miyadera, Keiki; Tokura, Taichiro; Marumo, Atushi; Omori, Ikuko; Sakaguchi, Masahiro; Fujiwara, Yusuke; Yui, Shunsuke; Ryotokuji, Takeshi; Arai, Kunihito; Kitano, Tomoaki; Wakita, Satoshi; Fukuda, Takahiro; Inokuchi, Koiti

2018-04-16

BCOR gene is a transcription regulatory factor that plays an essential role in normal hematopoiesis. The wider introduction of next-generation sequencing technology has led to reports in recent years of mutations in the BCOR gene in acute myeloid leukemia (AML), but the related clinical characteristics and prognosis are not sufficiently understood. We investigated the clinical characteristics and prognosis of 377 de novo AML cases with BCOR or BCORL1 mutation. BCOR or BCORL1 gene mutations were found in 28 cases (7.4%). Among cases aged 65 years or below that were also FLT3-ITD-negative and in the intermediate cytogenetic prognosis group, BCOR or BCORL1 gene mutations were observed in 11% of cases (12 of 111 cases), and this group had significantly lower 5-year overall survival (OS) (13.6% vs. 55.0%, P=0.0021) and relapse-free survival (RFS) (14.3% vs. 44.5%, P=0.0168) compared to cases without BCOR or BCORL1 gene mutations. Multivariate analysis demonstrated that BCOR mutations were an independent unfavorable prognostic factor (P=0.0038, P=0.0463) for both OS and RFS. In cases of AML that are FLT3-ITD-negative, aged 65 years or below, and in the intermediate cytogenetic prognosis group, which are considered to have relatively favorable prognosis, BCOR gene mutations appear to be an important prognostic factor. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

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Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide

CERN Document Server

Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P

2007-01-01

Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.

Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

DEFF Research Database (Denmark)

Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

Metal concentration at surface water using multivariate analysis and ...

African Journals Online (AJOL)

Metal concentration at surface water using multivariate analysis and human health risk assessment. F Azaman, H Juahir, K Yunus, A Azid, S.I. Khalit, A.D. Mustafa, M.A. Amran, C.N.C. Hasnam, M.Z.A.Z. Abidin, M.A.M. Yusri ...

Pretreatment neutrophil to lymphocyte ratio in determining the prognosis of head and neck cancer: a meta-analysis.

Science.gov (United States)

Yu, Yalian; Wang, Hongbo; Yan, Aihui; Wang, Hailong; Li, Xinyao; Liu, Jiangtao; Li, Wei

2018-04-04

Recent studies have reported a relationship between prognosis and neutrophil-to-lymphocyte ratio (NLR) in patients with head and neck cancer (HNC). As the results are still controversial, we conducted a meta-analysis of pretreatment NLR in peripheral blood and prognosis in HNC patients. We retrieved articles from PubMed, Medline, Cochrane Library, Embase and Web of Science. A comparative analysis was conducted for the effect of pretreatment NLR in peripheral blood on overall survival (OS), progression-free survival, disease-free survival (DFS), disease-specific survival, metastasis-free survival, and recurrence-free survival of HNC patients. The analysis applied the criteria for systematic reviews described in the Cochrane Handbook and was conducted using hazard ratios (HRs) to estimate effect size, and calculated by Stata/SE version 13.0. The meta-analysis included eligible cohort studies (5475 cases). The OS data indicated increased mortality risk in HNC patients with a high NLR (HR = 1.84, 95% confidence interval (CI): 1.53-2.23; P Analysis of subgroups stratified by NLR cutoff values revealed increased mortality risk and significantly shorter DFS in patients with high NLR compared to those with low NLR (HR = 2.18, 95% CI: 1.46-3.24; P analysis results were stable, as shown by sensitivity analysis. No publication bias was detected by the Egger test (P = 0.135). HNC patients with elevated pretreatment NLR in peripheral blood have poor prognosis and are prone to local invasion and distant metastasis. NLR values are easily obtained from routinely collected blood samples and could assist clinicians to determine prognosis of HNC patients.

Multivariate statistical analysis of atom probe tomography data

International Nuclear Information System (INIS)

Parish, Chad M.; Miller, Michael K.

2010-01-01

The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.

Colorectal cancer prognosis depends on T-cell infiltration and molecular characteristics of the tumor.

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Dahlin, Anna M; Henriksson, Maria L; Van Guelpen, Bethany; Stenling, Roger; Oberg, Ake; Rutegård, Jörgen; Palmqvist, Richard

2011-05-01

The aim of this study was to relate the density of tumor infiltrating T cells to cancer-specific survival in colorectal cancer, taking into consideration the CpG island methylator phenotype (CIMP) and microsatellite instability (MSI) screening status. The T-cell marker CD3 was stained by immunohistochemistry in 484 archival tumor tissue samples. T-cell density was semiquantitatively estimated and scored 1-4 in the tumor front and center (T cells in stroma), and intraepithelially (T cells infiltrating tumor cell nests). Total CD3 score was calculated as the sum of the three CD3 scores (range 3-12). MSI screening status was assessed by immunohistochemistry. CIMP status was determined by quantitative real-time PCR (MethyLight) using an eight-gene panel. We found that patients whose tumors were highly infiltrated by T cells (total CD3 score ≥7) had longer survival compared with patients with poorly infiltrated tumors (total CD3 score ≤4). This finding was statistically significant in multivariate analyses (multivariate hazard ratio, 0.57; 95% confidence interval, 0.31-1.00). Importantly, the finding was consistent in rectal cancer patients treated with preoperative radiotherapy. Although microsatellite unstable tumor patients are generally considered to have better prognosis, we found no difference in survival between microsatellite unstable and microsatellite stable (MSS) colorectal cancer patients with similar total CD3 scores. Patients with MSS tumors highly infiltrated by T cells had better prognosis compared with intermediately or poorly infiltrated microsatellite unstable tumors (log rank P=0.013). Regarding CIMP status, CIMP-low was associated with particularly poor prognosis in patients with poorly infiltrated tumors (multivariate hazard ratio for CIMP-low versus CIMP-negative, 3.07; 95% confidence interval, 1.53-6.15). However, some subset analyses suffered from low power and are in need of confirmation by independent studies. In conclusion, patients whose

Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

KAUST Repository

Malvaso, Federica

2015-05-05

Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

International Nuclear Information System (INIS)

Martin, Madhavi Z.; Labbe, Nicole; Rials, Timothy G.; Wullschleger, Stan D.

2005-01-01

In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate

Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

Energy Technology Data Exchange (ETDEWEB)

Martin, Madhavi Z. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States); Labbe, Nicole [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States)]. E-mail: nlabbe@utk.edu; Rials, Timothy G. [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States); Wullschleger, Stan D. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States)

2005-08-31

In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate.

Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

Czech Academy of Sciences Publication Activity Database

Novák, J.; Sůvová, H.; Vondráček, Jiří

2002-01-01

Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

Science.gov (United States)

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Science.gov (United States)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

International Nuclear Information System (INIS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-01-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Energy Technology Data Exchange (ETDEWEB)

Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

2004-06-15

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Multivariate statistics exercises and solutions

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

MLH1 Promoter Methylation and Prediction/Prognosis of Gastric Cancer: A Systematic Review and Meta and Bioinformatic Analysis.

Science.gov (United States)

Shen, Shixuan; Chen, Xiaohui; Li, Hao; Sun, Liping; Yuan, Yuan

2018-01-01

Background: The promoter methylation of MLH1 gene and gastric cancer (GC)has been investigated previously. To get a more credible conclusion, we performed a systematic review and meta and bioinformatic analysis to clarify the role of MLH1 methylation in the prediction and prognosis of GC. Methods: Eligible studies were targeted after searching the PubMed, Web of Science, Embase, BIOSIS, CNKI and Wanfang Data to collect the information of MLH1 methylation and GC. The link strength between the two was estimated by odds ratio with its 95% confidence interval. The Newcastle-Ottawa scale was used for quantity assessment . Subgroup and sensitivity analysis were conducted to explore sources of heterogeneity. The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were employed for bioinformatics analysis on the correlation between MLH1 methylation and GC risk, clinicopathological behavior as well as prognosis. Results: 2365 GC and 1563 controls were included in the meta-analysis. The pooled OR of MLH1 methylation in GC was 4.895 (95% CI: 3.149-7.611, PMLH1 methylation enhanced GC risk but might not related with GC clinicopathological features and prognosis. Conclusion: MLH1 methylation is an alive biomarker for the prediction of GC and it might not affect GC behavior. Further study could be conducted to verify the impact of MLH1 methylation on GC prognosis.

Influence of Preoperative Serum Aspartate Aminotransferase (AST Level on the Prognosis of Patients with Non-Small Cell Lung Cancer

Directory of Open Access Journals (Sweden)

Shu-Lin Chen

2016-09-01

Full Text Available The purpose of this work is to analyze preoperative serum aspartate aminotransferase (AST levels and their effect on the prognosis of patients with non-small cell lung cancer (NSCLC after surgical operation. These analyses were performed retrospectively in patients with NSCLC followed by surgery; participants were recruited between January 2004 and January 2008. All clinical information and laboratory results were collected from medical records. We explored the association between preoperative serum AST and recurrence-free survival (RFS, and the overall survival (OS of NSCLC patients. Kaplan–Meier analysis and Cox multivariate analysis, stratified by the AST median value, were used to evaluate the prognostic effect. A chi-squared test was performed to compare clinical characteristics in different subgroups. A p-value of ≤0.05 was considered to be statistically significant. A total of 231 patients were enrolled. The median RFS and OS were 22 and 59 months, respectively. The AST levels were divided into two groups, using a cut-off value of 19 U/L: High AST (>19 U/L, n = 113 vs. low AST (≤19 U/L, n = 118. Multivariate analysis indicated that preoperative serum AST > 19 U/L (hazard ratio (HR = 0.685, 95% confidence interval (CI: 0.493–0.994, p = 0.046 for RFS, HR = 0.646, 95% CI: 0.438–0.954, p = 0.028 for OS was an independent prognostic factor for both RFS and OS. High preoperative serum AST levels may serve as a valuable marker to predict the prognosis of NSCLC after operation.

Texture analysis of {sup 18}F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy

Energy Technology Data Exchange (ETDEWEB)

Nakajo, Masatoyo; Jinguji, Megumi; Nakabeppu, Yoshiaki; Higashi, Ryutarou; Fukukura, Yoshihiko; Yoshiura, Takashi [Kagoshima University, Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima (Japan); Nakajo, Masayuki [Nanpuh Hospital, Department of Radiology, Kagoshima (Japan); Sasaki, Ken; Uchikado, Yasuto; Natsugoe, Shoji [Kagoshima University, Department of Digestive Surgery, Breast and Thyroid Surgery, Graduate School of Medical and Dental Sciences, Kagoshima (Japan)

2017-02-15

This retrospective study was done to examine whether the heterogeneity in primary tumour F-18-fluorodeoxyglucose ({sup 18}F-FDG) distribution can predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy (CRT). The enrolled 52 patients with esophageal cancer underwent {sup 18}F-FDG-PET/CT studies before CRT. SUVmax, SUVmean, metabolic tumour volume (MTV, SUV ≥ 2.5), total lesion glycolysis (TLG) and six heterogeneity parameters assessed by texture analysis were obtained. Patients were classified as responders or non-responders according to Response Evaluation Criteria in Solid Tumors. Progression-free survival (PFS) and overall survival (OS) were calculated by the Kaplan-Meier method. Prognostic significance was assessed by Cox proportional hazards analysis. Thirty four non-responders showed significantly higher MTV (p = 0.006), TLG (p = 0.007), intensity variability (IV; p = 0.003) and size-zone variability (SZV; p = 0.004) than 18 responders. The positive and negative predictive values for non-responders were 77 % and 69 % in MTV, 76 % and 100 % in TLG, 78 % and 67 % in IV and 78 % and 82 % in SZV, respectively. Although PFS and OS were significantly shorter in patients with high MTV (PFS, p = 0.018; OS, p = 0.014), TLG (PFS, p = 0.009; OS, p = 0.025), IV (PFS, p = 0.013; OS, p = 0.007) and SZV (PFS, p = 0.010; OS, p = 0.007) at univariate analysis, none of them was an independent factor, while lymph node status, stage and tumour response status were independent factors at multivariate analysis. Texture features IV and SZV, and volumetric parameters MTV and TLG can predict tumour response, but all of them have limited value in prediction of prognosis of patients with esophageal cancer treated by CRT. (orig.)

Sarcopenia in COPD: relationship with COPD severity and prognosis

Science.gov (United States)

Costa, Tatiana Munhoz da Rocha Lemos; Costa, Fabio Marcelo; Moreira, Carolina Aguiar; Rabelo, Leda Maria; Boguszewski, César Luiz; Borba, Victória Zeghbi Cochenski

2015-01-01

Objective: To evaluate the prevalence of sarcopenia in COPD patients, as well as to determine whether sarcopenia correlates with the severity and prognosis of COPD. Methods: A cross-sectional study with COPD patients followed at the pulmonary outpatient clinic of our institution. The patients underwent dual-energy X-ray absorptiometry. The diagnosis of sarcopenia was made on the basis of the skeletal muscle index, defined as appendicular lean mass/height2 only for low-weight subjects and adjusted for fat mass in normal/overweight subjects. Disease severity (COPD stage) was evaluated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria. The degree of obstruction and prognosis were determined by the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity (BODE) index. Results: We recruited 91 patients (50 females), with a mean age of 67.4 ± 8.7 years and a mean BMI of 25.8 ± 6.1 kg/m2. Sarcopenia was observed in 36 (39.6%) of the patients, with no differences related to gender, age, or smoking status. Sarcopenia was not associated with the GOLD stage or with FEV1 (used as an indicator of the degree of obstruction). The BMI, percentage of body fat, and total lean mass were lower in the patients with sarcopenia than in those without (p < 0.001). Sarcopenia was more prevalent among the patients in BODE quartile 3 or 4 than among those in BODE quartile 1 or 2 (p = 0.009). The multivariate analysis showed that the BODE quartile was significantly associated with sarcopenia, regardless of age, gender, smoking status, and GOLD stage. Conclusions: In COPD patients, sarcopenia appears to be associated with unfavorable changes in body composition and with a poor prognosis. PMID:26578132

Small cell lung cancer with and without superior vena cava syndrome: a multivariate analysis of prognostic factors in 408 cases

International Nuclear Information System (INIS)

Wuerschmidt, Florian; Buenemann, Henry; Heilmann, Hans-Peter

1995-01-01

Purpose: Patients with small cell lung cancer (SCLC) and superior vena cava syndrome (SVCS) are widely believed to have a grave prognosis. The purpose of this study was to determine the prognosis of patients with SCLC and SVCS as compared to SCLC without SVCS. Methods and Materials: A retrospective analysis of 408 cases of SCLC ± SVCS was performed. Three-hundred and sixty showed no clinical signs of SVCS and 43 (11%) had SVCS; in 5 patients no adequate information was available about clinical signs of SVCS. All patients were classified as limited disease cases. About 98% received chemotherapy usually as the first treatment followed by radiotherapy. A median total dose of 46 Gy (range 30 to 70 Gy) was given at 2.0 Gy per fraction five times weekly. A prophylactic cranial irradiation was applied if a complete remission was achieved after chemotherapy or after 30 Gy of irradiation. Kaplan-Meier survival curves are shown and comparisons were made by the log-rank and the Gehan/Wilcoxon test. To adjust for prognostic factors, a proportional hazards analysis was done. Results: Patients without SVCS had 5-year survival rates (± SE) and a median survival time (MST; 95% confidence intervals) of 11% ± 2% and 13.7 months (12.7-14.5) in UICC Stage I to III; in Stage III the figures were 9% ± 2% and 12.6 months (11.2-13.7). In comparison, SCLC with SVCS had 5-year survival rates of 15% ± 7% and MST of 16.1 months (13.8-20.5). The difference was significant in univariate analysis (Stage III disease: p 0.008 by the log-rank test). In a multivariate analysis of all patients, Stage (Stage I + II > III; p = 0.0003), SVCS (yes > no; p = 0.005), and Karnofsky performance status (≤ 70 < 80-100%; p = 0.008) were of significant importance. Conclusions: SVCS is a favorable prognostic sign in SCLC. The treatment should be curatively intended

Study of ionically modified water performance in carbonate reservoir system by multivariate data analysis

DEFF Research Database (Denmark)

Sohal, Muhammad Adeel Nassar; Kucheryavskiy, Sergey V.; Thyne, Geoffrey

2017-01-01

the critical mechanisms at the pore scale. Better pore scale physico-chemical understanding will guide to formulate accurate reservoir-scale models. This paper presents a comprehensive meta-analysis of the proposed mechanisms using multivariate data analysis. Detailed review of the subject, including...... mechanisms with supporting and contradictory evidence has been presented by Sohal et al. (2016). In this study, the significance of each contributing factor to EOR was quantified and subjected to rigorous multivariate statistical analysis. The analysis was limited because there is no uniform methodology...

The studies of post-medieval glass by multivariate and X-ray fluorescence analysis

International Nuclear Information System (INIS)

Kierzek, J.; Kunicki-Goldfinger, J.

2002-01-01

Multivariate statistical analysis of the results obtained by energy dispersive X-ray fluorescence analysis has been used in the study of baroque vessel glasses originated from central Europe. X-ray spectrometry can be applied as a completely non-destructive, non-sampling and multi-element method. It is very useful in the studies of valuable historical artefacts. For the last years, multivariate statistical analysis has been developed as an important tool for the archaeometric purposes. Cluster, principal component and discriminant analysis were applied for the classification of the examined objects. The obtained results show that these statistical tools are very useful and complementary in the studies of historical objects. (author)

Lymphocyte-Related Inflammation and Immune-Based Scores Predict Prognosis of Chordoma Patients After Radical Resection

Directory of Open Access Journals (Sweden)

Wenhao Hu

2018-04-01

Full Text Available The inflammatory microenvironment plays a critical role in the development and progression of malignancies. In the present study, we aimed to evaluate the prognostic value of lymphocyte-related inflammation and immune-based prognostic scores in patients with chordoma after radical resection, including the neutrophil-lymphocyte ratio (NLR, platelet-lymphocyte ratio (PLR, monocyte-lymphocyte ratio (MLR, and systemic immune-inflammation index (SII. A total of 172 consecutive patients with chordoma who underwent radical resection were reviewed. R software was used to randomly select 86 chordoma patients as a training set and 86 chordoma patients as a validation set. Potential prognostic factors were also identified, including age, sex, tumor localization, KPS, Enneking stage, tumor size, and tumor metastasis. Overall survival (OS was calculated using the Kaplan–Meier method and multivariate Cox regression analyses. NLR, PLR, SII, Enneking stage, tumor differentiation and tumor metastasis were identified as significant factors from the univariate analysis in both the training and validation sets and were subjected to multivariate Cox proportional hazards analysis. The univariate analysis showed that NLR ≥1.65, PLR ≥121, and SII ≥370×109/L were significantly associated with poor OS. In the multivariate Cox proportional hazard analysis, SII, Enneking stage and tumor metastasis were significantly associated with OS. As noninvasive, low-cost, reproducible prognostic biomarkers, NLR, PLR and SII could help predict poor prognosis in patients with chordoma after radical resection. This finding may contribute to the development of more effective tailored therapy according to the characteristics of individual tumors.

Multivariable analysis: a practical guide for clinicians and public health researchers

National Research Council Canada - National Science Library

Katz, Mitchell H

2011-01-01

.... It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae...

p53 and PCNA expression in advanced colorectal cancer: response to chemotherapy and long-term prognosis.

Science.gov (United States)

Paradiso, A; Rabinovich, M; Vallejo, C; Machiavelli, M; Romero, A; Perez, J; Lacava, J; Cuevas, M A; Rodriquez, R; Leone, B; Sapia, M G; Simone, G; De Lena, M

1996-12-20

In a series of 71 patients with advanced colorectal cancer treated with biochemically modulated 5-fluorouracil (5-FU) and methotrexate (MTX), we investigated the relationship between the proliferating-cell nuclear antigen (PCNA) (PC10) and p53 (Pab1801) primary-tumor immunohistochemical expression with respect to clinical response and long-term prognosis. Nuclear p53 expression was demonstrated in 44% of samples (any number of positive tumor cells) while all tumors showed a certain degree of PCNA immunostaining. PCNA immunostaining was correlated with histopathologic grade and p53 expression, while p53 was not correlated with any of the parameters considered. The probability of clinical response to biochemically modulated 5-FU was independent of p53 and PCNA expression. p53 expression (all cut-off values) was not associated with short- or long-term clinical prognosis, whereas patients with higher PCNA primary-tumor expression showed longer survival from treatment and survival from diagnosis, according to univariate and multivariate analysis, particularly in the sub-set of colon-cancer patients. We conclude that the clinical response of advanced-colorectal-cancer patients to biochemically modulated 5-FU and MTX cannot be predicted by PCNA and p53 primary-tumor expression, but high PCNA expression appears to be independently related to long-term prognosis.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

Science.gov (United States)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Use of multivariate extensions of generalized linear models in the analysis of data from clinical trials

OpenAIRE

ALONSO ABAD, Ariel; Rodriguez, O.; TIBALDI, Fabian; CORTINAS ABRAHANTES, Jose

2002-01-01

In medical studies the categorical endpoints are quite often. Even though nowadays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed. multivariate analysis; multivariate logistic regression; multicategor...

Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

Science.gov (United States)

Lin, Deborah S; Taylor, Peter; Tibbett, Mark

2014-10-01

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Immunophenotypes and Immune Markers Associated with Acute Promyelocytic Leukemia Prognosis

Directory of Open Access Journals (Sweden)

Fang Xu

2014-01-01

Full Text Available CD2+, CD34+, and CD56+ immunophenotypes are associated with poor prognoses of acute promyelocytic leukemia (APL. The present study aimed to explore the role of APL immunophenotypes and immune markers as prognostic predictors on clinical outcomes. A total of 132 patients with de novo APL were retrospectively analyzed. Immunophenotypes were determined by flow cytometry. Clinical features, complete remission (CR, relapse, and five-year overall survival (OS rate were assessed and subjected to multivariate analyses. The CD13+CD33+HLA-DR-CD34− immunophenotype was commonly observed in patients with APL. Positive rates for other APL immune markers including cMPO, CD117, CD64, and CD9 were 68.7%, 26%, 78.4%, and 96.6%, respectively. When compared with patients with CD2− APL, patients with CD2+ APL had a significantly higher incidence of early death (50% versus 15.7%; P=0.016, lower CR rate (50% versus 91.1%; P=0.042, and lower five-year OS rate (41.7% versus 74.2%; P=0.018. White blood cell (WBC count before treatment was found to be the only independent risk factor of early death, CR failure, and five-year mortality rate. Flow cytometric immunophenotype analysis can facilitate prompt APL diagnosis. Multivariate analysis has demonstrated that WBC count before treatment is the only known independent risk factor that predicts prognosis for APL in this study population.

Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

International Nuclear Information System (INIS)

Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T.

1993-01-01

149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs

Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

Energy Technology Data Exchange (ETDEWEB)

Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

1993-07-01

149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

Classification of adulterated honeys by multivariate analysis.

Science.gov (United States)

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

Science.gov (United States)

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

EMMPRIN co-expressed with matrix metalloproteinases predicts poor prognosis in patients with osteosarcoma.

Science.gov (United States)

Futamura, Naohisa; Nishida, Yoshihiro; Urakawa, Hiroshi; Kozawa, Eiji; Ikuta, Kunihiro; Hamada, Shunsuke; Ishiguro, Naoki

2014-06-01

Several studies have focused on the relationships between the expression of extracellular matrix metalloproteinase inducer (EMMPRIN) and the prognosis of patients with malignant tumors. However, few of these have investigated the expression of EMMPRIN in osteosarcoma. We examined expression levels of EMMPRIN immunohistochemically in 53 cases of high-grade osteosarcoma of the extremities and analyzed the correlation of its expression with patient prognosis. The correlation between matrix metalloproteinases (MMPs) and EMMPRIN expression and the prognostic value of co-expression were also analyzed. Staining positivity for EMMPRIN was negative in 7 cases, low in 17, moderate in 19, and strong in 10. The overall and disease-free survivals (OS and DFS) in patients with higher EMMPRIN expression (strong-moderate) were significantly lower than those in the lower (weak-negative) group (0.037 and 0.024, respectively). In multivariate analysis, age (P=0.004), location (P=0.046), and EMMPRIN expression (P=0.038) were significant prognostic factors for overall survival. EMMPRIN expression (P=0.024) was also a significant prognostic factor for disease-free survival. Co-expression analyses of EMMPRIN and MMPs revealed that strong co-expression of EMMPRIN and membrane-type 1 (MT1)-MMP had a poor prognostic value (P=0.056 for DFS, P=0.006 for OS). EMMPRIN expression and co-expression with MMPs well predict the prognosis of patients with extremity osteosarcoma, making EMMPRIN a possible therapeutic target in these patients.

Application of monitoring, diagnosis, and prognosis in thermal performance analysis for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Kim, Hyeong Min; Heo, Gyun Young [Kyung Hee University, Yongin (Korea, Republic of); Na, Man Gyun [Chosun University, Gwangju (Korea, Republic of)

2014-12-15

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

Application of monitoring, diagnosis, and prognosis in thermal performance analysis for nuclear power plants

International Nuclear Information System (INIS)

Kim, Hyeong Min; Heo, Gyun Young; Na, Man Gyun

2014-01-01

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

Nuclear Ep-ICD expression is a predictor of poor prognosis in "low risk" prostate adenocarcinomas.

Directory of Open Access Journals (Sweden)

Jasmeet Assi

Full Text Available Molecular markers for predicting prostate cancer (PCa that would have poor prognosis are urgently needed for a more personalized treatment for patients. Regulated intramembrane proteolysis of Epithelial cell adhesion molecule results in shedding of the extracellular domain (EpEx and release of its intracellular domain (Ep-ICD which triggers oncogenic signaling and might correlate to tumor aggressiveness. This study aimed to explore the potential of Ep-ICD and EpEx to identify PCa that have poor prognosis.Immunohistochemical analysis of Ep-ICD and EpEx was carried out in normal prostate tissues (n = 100, benign prostate hyperplasia (BPH, n = 83, and prostate cancer (n = 249 using domain specific antibodies. The expression of Ep-ICD and EpEx was correlated with clinico- pathological parameters and disease free survival (DFS.Reduced expression of nuclear Ep-ICD and membrane EpEx was observed in PCa in comparison with BPH and normal prostate tissues (p = 0.006, p < 0.001 respectively. For patients who had PCa with Gleason Score less than 7, preserved nuclear Ep-ICD emerged as the most significant marker in multivariate analysis for prolonged DFS, where these patients did not have recurrence during follow up of up to 12 years (p = 0.001.Reduced expression of nuclear Ep-ICD was associated with shorter disease free survival in patients with a Gleason Score less than 7 and may be useful in identifying patients likely to have aggressive tumors with poor prognosis. Furthermore, nuclear Ep-ICD can differentiate between normal and prostate cancer tissues for ambiguous cases.

Denial-of-service attack detection based on multivariate correlation analysis

NARCIS (Netherlands)

Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

2011-01-01

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

Evolution of noninvasive tests of liver fibrosis is associated with prognosis in patients with chronic hepatitis C.

Science.gov (United States)

Vergniol, Julien; Boursier, Jérôme; Coutzac, Clélia; Bertrais, Sandrine; Foucher, Juliette; Angel, Camille; Chermak, Faiza; Hubert, Isabelle Fouchard; Merrouche, Wassil; Oberti, Frédéric; de Lédinghen, Victor; Calès, Paul

2014-07-01

No data are available about the prediction of long-term survival using repeated noninvasive tests of liver fibrosis in chronic hepatitis C (CHC). We aimed to assess the prognostic value of 3-year liver stiffness measurement (LSM), aspartate aminotransferase to platelet ratio index (APRI), and fibrosis 4 (FIB-4) evolution in CHC. CHC patients with two LSM (1,000-1,500 days interval) were prospectively included. Blood fibrosis tests APRI and FIB-4 were calculated the day of baseline (bLSM) and follow-up (fLSM) LSM. Evolution of fibrosis tests was expressed as delta: (follow-up-baseline results)/duration. Date and cause of death were recorded during follow-up that started the day of fLSM. In all, 1,025 patients were included. Median follow-up after fLSM was 38.0 months (interquartile range [IQR]: 27.7-46.1) during which 35 patients died (14 liver-related death) and seven had liver transplantation. Prognostic accuracy (Harrell C-index) of multivariate models including baseline and delta results was not significantly different between LSM and FIB-4 (P ≥ 0.24), whereas FIB-4 provided more accurate prognostic models than APRI (P = 0.03). By multivariate analysis including LSM variables, overall survival was independently predicted by bLSM, delta (dLSM), and sustained virological response (SVR). Prognosis was excellent in patients having bLSM 0 kPa/year) in ≥ 14 kPa bLSM had the worst prognosis. Baseline and delta FIB-4 also identified patient subgroups with significantly different prognosis. Three-year evolution of noninvasive tests of liver fibrosis has a strong prognostic value in CHC patients. These tests should be repeated to monitor patients and predict their outcome. © 2014 by the American Association for the Study of Liver Diseases.

Association between H3K4 methylation and cancer prognosis: A meta-analysis.

Science.gov (United States)

Li, Simin; Shen, Luyan; Chen, Ke-Neng

2018-05-08

Histone H3 lysine 4 methylation (H3K4 methylation), including mono-methylation (H3K4me1), di-methylation (H3K4me2), or tri-methylation (H3K4me3), is one of the epigenetic modifications to histone proteins, which are related to the transcriptional activation of genes. H3K4 methylation has both tumor inhibiting and promoting effects, and the prognostic value of H3K4 methylation in cancer remains controversial. Therefore, we performed a systematic review and meta-analysis to examine the association between H3K4 methylation and cancer prognosis. A comprehensive search of PubMed, Web of Science, ScienceDirect, Embase, and Ovid databases was conducted to identify studies investigating the association between H3K4 methylation and prognosis of patients with malignant tumors. The data and characteristics of each study were extracted, and the hazard ratio (HR) at a 95% confidence interval (CI) was calculated to estimate the effect. A total of 1474 patients in 10 studies were enrolled in this meta-analysis. The pooled HR of 1.52 (95% CI 1.02-2.26) indicated that patients with a lower level of H3K4me2 expression were expected to have shorter overall survival, while the pooled HR of 0.45 (95% CI 0.27-0.74) indicated that patients with a lower level of H3K4me3 expression were expected to have longer overall survival. This meta-analysis indicates that increased H3K4me3 expression and decreased H3K4me2 expression might be predictive factors of poor prognosis in cancer. Further large cohort studies are needed to confirm these findings. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Long-term prognosis in patients continuing taking antithrombotics after peptic ulcer bleeding.

Science.gov (United States)

Wang, Xi-Xu; Dong, Bo; Hong, Biao; Gong, Yi-Qun; Wang, Wei; Wang, Jue; Zhou, Zhen-Yu; Jiang, Wei-Jun

2017-01-28

To investigate the long-term prognosis in peptic ulcer patients continuing taking antithrombotics after ulcer bleeding, and to determine the risk factors that influence the prognosis. All clinical data of peptic ulcer patients treated from January 1, 2009 to January 1, 2014 were retrospectively collected and analyzed. Patients were divided into either a continuing group to continue taking antithrombotic drugs after ulcer bleeding or a discontinuing group to discontinue antithrombotic drugs. The primary outcome of follow-up in peptic ulcer bleeding patients was recurrent bleeding, and secondary outcome was death or acute cardiovascular disease occurrence. The final date of follow-up was December 31, 2014. Basic demographic data, complications, and disease classifications were analyzed and compared by t - or χ 2 -test. The number of patients that achieved various outcomes was counted and analyzed statistically. A survival curve was drawn using the Kaplan-Meier method, and the difference was compared using the log-rank test. COX regression multivariate analysis was applied to analyze risk factors for the prognosis of peptic ulcer patients. A total of 167 patients were enrolled into this study. As for the baseline information, differences in age, smoking, alcohol abuse, and acute cardiovascular diseases were statistically significant between the continuing and discontinuing groups (70.8 ± 11.4 vs 62.4 ± 12.0, P peptic ulcer bleeding, continuing antithrombotics increases the risk of recurrent bleeding events, while discontinuing antithrombotics would increase the risk of death and developing cardiovascular disease. This suggests that clinicians should comprehensively consider the use of antithrombotics after peptic ulcer bleeding.

Lymphotoxin alpha (LTA polymorphism is associated with prognosis of non-Hodgkin's lymphoma in a Chinese population.

Directory of Open Access Journals (Sweden)

Yan Zhang

Full Text Available BACKGROUND: Non-Hodgkin's lymphoma (NHL has been widely reported to be associated with autoimmune and pro-inflammatory response, and genetic polymorphisms of candidate genes involved in autoimmune and pro-inflammatory response may influence the survival and prognosis of NHL patients. To evaluate the role of such genetic variations in prognosis of NHL, we conducted this study in a Chinese population. METHODS: We used the TaqMan assay to genotype six single nucleotide polymorphisms (SNPs (TNF rs1799964T>C, LTA rs1800683G>A, IL-10 rs1800872T>G, LEP rs2167270G>A, LEPR rs1327118C>G, TNFAIP8 rs1045241C>T for 215 NHL cases. Kaplan-Meier analysis was performed to compare progression free survival among two common genotypes. Cox proportional hazard models were used to identify independent risk factors. RESULTS: We observed that LTA rs1800683G>A was significantly associated with risk of progression or relapse in NHL patients (HR = 1.63, 95%CI = 1.06-2.51; P = 0.028, particularly in Diffuse large B cell lymphoma (DLBCL cases (HR = 1.50, 95%CI = 1.10-2.04, P = 0.01. Both univariate and multivariate Cox regression analysis showed that in DLBCL patients, Ann Arbor stage III/IV, elevated LDH level before treatment and LTA rs1800683 AA genotype carrier were independent risk factors for progression or relapse. While in NK/T cell lymphoma, Ann Arbor stage III/IV and elevated β2-MG level before treatment indicated poorer prognosis. CONCLUSIONS: The polymorphism of LTA rs1800683G>A contributes to NHL prognosis in a Chinese population. Further large-scale and well-designed studies are needed to confirm these results.

Serum alpha-fetoprotein response can predict prognosis in hepatocellular carcinoma patients undergoing radiofrequency ablation therapy

Energy Technology Data Exchange (ETDEWEB)

Kao, W.-Y. [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China); Chiou, Y.-Y., E-mail: yychiou@vghtpe.gov.tw [Department of Radiology, Taipei Veterans General Hospital, Taiwan (China); Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Hung, H.-H. [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China); Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Su, C.-W., E-mail: cwsu2@vghtpe.gov.tw [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China); Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Chou, Y.-H. [Department of Radiology, Taipei Veterans General Hospital, Taiwan (China); Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Wu, J.-C. [Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Department of Medical Research and Education, Taipei Veterans General Hospital, Taiwan (China); Huo, T.-I. [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China); Institute of Pharmacology, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Huang, Y.-H. [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China); Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan (China); Wu, W.-C. [Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (China)

2012-05-15

Aims: To evaluate the clinical inference of serum alpha-fetoprotein (AFP) response in hepatocellular carcinoma (HCC) patients undergoing percutaneous radiofrequency ablation (RFA). Materials and methods: Three hundred and thirteen previously untreated HCC patients were enrolled in the study. The optimal AFP response was defined as >20% decrease from baseline after 1 month of RFA for those with a baseline AFP level of {>=}100 ng/ml. The impact of AFP response on prognosis was analysed and prognostic factors were assessed. Results: After a median follow-up of 26.7 {+-} 19.1 months, 49 patients died and 264 patients were alive. The cumulative 5 year survival rates were 75.3 and 57.4% in patients with an initial AFP of <100 ng/ml and {>=}100 ng/ml, respectively (p = 0.003). In the 58 patients with a baseline AFP of {>=}100 ng/ml and initial completed tumour necrosis after RFA, the cumulative 5 year survival rates were 62.4 and 25.7% in optimal and non-optimal AFP responders, respectively (p = 0.001). By multivariate analysis, the prothrombin time international normalized ratio >1.1 (p = 0.009), non-optimal AFP response (p = 0.023), and creatinine >1.5 mg/dl (p = 0.021) were independent risk factors predictive of poor overall survival. Besides, the cumulative 5 year recurrence rates were 83.4 and 100% in optimal and non-optimal AFP responders, respectively (p < 0.001). Multivariate analysis demonstrated platelet count {<=}10{sup 5}/mm{sup 3} (p = 0.048), tumour size >2 cm (p = 0.027), and non-optimal AFP response (p < 0.001) were independent risk factors associated with tumour recurrence after RFA. Conclusions: Serum AFP response may be a useful marker for predicting prognosis in HCC patients undergoing RFA.

Hierarchical multivariate covariance analysis of metabolic connectivity.

Science.gov (United States)

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Gender, histology, and time of diagnosis are important factors for prognosis: analysis of 1499 never-smokers with advanced non-small cell lung cancer in Japan.

Science.gov (United States)

Kawaguchi, Tomoya; Takada, Minoru; Kubo, Akihito; Matsumura, Akihide; Fukai, Shimao; Tamura, Atsuhisa; Saito, Ryusei; Kawahara, Masaaki; Maruyama, Yosihito

2010-07-01

There has been a growing interest in lung cancer in never-smokers. Utilizing a database from the National Hospital Study Group for Lung Cancer, information for never-smokers and ever-smokers with advanced non-small cell lung cancer was obtained from 1990 to 2005, including clinicopathologic characteristics, chemotherapy response, and survival data. Time of diagnosis was classified into two periods: 1990-1999 and 2000-2005. Multivariate analysis was performed using the Cox regression and logistic regression method, including gender, age, performance status, histology, stage, and period of diagnosis. There were 1499 never-smokers and 3455 ever-smokers with advanced stage IIIB and IV diseases who received cytotoxic chemotherapy. Never-smokers generally included more females, were younger, with better performance status and more adenocarcinoma diagnosed (p time of diagnosis are important factors for prognosis in these patients.

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

DEFF Research Database (Denmark)

Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

2006-01-01

Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

Understanding Cancer Prognosis

Medline Plus

Full Text Available ... disease will go for you is called prognosis. It can be hard to understand what prognosis means ... prognosis include: The type of cancer and where it is in your body The stage of the ...

TROP2 correlates with microvessel density and poor prognosis in hilar cholangiocarcinoma.

Science.gov (United States)

Ning, Shanglei; Guo, Sen; Xie, Jianjun; Xu, Yunfei; Lu, Xiaofei; Chen, Yuxin

2013-02-01

Trophoblast cell surface antigen 2 (TROP2) was found to be associated with tumor progression and poor prognosis in a variety of epithelial carcinomas. The aim of the study was to investigate TROP2 expression and its prognostic impact in hilar cholangiocarcinoma. Immunohistochemistry and quantitative real-time PCR were used to determine TROP2 expression in surgical specimens from 70 hilar cholangiocarcinoma patients receiving radical resection. The relationship between TROP2 expression and microvessel density was investigated and standard statistical analysis was used to evaluate TROP2 prognosis significance in hilar cholangiocarcinoma. High TROP2 expression by immunohistochemistry was found in 43 (61.4 %) of the 70 tumor specimens. Quantitative real-time PCR confirmed that TROP2 level in tumor was significantly higher than in non-tumoral biliary tissues (P = 0.001). Significant correlations were found between TROP2 expression and histological differentiation (P = 0.016) and tumor T stage (P = 0.031) in hilar cholangiocarcinoma. TROP2 expression correlated with microvessel density in hilar cholangiocarcinoma (P = 0.026). High TROP2 expression patients had a significantly poorer overall survival rate than those with low TROP2 expression (30 vs. 68.5 %, P = 0.001), and multivariate Cox regression analysis indicated TROP2 as an independent prognostic factor for hilar cholangiocarcinoma (P = 0.004). TROP2 expression correlates with microvessel density significantly and is an independent prognostic factor in human hilar cholangiocarcinoma.

Immunological network analysis in HPV associated head and neck squamous cancer and implications for disease prognosis.

Science.gov (United States)

Chen, Xiaohang; Yan, Bingqing; Lou, Huihuang; Shen, Zhenji; Tong, Fangjia; Zhai, Aixia; Wei, Lanlan; Zhang, Fengmin

2018-04-01

Human papillomavirus-positive (HPV+) head and neck squamous cell cancer (HNSCC) exhibits a better prognosis than HPV-negative (HPV-) HNSCC. This difference may in part be due to enhanced immune activation in the HPV+ HNSCC tumor microenvironment. To characterize differences in immune activation between HPV+ and HPV- HNSCC tumors, we identified and annotated differentially expressed genes based upon mRNA expression data from The Cancer Genome Atlas (TCGA). Immune network between immune cells and cytokines was constructed by using single sample Gene Set Enrichment Analysis and conditional mutual information. Multivariate Cox regression analysis was used to determine the prognostic value of immune microenvironment characterization. A total of 1673 differentially expressed genes were functionally annotated. We found that genes upregulated in HPV+ HNSCC are enriched in immune-associated processes. And the up-regulated gene sets were validated by Gene Set Enrichment Analysis. The microenvironment of HPV+ HNSCC exhibited greater numbers of infiltrating B and T cells and fewer neutrophils than HPV- HNSCC. These findings were validated by two independent datasets in the Gene Expression Omnibus (GEO) database. Further analyses of T cell subtypes revealed that cytotoxic T cell subtypes predominated in HPV+ HNSCC. In addition, the ratio of M1/M2 macrophages was much higher in HPV+ HNSCC. The infiltration of these immune cells was correlated with differentially expressed cytokine-associated genes. Enhanced infiltration of B cells and CD8+ T cells were identified as independent protective factors, while high neutrophil infiltration was a risk enhancing factor for HPV+ HNSCC patients. A schematic model of immunological network was established for HPV+ HNSCC to summarize our findings. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multivariate statistical analysis of major and trace element data for ...

African Journals Online (AJOL)

Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

Prognosis of status epilepticus in elderly patients.

Science.gov (United States)

Vilella, L; González Cuevas, M; Quintana Luque, M; Toledo, M; Sueiras Gil, M; Guzmán, L; Salas Puig, J; Santamarina Pérez, E

2018-03-01

To evaluate the clinical features and prognosis of status epilepticus (SE) in patients above 70 years old. Retrospective analysis of all patients ≥70 years old with SE registered prospectively during 4 years. Follow-up after discharge was performed. Ninety patients were evaluated. Acute symptomatic etiology was the most prevalent. The mean number of antiepileptic drugs (AEDs) used was 2.7 ± 1.2, and 21% of the patients required sedation. A poor outcome was considered when death (31.1%) or developing of new neurological impairment at discharge (32.2%) occurred. After multivariate analysis, four variables predicted a poor outcome: acute symptomatic etiology (OR: 6.320; 95% CI: 1.976-20.217; P = .002), focal motor SE type (OR: 9.089; 95% CI: 2.482-33.283; P = .001), level of consciousness (OR: 4.596; 95% CI: 1.903-11.098; P = .001), and SE duration >12 hours (OR: 3.763; 95% CI: 1.130-12.530; P = .031). Independent predictive factors of mortality were SE duration >12 hours (OR: 4.306; 95% CI: 1.044-17.757; P = .043), modified Status Epilepticus Severity Score (mSTESS) (OR: 2.216; 95% CI: 1.313-3.740; P = .003), and development of complications (OR: 3.334; 95% CI: 1.004-11.070, P = .049). Considering long-term mortality, age (HR 1.036; 95% CI 1.001-1.071; P = .044), a potentially fatal underlying cause (HR 2.609; 95% CI 1.497- 4.548; P = .001), and mSTESS score >4 (HR 1.485; 95% CI 1.158-1.903; P = .002) remained as predictive factors. There was no association between sedation and the number of AEDs used with outcome at discharge or long-term mortality (P > .05). SE above 70 years old has a high morbimortality. Prognosis is not related to treatment aggressiveness. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The tobacco paradox in acute coronary syndrome. The prior cessation of smoking as a marker of a better short-term prognosis.

Science.gov (United States)

Bastos-Amador, P; Almendro-Delia, M; Muñoz-Calero, B; Blanco-Ponce, E; Recio-Mayoral, A; Reina-Toral, A; Cruz-Fernandez, J M; García-Alcántara, A; Hidalgo-Urbano, R; García-Rubira, J C

2016-01-01

The tobacco paradox is a phenomenon insufficiently explained by previous studies. This study analyses the prognostic role of prior or active smoking in patients with acute coronary syndrome. We obtained data from the ARIAM registry, between 2001 and 2012. The study included 42,827 patients with acute coronary syndrome (mean age, 65±13 years; 26.4% women). The influence of smoking and that of being an ex-smoker on mortality was analysed using a multivariate analysis. The smokers were younger, were more often men, had less diabetes, hypertension and prior history of heart failure, stroke, arrhythmia and renal failure and more frequently had ST-elevation and a family history of smoking. The ex-smokers had more dyslipidaemia and history of angina, myocardial infarction, ischemic heart disease, peripheral vasculopathy and chronic bronchial disease. Smokers and ex-smokers less frequently developed cardiogenic shock (smokers 4.2%, ex-smokers 4.7% and nonsmokers 6.9%, P<.001). Hospital mortality was 7.8% for the nonsmokers, 4.9% for the ex-smokers and 3.1% for the smokers (P<.001). In the multivariate analysis, the smoker factor lost its influence in the prognosis (-0.26%, p=.52 using an inverse probability calculation; and+0.26%, P=.691 using a propensity analysis). However, the exsmoker factor showed a significant reduction in mortality in both tests (-2.4% in the inverse probability analysis, P<.001; and -1.5% in the propensity analysis, P=.005). The tobacco paradox is a finding that could be explained by other prognostic factors. Smoking cessation prior to hospitalization for acute coronary syndrome is associated with a better prognosis. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI). All rights reserved.

Chemokine-like factor-like MARVEL transmembrane domain-containing 3 expression is associated with a favorable prognosis in esophageal squamous cell carcinoma.

Science.gov (United States)

Han, Tianci; Shu, Tianci; Dong, Siyuan; Li, Peiwen; Li, Weinan; Liu, Dali; Qi, Ruiqun; Zhang, Shuguang; Zhang, Lin

2017-05-01

Decreased expression of human chemokine-like factor-like MARVEL transmembrane domain-containing 3 (CMTM3) has been identified in a number of human tumors and tumor cell lines, including gastric and testicular cancer, and PC3, CAL27 and Tca-83 cell lines. However, the association between CMTM3 expression and the clinicopathological features and prognosis of esophageal squamous cell carcinoma (ESCC) patients remains unclear. The aim of the present study was to investigate the correlation between CMTM3 expression and clinicopathological parameters and prognosis in ESCC. CMTM3 mRNA and protein expression was analyzed in ESCC and paired non-tumor tissues by quantitative real-time polymerase chain reaction, western blotting and immunohistochemical analysis. The Kaplan-Meier method was used to plot survival curves and the Cox proportional hazards regression model was also used for univariate and multivariate survival analysis. The results revealed that CMTM3 mRNA and protein expression levels were lower in 82.5% (30/40) and 75% (30/40) of ESCC tissues, respectively, when compared with matched non-tumor tissues. Statistical analysis demonstrated that CMTM3 expression was significantly correlated with lymph node metastasis (P=0.002) and clinical stage (P<0.001) in ESCC tissues. Furthermore, the survival time of ESCC patients exhibiting low CMTM3 expression was significantly shorter than that of ESCC patients exhibiting high CMTM3 expression (P=0.01). In addition, Kaplan-Meier survival analysis revealed that the overall survival time of patients exhibiting low CMTM3 expression was significantly decreased compared with patients exhibiting high CMTM3 expression (P=0.010). Cox multivariate analysis indicated that CMTM3 protein expression was an independent prognostic predictor for ESCC after resection. This study indicated that CMTM3 expression is significantly decreased in ESCC tissues and CMTM3 protein expression in resected tumors may present an effective prognostic

Development of Social Systems in the Context of Prognosis

Directory of Open Access Journals (Sweden)

Kvesko Svetlana

2018-01-01

Full Text Available The paper dwells on the prognosis of the social systems development. The prognostic analysis is based on the systemic approach to the issue; it shows that the conditions of the transference to the knowledge society determine the formation of the brand new features in social prognoses. The paper states the specific features and conditions of social prognosis, which ensure the validity of control actions. Besides, the current analysis provides recommendations on how to devise social prognostic strategies (within the frame of a transference to the knowledge society and b environmental instability. Finally, the functional load of social prognosis in the contemporary conditions is outlined.

Identification of a claudin-4 and E-cadherin score to predict prognosis in breast cancer.

Science.gov (United States)

Szasz, Attila M; Nemeth, Zsuzsanna; Gyorffy, Balazs; Micsinai, Mariann; Krenacs, Tibor; Baranyai, Zsolt; Harsanyi, Laszlo; Kiss, Andras; Schaff, Zsuzsa; Tokes, Anna-Maria; Kulka, Janina

2011-12-01

The elevated expression of claudins (CLDN) and E-cadherin (CDH-1) was found to correlate with poor prognostic features. Our aim was to perform a comprehensive analysis to assess their potential to predict prognosis in breast cancer. The expression of CLDN-1, -3-5, -7, -8, -10, -15, -18, and E-cadherin at the mRNA level was evaluated in correlation with survival in datasets containing expression measurements of 1809 breast cancer patients. The breast cancer tissues of 197 patients were evaluated with tissue microarray technique and immunohistochemical method for CLDN-1-5, -7, and E-cadherin protein expression. An additional validation set of 387 patients was used to test the accuracy of the resulting prognostic score. Based on the bioinformatic screening of publicly-available datasets, the metagene of CLDN-3, -4, -7, and E-cadherin was shown to have the most powerful predictive power in the survival analyses. An immunohistochemical protein profile consisting of CLDN-2, -4, and E-cadherin was able to predict outcome in the most effective manner in the training set. Combining the overlapping members of the above two methods resulted in the claudin-4 and E-cadherin score (CURIO), which was able to accurately predict relapse-free survival in the validation cohort (P = 0.029). The multivariate analysis, including clinicopathological variables and the CURIO, showed that the latter kept its predictive power (P = 0.040). Furthermore, the CURIO was able to further refine prognosis, separating good versus poor prognosis subgroups in luminal A, luminal B, and triple-negative breast cancer intrinsic subtypes. In breast cancer, the CURIO provides additional prognostic information besides the routinely utilized diagnostic approaches and factors. © 2011 Japanese Cancer Association.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group.

Science.gov (United States)

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-13

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 The Authors.

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.

Science.gov (United States)

Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh

2018-03-01

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

Science.gov (United States)

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Elevated Preoperative Neutrophil-Lymphocyte Ratio Is Associated with Poor Prognosis in Hepatocellular Carcinoma Patients Treated with Liver Transplantation: A Meta-Analysis

Directory of Open Access Journals (Sweden)

Xiao-Dong Sun

2016-01-01

Full Text Available This study aims to investigate the prognostic value of neutrophil to lymphocyte ratio (NLR in hepatocellular carcinoma (HCC patients treated with liver transplantation (LT through meta-analysis. Relevant articles were sought in PubMed, Embase, and Wangfang databases up to July 2015. A total of 1687 patients from 10 studies were included in this meta-analysis. Meta-analysis results showed that elevated NLR was significantly associated with poorer overall survival (OS (HR = 2.71, 95% CI: 1.91–3.83 and poorer disease-free survival (DFS (HR = 3.61, 95% CI: 2.23–5.84 in HCC patients treated with LT. Moreover, subgroup analysis showed the significant association between elevated preoperative NLR and poor prognosis was not altered by cutoff values of NLR or types of LT. Therefore, elevated preoperative NLR is associated with poor prognosis in HCC patients treated with LT. Preoperative NLR should be used to predict the prognosis of HCC after LT in our clinical work.

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis

NARCIS (Netherlands)

Brindle, R. C.; Ginty, A. T.; Jones, A.; Phillips, A. C.; Roseboom, T. J.; Carroll, D.; Painter, R. C.; de Rooij, S. R.

2016-01-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure

Myocardial technetium-99m sestamibi single-photon emission tomography as a prognostic tool in coronary artery disease: multivariate analysis in a long-term prospective study

International Nuclear Information System (INIS)

Zanco, P.; Zampiero, A.; Favero, A.; Borsato, N.; Chierichetti, F.; Rubello, D.; Saitta, B.; Ferlin, G.

1995-01-01

A prospective study was started in 1988 and at present 176 consecutive, and thus unselected, patients have been enrolled. All of them have been submitted to stress-rest MIBI SPET for the diagnosis or evaluation of CAD; 147 patients (121 males and 26 females, aged 53±9 years) have completed a surveillance period of at least 36 months following the scintigraphic study (range 36-60 months, mean 43). Sixty-one patients had a documented previous myocardial infarction. The mean pre-test likelihood of CAD was 44% in the patients without prior infarction. The main anamnestic, clinical, EKG and scintigraphic findings were evaluated and statistically correlated with the incidence of ensuing cardiac events using both univariate (chi-square test) and multivariate analysis (logistic regression model). Twenty-nine patients suffered from a cardiac event during the follow-up period (i.e. three cardiac deaths, six myocardial infarctions and 20 cases of unstable angina). Statistical multivariate analysis identified MIBI scan as the only highly significant and independent prognostic predictor. In detail, the most important scintigraphic parameters were the presence of a reversible defect and the extension of the stress perfusion defect. The presence of typical angina proved to be a slightly significant predictor, while no other examined parameter showed a significant correlation with a bad prognosis. In conclusion, MIBI SPET can be considered a useful tool in the risk stratification of CAD patients. (orig.). With 3 tabs

Metabolic tumor volume measured by F 18 FDG PET/CT can further stratify the prognosis of patients with stage IV Non Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Yoo, Su Woong; Kim, Ja Hae; Chong, Ar I; Kwon, Seong Young; Min, Jung Joon; Song, Ho Chun; Bom, Hee Seung [Chonnam National Univ. Hwasun Hospital, Gwangju (Korea, Republic of)

2012-12-15

This study aimed to further stratify prognostic factors in patients with stage IV non small cell lung cancer (NSCLC) by measuring their metabolic tumor volume (MTV) using F 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). The subjects of this retrospective study were 57 patients with stage IV NSCLC. MTV, total lesion glycolysis (TLG), and maximum standardized uptake value (SUVmax) were measured on F 18 FDG PET/CT in both the primary lung lesion as well as metastatic lesions in torso. Optimal cutoff values of PET parameters were mea measured by receiver operating characteristic (ROC) curve anal analysis. Kaplan Meier survival (PET). The univariate and multivariate cox proportional hazards models were used to select the significant prognostic factors. Univariate analysis showed that both MTV and TLG of primary lung lesion (MTV lung and TLG lung) were significant factors for prediction of PFS ( <0.001 =0.038, respectively). Patients showing lower values of MTV lung and TLG lung than the cutoff values had significantly longer mean PFS than those with higher values. hazard ratios (95% confidence interval) of MTV lung and TLG lung measured by univariate analysis were 6.4 (2.5 16.3) and 2.4 (1.0 5.5), respectively. multivariate analysis revealed that MTV lung was the only significant factor for prediction of prognosis. Hazard ratio was 13,5 (1.6 111.1, =0,016). patients with stage IV NSCLC could be further stratified into subgroups of significantly better and worse prognosis by MTV of primary lung lesion.

Metabolic tumor volume measured by F 18 FDG PET/CT can further stratify the prognosis of patients with stage IV Non Small Cell Lung Cancer

International Nuclear Information System (INIS)

Yoo, Su Woong; Kim, Ja Hae; Chong, Ar I; Kwon, Seong Young; Min, Jung Joon; Song, Ho Chun; Bom, Hee Seung

2012-01-01

This study aimed to further stratify prognostic factors in patients with stage IV non small cell lung cancer (NSCLC) by measuring their metabolic tumor volume (MTV) using F 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). The subjects of this retrospective study were 57 patients with stage IV NSCLC. MTV, total lesion glycolysis (TLG), and maximum standardized uptake value (SUVmax) were measured on F 18 FDG PET/CT in both the primary lung lesion as well as metastatic lesions in torso. Optimal cutoff values of PET parameters were mea measured by receiver operating characteristic (ROC) curve anal analysis. Kaplan Meier survival (PET). The univariate and multivariate cox proportional hazards models were used to select the significant prognostic factors. Univariate analysis showed that both MTV and TLG of primary lung lesion (MTV lung and TLG lung) were significant factors for prediction of PFS ( <0.001 =0.038, respectively). Patients showing lower values of MTV lung and TLG lung than the cutoff values had significantly longer mean PFS than those with higher values. hazard ratios (95% confidence interval) of MTV lung and TLG lung measured by univariate analysis were 6.4 (2.5 16.3) and 2.4 (1.0 5.5), respectively. multivariate analysis revealed that MTV lung was the only significant factor for prediction of prognosis. Hazard ratio was 13,5 (1.6 111.1, =0,016). patients with stage IV NSCLC could be further stratified into subgroups of significantly better and worse prognosis by MTV of primary lung lesion

Elevated red cell distribution width contributes to a poor prognosis in patients with esophageal carcinoma.

Science.gov (United States)

Wan, Guo-Xing; Chen, Ping; Cai, Xiao-Jun; Li, Lin-Jun; Yu, Xiong-Jie; Pan, Dong-Feng; Wang, Xian-He; Wang, Xuan-Bin; Cao, Feng-Jun

2016-01-15

The red cell distribution width (RDW) has also been reported to reliably reflect the inflammation and nutrition status and predict the prognosis across several types of cancer, however, the prognostic value of RDW in esophageal carcinoma has seldom been studied. A retrospective study was performed to assess the prognostic value of RDW in patients with esophageal carcinoma by the Kaplan-Meier analysis and multivariate Cox regression proportional hazard model. All enrolled patients were divided into high RDW group (≧15%) and low RDW group (<15%) according to the detected RDW values. Clinical and laboratory data from a total of 179 patients with esophageal carcinoma were retrieved. With a median follow-up of 21months, the high RDW group exhibited a shorter disease-free survival (DFS) (p<0.001) and an unfavorable overall survival (OS) (p<0.001) in the univariate analysis. The multivariate analysis revealed that elevated RDW at diagnosis was an independent prognostic factor for shorter PFS (p=0.043, HR=1.907, 95% CI=1.020-3.565) and poor OS (p=0.042, HR=1.895, 95% CI=1.023-3.508) after adjustment with other cancer-related prognostic factors. The present study suggests that elevated preoperative RDW(≧15%) at the diagnosis may independently predict poorer disease-free and overall survival among patients with esophageal carcinoma. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterization and Long-Term Prognosis of Postmyocarditic Dilated Cardiomyopathy Compared With Idiopathic Dilated Cardiomyopathy.

Science.gov (United States)

Merlo, Marco; Anzini, Marco; Bussani, Rossana; Artico, Jessica; Barbati, Giulia; Stolfo, Davide; Gigli, Marta; Muça, Matilda; Naso, Paola; Ramani, Federica; Di Lenarda, Andrea; Pinamonti, Bruno; Sinagra, Gianfranco

2016-09-15

Dilated cardiomyopathy (DC) is the final common pathway of different pathogenetic processes and presents a significant prognostic heterogeneity, possibly related to its etiologic variety. The characterization and long-term prognosis of postmyocarditic dilated cardiomyopathy (PM-DC) remain unknown. This study assesses the clinical-instrumental evolution and long-term prognosis of a large cohort of patients with PM-DC. We analyzed 175 patients affected with DC consecutively enrolled from 1993 to 2008 with endomyocardial biopsy (EMB) data available. PM-DC was defined in the presence of borderline myocarditis at EMB or persistent left ventricular dysfunction 1 year after diagnosis of active myocarditis at EMB. Other patients were defined as affected by idiopathic dilated cardiomyopathy (IDC). Analysis of follow-up evaluations was performed at 24, 60, and 120 months. We found 72 PM-DC of 175 enrolled patients (41%). Compared with IDC, patients with PM-DC were more frequently females and less frequently presented a familial history of DC. No other baseline significant differences were found. During the long-term follow-up (median 154, first to third interquartile range 78 to 220 months), patients with PM-DC showed a trend toward slower disease progression. Globally, 18 patients with PM-DC (25%) versus 49 with IDC (48%) experienced death/heart transplantation (p = 0.045). The prognostic advantage for patients with PM-DC became significant beyond 40 months of follow-up. At multivariable time-dependent Cox analysis, PM-DC was confirmed to have a global independent protective role (hazard ratio 0.53, 95% confidence interval 0.28 to 0.97, p = 0.04). In conclusion, PM-DC is characterized by better long-term prognosis compared with IDC. An exhaustive etiologic characterization appears relevant in the prognostic assessment of DC. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate analysis between air pollutants and meteorological variables in Seoul

International Nuclear Information System (INIS)

Kim, J.; Lim, J.

2005-01-01

Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

Energy Technology Data Exchange (ETDEWEB)

Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

International Nuclear Information System (INIS)

Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

2013-01-01

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

The associations between immunity-related genes and breast cancer prognosis in Korean women.

Directory of Open Access Journals (Sweden)

Jaesung Choi

Full Text Available We investigated the role of common genetic variation in immune-related genes on breast cancer disease-free survival (DFS in Korean women. 107 breast cancer patients of the Seoul Breast Cancer Study (SEBCS were selected for this study. A total of 2,432 tag single nucleotide polymorphisms (SNPs in 283 immune-related genes were genotyped with the GoldenGate Oligonucleotide pool assay (OPA. A multivariate Cox-proportional hazard model and polygenic risk score model were used to estimate the effects of SNPs on breast cancer prognosis. Harrell's C index was calculated to estimate the predictive accuracy of polygenic risk score model. Subsequently, an extended gene set enrichment analysis (GSEA-SNP was conducted to approximate the biological pathway. In addition, to confirm our results with current evidence, previous studies were systematically reviewed. Sixty-two SNPs were statistically significant at p-value less than 0.05. The most significant SNPs were rs1952438 in SOCS4 gene (hazard ratio (HR = 11.99, 95% CI = 3.62-39.72, P = 4.84E-05, rs2289278 in TSLP gene (HR = 4.25, 95% CI = 2.10-8.62, P = 5.99E-05 and rs2074724 in HGF gene (HR = 4.63, 95% CI = 2.18-9.87, P = 7.04E-05. In the polygenic risk score model, the HR of women in the 3rd tertile was 6.78 (95% CI = 1.48-31.06 compared to patients in the 1st tertile of polygenic risk score. Harrell's C index was 0.813 with total patients and 0.924 in 4-fold cross validation. In the pathway analysis, 18 pathways were significantly associated with breast cancer prognosis (P<0.1. The IL-6R, IL-8, IL-10RB, IL-12A, and IL-12B was associated with the prognosis of cancer in data of both our study and a previous study. Therefore, our results suggest that genetic polymorphisms in immune-related genes have relevance to breast cancer prognosis among Korean women.

Multivariate analysis of data in sensory science

CERN Document Server

Naes, T; Risvik, E

1996-01-01

The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group

DEFF Research Database (Denmark)

Christophersen, J; Menné, T; Tanghøj, P

1989-01-01

The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba......-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis....

Comorbidity predicts poor prognosis in nasopharyngeal carcinoma: Development and validation of a predictive score model

International Nuclear Information System (INIS)

Guo, Rui; Chen, Xiao-Zhong; Chen, Lei; Jiang, Feng; Tang, Ling-Long; Mao, Yan-Ping; Zhou, Guan-Qun; Li, Wen-Fei; Liu, Li-Zhi; Tian, Li; Lin, Ai-Hua; Ma, Jun

2015-01-01

Background and purpose: The impact of comorbidity on prognosis in nasopharyngeal carcinoma (NPC) is poorly characterized. Material and methods: Using the Adult Comorbidity Evaluation-27 (ACE-27) system, we assessed the prognostic value of comorbidity and developed, validated and confirmed a predictive score model in a training set (n = 658), internal validation set (n = 658) and independent set (n = 652) using area under the receiver operating curve analysis. Results: Comorbidity was present in 40.4% of 1968 patients (mild, 30.1%; moderate, 9.1%; severe, 1.2%). Compared to an ACE-27 score ⩽1, patients with an ACE-27 score >1 in the training set had shorter overall survival (OS) and disease-free survival (DFS) (both P < 0.001), similar results were obtained in the other sets (P < 0.05). In multivariate analysis, ACE-27 score was a significant independent prognostic factor for OS and DFS. The combined risk score model including ACE-27 had superior prognostic value to TNM stage alone in the internal validation set (0.70 vs. 0.66; P = 0.02), independent set (0.73 vs. 0.67; P = 0.002) and all patients (0.71 vs. 0.67; P < 0.001). Conclusions: Comorbidity significantly affects prognosis, especially in stages II and III, and should be incorporated into the TNM staging system for NPC. Assessment of comorbidity may improve outcome prediction and help tailor individualized treatment

Decomposition of multivariate phenotypic means in multigroup genetic covarinace structure analysis

NARCIS (Netherlands)

Dolan, C.V.; Molenaar, P.C.M.; Boomsma, D.I.

1992-01-01

Uses D. Sorbom's (1974) method to study differences in latent means in multivariate twin data. By restricting the analysis to a comparison between groups, the results pertain only to the additive contributions of common genetic and environmental factors to the deviation of the group means from what

Prognosis after surgical treatment for pancreatic cancer in patients aged 80 years or older: a multicenter study.

Science.gov (United States)

Sho, Masayuki; Murakami, Yoshiaki; Kawai, Manabu; Motoi, Fuyuhiko; Satoi, Sohei; Matsumoto, Ippei; Honda, Goro; Uemura, Kenichiro; Yanagimoto, Hiroaki; Kurata, Masanao; Akahori, Takahiro; Kinoshita, Shoichi; Nagai, Minako; Nishiwada, Satoshi; Fukumoto, Takumi; Unno, Michiaki; Yamaue, Hiroki; Nakajima, Yoshiyuki

2016-03-01

The optimal therapeutic strategy for very elderly pancreatic cancer patients remains to be determined. The aim of this study was to clarify the role of pancreatic resection in patients 80 years of age or older. A retrospective multicenter analysis of 1401 patients who had undergone pancreatic resection for pancreatic cancer was performed. The patients aged ≥ 80 years (n = 99) were compared with a control group <80 years of age (n = 1302). There were no significant differences in the postoperative complications and mortality between the two groups. However, the prognosis of octogenarians was poorer than that of younger patients for both resectable and borderline resectable tumors. Importantly, there were few long-term survivors in the elderly group, especially among those with borderline resectable pancreatic cancer. A multivariate analysis of the prognostic factors in the very elderly patients indicated that the completion of adjuvant chemotherapy was the only significant factor. In addition, preoperative albumin level was the only independent risk factor for a failure to complete adjuvant chemotherapy. This study demonstrates that the postoperative prognosis in octogenarian patients was not good as that in younger patients possibly due to less frequent completion of adjuvant chemotherapy. © 2016 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

International Nuclear Information System (INIS)

Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

2008-01-01

Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

Identification of multivariate models for noise analysis of nuclear plant

International Nuclear Information System (INIS)

Zwingelstein, G.C.; Upadhyaya, B.R.

1979-01-01

During the normal operation of a pressurized water reactor, neutron noise analysis with multivariate autoregressive procedures in a valuable diagnostic tool to extract dynamic characteristics for incipient failure detection. The first part of the paper will describe in details the equations for estimating the multivariate autoregressive model matrices and the structure of various matrices. The matrices are estimated by solving a set of matrix operations, called Yule-Walker equations. The selection of optimal model order will also be discussed. Once the optimal parameter set is obtained, simple and fast calculations are used to determine the auto power spectral density, cross spectra, coherence function, phase. In addition the spectra may be decomposed into components being contributed from different noise sources. An application using neutron flux data collected on a nuclear plant will illustrate the efficiency of the method

Handbook of univariate and multivariate data analysis with IBM SPSS

CERN Document Server

Ho, Robert

2013-01-01

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

International Nuclear Information System (INIS)

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-01-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component

Utility of Inflammatory Marker- and Nutritional Status-based Prognostic Factors for Predicting the Prognosis of Stage IV Gastric Cancer Patients Undergoing Non-curative Surgery.

Science.gov (United States)

Mimatsu, Kenji; Fukino, Nobutada; Ogasawara, Yasuo; Saino, Yoko; Oida, Takatsugu

2017-08-01

The present study aimed to compare the utility of various inflammatory marker- and nutritional status-based prognostic factors, including many previous established prognostic factors, for predicting the prognosis of stage IV gastric cancer patients undergoing non-curative surgery. A total of 33 patients with stage IV gastric cancer who had undergone palliative gastrectomy and gastrojejunostomy were included in the study. Univariate and multivariate analyses were performed to evaluate the relationships between the mGPS, PNI, NLR, PLR, the CONUT, various clinicopathological factors and cancer-specific survival (CS). Among patients who received non-curative surgery, univariate analysis of CS identified the following significant risk factors: chemotherapy, mGPS and NLR, and multivariate analysis revealed that the mGPS was independently associated with CS. The mGPS was a more useful prognostic factor than the PNI, NLR, PLR and CONUT in patients undergoing non-curative surgery for stage IV gastric cancer. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

A large cohort study reveals the association of elevated peripheral blood lymphocyte-to-monocyte ratio with favorable prognosis in nasopharyngeal carcinoma.

Directory of Open Access Journals (Sweden)

Jing Li

Full Text Available BACKGROUND: Nasopharyngeal carcinoma (NPC is an endemic neoplasm in southern China. Although NPC sufferers are sensitive to radiotherapy, 20-30% of patients finally progress with recurrence and metastases. Elevated lymphocyte-to-monocyte ratio (LMR has been reported to be associated with favorable prognosis in some hematology malignancies, but has not been studied in NPC. The aim of this study was to evaluate whether LMR could predict the prognosis of NPC patients. METHODS: A retrospective cohort of 1,547 non-metastatic NPC patients was recruited between January 2005 and June 2008. The counts for peripheral lymphocyte and monocyte were retrieved, and the LMR was calculated. Receiver operating characteristic curve analysis, univariate and multivariate COX proportional hazards analyses were applied to evaluate the associations of LMR with overall survival (OS, disease-free survival (DFS, distant metastasis-free survival (DMFS and loco-regional recurrence-free survival (LRRFS, respectively. RESULTS: Univariate analysis revealed that higher LMR level (≥ 5.220 was significantly associated with superior OS, DFS and DMFS (P values <0.001. The higher lymphocyte count (≥ 2.145 × 10(9/L was significantly associated with better OS (P = 0.002 and DMFS (P = 0.031, respectively, while the lower monocyte count (<0.475 × 10(9/L was associated with better OS (P = 0.012, DFS (P = 0.011 and DMFS (P = 0.003, respectively. Multivariate Cox proportional hazard analysis showed that higher LMR level was a significantly independent predictor for superior OS (hazard ratio or HR = 0.558, 95% confidence interval or 95% CI = 0.417-0.748; P<0.001, DFS (HR = 0.669, 95% CI = 0.535-0.838; P<0.001 and DMFS (HR = 0.543, 95% CI = 0.403-0.732; P<0.001, respectively. The advanced T and N stages were also independent indicators for worse OS, DFS, and DMFS, except that T stage showed borderline statistical significance for DFS (P = 0.053 and DMFS (P = 0.080. CONCLUSIONS: The

HLA-G 3'UTR Polymorphisms Impact the Prognosis of Stage II-III CRC Patients in Fluoropyrimidine-Based Treatment.

Science.gov (United States)

Garziera, Marica; Bidoli, Ettore; Cecchin, Erika; Mini, Enrico; Nobili, Stefania; Lonardi, Sara; Buonadonna, Angela; Errante, Domenico; Pella, Nicoletta; D'Andrea, Mario; De Marchi, Francesco; De Paoli, Antonino; Zanusso, Chiara; De Mattia, Elena; Tassi, Renato; Toffoli, Giuseppe

2015-01-01

An important hallmark of CRC is the evasion of immune surveillance. HLA-G is a negative regulator of host's immune response. Overexpression of HLA-G protein in primary tumour CRC tissues has already been associated to worse prognosis; however a definition of the role of immunogenetic host background is still lacking. Germline polymorphisms in the 3'UTR region of HLA-G influence the magnitude of the protein by modulating HLA-G mRNA stability. Soluble HLA-G has been associated to 3'UTR +2960 Ins/Ins and +3035 C/T (lower levels) and +3187 G/G (high levels) genotypes. HLA-G 3'UTR SNPs have never been explored in CRC outcome. The purpose of this study was to investigate if common HLA-G 3'UTR polymorphisms have an impact on DFS and OS of 253 stage II-III CRC patients, after primary surgery and ADJ-CT based on FL. The 3'UTR was sequenced and SNPs were analyzed for their association with survival by Kaplan-Meier and multivariate Cox models; results underwent internal validation using a resampling method (bootstrap analysis). In a multivariate analysis, we estimated an association with improved DFS in Ins allele (Ins/Del +Ins/Ins) carriers (HR 0.60, 95% CI 0.38-0.93, P = 0.023) and in patients with +3035 C/T genotype (HR 0.51, 95% CI 0.26-0.99, P = 0.045). The +3187 G/G mutated carriers (G/G vs A/A+A/G) were associated to a worst prognosis in both DFS (HR 2.46, 95% CI 1.19-5.05, P = 0.015) and OS (HR 2.71, 95% CI 1.16-6.63, P = 0.022). Our study shows a prognostic and independent role of 3 HLA-G 3'UTR SNPs, +2960 14-bp INDEL, +3035 C>T, and +3187 A>G.

Differential Expression of Circular RNAs in Glioblastoma Multiforme and Its Correlation with Prognosis

Directory of Open Access Journals (Sweden)

Junle Zhu

2017-04-01

Full Text Available OBJECTIVE: The present study aimed to explore the expression profiles of circular RNAs (circRNAs in glioblastoma multiforme (GBM in an attempt to identify potential core genes in the pathogenesis of this tumor. METHODS: Differentially expressed circRNAs were screened between tumor tissues from five GBM patients and five normal brain samples using Illumina Hiseq. Bioinformatics analysis was used to analyze their potential function. CircBRAF was further detected in different WHO grades glioma tissues and normal brain tissues. Kaplan-Meier curves and multivariate Cox's analysis were used to analyze the association between circBRAF expression level and prognosis of glioma patients. RESULTS: A total of 1411 differentially expressed circRNAs were identified in GBM patients including 206 upregulated circRNAs and 1205 downregulated circRNAs. Differential expression of circRNAs was closely associated with the biological process and molecular function. The downregulated circRNAs were mainly associated with ErbB and Neurotrophin signaling pathways. Moreover, the expression level of circBRAF in normal brain tissues was significantly higher than that in glioma tissues (P < .001. CircBRAF was significantly lower in glioma patients with high pathological grade (WHO III & IV than those with low grade (WHO I & II (P < .001. Cox analysis revealed that high circBRAF expression was an independent biomarker for predicting good progression-free survival and overall survival in glioma patients (HR = 0.413, 95% CI 0.201-0.849; HR = 0.299, 95% CI 0.135-0.661; respectively. CONCLUSION: The present study identified a profile of dysregulated circRNAs in GBM. Bioinformatics analysis showed that dysregulated circRNAs might be associated with tumorigenesis and development of GBM. In addition, circBRAF could severe as a biomarker for predicting pathological grade and prognosis in glioma patients.

Multivariate statistical methods a first course

CERN Document Server

Marcoulides, George A

2014-01-01

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

Preoperative C-reactive protein as a prognostic predictor for upper tract urothelial carcinoma: A systematic review and meta-analysis.

Science.gov (United States)

Luo, You; Fu, Sheng Jun; She, Dong Li; Xiong, H U; Yang, L I

2015-07-01

Upper tract urothelial carcinoma (UTUC) is a relatively rare and highly aggressive tumor. However, the prognosis of UTUC is rarely predicted accurately due to the lack of reliable biomarkers. C-reactive protein (CRP) has been found to be correlated with several types of cancer. In this study, we performed a systematic review and meta-analysis to determine the association between CRP levels and prognosis in UTUC. A computerized search was conducted through PubMed, Embase, Web of Science, the Cochrane Library and CBM databases to identify clinical studies that have evaluated the association between preoperative CRP levels and prognosis of UTUC. The prognostic outcomes included recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). We extracted and synthesized corresponding hazard ratios (HRs) and confidence intervals (CIs) using Review Manager 5.3 software. We identified 7 retrospective cohort studies including a total of 1,919 patients and analyzed these studies using univariate and multivariate models. Our meta-analysis results revealed that RFS and CSS were significantly different between patients with elevated CRP levels and those with low CRP levels (P<0.0001 and P<0.00001, respectively); however, that was not the case for OS (P=0.22) in the multivariate or the univariate model. The pooled HR of RFS was 2.90 (95% CI: 1.87-4.51, P<0.00001) in the univariate analysis and 1.57 (95% CI: 1.26-1.97, P<0.0001) in the multivariate analysis. The pooled HRs of CSS were 2.78 (95% CI: 1.75-4.43, P<0.0001) and 1.64 (95% CI: 1.32-2.03, P<0.00001) in the univariate and multivariate analysis, respectively. However, the pooled HRs of OS were not significant in the univariate [1.24 (95% CI: 0.72-2.15, P=0.43)] or the multivariate analysis [1.24 (95% CI: 0.88-1.75, P=0.22)]. In conclusion, our meta-analysis results suggested that CRP level may be a prognostic predictor in UTUC.

Clinical analysis on 159 cases of mechanical ocular trauma

Directory of Open Access Journals (Sweden)

Zi-Yao Liu

2013-08-01

Full Text Available AIM: To provide the basis of security guidance and decreasing the incidence through a general investigation of the mechanical ocular trauma among all the common causes, occasions where getting hurt as well as the characteristics of the high-risk group, and by further analysis and monitoring of the clinical cases and follow-up visit, study the related key factors of influencing the prognosis statistically. METHODS: The data of the 159 cases with mechanical ocular trauma were recorded.RESULTS: We obtained the 159 subjects' ages, genders as well as mechanical ocular trauma characteristic data, such as ocular distributions, the seasons of the injuries occurring, the causes and the occasions of the injuries, the high-risks group and so on. The factors affecting the visual prognosis,univariate analysis showed that the difference between urban and rural areas was a related influencing factor while the consulting hours and the ages of the patients were irrelevant. In the multivariate Logistic regression model of complications that affected the visual prognosis, there were four main factors leading to poor eyesight: endophthalmitis, retinal detachment, luxation or subluxation of the lens, prolapse of vitreous. In the multivariate Logistic regression model of the visual prognosis of mechanical eye injury, there were three factors of concern that corresponded to poor eyesight: the ages less than 10, zonation Ⅲ, grade of injury more than 3. CONCLUSION: The epidemiologic features of the mechanical ocular trauma in our hospital correspond to the reports from other areas. Appropriate medical care can improve the visual prognosis. Factors such as zonation Ⅲ, ages less than 10, grade of injury more than 3, endophthalmitis with the eye injury, prolapse of vitreous, luxation or subluxation of the lens and so on, indicate poor visual prognosis.

An Introduction to Applied Multivariate Analysis

CERN Document Server

Raykov, Tenko

2008-01-01

Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions.

Science.gov (United States)

Bathke, Arne C; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-03-22

To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.

Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

Science.gov (United States)

Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

2017-09-01

For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Resent state and multivariate analysis of a few juniper forests of baluchistan, pakistan

International Nuclear Information System (INIS)

Ahmed, M.; Siddiqui, M.F.

2015-01-01

Quantitative multivariate investigations were carried out to explore various forms of Juniper trees resulting human disturbances and natural phenomenon. Thirty stands were sampled by point centered quarter method and data were analysed using Wards cluster analysis and Bray-Curtis ordination. On the basis of multivariate analysis eight various forms i.e. healthy, unhealthy, over mature, disturbed, dieback, standing dead, logs and cut stem were recognized. Structural attributes were computed. Highest numbers (130-133 stem ha-1) of logs were recorded from Cautair and Khunk forests. Highest density ha-1 (229 ha-1) of healthy plants was estimated from Tangi Top area while lowest number (24 ha-1) of healthy plants was found from Saraghara area. Multivariate analysis showed five groups in cluster and ordination diagrams. These groups are characterized on the basis of healthy, over mature, disturbed and logged trees of Juniper. Higher number (115, 96, 84, 80 ha-1) of disturbed trees were distributed at Speena Sukher, Srag Kazi, Prang Shella and Tangi Top respectively. Overall density does not show any significant relation with basal area m2 ha-1, degree of slopes and the elevation of the sampling stands. Present study show that each and every Juniper stands are highly disturbed mostly due to human influence, therefore prompt conservational steps should be taken to safe these forests. (author)

Geoscientific long-term prognosis. Preliminary safety analysis for the site Gorleben

International Nuclear Information System (INIS)

Mrugalla, Sabine

2011-07-01

The preliminary safety analysis of the site Gorleben includes the following chapters: (1) Introduction; (2) Aim and content of the geoscientific long-term prognosis for the site Gorleben; (3) Boundary conditions at the site Gorleben: climate; geomorphology; overlying rocks and adjoining rocks; hydrogeology; salt deposit Gorleben. (4) Probable future geological developments at the site Gorleben: supraregional developments with effects on the site Gorleben; glacial period developments; developments of the geomorphology, overlying and adjoining rocks; future developments of the hydrological systems at the site Gorleben; future saliniferous specific developments of the salt deposit Gorleben. (5) Commentary on the unlikely or excludable developments of the site Gorleben.

Understanding Cancer Prognosis

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Full Text Available ... Prognosis Questions to Ask about Your Diagnosis Research Understanding Cancer Prognosis Oncologist Anthony L. Back, M.D., a national expert on doctor-patient communications, talks with one of his patients about what ...

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

Validation of podocalyxin-like protein as a biomarker of poor prognosis in colorectal cancer

International Nuclear Information System (INIS)

Larsson, Anna; Fridberg, Marie; Gaber, Alexander; Nodin, Björn; Levéen, Per; Jönsson, Göran; Uhlén, Mathias; Birgisson, Helgi; Jirström, Karin

2012-01-01

Podocalyxin-like 1 (PODXL) is a cell-adhesion glycoprotein and stem cell marker that has been associated with an aggressive tumour phenotype and adverse outcome in several cancer types. We recently demonstrated that overexpression of PODXL is an independent factor of poor prognosis in colorectal cancer (CRC). The aim of this study was to validate these results in two additional independent patient cohorts and to examine the correlation between PODXL mRNA and protein levels in a subset of tumours. PODXL protein expression was analyzed by immunohistochemistry in tissue microarrays with tumour samples from a consecutive, retrospective cohort of 270 CRC patients (cohort 1) and a prospective cohort of 337 CRC patients (cohort 2). The expression of PODXL mRNA was measured by real-time quantitative PCR in a subgroup of 62 patients from cohort 2. Spearman´;s Rho and Chi-Square tests were used for analysis of correlations between PODXL expression and clinicopathological parameters. Kaplan Meier analysis and Cox proportional hazards modelling were applied to assess the relationship between PODXL expression and time to recurrence (TTR), disease free survival (DFS) and overall survival (OS). High PODXL protein expression was significantly associated with unfavourable clinicopathological characteristics in both cohorts. In cohort 1, high PODXL expression was associated with a significantly shorter 5-year OS in both univariable (HR = 2.28; 95% CI 1.43-3.63, p = 0.001) and multivariable analysis (HR = 2.07; 95% CI 1.25-3.43, p = 0.005). In cohort 2, high PODXL expression was associated with a shorter TTR (HR = 2.93; 95% CI 1.26-6.82, p = 0.013) and DFS (HR = 2.44; 95% CI 1.32-4.54, p = 0.005), remaining significant in multivariable analysis, HR = 2.50; 95% CI 1.05-5.96, p = 0.038 for TTR and HR = 2.11; 95% CI 1.13-3.94, p = 0.019 for DFS. No significant correlation could be found between mRNA levels and protein expression of PODXL and there was no association between mRNA levels

Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

Science.gov (United States)

Rajagopalan, Vidya; Schwartzman, Armin; Hua, Xue; Leow, Alex; Thompson, Paul; Lepore, Natasha

2015-01-01

We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2014-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2013-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

Comorbid renal tubular damage and hypoalbuminemia exacerbate cardiac prognosis in patients with chronic heart failure.

Science.gov (United States)

Otaki, Yoichiro; Watanabe, Tetsu; Takahashi, Hiroki; Funayama, Akira; Kinoshita, Daisuke; Yokoyama, Miyuki; Takahashi, Tetsuya; Nishiyama, Satoshi; Arimoto, Takanori; Shishido, Tetsuro; Miyamoto, Takuya; Konta, Tsuneo; Kubota, Isao

2016-02-01

Renal tubular damage (RTD) and hypoalbuminemia are risks for poor prognosis in patients with chronic heart failure (CHF). Renal tubules play a pivotal role in amino acid and albumin reabsorption, which maintain serum albumin levels. The aims of the present study were to (1) examine the association of RTD with hypoalbuminemia, and (2) assess the prognostic importance of comorbid RTD and hypoalbuminemia in patients with CHF. We measured N-acetyl-β-D-glucosamidase (NAG) levels and the urinary β2-microglobulin to creatinine ratio (UBCR) in 456 patients with CHF. RTD was defined as UBCR ≥ 300 μg/g or NAG ≥ 14.2 U/g. There were moderate correlations between RTD markers and serum albumin (NAG, r = -0.428, P < 0.0001; UBCR, r = -0.399, P < 0.0001). Multivariate logistic analysis showed that RTD was significantly related to hypoalbuminemia in patients with CHF. There were 134 cardiac events during a median period of 808 days. The comorbidity of RTD and hypoalbuminemia was increased with advancing New York Heart Association functional class. Multivariate Cox proportional hazard regression analysis showed that the presence of RTD and hypoalbuminemia was associated with cardiac events. The net reclassification index was significantly improved by adding RTD and hypoalbuminemia to the basic risk factors. Comorbid RTD and hypoalbuminemia are frequently observed and increase the risk for extremely poor outcome in patients with CHF.

Understanding Cancer Prognosis

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Full Text Available ... treatments being used today. Still, your doctor may tell you that you have a good prognosis if ... to respond well to treatment. Or, he may tell you that you have a poor prognosis if ...

Understanding Cancer Prognosis

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Full Text Available ... Understanding Cancer Prognosis Oncologist Anthony L. Back, M.D., a national expert on doctor-patient communications, talks with one of his patients about what she'd like to know of her prognosis. Credit: National ...

Understanding Cancer Prognosis

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Full Text Available ... Overview Research Cancer Screening Cancer Screening Overview Screening Tests Research Diagnosis and Staging Symptoms Diagnosis Staging Prognosis ... Cancer Prevention Overview Screening Cancer Screening Overview Screening Tests Diagnosis & Staging Symptoms Diagnosis Staging Prognosis Treatment Types ...

Understanding Cancer Prognosis

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Full Text Available ... doctor may tell you that you have a good prognosis if statistics suggest that your cancer is ... about how to discuss prognosis with their patients. Good communication, he says, is part of providing good ...

Understanding Cancer Prognosis

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Full Text Available ... during a certain period of time after diagnosis. Disease-free survival This statistic is the percentage of ... discuss cancer prognosis (the likely course of the disease). Learn key points about prognosis and how to ...

Understanding Cancer Prognosis

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Full Text Available ... your situation is in the best position to discuss your prognosis and explain what the statistics may ... situation best is in the best position to discuss your prognosis. Survival statistics most often come from ...

Low levels of tumor suppressor candidate 3 predict poor prognosis of patients with hepatocellular carcinoma

Directory of Open Access Journals (Sweden)

Sheng XR

2018-02-01

that the expression of TUSC3 was strongly correlated with overall survival (OS and disease-free survival (DFS after radical surgery in HCC patients (P<0.001, P<0.001, respectively. Multivariate analysis revealed that the TUSC3 level was an independent risk factor for OS and DFS in HCC patients (P=0.001, P<0.001, respectively. Results of qRT-PCR and Western blot assays indicated that the level of TUSC3 in HCC tissues was significantly lower than that in the corresponding adjacent noncancerous tissues (P<0.01, P<0.001. Conclusion: The expression of TUSC3 in HCC was significantly downregulated and was correlated with tumor progression and prognosis, which could be used as an independent predictor of prognosis in HCC patients. Keywords: TUSC3, hepatocellular carcinoma, prognosis, immunohistochemistry, overall survival

A robust multivariate long run analysis of European electricity prices

OpenAIRE

Bruno Bosco; Lucia Parisio; Matteo Pelagatti; Fabio Baldi

2007-01-01

This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electrici...

Multivariate analysis of the prognostic factors of squamous cell cervical cancer treated by radical hysterectomy or combined radiation therapy

International Nuclear Information System (INIS)

Coelho, Francisco Ricardo Gualda; Kowalski, Luiz Paulo; Abrao, Fauzer Simao; Franco, Eduardo Luiz; Zeferino, Luiz Carlos; Brentani, Maria Mitzi

1996-01-01

Six hundred and nine cases of invasive squamous cell carcinoma of the cervix uteri in a retrospective analysis (1953-1982) at the A.C. Camargo Hospital, Antonio Prudente Foundation, Sao Paulo, Brazil. The patients were submitted to radical surgery and radiation therapy, individually or in combination. A multivariate analysis of the different variables were performed according to the Cox's regression method. The variables of prognosis value, in decreasing order of importance, were: the decade of patient's admission, the modality of therapy employed, the presence of residual tumor in the surgical specimens and the clinical stage of the disease. Other variables like ethnic group, age of first menstrual flux, menopause, number of pregnancy, kind of delivery, number and kind of abortion, were found to be of no prognostic importance. The decade of admission was of independent prognostic significance. The presence of residual tumor in the surgical specimens was more important than lymph nodes spreading, but the overall survival was affected by the increase in the number of positive lymph nodes. Patient's age was a weak prognostic factor accounting for a reduction in the survival time among cases with age upper to 45 years old. Radiation therapy sterilizes a considerable number of lymph nodes but not all of them in every patient. There are a specific group of patients where the radical surgery is necessary in order to carry a complete debulking of the disease. (author)

Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach

DEFF Research Database (Denmark)

Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale

2011-01-01

deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... showed that it was possible to differentiate between the different batches depending on their history and that some batches were more oxidised than others. Furthermore, based on the results from the data analysis, critical control points in the entire production chain will be identified and strategies...

Authentication of Trappist beers by LC-MS fingerprints and multivariate data analysis.

Science.gov (United States)

Mattarucchi, Elia; Stocchero, Matteo; Moreno-Rojas, José Manuel; Giordano, Giuseppe; Reniero, Fabiano; Guillou, Claude

2010-12-08

The aim of this study was to asses the applicability of LC-MS profiling to authenticate a selected Trappist beer as part of a program on traceability funded by the European Commission. A total of 232 beers were fingerprinted and classified through multivariate data analysis. The selected beer was clearly distinguished from beers of different brands, while only 3 samples (3.5% of the test set) were wrongly classified when compared with other types of beer of the same Trappist brewery. The fingerprints were further analyzed to extract the most discriminating variables, which proved to be sufficient for classification, even using a simplified unsupervised model. This reduced fingerprint allowed us to study the influence of batch-to-batch variability on the classification model. Our results can easily be applied to different matrices and they confirmed the effectiveness of LC-MS profiling in combination with multivariate data analysis for the characterization of food products.

Multivariable nonlinear analysis of foreign exchange rates

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2003-05-01

We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

Understanding Cancer Prognosis

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Full Text Available ... Questions to Ask about Your Diagnosis Research Understanding Cancer Prognosis Oncologist Anthony L. Back, M.D., a ... for provider care teams (PDF-210KB). Understanding Your Cancer Prognosis Video View this video on YouTube. Three ...

Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

Directory of Open Access Journals (Sweden)

Shikha Awasthi

2017-06-01

Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

Podocalyxin Is a Marker of Poor Prognosis in Pancreatic Ductal Adenocarcinoma.

Directory of Open Access Journals (Sweden)

Kapo Saukkonen

Full Text Available Podocalyxin-like 1 is a transmembrane glyco-protein whose overexpression associates in many cancers with poor prognosis and unfavorable clinicopathological characteristics. Until now, its prognostic value has never been studied in pancreatic ductal adenocarcinoma (PDAC. The aim of this study was to investigate podocalyxin expression in PDAC by a novel monoclonal antibody and a commercially available polyclonal antibody.With tissue microarrays and immuno-histochemistry, podocalyxin expression evaluation involved 168 PDAC patients. The associations of the podocalyxin tumor expression with clinicopathological variables were explored by Fisher's exact test and the linear-by-linear test. Survival analyses were by Kaplan-Meier analysis and the Cox proportional hazard model.The polyclonal antibody revealed membranous podocalyxin expression in 73 (44.0% specimens and the monoclonal antibody was highly expressed in 36 (21.8% cases. Membranous expression by the polyclonal antibody was associated with T classification (p=0.045 and perineural invasion (p=0.005, and high expression by the mono-clonal antibody with poor differentiation (p=0.033. High podocalyxin expression associated significantly with higher risk of death from PDAC by both the polyclonal antibody (hazard ratio (HR = 1.62; 95% confidence interval (CI 1.12-2.33; p=0.01 and the monoclonal antibody (HR = 2.10, 95% CI 1.38-3.20; p<0.001. The results remained significant in multivariate analysis, adjusted for age, gender, stage, lymph node ratio (≥/< 20%, and perivascular invasion (respectively as HR = 2.03; 95% CI 1.32-3.13, p=0.001; and as HR = 2.36; 95% CI 1.47-3.80, p<0.001.We found podocalyxin to be an independent factor for poor prognosis in PDAC. To our knowledge, this is the first such report of its prognostic value.

The single-nucleotide polymorphisms in CHD5 affect the prognosis of patients with hepatocellular carcinoma

Science.gov (United States)

Zhu, Xiao; Kong, Qingming; Xie, Liwei; Chen, Zhihong; Li, Hongmei; Zhu, Zhu; Huang, Yongmei; Lan, Feifei; Luo, Haiqing; Zhan, Jingting; Ding, Hongrong; Lei, Jinli; Xiao, Qin; Fu, Weiming; Fan, Wenguo; Zhang, Jinfang; Luo, Hui

2018-01-01

Previous studies showed that the low expressions of chromodomain-helicase-DNA-binding protein 5 (CHD5) were intensively associated with deteriorative biologic and clinical characteristics as well as outcomes in many tumors. The aim of this study is to determine whether CHD5 single nucleotide polymorphisms (SNPs) contribute to the prognosis of hepatocellular carcima (HCC). The SNPs were selected according to their linkage disequilibrium (LD) in the targeted next-generation sequencing (NGS) and then genotyped with TaqMan probers. We revealed a rare haplotype AG in CHD5 (SNPs: rs12564469-rs9434711) was markedly associated with HCC prognosis. The univariate and multivariate regression analyses revealed the patients with worse overall survival time were those with tumor metastasis and haplotype AG, as well as cirrhosis, poor differentiation and IV-TNM stage. Based on the available public databases, we discovered the significant association between haplotype AG and CHD5 mRNA expressions only existed in Chinese. These data proposed that the potentially genetic haplotype might functionally contribute to HCC prognosis and CHD5 mRNA expressions. PMID:29568352

Spatial compression algorithm for the analysis of very large multivariate images

Science.gov (United States)

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

Understanding Cancer Prognosis

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Full Text Available ... hard to talk about, even for doctors. Many Factors Can Affect Your Prognosis Some of the factors that affect prognosis include: The type of cancer ... that cancer will come back later. For this reason, doctors cannot say for sure that you are ...

THE PROGNOSIS OF RUSSIAN DEFENSE INDUSTRY DEVELOPMENT IMPLEMENTED THROUGH REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

L.M. Kapustina

2007-03-01

Full Text Available The article illustrates the results of investigation the major internal and external factors which influence the development of the defense industry, as well as the results of regression analysis which quantitatively displays the factorial contribution in the growth rate of Russian defense industry. On the basis of calculated regression dependences the authors fulfilled the medium-term prognosis of defense industry. Optimistic and inertial versions of defense product growth rate for the period up to 2009 are based on scenario conditions in Russian economy worked out by the Ministry of economy and development. In conclusion authors point out which factors and conditions have the largest impact on successful and stable operation of Russian defense industry.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

Science.gov (United States)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

Is the prognosis for Japanese and German patients with gastric cancer really different?

Science.gov (United States)

Bollschweiler, E; Boettcher, K; Hoelscher, A H; Sasako, M; Kinoshita, T; Maruyama, K; Siewert, J R

1993-05-15

Differing survival rates have been reported between patients having undergone surgical intervention for the treatment of gastric carcinoma in Japan and Western industrialized countries. Through the actual availability of the data compiled at a major Japanese medical center (National Cancer Center, Tokyo), it was possible, for the first time, to compare the patients and therapeutic results of a Japanese center (n = 1475) with that of a German center (Department of Surgery, Technical University of Munich, Munich; n = 453). The prognostic factors involving both groups were compared. Survival rates were analyzed in univariate and multivariate fashions. Some of the examined prognostic factors, such as sex, histologic type, tumor size, and Borrmann classification, were similarly distributed. Differences in frequency were discovered concerning pathologic tumor (pT), node (pN), and metastasis (pM) categories, localization, and age groups. Univariate analysis showed a 2-year survival rate of 88% for all Japanese patients with gastric cancer compared with 58% for German patients. The 5-year survival rates were 77% and 44%, respectively. The difference in the 2-year and 5-year survival rates for both departments may be related to differences in frequencies of several characteristics. In performing the same analysis in a multivariate fashion for the patient populations at both centers, it became clear that an important prognostic factor was the center itself. The survival curves of patients from Tokyo and Munich with the same prognostic factors demonstrate this difference. These differences, however, were small in comparison with those of univariate analysis. Using a similar classification of the tumor stage and similar prognostic characteristics, the prognosis for gastric cancer in Japan and Germany may be the same.

Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

International Nuclear Information System (INIS)

Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

2004-01-01

The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

Science.gov (United States)

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

[Computer-aided Prognosis for Breast Cancer Based on Hematoxylin & Eosin Histopathology Image].

Science.gov (United States)

Chen, Jiamei; Qu, Aiping; Liu, Wenlou; Wang, Linwei; Yuan, Jingping; Liu, Juan; Li, Yan

2016-06-01

Quantitatively analyzing hematoxylin &eosin(H&E)histopathology images is an emerging field attracting increasing attentions in recent years.This paper reviews the application of computer-aided image analysis in breast cancer prognosis.The traditional prognosis based on H&E histopathology image for breast cancer is firstly sketched,followed by a detailed description of the workflow of computer-aided prognosis including image acquisition,image preprocessing,regions of interest detection and object segmentation,feature extraction,and computer-aided prognosis.In the end,major technical challenges and future directions in this field are summarized.

Refining prognosis in patients with hepatocellular carcinoma through incorporation of metabolic imaging biomarkers

Energy Technology Data Exchange (ETDEWEB)

Takeuchi, Satoshi [Hokkaido University Graduate School of Medicine, Department of Medical Oncology, Sapporo (Japan); The University of Texas MD Anderson Cancer Center, Department of Nuclear Medicine, Houston, TX (United States); Rohren, Eric M. [The University of Texas MD Anderson Cancer Center, Department of Nuclear Medicine, Houston, TX (United States); Baylor College of Medicine, Department of Radiology, Houston, TX (United States); Abdel-Wahab, Reham [The University of Texas MD Anderson Cancer Center, Department of Gastrointestinal Medical Oncology, Houston, TX (United States); Assiut University Hospital, Clinical Oncology Department, Assiut (Egypt); Xiao, Lianchun; Morris, Jeffrey S. [The University of Texas MD Anderson Cancer Center, Department of Biostatistics, Houston, TX (United States); Macapinlac, Homer A. [The University of Texas MD Anderson Cancer Center, Department of Nuclear Medicine, Houston, TX (United States); Hassan, Manal M. [Baylor College of Medicine, Department of Radiology, Houston, TX (United States); Kaseb, Ahmed O. [The University of Texas MD Anderson Cancer Center, Department of Gastrointestinal Medical Oncology, Houston, TX (United States)

2017-06-15

{sup 18}F-fluorodeoxyglucose positron emission tomopraphy/computed tomography (FDGPET/CT) has been proven to be useful for imaging many types of cancer; however, its role is not well defined in hepatocellular carcinoma (HCC). We assessed the prognostic value of metabolic imaging biomarkers as established by baseline pretreatment FDG PET/CT in patients with HCC. We retrospectively analyzed the records of patients with HCC who underwent FDG PET/CT before initial treatment from May 2013 through May 2014. Four PET/CT parameters were measured: maximum standardized uptake value (SUV{sub max}), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal-liver SUV ratio (TNR). Optimal cut-off values for the PET/CT parameters to stratify patients in terms of overall survival (OS) were determined. Multivariate analysis was performed to determine whether the PET/CT parameters could add to the prognostic value of the Cancer of the Liver Italian Program (CLIP) scoring system and the Barcelona-Clinic Liver Cancer (BCLC) staging system. The analysis included 56 patients. Univariate analysis of the association between OS and continuous variables, including the PET/CT parameters SUV{sub max}, TLG, tumor size, total bilirubin level, and alkaline phosphatase level were significant predictors of OS. SUV{sub max} ≥ 11.7, TLG ≥ 1,341, MTV ≥ 230 mL, and TNR ≥ 4.8 were identified as cut-off values. Multivariate analysis revealed that SUV{sub max} ≥ 11.7 and TNR ≥ 4.8 were independent factors predicting a poor prognosis in both the CLIP scoring system and the BCLC staging system, as was TLG in the BCLC staging system. Pretreatment FDG PET/CT in patients with HCC can add to the prognostic value of standard clinical measures. Incorporation of imaging biomarkers derived from FDG PET/CT into HCC staging systems should be considered. (orig.)

Understanding Cancer Prognosis

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Full Text Available ... talk about, even for doctors. Many Factors Can Affect Your Prognosis Some of the factors that affect prognosis include: The type of cancer and where ... at the National Institutes of Health FOLLOW US Facebook Twitter Instagram YouTube Google+ LinkedIn GovDelivery RSS CONTACT ...

A primer of multivariate statistics

CERN Document Server

Harris, Richard J

2014-01-01

Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2012-01-01

Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

CD147/EMMPRIN overexpression and prognosis in cancer: A systematic review and meta-analysis

OpenAIRE

Xin, Xiaoyan; Zeng, Xianqin; Gu, Huajian; Li, Min; Tan, Huaming; Jin, Zhishan; Hua, Teng; Shi, Rui; Wang, Hongbo

2016-01-01

CD147/EMMPRIN (extracellular matrix metalloproteinase inducer) plays an important role in tumor progression and a number of studies have suggested that it is an indicator of tumor prognosis. This current meta-analysis systematically reevaluated the predictive potential of CD147/EMMPRIN in various cancers. We searched PubMed and Embase databases to screen the literature. Fixed-effect and random-effect meta-analytical techniques were used to correlate CD147 expression with outcome measures. A t...

Understanding Cancer Prognosis

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Full Text Available ... D., a national expert on doctor-patient communications, talks with one of his patients about what she'd like to ... how to discuss cancer prognosis (the likely course of the disease). Learn key points about prognosis and how to talk about it, and gain valuable insight from the ...

Multivariate statistical analysis of wildfires in Portugal

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Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

2013-04-01

Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

Integrated environmental monitoring and multivariate data analysis-A case study.

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Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate

Structural analysis and design of multivariable control systems: An algebraic approach

Science.gov (United States)

Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

1988-01-01

The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Benoit Parmentier

2014-12-01

Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

Aldehyde dehydrogenase 1A1 circumscribes high invasive glioma cells and predicts poor prognosis

Science.gov (United States)

Xu, Sen-Lin; Liu, Sha; Cui, Wei; Shi, Yu; Liu, Qin; Duan, Jiang-Jie; Yu, Shi-Cang; Zhang, Xia; Cui, You-Hong; Kung, Hsiang-Fu; Bian, Xiu-Wu

2015-01-01

Glioma is the most aggressive brain tumor with high invasiveness and poor prognosis. More reliable, sensitive and practical biomarkers to reveal glioma high invasiveness remain to be explored for the guidance of therapy. We herein evaluated the diagnostic and prognostic value of aldehyde dehydrogenase 1A1 (ALDH1A1) in the glioma specimens from 237 patients, and found that ADLH1A1 was frequently overexpressed in the high-grade glioma (WHO grade III-IV) as compared to the low-grade glioma (WHO grade I-II) patients. The tumor cells with ALDH1A1 expression were more abundant in the region between tumor and the borderline of adjacent tissue as compared to the central part of the tumor. ALDH1A1 overexpression was associated with poor differentiation and dismal prognosis. Notably, the overall and disease-free survivals of the patients who had ALDH1A1+ tumor cells sparsely located in the adjacent tissue were much worse. Furthermore, ALDH1A1 expression was correlated with the “classical-like” (CL) subtype as we examined GBM specimens from 72 patients. Multivariate Cox regression analysis revealed that ALDH1A1 was an independent marker for glioma patients’ outcome. Mechanistically, both in vitro and in vivo studies revealed that ALDH1A1+ cells isolated from either a glioblastoma cell line U251 or primary glioblastoma cells displayed significant invasiveness, clonogenicity, and proliferation as compared to ALDH1A1- cells, due to increased levels of mRNA and protein for matrix metalloproteinase 2, 7 and 9 (MMP2, MMP7 and MMP9). These results indicate that ALDH1A1+ cells contribute to the progression of glioma including invasion, proliferation and poor prognosis, and suggest that targeting ALDH1A1 may have important implications for the treatment of highly invasive glioma. PMID:26101711

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

Science.gov (United States)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

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Jankulovska Mirjana

2014-01-01

Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

Science.gov (United States)

D'Amico, E J; Neilands, T B; Zambarano, R

2001-11-01

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

Science.gov (United States)

Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

2013-06-01

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

Science.gov (United States)

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

Directory of Open Access Journals (Sweden)

Philip A Kragel

2012-11-01

Full Text Available Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA of functional magnetic resonance imaging (fMRI data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that nonlinear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Association of Diabetes and Prognosis of Minor Stroke and Its Subtypes: A Prospective Observational Study.

Directory of Open Access Journals (Sweden)

Yuesong Pan

Full Text Available The association between diabetes mellitus (DM and prognosis of minor stroke is unclear. The aim of this study is to investigate whether DM contributes to the prognosis of minor stroke or its specific subtype.All minor ischemic stroke patients were derived from the China National Stroke Registry and classified into 5 subtypes according to the TOAST (Trial of Org 10172 in Acute Stroke Treatment criteria. DM was defined as either self-reported physician diagnosis of diabetes or use of hypoglycemic medications during hospitalization or at discharge. Patients were followed up for 1 year for clinical outcomes of recurrent stroke, death and functional outcome. Poor functional outcomes were defined as a score of 2-6 for modified Rankin Score. Associations between DM and prognosis of minor stroke and its subtypes were analyzed by univariable and multivariable logistic regression.Of 4,548 patients with minor stroke, 1,230(27.0% patients had DM, 1,038(22.8% had poor outcomes and 570(13.0% of 4,401 patients had recurrent stroke at 1 year. In multivariable analyses, DM were significantly associated with 1-year stroke recurrence (Odds Ratio [OR], 1.31; 95% confidence interval [CI]: 1.08-1.59 and poor outcome (OR, 1.51; 95%CI: 1.28-1.77. Among the subtypes of minor stroke, DM was only significantly associated with 1-year stroke recurrence (OR, 1.63; 95%CI: 1.07-2.50 and poor outcome (OR, 1.73; 95%CI: 1.22-2.45 in the small-artery occlusion subtype.DM significantly increased the risk of stroke recurrence and poor outcome in the small-artery occlusion subtype, but not in other subtypes of minor stroke.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

Science.gov (United States)

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

Recurrent endometrial cancer: patterns of recurrent disease and assessment of prognosis

Energy Technology Data Exchange (ETDEWEB)

Sohaib, S.A. [Department of Radiology, Royal Marsden Hospital, London (United Kingdom); Houghton, S.L. [Department of Radiology, Royal Marsden Hospital, London (United Kingdom); Meroni, R. [Department of Academic Radiology, St Bartholomew' s Hospital, London (United Kingdom); Rockall, A.G. [Department of Academic Radiology, St Bartholomew' s Hospital, London (United Kingdom); Blake, P. [Department of Gynaecological Oncology, Royal Marsden Hospital, London (United Kingdom); Reznek, R.H. [Department of Academic Radiology, St Bartholomew' s Hospital, London (United Kingdom)

2007-01-15

Aim: To evaluate patterns of disease and identify factors predicting outcome in patients presenting with recurrent endometrial adenocarcinoma following primary surgery. Materials and methods: A retrospective review was performed of the imaging and clinical data in 86 patients (median age 66 years, range 42-88 years) presenting with recurrent endometrial adenocarcinoma following primary surgery. Results: Following primary surgery recurrent disease occurred within 2 years in 64% and within 3 years in 87%. Relapse was seen within lymph nodes in 41 (46%), the vagina in 36 (42%) the peritoneum in 24 (28%) and the lung in 21 (24%). Unusual sites of disease included spleen, pancreas, rectum, muscle and brain. Univariate survival analysis showed the factors significant for poor outcome were: multiple sites of disease, liver and splenic disease, haematogenous, peritoneal and nodal spread, poorly differentiated tumour, and early relapse. The presence of disease within the vagina, bladder or lung was not associated with poor prognosis. Multivariate analysis identified multiple sites of disease, liver and splenic metastases to be independent predictors of poor outcome. Conclusion: The most frequently observed sites of relapse are: lymph nodes, vagina, peritoneum and lung. Significant predictors of poor outcome in recurrent disease are multiple sites of disease and liver and splenic metastases.

Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.

Science.gov (United States)

Grinchuk, Oleg V; Yenamandra, Surya P; Iyer, Ramakrishnan; Singh, Malay; Lee, Hwee Kuan; Lim, Kiat Hon; Chow, Pierce Kah-Hoe; Kuznetsov, Vladamir A

2018-01-01

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro-oncogenic pathways in primary tumors (PT) and adjacent non-malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10 -6 ) and cross-cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross-validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, quantitative RT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for

Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

DEFF Research Database (Denmark)

Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

2005-01-01

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable...... data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later....

[Clinical value of angiogenin in predicting the prognosis of patients with idiopathic pulmonary fibrosis].

Science.gov (United States)

Bai, Yanling; Zhu, Haiyan; Sun, Qiyu; Gu, Guozhong; Zhang, Lingyu; Li, Ying; Yang, Baofeng

2017-09-01

To explore the relationship between angiogenin-1/2 (Ang-1/2) and clinical parameters of idiopathic pulmonary fibrosis (IPF), and to assess the value of Ang-1/2 in predicting the prognosis of patients with IPF. A retrospective analysis was conducted. Ninety-one patients diagnosed as IPF by high resolution CT (HRCT) and lung biopsy admitted to Daqing Oil Field General Hospital from March 2014 to January 2015 were enrolled. The general data, serum parameters and pulmonary function parameters of all patients were collected. After treatment, all of the 91 patients were followed-up to 2 years. The patients were divided into favorable prognosis group and unfavorable prognosis group according to follow-up results. The differences in all parameters between the two groups were compared. The relationship between Ang-1, Ang-2 and lung function parameters was analyzed by Pearson correlation analysis. Cox proportional hazard regression model was used to evaluate the effect of clinical parameters on the prognosis of patients with IPF. The effect of Ang-2 in predicting prognosis of patients with IPF was analyzed by receiver operating characteristic (ROC) curve. During the 2-year follow-up period, 30 of 91 patients showed a favorable prognosis, and 55 showed an unfavorable prognosis with a poor prognosis rate of 64.71%, and 6 patients withdrew from the study due to loss of follow-up and death. Compared with the favorable prognosis group, Ang-2 level in the unfavorable prognosis group was significantly increased (μg/L: 2.88±1.63 vs. 1.89±1.22, t = 2.909, P = 0.005), but Ang-1 only showed a slight increase (μg/L: 28.70±14.26 vs. 25.62±11.95, t = 1.005, P = 0.318). The results of Pearson correlation analysis showed that Ang-2 level was negatively correlated with forced expiratory volume in 1 second (FVC1) and the percentage of carbon monoxide diffusing capacity accounting for the expected value (DLCO%: r value was -0.227 and -0.206, and P value was 0.147 and 0.253, respectively

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

Science.gov (United States)

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

Multivariate missing data in hydrology - Review and applications

Science.gov (United States)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

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Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Single nucleotide polymorphisms in MLH1 predict poor prognosis of hepatocellular carcinoma in a Chinese population.

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Zhu, Xiaonian; Liu, Wei; Qiu, Xiaoqiang; Wang, Zhigang; Tan, Chao; Bei, Chunhua; Qin, Linyuan; Ren, Yuan; Tan, Shengkui

2017-10-03

Hepatocellular carcinoma (HCC) is a malignant cancer causing deleterious health effect worldwide, especially in China. So far clinical cure rate and long-term survival rate of HCC remains low. Most HCC patients after cancer resection have recurrence or metastasis within 5 years. This study aims to explore the genetic association of mutL homolog 1 ( MLH1 ) polymorphisms with HCC risk and prognosis. Four candidate MLH1 polymorphisms, rs1800734, rs10849, rs3774343 and rs1540354 were studied from a hospital-based case-control study including 1,036 cases (HCC patients) and 1,036 controls (non-HCC patients) in Guangxi, China. All these SNPs interacted with environmental risk factors, such as HBV infection, alcohol intake and smoking in the pathogenesis of HCC. However, only rs1800734 had significant difference between cases and controls. Compared to the AA genotype, patients with AG, GG and AG/GG genotype of rs1800734 had an increased risk of HCC [ORs (95% CI) = 1.217 (1.074∼1.536), 1.745 (1.301∼2.591) and 1.291 (1.126∼1.687)] and a decreased survival time [co-dominant, HR (95% CI) = 1.553 (1.257∼1.920); dominant, HR (95% CI) = 2.207 (1.572∼3.100)]. Furthermore, we found that tumor number, tumor staging, metastasis and rs1800734 were associated with the overall survival of HCC patients by multivariate COX regression analysis. No significant difference was found between the other three MLH1 polymorphisms with HCC risk and prognosis. Our study suggests MLH1 SNP, rs1800734 as a new predictor for poor prognosis of HCC patients.

Chemotherapy-induced neutropenia and the prognosis of colorectal cancer: a meta-analysis of cohort studies.

Science.gov (United States)

Tan, XiangZhou; Wen, QiaoCheng; Wang, Ran; Chen, ZhiKang

2017-11-01

Recently, there has been a controversial discussion about the prognostic value of chemotherapy-induced neutropenia (CIN) in colorectal cancer patients. Thus, a meta-analysis was conducted to determine the relationship between CIN and the prognosis of colorectal cancer patients. We searched the PubMed, EMBASE, and Cochrane library databases to identify studies evaluating the association between CIN and colorectal cancer prognosis. Pooled random/fixed effect models were used to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association. Eight studies were selected for the meta-analysis, for a total of 2,745 patients. There was significant improved survival among colorectal cancer patients with CIN (HR = 0.62, 95% CI = 0.47-0.76). However, significant heterogeneity was found (p = 0.000, Ι 2  = 75.0%). Through subgroup analysis, we could greatly eliminate the heterogeneity and found that neutropenia was associated with better survival in stage IV colorectal cancer patients, no matter the HR calculated by overall survival (OS) or progression-free survival (PFS). Meanwhile, the prognostic value of neutropenia in stage II/III colorectal cancer can be found when the HR is calculated by disease-free survival (DFS). Additionally, we observed significant differences after stratification according to various tumor stages, endpoints, and the use of G-CSF. Our results which, based on a cohort study, indicate that CIN is associated with improved survival in patients with colorectal cancer. However, further randomized controlled trials are warranted.

Understanding Your Cancer Prognosis

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Understanding Your Cancer Prognosis is the main video in the NCI Prognosis Video Series, which offers the perspectives of three cancer patients and their doctor, an oncologist who is also a national expert in doctor-patient communication.

Reduction of the dimensionality and comparative analysis of multivariate radiological data

International Nuclear Information System (INIS)

Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

2009-01-01

Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of 238 U, 226 Ra or 232 Th, combined with the concentration of 40 K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

A Novel and Effective Multivariate Method for Compositional Analysis using Laser Induced Breakdown Spectroscopy

International Nuclear Information System (INIS)

Wang, W; Qi, H; Ayhan, B; Kwan, C; Vance, S

2014-01-01

Compositional analysis is important to interrogate spectral samples for direct analysis of materials in agriculture, environment and archaeology, etc. In this paper, multi-variate analysis (MVA) techniques are coupled with laser induced breakdown spectroscopy (LIBS) to estimate quantitative elemental compositions and determine the type of the sample. In particular, we present a new multivariate analysis method for composition analysis, referred to as s pectral unmixing . The LIBS spectrum of a testing sample is considered as a linear mixture with more than one constituent signatures that correspond to various chemical elements. The signature library is derived from regression analysis using training samples or is manually set up with the information from an elemental LIBS spectral database. A calibration step is used to make all the signatures in library to be homogeneous with the testing sample so as to avoid inhomogeneous signatures that might be caused by different sampling conditions. To demonstrate the feasibility of the proposed method, we compare it with the traditional partial least squares (PLS) method and the univariate method using a standard soil data set with elemental concentration measured a priori. The experimental results show that the proposed method holds great potential for reliable and effective elemental concentration estimation

C-Reactive Protein Is an Important Biomarker for Prognosis Tumor Recurrence and Treatment Response in Adult Solid Tumors: A Systematic Review.

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Shrotriya, Shiva; Walsh, Declan; Bennani-Baiti, Nabila; Thomas, Shirley; Lorton, Cliona

2015-01-01

A systematic literature review was done to determine the relationship between elevated CRP and prognosis in people with solid tumors. C-reactive protein (CRP) is a serum acute phase reactant and a well-established inflammatory marker. We also examined the role of CRP to predict treatment response and tumor recurrence. MeSH (Medical Subject Heading) terms were used to search multiple electronic databases (PubMed, EMBASE, Web of Science, SCOPUS, EBM-Cochrane). Two independent reviewers selected research papers. We also included a quality Assessment (QA) score. Reports with QA scores <50% were excluded. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology was utilized for this review (S1 PRISMA Checklist). 271 articles were identified for final review. There were 45% prospective studies and 52% retrospective. 264 had intermediate QA score (≥50% but <80%); Seven were adequate (80% -100%); A high CRP was predictive of prognosis in 90% (245/271) of studies-80% of the 245 studies by multivariate analysis, 20% by univariate analysis. Many (52%) of the articles were about gastrointestinal malignancies (GI) or kidney malignancies. A high CRP was prognostic in 90% (127 of 141) of the reports in those groups of tumors. CRP was also prognostic in most reports in other solid tumors primary sites. A high CRP was associated with higher mortality in 90% of reports in people with solid tumors primary sites. This was particularly notable in GI malignancies and kidney malignancies. In other solid tumors (lung, pancreas, hepatocellular cancer, and bladder) an elevated CRP also predicted prognosis. In addition there is also evidence to support the use of CRP to help decide treatment response and identify tumor recurrence. Better designed large scale studies should be conducted to examine these issues more comprehensively.

Multivariate factor analysis of Girgentana goat milk composition

Directory of Open Access Journals (Sweden)

Pietro Giaccone

2010-01-01

Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

Tumor hypoxia, p53, and prognosis in cervical cancers

International Nuclear Information System (INIS)

Haensgen, Gabriele; Krause, Ulf; Becker, Axel; Stadler, Peter; Lautenschlaeger, Christine; Wohlrab, Wolfgang; Rath, Friedrich W.; Molls, Michael; Dunst, Juergen

2001-01-01

Background: The p53 protein is involved in the regulation of initiation of apoptosis. In vitro, p53-deficient cells do not respond to hypoxia with apoptosis as do p53-normal cells, and this may lead to a relative growth advantage of cells without a functioning p53 under hypoxia. On the basis of this hypothesis, a selection of cells with a functionally inactive p53 may occur in hypoxic tumors. The development of uterine cervical carcinomas is closely associated with infections of human papilloma viruses, which may cause a degradation of the tumor suppressor gene p53, resulting in a restriction of apoptosis. Thus, cervical cancers have often a functionally inactive p53. The purpose of our clinical study was therefore to investigate the association between p53, hypoxia, and prognosis in cervical cancers in which the oxygenation status can be determined by clinical methods. Material and Methods: Seventy patients with locally advanced squamous cell cervical cancer Stages IIB (n=14), IIIB (n=49), and IVA (n=7) were investigated in the period from 1996 through 1999. All were treated with definitive radiotherapy with curative intent by a combination of external radiotherapy plus high-dose-rate afterloading. Before therapy, tumor oxygenation was measured with a needle probe polarographically using the Eppendorf histograph. Hypoxic tumors were defined as those with pO 2 measurements below 5 mm Hg (HF5). Pretreatment biopsies were taken and analyzed immunohistologically for p53 protein expression with the DO-7 antibody. The DNA index was measured by flow cytometry. The statistical data analysis was done with SPSS 9.0 for Windows. Results: The 3-year overall survival was 55% for the whole group of patients. Clinical prognostic factors in a multivariate analysis were pretreatment hemoglobin level (3-year survival 62% for patients with a pretreatment hemoglobin ≥11 g/dl vs. 27% for hemoglobin <11 g/dl, p=0.006) and FIGO stage (Stage IIB: 65%; Stage IIIB: 60%; Stage IVA: 29%, p

A multivariate analysis of Antarctic sea ice since 1979

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Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)

2012-03-15

Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)

Preoperative carcinoembryonic antigen and prognosis of colorectal cancer. An independent prognostic factor still reliable.

Science.gov (United States)

Li Destri, Giovanni; Rubino, Antonio Salvatore; Latino, Rosalia; Giannone, Fabio; Lanteri, Raffaele; Scilletta, Beniamino; Di Cataldo, Antonio

2015-04-01

To evaluate whether, in a sample of patients radically treated for colorectal carcinoma, the preoperative determination of the carcinoembryonic antigen (p-CEA) may have a prognostic value and constitute an independent risk factor in relation to disease-free survival. The preoperative CEA seems to be related both to the staging of colorectal neoplasia and to the patient's prognosis, although this-to date-has not been conclusively demonstrated and is still a matter of intense debate in the scientific community. This is a retrospective analysis of prospectively collected data. A total of 395 patients were radically treated for colorectal carcinoma. The preoperative CEA was statistically compared with the 2010 American Joint Committee on Cancer (AJCC) staging, the T and N parameters, and grading. All parameters recorded in our database were tested for an association with disease-free survival (DFS). Only factors significantly associated (P < 0.05) with the DFS were used to build multivariate stepwise forward logistic regression models to establish their independent predictors. A statistically significant relationship was found between p-CEA and tumor staging (P < 0.001), T (P < 0.001) and N parameters (P = 0.006). In a multivariate analysis, the independent prognostic factors found were: p-CEA, stages N1 and N2 according to AJCC, and G3 grading (grade). A statistically significant difference (P < 0.001) was evident between the DFS of patients with normal and high p-CEA levels. Preoperative CEA makes a pre-operative selection possible of those patients for whom it is likely to be able to predict a more advanced staging.

Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

OpenAIRE

Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

2017-01-01

Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

A comparison of multivariate genome-wide association methods

DEFF Research Database (Denmark)

Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

2014-01-01

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

Combination of blood tests for significant fibrosis and cirrhosis improves the assessment of liver-prognosis in chronic hepatitis C.

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Boursier, J; Brochard, C; Bertrais, S; Michalak, S; Gallois, Y; Fouchard-Hubert, I; Oberti, F; Rousselet, M-C; Calès, P

2014-07-01

Recent longitudinal studies have emphasised the prognostic value of noninvasive tests of liver fibrosis and cross-sectional studies have shown their combination significantly improves diagnostic accuracy. To compare the prognostic accuracy of six blood fibrosis tests and liver biopsy, and evaluate if test combination improves the liver-prognosis assessment in chronic hepatitis C (CHC). A total of 373 patients with compensated CHC, liver biopsy (Metavir F) and blood tests targeting fibrosis (APRI, FIB4, Fibrotest, Hepascore, FibroMeter) or cirrhosis (CirrhoMeter) were included. Significant liver-related events (SLRE) and liver-related deaths were recorded during follow-up (started the day of biopsy). During the median follow-up of 9.5 years (3508 person-years), 47 patients had a SLRE and 23 patients died from liver-related causes. For the prediction of first SLRE, most blood tests allowed higher prognostication than Metavir F [Harrell C-index: 0.811 (95% CI: 0.751-0.868)] with a significant increase for FIB4: 0.879 [0.832-0.919] (P = 0.002), FibroMeter: 0.870 [0.812-0.922] (P = 0.005) and APRI: 0.861 [0.813-0.902] (P = 0.039). Multivariate analysis identified FibroMeter, CirrhoMeter and sustained viral response as independent predictors of first SLRE. CirrhoMeter was the only independent predictor of liver-related death. The combination of FibroMeter and CirrhoMeter classifications into a new FM/CM classification improved the liver-prognosis assessment compared to Metavir F staging or single tests by identifying five subgroups of patients with significantly different prognoses. Some blood fibrosis tests are more accurate than liver biopsy for determining liver prognosis in CHC. A new combination of two complementary blood tests, one targeted for fibrosis and the other for cirrhosis, optimises assessment of liver-prognosis. © 2014 John Wiley & Sons Ltd.

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

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Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.

Science.gov (United States)

Chen, Jia-Mei; Li, Yan; Xu, Jun; Gong, Lei; Wang, Lin-Wei; Liu, Wen-Lou; Liu, Juan

2017-03-01

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature-based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1975-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

Effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer: A Meta-analysis

Directory of Open Access Journals (Sweden)

Zi-long CHEN

2018-04-01

Full Text Available Objective To systematically evaluate the effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer. Methods The database including PubMed, Embase, CNKI and Wangfang, were electronically searched with no language restriction from their inception to March 2017 to collect the studies about the effect of metformin on the prognosis of diabetic patients combined with gynecologic cancer. The references in reviews were also searched. According to the inclusion and exclusion criteria, two reviewers screened the literatures independently, extracted data and assessed methodological quality by the Newcastle-Ottawa scale. The primary end points included overall survival (OS and progress free survival (PFS. The outcome measures were the pooled hazard ratios (HR and 95% confidence intervals (95% CI. I2 was performed in a heterogeneity assessment. Publication bias was evaluated by using Begg's funnel plot and Egger's test, and the sensitivity analysis was conducted to confirm robustness. The Meta-analysis was performed using STATA 12.0 software. Results Sixteen eligible retrospective cohort studies were included and the score of quality assessment were ranged from 6 to 9. The Meta-analysis showed that metformin could improve the OS of diabetic patients with gynecologic tumors (HR=0.71, 95%CI 0.59-0.85, P=0.000. Subgroup analysis revealed that metformin could improve the OS of diabetic patients combined with endometrial cancer (HR=0.70, 95%CI 0.54-0.89, P=0.004 and diabetic patients combined with cervical cancer (HR=0.95, 95%CI 0.90-1.00, P=0.048. Meanwhile metformin improved the OS (HR=0.56, 95%CI 0.38-0.83, P=0.004 and PFS (HR=0.45, 95%CI 0.30-0.68, P=0.000 of diabetic patients with ovarian cancer after adjusting for confounders. Conclusions The use of metformin is positive for the prognosis of diabetic patients combined with gynecologic cancer. It may improve the OS of diabetic patients with endometrial cancer and diabetic

Multivariate survivorship analysis using two cross-sectional samples.

Science.gov (United States)

Hill, M E

1999-11-01

As an alternative to survival analysis with longitudinal data, I introduce a method that can be applied when one observes the same cohort in two cross-sectional samples collected at different points in time. The method allows for the estimation of log-probability survivorship models that estimate the influence of multiple time-invariant factors on survival over a time interval separating two samples. This approach can be used whenever the survival process can be adequately conceptualized as an irreversible single-decrement process (e.g., mortality, the transition to first marriage among a cohort of never-married individuals). Using data from the Integrated Public Use Microdata Series (Ruggles and Sobek 1997), I illustrate the multivariate method through an investigation of the effects of race, parity, and educational attainment on the survival of older women in the United States.

Assessment of the pyramidal tract by diffusion tensor analysis in brain hemorrhage patients for motor function prognosis

International Nuclear Information System (INIS)

Kawamo, Michiaki; Abe, Takumi; Izumiyama, Hitoshi

2008-01-01

In patients with brain hemorrhage, the entire visualized pyramidal tract was established as an area of interest (ROI). Its Fractional Anisotropy (FA) value was determined by diffusion tensor analysis (DTA), and its relationship to motor function at the onset and three months later was investigated. In 30 patients with brain hemorrhage accompanying paralysis, MRI was performed during the subacute phase (6-14 days after onset). In addition, using a workstation, DTA was performed in order to visualize the pyramidal tract. The FA of the ROI was measured on the affected and unaffected sides, and as previously reported, the ratio of FA in the affected and unaffected sides was calculated. Subsequently, we examined the relationship between the FA ratio and motor function prognosis. Motor function prognosis was assessed based on the sum of the Brunnstrom stage at the onset and three months later. A strong correlation coefficient existed between the FA ratio of the entire pyramidal tract and the sum of the Brunnstrom stage three months after onset (0.74, p<0.001), and prognosis of motor function tended to improve in patients with FA ratios of 0.95 or higher. Patients with mild paralysis were identified in order to ascertain the degree of improvement in paralysis, and a significant correlation between the FA ratio of the entire pyramidal tract and the degree of improvement in the Brunnstrom stage was observed (correlation coefficient 0.77, p<0.001). When compared to putamen hemorrhage, the FA ratio affected the prognosis of paralysis more in thalamic hemorrhage. The results suggest that in patients with an FA ratio of 1.0, the recovery rate of paralysis three months after onset is markedly high. In brain hemorrhage patients, a reduction in the FA ratio of the entire pyramidal tract was correlated with the functional prognosis of motor paralysis, and in thalamic hemorrhage, it may be possible to predict motor function based on FA ratios. Hence, the DTA of the pyramidal tract

Imbalance between vascular endothelial growth factor and endostatin correlates with the prognosis of operable non-small cell lung cancer.

Science.gov (United States)

Hu, Y; Hu, M-m; Shi, G-L; Han, Y; Li, B-L

2014-09-01

Angiogenesis is regulated by a balance of pro-angiogenic and anti-angiogenic factors. Vascular endothelial growth factor (VEGF) and endostatin respectively represents a frequent component of inducers and inhibitors in the process of angiogenesis. The ratio of VEGF/endostatin may reflect the balance of angiogenic switch. This study aimed to determine whether an imbalance between VEGF/endostatin exists in operable non-small cell lung cancer (NSCLC) patients and to assess the correlation, if any, between the imbalance and the prognosis. Preoperative serum levels of VEGF and endostatin were simultaneously determined by quantitiative enzyme-linked immunosorbent assay (ELISA) and the ratio of them was calculated among 98 NSCLC patients and 51 healthy controls. The relationship between these factors and clinicopathological features, including prognosis, was examined. The ratio of VEGF/endostatin levels was significantly higher in operable NSCLC patients [median, 10.4; interquartile range (IQR), 5.9-19.8] than in normal controls [median, 5.1; IQR, 3.3-9.7] (P = 0.002). While the ratio in patients who were still alive for more than 60 months was 8.3 (IQR, 4.3-17.9), the ratio in those who died was 12.9 (IQR, 8.0-22.1) (p = 0.017). In subgroup analysis of patients with pathological stage N0, there was a statistically significant increase of the survival time in the group with a lower ratio than in the group with a higher ratio (p = 0.032). Multivariate analysis confirmed that the VEGF/endostatin ratio was an independent prognostic factor (p = 0.018). There was an imbalance between VEGF and endostatin in serum of operable NSCLC patients. The imbalance correlated with the prognosis of operable NSCLC. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Application of instrumental neutron activation analysis and multivariate statistical methods to archaeological Syrian ceramics

International Nuclear Information System (INIS)

Bakraji, E. H.; Othman, I.; Sarhil, A.; Al-Somel, N.

2002-01-01

Instrumental neutron activation analysis (INAA) has been utilized in the analysis of thirty-seven archaeological ceramics fragment samples collected from Tal AI-Wardiate site, Missiaf town, Hamma city, Syria. 36 chemical elements were determined. These elemental concentrations have been processed using two multivariate statistical methods, cluster and factor analysis in order to determine similarities and correlation between the various samples. Factor analysis confirms that samples were correctly classified by cluster analysis. The results showed that samples can be considered to be manufactured using three different sources of raw material. (author)

Expression of miR-146a-5p in patients with intracranial aneurysms and its association with prognosis.

Science.gov (United States)

Zhang, H-L; Li, L; Cheng, C-J; Sun, X-C

2018-02-01

The study aims to detect the association of miR-146a-5p with intracranial aneurysms (IAs). The expression of miR-146a-5p was compared from plasma samples between 72 patients with intracranial aneurysms (IAs) and 40 healthy volunteers by quantitative Real-time polymerase chain reaction (qRT-PCR). Statistical analysis was performed to analyze the relationship between miR-146a-5p expression and clinical data and overall survival (OS) time of IAs patients. Univariate and multivariate Cox proportional hazards have also been performed. Notably, higher miR-146a-5p expression was found in plasma samples from 72 patients with intracranial aneurysms (IAs) compared with 40 healthy controls. Higher miR-146a-5p expression was significantly associated with rupture and Hunt-Hess level in IAs patients. Kaplan-Meier survival analysis verified that higher miR-146a-5p expression predicted a shorter overall survival (OS) compared with lower miR-146a-5p expression in IAs patients. Univariate and multivariate Cox proportional hazards demonstrated that higher miR-146a-5p expression, rupture, and Hunt-Hess were independent risk factors of OS in patients with intracranial aneurysms (IAs). MiR-146a-5p expression may serve as a biomarker for predicting prognosis in patients with IAs.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

Science.gov (United States)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Tumor-stroma ratio(TSR) as a potential novel predictor of prognosis in digestive system cancers: A meta-analysis.

Science.gov (United States)

Zhang, Runjin; Song, Wei; Wang, Kai; Zou, Shubing

2017-09-01

The tumor-stroma ratio (TSR) has been reported as a prognosis predictor in multiple cancers. The aim of this meta-analysis was to investigate the potential value of TSR as a prognostic predictor of cancer in the digestive system. We searched PubMed, Embase, Elsevier and Web of Science. All studies exploring the association of TSR with overall survival (OS) or disease-free survival (DFS), and lymph node metastasis (LNM) were identified. In total, eight studies were eligible for analysis, and they included 1959 patients. Meta-analysis showed that the low TSR in the tumor could predict poor overall survival (OS) in multiple cancers (pooled Hazard Ratio [HR]: 2.15, 95%CI: 1.80-2.57, P<0.00001, fixed effects). For disease-free survival (DFS), low TSR was also a significant predictor (pooled Hazard Ratio [HR]: 2.31, 95%CI: 1.88-2.83, P<0.00001, fixed effects). In addition, low TSR was correlated with tumor stage. The tumor-stroma ratio (TSR) may potentially serve as a poor prognostic predictor for the metastasis and prognosis of cancer. Copyright © 2017. Published by Elsevier B.V.

Prognosis of synchronous bilateral breast cancer

DEFF Research Database (Denmark)

Holm, Marianne; Tjønneland, Anne; Balslev, Eva

2014-01-01

Currently, no consistent evidence-based guidelines for the management of synchronous bilateral breast cancer (SBBC) exist and it is uncertain how presenting with SBBC affects patients' prognosis. We conducted a review of studies analyzing the association between SBBC and prognosis. The studies...... that reported adjusted effect measures were included in meta-analyses of effect of bilaterality on breast cancer mortality. From 57 initially identified records 17 studies from 11 different countries including 8,050 SBBC patients were included. The quality of the studies varied but was generally low with small...... sample sizes, and lack of consistent, detailed histo-pathological information. When doing meta-analysis on the subgroup of studies that provided adjusted effect estimates on breast cancer mortality (nine studies including 3,631 SBBC cases), we found that bilaterality in itself had a negative impact...

A method of signal transmission path analysis for multivariate random processes

International Nuclear Information System (INIS)

Oguma, Ritsuo

1984-04-01

A method for noise analysis called ''STP (signal transmission path) analysis'' is presentd as a tool to identify noise sources and their propagation paths in multivariate random proceses. Basic idea of the analysis is to identify, via time series analysis, effective network for the signal power transmission among variables in the system and to make use of its information to the noise analysis. In the present paper, we accomplish this through two steps of signal processings; first, we estimate, using noise power contribution analysis, variables which have large contribution to the power spectrum of interest, and then evaluate the STPs for each pair of variables to identify STPs which play significant role for the generated noise to transmit to the variable under evaluation. The latter part of the analysis is executed through comparison of partial coherence function and newly introduced partial noise power contribution function. This paper presents the procedure of the STP analysis and demonstrates, using simulation data as well as Borssele PWR noise data, its effectiveness for investigation of noise generation and propagation mechanisms. (author)

Biodegradable blends of starch/polyvinyl alcohol/glycerol: multivariate analysis of the mechanical properties

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Juliano Zanela

Full Text Available Abstract The aim of the work was to study the mechanical properties of extruded starch/polyvinyl alcohol (PVA/glycerol biodegradable blends using multivariate analysis. The blends were produced as cylindrical strands by extrusion using PVAs with different hydrolysis degrees and viscosities, at two extrusion temperature profiles (90/170/170/170/170 °C and 90/170/200/200/200 °C and three conditioning relative humidities of the samples (33, 53, and 75%. The mechanical properties showed a great variability according to PVA type, as well as the extrusion temperature profile and the conditioning relative humidity; the tensile strength ranged from 0.42 to 5.40 MPa, elongation at break ranged from 10 to 404% and Young’s modulus ranged from 0.93 to 13.81 MPa. The multivariate analysis was a useful methodology to study the mechanical properties behavior of starch/PVA/glycerol blends, and it can be used as an exploratory technique to select of the more suitable PVA type and extrusion temperature to produce biodegradable materials.

Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

NARCIS (Netherlands)

Molenaar, P.C.M.

1987-01-01

Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic

Principal response curves: analysis of time-dependent multivariate responses of biological community to stress

NARCIS (Netherlands)

Brink, van den P.J.; Braak, ter C.J.F.

1999-01-01

In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity

Identification of key pathways and genes influencing prognosis in bladder urothelial carcinoma

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Ning X

2017-03-01

Full Text Available Xin Ning, Yaoliang Deng Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, People’s Republic of China Background: Genomic profiling can be used to identify the predictive effect of genomic subsets for determining prognosis in bladder urothelial carcinoma (BUC after radical cystectomy. This study aimed to investigate potential gene and pathway markers associated with prognosis in BUC.Methods: A microarray dataset of BUC was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs were identified by DESeq of the R platform. Kaplan–Meier analysis was applied for prognostic markers. Key pathways and genes were identified using bioinformatics tools, such as gene set enrichment analysis, gene ontology, the Kyoto Encyclopedia of Genes and Genomes, gene multiple association network integration algorithm (GeneMANIA, Search Tool for the Retrieval of Interacting Genes/Proteins, and Molecular Complex Detection.Results: A comparative gene set enrichment analysis of tumor and adjacent normal tissues suggested BUC tumorigenesis resulted mainly from enrichment of cell cycle and DNA damage and repair-related biological processes and pathways, including TP53 and mitotic recombination. Two hundred and fifty-six genes were identified as potential prognosis-related DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the potential prognosis-related DEGs were enriched in angiogenesis, including the cyclic adenosine monophosphate biosynthetic process, cyclic guanosine monophosphate-protein kinase G, mitogen-activated protein kinase, Rap1, and phosphoinositide-3-kinase-AKT signaling pathway. Nine hub genes, TAGLN, ACTA2, MYH11, CALD1, MYLK, GEM, PRELP, TPM2, and OGN, were identified from the intersection of protein–protein interaction and GeneMANIA networks. Module analysis of protein–protein interaction and GeneMANIA networks mainly showed

Younger age is an independent predictor of worse prognosis among Lebanese nonmetastatic breast cancer patients: analysis of a prospective cohort

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El Chediak A

2017-06-01

Full Text Available Alissar El Chediak,1 Raafat S Alameddine,1 Ayman Hakim,1 Lara Hilal,2 Sarah Abdel Massih,1 Lana Hamieh,3 Deborah Mukherji,1 Sally Temraz,1 Maya Charafeddine,1 Ali Shamseddine1 1Division of Hematology/Oncology, Department of Internal Medicine, 2Department of Radiation Oncology, American University of Beirut Medical Center, Beirut, Lebanon; 3Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA Background: Several retrospective studies have reported that younger age at presentation is associated with a worse prognosis for nonmetastatic breast cancer patients. In this study, we prospectively assessed the association between different baseline characteristics (age, tumor characteristics, mode of treatment, etc and outcomes among newly diagnosed nonmetastatic Lebanese breast cancer patients.Methods: We recruited a sample of 123 women newly diagnosed with nonmetastatic breast cancer presenting to American University of Beirut Medical Center. Immunohistochemical, molecular (vitamin D receptor, methylene tetrahydrofolate reductase polymorphisms, and genetic assays were performed. Patient characteristics were compared by age group (<40 and ≥40 years. A Cox regression analysis was performed to evaluate the variables affecting the disease-free survival (DFS. Outcome data were obtained, and DFS was estimated.Results: Among the 123 patients, 47 were 40 years of age or younger, and 76 were older than 40 years. Median follow-up duration was 58 months. Nine out of 47 patients <40 years (19.1% experienced disease relapse in contrast to four out of 76 patients >40 years (5.2%. A wide immunohistochemical panel included Ki-67, cyclin B1, p53, platelet-derived growth factor receptor, and vascular endothelial growth factor receptor, and did not reveal any significant difference in these markers between the two age groups. Older patients had a larger percentage of Luminal A than younger patients. On multivariate analysis

Morphological analysis of enlarged ventricle on CT image, using multivariate analysis

International Nuclear Information System (INIS)

Iwasaki, Satoru; Kichikawa, Kimihiko; Otsuji, Hideyuki; Fukusumi, Akio; Kobayashi, Yasuo.

1983-01-01

Multivariate analysis of enlarged cerebral ventricle on CT was undertaken to study the characteristics of ventricular morphology. Several ventricular segments of enlarged ventricle, defined on the basis of the study of normal group, were linearly measured on CT image. Then the discriminant analysis with the increase and decrease of variable was applied. The following are the results obtained. The error ratio of discrimination between pressure hydrocephalus and cerebral atrophy was 8.4 %, and between obstructive hydrocephalus and communicating hydrocephalus was 11.3 %. Ventricular segments were divided into three groups according to their character of enlargement: (1) the temporal horn and trigone are large in pressure hydrocephalus; (2) the hypothalamic segment of the third ventricle and the body of lateral ventricle are larger in obstructive hydrocephalus than in communicating hydrocephalus; (3) the anterior horn, cellae mediae at the level of the head of caudate nuclei and thalamic segment of the third ventricle are relatively large in cerebral atrophy and communicating hydrocephalus. The hypothalamic segment of the third ventricle assumes a round or oval shape in pressure hydrocephalus but a rectangular or teardrop shape in cerebral atrophy. These findings are contributory to pathological evaluation of ventricular enlargement. (author)

Multivariate analysis for customer segmentation based on RFM

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Álvaro Julio Cuadros López

2018-02-01

Full Text Available Context: To build a successful relationship management (CRM, companies must start with the identification of the true value of customers, as this provides basic information to implement more targeted and customized marketing strategies. The RFM methodology, a classic analysis tool that uses three evaluation parameters, allows companies to understand customer behavior, and to establish customer segments. The addition of a new parameter in the traditional technique is an opportunity to refine the possible outcomes of a customer segmentation since it not only provides a new element of evaluation to identify the most valuable customers, but it also makes it possible to differentiate and get to know customers even better. Method: The article presents a methodology that allows to establish customer segments using an extended RFM method with new variables, selected through multivariate analysis..  Results: The proposed implementation was applied in a company in which variables such as profit, profit percentage, and billing due date were tested. Therefore, it was possible to establish a more detailed customer segmentation than with the classic RFM. Conclusions: the RFM analysis is a method widely used in the industry for its easy understanding and applicability. However, it can be improved with the use of statistical procedures and new variables, which will allow companies to have deeper information about the behavior of the clients, and will facilitate the design of specific marketing strategies.

Prognosis of 18 H7N9 avian influenza patients in Shanghai.

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Shuihua Lu

Full Text Available To provide prognosis of an 18 patient cohort who were confirmed to have H7N9 lung infection in Shanghai.Patients' history, clinical manifestation, laboratory test, treatment strategy and mortality were followed and recorded for data analysis.A total of 18 patients had been admitted into Shanghai Public Health Clinical Center from April 8th to July 29, 2013. 22.2% of the patients were found to have live poultry contact history and 80% were aged male patients with multiple co-morbidities including diabetes, hypertension and/or chronic obstructive pulmonary disease (COPD. This group of patients was admitted to the clinical center around 10 days after disease onset. According to laboratory examinations, increased C reactive protein (CRP, Procalcitonin (PCT, Plasma thromboplastin antecedent (PTA and virus positive time (days were indicative of patients' mortality. After multivariate analysis, only CRP level showed significant prediction of mortality (P = 0.013 while results of prothrombin time (PT analysis almost reached statistical significance (P = 0.056.H7N9 infection induced pneumonia of different severity ranging from mild to severe pneumonia or acute lung injury/acute respiratory distress syndrome to multiple organ failure. Certain laboratory parameters such as plasma CRP, PCT, PTA and virus positive days predicted mortality of H7N9 infection and plasma CRP is an independent predictor of mortality in these patients.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

Science.gov (United States)

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

No impact of perioperative blood transfusion on prognosis after curative resection for hepatocellular carcinoma: a propensity score matching analysis.

Science.gov (United States)

Peng, T; Zhao, G; Wang, L; Wu, J; Cui, H; Liang, Y; Zhou, R; Liu, Z; Wang, Q

2017-10-27

The relationship between perioperative blood transfusion and long-term survival after curative resection for hepatocellular carcinoma (HCC) remains controversial. The aim of the present study was to investigate the impact of blood transfusion on the long-term prognosis of HCC patients. Patients with primary HCC who underwent a curative hepatectomy from 2003 to 2011 were enrolled and then retrospectively studied. The clinicopathologic characteristics between patients in the blood transfusion and non-transfusion groups were matched using a propensity score matching (PSM) analysis. Univariate and multivariate Cox regression analyses were used to identify whether perioperative blood transfusion affects long-term survival after resection for HCC. A total of 374 patients were enrolled and 113 patients received perioperative transfusions. The 1-, 3- and 5-year disease-free and overall survival rates of the entire cohort were 65.0, 37.3 and 23.9%, and 90.9, 70.7 and 57.5%, respectively. The disease-free and overall survival rates of the blood transfusion group were significantly worse than the disease-free and overall survival rates of the non-transfusion group in the entire cohort (p blood transfusion was not an independent predictor of disease-free and overall survival in the propensity-matched cohort (p = 0.154, p = 0.667). The present study demonstrates that perioperative blood transfusion has no impact on disease-free and overall survival after curative resection for HCC.

Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients

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M. Agostini

2013-01-01

Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

Energy Technology Data Exchange (ETDEWEB)

Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

2011-09-30

Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known

Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

International Nuclear Information System (INIS)

Rodrigues, J.A.; Barros, A.S.; Carvalho, B.; Brandao, T.; Gil, Ana M.

2011-01-01

Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: · Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. · Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. · Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. · 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5

Synthetic environmental indicators: A conceptual approach from the multivariate statistics

International Nuclear Information System (INIS)

Escobar J, Luis A

2008-01-01

This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

Analysis of the Role of PET/CT SUVmax in Prognosis and Its Correlation with 
Clinicopathological Characteristics in Resectable Lung Squamous Cell Carcinoma

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Hongliang REN

2016-04-01

Full Text Available Background and objective Lung cancer is the leading cause of cancer death in men and women in the world, more than one-half of cases are diagnosed at a advanced stage, and the overall 5-year survival rate for lung cancer is 18%. Lung cancer is divided into non-small cell lung carcinoma (NSCLC and small cell lung carcinoma (SCLC. Approximately 80%-85% of cases are NSCLC which includes three main types: adenocarcinoma (40%, squamous cell carcinoma (SCC (20%-30%, and large cell carcinoma (10%. Although therapies that target driver mutations in adenocarcinomas are showing some promise, they are proving ineffective in smoking-related SCC. We need pay more attention to the diagnosis and treatment of SCC. 18F-FDG positron emission tomography (PET/computed tomography (CT has emerged as an accurate staging modality in lung cancer diagnosis. The aim of this study is to investigate the role of maximum standardized uptake value (SUVmax on PET-CT in prognosis and its correlation with clinicopathological characteristics in resectable SCC. Methods One hundred and eighty-two resectable SCC patients who underwent PET/CT imaging between May 2005 and October 2014 were enrolled into this retrospectively study. All the enrolled patients had underwent pulmonary resection with mediastinal lymph node dissection without preoperative chemotherapy or radiotherapy. Survival outcomes were analyzed using the Kaplan-Meier method and multivariate Cox proportional hazards model. Correlation between SUVmax and clinicopathological factors was analysed using Pearson correlation analysis and Spearman rank correlation analysis. Results The patients were divided into two groups on the basis of SUVmax 13.0 as cutoff value, and patients with SUVmax more than 13.0 had shorter median overall survival than patients less than 13.0 in univariate analysis (56 months vs 87 months; P=0.022. There was remarkable correlation between SUVmax and gender, tumor size, tumor-node-metastasis (TNM stage

The Value of the Electrocardiogram for Evaluating Prognosis in Patients with Idiopathic Pulmonary Arterial Hypertension.

Science.gov (United States)

Cheng, Xiao-Ling; He, Jian-Guo; Liu, Zhi-Hong; Gu, Qing; Ni, Xin-Hai; Zhao, Zhi-Hui; Luo, Qin; Xiong, Chang-Ming

2017-02-01

Association between electrocardiography (ECG) features and right ventricular anatomy and physiology has been established. This study is aimed to identify the value of 12-lead ECG in evaluating prognosis of patients with idiopathic pulmonary arterial hypertension (IPAH). 194 patients with newly diagnosed IPAH were included in this study. Correlations between electrocardiography variables and hemodynamics were assessed. Univariate and multivariable cox regression analysis were performed to identify ECG variables for predicting all-cause mortality in IPAH. Partial correlation analysis showed that P wave amplitude in lead II correlated with the mean pulmonary arterial pressure (mPAP, r = 0.349, p ≤ 0.001) and cardiac index (CI, r = -0.224, p = 0.002); R wave amplitude in V1 correlated with mPAP (r = 0.359, p ≤ 0.001); S wave amplitude in V6 correlated with mPAP (r = 0.259, p = 0.030) and CI (r = -0.220, p = 0.003). P wave amplitude in lead II (HR 1.555, p = 0.033) and R wave amplitude in lead aVR (HR 5.058, p < 0.001) were the independent predictors of all-cause mortality. Kaplan-Meier survival curves showed patients with a p ≥ 0.25 mv in lead II, and R ≥ 0.4 mv in lead aVR had lower 3-year survival (55 vs. 91%, p < 0.001). Specific lead-12 ECG features could reflect right ventricular overload hemodynamics, and are useful to evaluate prognosis of patients with IPAH.

Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis

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Wondimu Ahmed

2017-09-01

Full Text Available This study investigated the relationship between motivation and self-regulated learning (SRL in a nationally representative sample of 5245, 15-year-old students in the USA. A multivariate multilevel analysis was conducted to examine the role of three motivational variables (self-efficacy, intrinsic value & instrumental value in predicting three SRL strategies (memorization, elaboration & control. The results showed that compared to self-efficacy, intrinsic value and instrumental value of math were stronger predictors of memorization, elaboration and control strategies. None of the motivational variables had a stronger effect on one strategy than the other. The findings suggest that the development of self-regulatory skills in math can be greatly enhanced by helping students develop positive value of and realistic expectancy for success in math.

Multivariate co-integration analysis of the Kaya factors in Ghana.

Science.gov (United States)

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-05-01

The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

Increased serum levels of tumour-associated trypsin inhibitor independently predict a poor prognosis in colorectal cancer patients

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Gaber Alexander

2010-09-01

Full Text Available Abstract Background There is an insufficient number of reliable prognostic and response predictive biomarkers in colorectal cancer (CRC management. In a previous study, we found that high tumour tissue expression of tumour-associated trypsin inhibitor (TATI correlated with liver metastasis and an impaired prognosis in CRC. The aim of this study was to investigate the prognostic validity of serum TATI (s-TATI in CRC. We further assessed the prognostic value of carcino-embryonic antigen in serum (s-CEA and the interrelationship between s-TATI and TATI in tissue (t-TATI. Methods Using an immunofluorometric assay, s-TATI levels were analysed in 334 preoperatively collected serum samples from patients with CRC. Spearman's Rho and Chi-square test were used for analysis of correlations between s-TATI and clinicopathological parameters, s-CEA and t-TATI. Kaplan-Meier analysis and Cox uni- and multivariate regression analysis were used to estimate disease free survival (DFS and overall survival (OS according to quartiles of s-TATI and cut-offs derived from ROC-analysis of s-TATI and s-CEA. Results Increased levels of s-TATI were associated with a reduced DFS (HR = 2.00; 95% CI 1.40-2.84, P P P = 0.034 for DFS and HR = 1.78; 95% CI 1.25-2.53, P = 0.001 for OS. There was no significant association between s-TATI and t-TATI. The prognostic value of s-CEA was also evident, but somewhat weaker than for s-TATI. Conclusions High preoperative s-TATI levels predict a poor prognosis in patients with CRC, and the prognostic value is independent of established prognostic parameters and t-TATI expression. These data suggest that s-TATI might be a useful marker for prognostic stratification in CRC.

Peritumoral vascular invasion and NHERF1 expression define an immunophenotype of grade 2 invasive breast cancer associated with poor prognosis

International Nuclear Information System (INIS)

Malfettone, Andrea; Saponaro, Concetta; Paradiso, Angelo; Simone, Giovanni; Mangia, Annita

2012-01-01

Traditional determinants proven to be of prognostic importance in breast cancer include the TNM staging, histological grade, proliferative activity, hormone receptor status and HER2 overexpression. One of the limitations of the histological grading scheme is that a high percentage of breast cancers are still classified as grade 2, a category with ambiguous clinical significance. The aim of this study was to best characterize tumors scored as grade 2. We investigated traditional prognostic factors and a panel of tumor markers not used in routine diagnosis, such as NHERF1, VEGFR1, HIF-1α and TWIST1, in 187 primary invasive breast cancers by immunohistochemistry, stratifying patients into good and poor prognostic groups by the Nottingham Prognostic Index. Grade 2 subgroup analysis showed that the PVI (p = 0.023) and the loss of membranous NHERF1 (p = 0.028) were adverse prognostic factors. Relevantly, 72% of grade 2 tumors were associated to PVI+/membranous NHERF1- expression phenotype, characterizing an adverse prognosis (p = 0.000). Multivariate logistic regression analysis in the whole series revealed poor prognosis correlated with PVI and MIB1 (p = 0.000 and p = 0.001, respectively). Furthermore, in the whole series of breast cancers we found cytoplasmic NHERF1 expression positively correlated to VEGFR1 (r = 0.382, p = 0.000), and in VEGFR1-overexpressing tumors the oncogenic receptor co-localized with NHERF1 at cytoplasmic level. The PVI+/membranous NHERF1- expression phenotype identifies a category of grade 2 tumors with the worst prognosis, including patient subgroup with a family history of breast cancer. These observations support the idea of the PVI+/membranous NHERF1- expression immunophenotype as a useful marker, which could improve the accuracy of predicting clinical outcome in grade 2 tumors

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-06

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-01

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response

International Nuclear Information System (INIS)

Skwarchuk, Mark W.; Jackson, Andrew; Zelefsky, Michael J.; Venkatraman, Ennapadam S.; Cowen, Didier M.; Levegruen, Sabine; Burman, Chandra M.; Fuks, Zvi; Leibel, Steven A.; Ling, C. Clifton

2000-01-01

Purpose: The purpose of this paper is to use the outcome of a dose escalation protocol for three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer to study the dose-response for late rectal toxicity and to identify anatomic, dosimetric, and clinical factors that correlate with late rectal bleeding in multivariate analysis. Methods and Materials: Seven hundred forty-three patients with T1c-T3 prostate cancer were treated with 3D-CRT with prescribed doses of 64.8 to 81.0 Gy. The 5-year actuarial rate of late rectal toxicity was assessed using Kaplan-Meier statistics. A retrospective dosimetric analysis was performed for patients treated to 70.2 Gy (52 patients) or 75.6 Gy (119 patients) who either exhibited late rectal bleeding (RTOG Grade 2/3) within 30 months after treatment (i.e., 70.2 Gy--13 patients, 75.6 Gy--36 patients) or were nonbleeding for at least 30 months (i.e., 70.2 Gy--39 patients, 75.6 Gy--83 patients). Univariate and multivariate logistic regression was performed to correlate late rectal bleeding with several anatomic, dosimetric, and clinical variables. Results: A dose response for ≥ Grade 2 late rectal toxicity was observed. By multivariate analysis, the following factors were significantly correlated with ≥ Grade 2 late rectal bleeding for patients prescribed 70.2 Gy: 1) enclosure of the outer rectal contour by the 50% isodose on the isocenter slice (i.e., Iso50) (p max (p max

Multivariate methods in nuclear waste remediation: Needs and applications

International Nuclear Information System (INIS)

Pulsipher, B.A.

1992-05-01

The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs

External validation of the DRAGON score in an elderly Spanish population: prediction of stroke prognosis after IV thrombolysis.

Science.gov (United States)

Giralt-Steinhauer, Eva; Rodríguez-Campello, Ana; Cuadrado-Godia, Elisa; Ois, Ángel; Jiménez-Conde, Jordi; Soriano-Tárraga, Carolina; Roquer, Jaume

2013-01-01

Intravenous (i.v.) thrombolysis within 4.5 h of symptom onset has proven efficacy in acute ischemic stroke treatment, although half of all outcomes are unfavorable. The recently published DRAGON score aims to predict the 3-month outcome in stroke patients who have received i.v. alteplase. The purpose of this study was an external validation of the results of the DRAGON score in a Spanish cohort. Patients with acute stroke treated with alteplase were prospectively registered in our BasicMar database. We collected demographic characteristics, vascular risk factors, the time from stroke onset to treatment, baseline serum glucose levels and stroke severity for this population. We then reviewed hyperdense cerebral artery signs and signs of early infarct on the admission CT scan. We calculated the DRAGON score and used the developers' 3-month prognosis categories: good [modified Rankin Scale score (mRS) 0-2], poor (mRS 3-6) and miserable (mRS 5-6) outcome. Discrimination was tested using the area under the receiver operator curve (AUC-ROC). Calibration was assessed by the Hosmer-Lemeshow test. Our final cohort of 297 patients was older (median age 74 years, IQR 65-80) and had more risk factors and severe strokes [median National Institutes of Health Stroke Scale (NIHSS) points 13, IQR 7-19] than the original study population. Poor prognosis was observed in 143 (48.1%) patients. Higher DRAGON scores were associated with a higher risk of poor prognosis. None of our treated stroke patients with a DRAGON score ≥8 at admission experienced a favorable outcome after 3 months. All DRAGON variables were significantly associated with a worse outcome in the multivariate analysis except for onset-to-treatment time (p = 0.334). Discrimination to predict poor prognosis was very good (AUC-ROC 0.84) and the score had good Hosmer-Lemeshow calibration (p = 0.84). The DRAGON score is easy to perform and offers a rapid, reliable prediction of poor prognosis in acute-stroke patients

Up-regulation of 91H promotes tumor metastasis and predicts poor prognosis for patients with colorectal cancer.

Directory of Open Access Journals (Sweden)

Qiwen Deng

Full Text Available Long noncoding RNAs (lncRNAs play widespread roles in gene regulation and cellular processes. However, the functional roles of lncRNAs in colorectal cancer (CRC are not yet well elucidated. The aim of the present study was to measure the levels of lncRNA 91H expression in CRC and evaluate its clinical significance and biological roles in the development and progression of CRC.91H expression and copy number variation (CNV were measured in 72 CRC tumor tissues and adjacent normal tissues by real-time PCR. The biological roles of 91H were evaluated by MTT, scratch wound assay, migration and invasion assays, and flow cytometry.91H was significantly overexpressed in cancerous tissue and CRC cell lines compared with adjacent normal tissue and a normal human intestinal epithelial cell line. Moreover, 91H overexpression was closely associated with distant metastasis and poor prognosis in patients with CRC, except for CNV of 91H. Multivariate analysis indicated that 91H expression was an independent prognostic indicator, as well as distant metastasis. Our in vitro data indicated that knockdown of 91H inhibited the proliferation, migration, and invasiveness of CRC cells.91H played an important role in the molecular etiology of CRC and might be regarded as a novel prognosis indicator in patients with CRC.

MicroRNA-17 and the prognosis of human carcinomas: a systematic review and meta-analysis

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Huang, Chengzhi; Yu, Mengya

2018-01-01

Objective Although the role of microRNA-17 (miR-17) has been identified as a tumour biomarker in various studies, its prognostic value in cancers remains unclear. Therefore, we performed a systematic review and meta-analysis to analyse and summarise the relationship between the miR-17 status and clinical outcome in a variety of human cancers. Design Systematic review and meta-analysis. Data sources PubMed, Web of Science and Embase from the first year of records to 15 May 2017. Outcomes The patients’ survival results were pooled, and pooled HRs with 95% CIs were calculated and used for measuring the strength of association between miR-17 and the prognosis of cancers, including hepatocellular carcinoma, lung cancer, osteosarcoma, glioma, T-cell lymphoblastic lymphoma and colon cancer. Heterogeneity, publication bias and subgroup analysis were also conducted. Results A total of 1096 patients were included in this meta-analysis from 12 articles. The results indicated that the increased expression of miR-17 played an unfavourable role in overall survival in various human carcinomas with the HR of 1.342 taking into account the publication bias. In subgroup analysis, HR of ethnicity (Caucasian HR=1.48 and Asian HR=1.40), disease (digestive system HR=1.36 and blood system cancer (HR=2.38), detection method (quantitative real-time PCR HR=1.40 and in situ hybridisation, HR=2.59) and detection sample (tissue HR=1.45 and serum HR=1.32) were significant with p<0.05. For the analysis of disease-free survival and recurrence-free survival, the increased expression of miR-17 was associated with unfavourable prognosis (HR=1.40). Conclusions miR-17 may be a useful biomarker in predicting the clinical outcome of human cancers, but due to the limitations of the current studies, further verification of the role of miR-17 in human malignancies is urgently needed. PROSPERO registration number CRD42017065749 PMID:29858404

Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

DEFF Research Database (Denmark)

Bro, R.; van den Berg, F.; Thybo, A.

2002-01-01

This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics-multivariate data analysis - it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems...

Multivariate quantitative structure-pharmacokinetic relationships (QSPKR) analysis of adenosine A(1) receptor agonists in rat

NARCIS (Netherlands)

Van der Graaf, PH; Nilsson, J; Van Schaick, EA; Danhof, M

The aim of this study was to investigate the feasibility of a quantitative structure-pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and

Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data.

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Ultsch, Alfred; Lötsch, Jörn

2015-01-01

Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data. The method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.

Letter regarding Li JS et al. entitled "ERCC polymorphisms and prognosis of patients with osteosarcoma".

Science.gov (United States)

Jian, Yuekui; Tian, Xiaobin; Li, Bo; Zhou, Zhuojia; Wu, Xinglin

2015-05-01

With great interest, we read the article "ERCC polymorphisms and prognosis of patients with osteosarcoma" (by Li JS et al.), which has reached important conclusions about the relationship between ERCC polymorphisms and osteosarcoma prognosis. Through quantitative analysis, the meta-analysis showed that ERCC2 Lys751Gln (ORGG vs. AA = 0.40 (95%CI = 0.1-0.86), P heterogeneity = 0.502; I (2) = 0 %) and ERCC5 His46His (ORCC vs. TT = 0.37 (95%CI = 0.15-0.93), P heterogeneity = 0.569; I (2) = 0 %) polymorphisms might influence the prognosis of patients with osteosarcoma [1]. The meta-analysis results are encouraging. Nevertheless, some deficiencies still existed that we would like to raise.

The prognosis of infective endocarditis treated with biological valves versus mechanical valves: A meta-analysis.

Science.gov (United States)

Tao, Ende; Wan, Li; Wang, WenJun; Luo, YunLong; Zeng, JinFu; Wu, Xia

2017-01-01

Surgery remains the primary form of treatment for infective endocarditis (IE). However, it is not clear what type of prosthetic valve provides a better prognosis. We conducted a meta-analysis to compare the prognosis of infective endocarditis treated with biological valves to cases treated with mechanical valves. Pubmed, Embase and Cochrane databases were searched from January 1960 to November 2016.Randomized controlled trials, retrospective cohorts and prospective studies comparing outcomes between biological valve and mechanical valve management for infective endocarditis were analyzed. The Newcastle-Ottawa Scale(NOS) was used to evaluate the quality of the literature and extracted data, and Stata 12.0 software was used for the meta-analysis. A total of 11 publications were included; 10,754 cases were selected, involving 6776 cases of biological valves and 3,978 cases of mechanical valves. The all-cause mortality risk of the biological valve group was higher than that of the mechanical valve group (HR = 1.22, 95% CI 1.03 to 1.44, P = 0.023), as was early mortality (RR = 1.21, 95% CI 1.02 to 1.43, P = 0.033). The recurrence of endocarditis (HR = 1.75, 95% CI 1.26 to 2.42, P = 0.001), as well as the risk of reoperation (HR = 1.79, 95% CI 1.15 to 2.80, P = 0.010) were more likely to occur in the biological valve group. The incidence of postoperative embolism was less in the biological valve group than in the mechanical valve group, but this difference was not statistically significant (RR = 0.90, 95% CI 0.76 to 1.07, P = 0.245). For patients with prosthetic valve endocarditis (PVE), there was no significant difference in survival rates between the biological valve group and the mechanical valve group (HR = 0.91, 95% CI 0.68 to 1.21, P = 0.520). The results of our meta-analysis suggest that mechanical valves can provide a significantly better prognosis in patients with infective endocarditis. There were significant differences in the clinical features of patients

Multivariate data analysis as a fast tool in evaluation of solid state phenomena

DEFF Research Database (Denmark)

Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja

2006-01-01

of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets...... the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles...... or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information....

Uveal melanoma: Estimating prognosis

Directory of Open Access Journals (Sweden)

Swathi Kaliki

2015-01-01

Full Text Available Uveal melanoma is the most common primary malignant tumor of the eye in adults, predominantly found in Caucasians. Local tumor control of uveal melanoma is excellent, yet this malignancy is associated with relatively high mortality secondary to metastasis. Various clinical, histopathological, cytogenetic features and gene expression features help in estimating the prognosis of uveal melanoma. The clinical features associated with poor prognosis in patients with uveal melanoma include older age at presentation, male gender, larger tumor basal diameter and thickness, ciliary body location, diffuse tumor configuration, association with ocular/oculodermal melanocytosis, extraocular tumor extension, and advanced tumor staging by American Joint Committee on Cancer classification. Histopathological features suggestive of poor prognosis include epithelioid cell type, high mitotic activity, higher values of mean diameter of ten largest nucleoli, higher microvascular density, extravascular matrix patterns, tumor-infiltrating lymphocytes, tumor-infiltrating macrophages, higher expression of insulin-like growth factor-1 receptor, and higher expression of human leukocyte antigen Class I and II. Monosomy 3, 1p loss, 6q loss, and 8q and those classified as Class II by gene expression are predictive of poor prognosis of uveal melanoma. In this review, we discuss the prognostic factors of uveal melanoma. A database search was performed on PubMed, using the terms "uvea," "iris," "ciliary body," "choroid," "melanoma," "uveal melanoma" and "prognosis," "metastasis," "genetic testing," "gene expression profiling." Relevant English language articles were extracted, reviewed, and referenced appropriately.

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

Science.gov (United States)

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Multivariate analysis of prognostic factors in male breast cancer in Serbia.

Science.gov (United States)

Sipetic-Grujicic, Sandra Branko; Murtezani, Zafir Hajdar; Neskovic-Konstatinovic, Zora Borivoje; Marinkovic, Jelena Milutin; Kovcin, Vladimir Nikola; Andric, Zoran Gojko; Kostic, Sanja Vladeta; Ratkov, Isidora Stojan; Maksimovic, Jadranka Milutin

2014-01-01

The aim of this study was to analyze the demographic and clinical characteristics of male breast cancer patients in Serbia, and furthermore to determine overall survival and predictive factors for prognosis. In the period of 1996-2006 histopathological diagnosis of breast cancer was made in 84 males at the Institute for Oncology and Radiology of Serbia. For statistical analyses the Kaplan-Meier method, long-rank test and Cox proportional hazards regression model were used. The mean age at diagnosis with breast cancer was 64.3±10.5 years with a range from 35-84 years. Nearly 80% of the tumors showed ductal histology. About 44% had early tumor stages (I and II) whereas 46.4% and 9.5% of the male exhibited stages III and IV, respectively. Only 7.1% of male patients were grade one. One-fifth of all patients had tumors measuring ≤2 cm, and 14.3% larger than 5 cm. Lymph node metastasis was recorded in 40.4% patients and 47% relapse. Estrogen and progesterone receptor expression was positive in 66.7% and 58.3%, respectively. Among 14.3% of individuals tumor was HER2 positive. About two-thirds of all male patients had radical mastectomy (66.7%). Adjuvant hormonal (tamoxifene), systematic chemotherapy (CMF or FAC) and adjuvant radiotherapy were given to 59.5%, 35.7% and 29.8% patients respectively. Overall survival rates at five and ten years for male breast cancer were 55.0% and 43.9%, respectively. According to the multivariate Cox regression predictive model, a lower initial disease stage, a lower tumor grade, application of adjuvant hormone therapy and no relapse occurrence were significant independent predictors for good overall survival. Results of the treatment would be better if disease is discovered earlier and therefore health education and screening are an imperative in solving this problem.

Multi-variable systems in nuclear power plant

International Nuclear Information System (INIS)

Collins, G.B.; Howell, J.

1982-01-01

Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)

Multivariate statistics high-dimensional and large-sample approximations

CERN Document Server

Fujikoshi, Yasunori; Shimizu, Ryoichi

2010-01-01

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

KAUST Repository

Migliorati, Giovanni

2016-01-01

We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

Science.gov (United States)

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

International Nuclear Information System (INIS)

Yu, P.

2008-01-01

More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

Science.gov (United States)

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

[Primary Neuroendocrine Carcinoma of Thymus Caused Cushing Syndrome: Surgical Treatment and Prognosis Analysis].

Science.gov (United States)

Li, Li; Chen, Yeye; Li, Shanqing; Liu, Hongsheng; Huang, Cheng; Qin, Yingzhi

2015-07-01

Primary neuroendocrine carcinoma of thymus (pNECT) is a rare thymic neoplasm. Some pNECTs could produce an adrenocorticotropic hormone and cause Cushing syndrome (CS). The aim os this study is to discuss the diagnostic technique and surgical management of pNECT-caused CS and analyze prognosis factors to improve the clinical experience of the disease. The outcome of surgery and follow-up of 14 cases (eight males and six females) of pNECT-caused CS were retrospectively analyzed from November 1987 to June 2013. The median age of the patients was 29, and the median duration of the disease was four months (1 month-44 months). All cases exhibited clinical evidence for the diagnosis of CS, and thoracic computed tomography (CT) was used to detect thymic tumors. Surgical treatment significantly decreased the concentration of both serum cortisol and adrenocorticotropic hormone (Pdisease with aggressive characteristics and unclear prognosis. Early diagnosis and therapy is a challenge for clinicians. Thoracic CT is important for disease location and preoperative evaluation and should be routinely applied to all CS patients to allow early surgery and improved prognosis.

Ischemic risk stratification by means of multivariate analysis of the heart rate variability

International Nuclear Information System (INIS)

Valencia, José F; Vallverdú, Montserrat; Caminal, Pere; Porta, Alberto; Voss, Andreas; Schroeder, Rico; Vázquez, Rafael; Bayés de Luna, Antonio

2013-01-01

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures. (paper)

A Damage Prognosis Method of Girder Structures Based on Wavelet Neural Networks

Directory of Open Access Journals (Sweden)

Rumian Zhong

2014-01-01

Full Text Available Based on the basic theory of wavelet neural networks and finite element model updating method, a basic framework of damage prognosis method is proposed in this paper. Firstly, a damaged I-steel beam model testing is used to verify the feasibility and effectiveness of the proposed damage prognosis method. The results show that the predicted results of the damage prognosis method and the measured results are very well consistent, and the maximum error is less than 5%. Furthermore, Xinyihe Bridge in the Beijing-Shanghai Highway is selected as the engineering background, and the damage prognosis is conducted based on the data from the structural health monitoring system. The results show that the traffic volume will increase and seasonal differences will decrease in the next year and a half. The displacement has a slight increase and seasonal characters in the critical section of mid span, but the strain will increase distinctly. The analysis results indicate that the proposed method can be applied to the damage prognosis of girder bridge structures and has the potential for the bridge health monitoring and safety prognosis.

Body Mass Index, Diet-Related Factors, and Bladder Cancer Prognosis: A Systematic Review and Meta-Analysis

NARCIS (Netherlands)

Westhoff, E.; Witjes, J.A.; Fleshner, N.E.; Lerner, S.P.; Shariat, S.F.; Steineck, G.; Kampman, E.; Kiemeney, L.A.L.M.; Vrieling, A.

2018-01-01

Background: Urologists are frequently confronted with questions of urinary bladder cancer (UBC) patients about what they can do to improve their prognosis. Unfortunately, it is largely unknown which lifestyle factors can influence prognosis. Objective: To systematically review the available evidence

The correlations between alteration of p16 gene and clinicopathological factors and prognosis in squamous cell carcinomas of the buccal mucosa.

Science.gov (United States)

Dong, Yuying; Wang, Jie; Dong, Fusheng; Wang, Xu; Zhang, Yinghuai

2012-07-01

To evaluate relationships between the alteration of p16 gene and the clinical status and prognosis of the patients with squamous cell carcinoma of the buccal mucosa. Thirty buccal cancers were included in the analysis. Deletion analysis was performed by PCR. Point mutation analysis was used by PCR-SSCP and direct sequencing. Methylation-specific PCR methods were adopted for the evaluation of p16 methylation. The correlation between alteration of p16 gene and clinicopathological factors buccal cancer was evaluated by Fisher's exact test. Kaplan-Meier and Cox regression were used to investigate the relationship between p16 alteration and survival time. The frequency of p16 alteration was 63.3% in buccal carcinomas. P16 deletion was associated significantly with tumor size (P = 0.01). P16 point mutation was associated significantly with differentiation (P = 0.006). P16 methylation was associated significantly with nodes metastasis (P = 0.027). The overall survival rate of 30 buccal carcinomas was 53.3%. The Log-rank test (P = 0.021) and univariate Cox regression analysis (P = 0.030) revealed that p16 methylation was significantly associated with the overall survival rate. Multivariate analysis showed that p16 deletion, p16 mutation, and p16 methylation were not statistically significant. The alterations of p16 gene may play a major role in malignancy and development and metastases of buccal carcinoma and may be an excellent marker of aggressive clinical behavior. P16 methylation has a prognostic value in buccal carcinoma but not an independent prognosis factor. P16 point mutation and p16 deletion have not prognostic significance in buccal carcinoma. © 2012 John Wiley & Sons A/S.

Does in-hospital ventricular fibrillation affect prognosis after myocardial infarction?

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Jensen, G V; Torp-Pedersen, C; Hildebrandt, P

1997-01-01

with ventricular fibrillation in time intervals, indicated that the importance of ventricular fibrillation for risk of death was exhausted during the initial 60 days after infarction. CONCLUSION: Ventricular fibrillation is associated with an independent increased risk of death within 0-60 days after infarction......AIM: The aim of this study was to estimate the prognostic information to be gained from ventricular fibrillation in patients with myocardial infarction. METHODS AND RESULTS: We studied 4259 consecutive patients with myocardial infarction admitted to one centre in 1977-1988. Five hundred and twenty......-eight (12.4%) of the patients had ventricular fibrillation in hospital. The following risk factors were included in multivariate models to estimate their importance for 30-day and long-term (median 7 year) prognosis: age, gender, ventricular fibrillation, congestive heart failure, pulmonary oedema...

Multivariate analysis of microarray data: differential expression and differential connection.

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Kiiveri, Harri T

2011-02-01

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...

Mining novel biomarkers for prognosis of gastric cancer with serum proteomics

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Sui Mei-Hua

2009-09-01

Full Text Available Abstract Background Although gastric caner (GC remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics. Methods Sera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption/ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System (ZJU-PDAS. CEA level were evaluated by chemiluminescence immunoassay. Results After median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients (overall survival more than 24 months and 19 poor-prognosis GC patients (no more than 24 months. The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen (CEA and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage (III+IV and short survival (p Conclusion We have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

Multivariate pattern dependence.

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Stefano Anzellotti

2017-11-01

Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

HIF1-alpha overexpression indicates a good prognosis in early stage squamous cell carcinomas of the oral floor

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Joos Ulrich

2005-07-01

Full Text Available Abstract Background Hypoxia-inducible factor 1 (HIF-1 is a transcription factor, which plays a central role in biologic processes under hypoxic conditions, especially concerning tumour angiogenesis. HIF-1α is the relevant, oxygen-dependent subunit and its overexpression has been associated with a poor prognosis in a variety of malignant tumours. Therefore, HIF-1α expression in early stage oral carcinomas was evaluated in relation to established clinico-pathological features in order to determine its value as a prognostic marker. Methods 85 patients with histologically proven surgically treated T1/2 squamous cell carcinoma (SCC of the oral floor were eligible for the study. Tumor specimens were investigated by means of tissue micro arrays (TMAs and immunohistochemistry for the expression of HIF-1. Correlations between clinical features and the expression of HIF-1 were evaluated by Kaplan-Meier curves, log-rank tests and multivariate Cox regression analysis. Results HIF-1α was frequently overexpressed in a probably non-hypoxia related fashion. The expression of HIF-1α was related with a significantly improved 5-year survival rate (p Conclusion HIF-1α overexpression is an indicator of favourable prognosis in T1 and T2 SCC of the oral floor. Node negative patients lacking HIF-1α expression may therefore be considered for adjuvant radiotherapy.

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

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Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

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Isabel Gallego-Alvarez

2014-11-01

Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan

International Nuclear Information System (INIS)

Adam, Abdel Majid A.; Eltayeb, Mohamed Ahmed H.

2012-01-01

Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q–Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate. - Highlights: ► Multivariate statistical techniques were used to characterize radioactive data. ► U-238 in Uro phosphate shows higher degree of mobility (CV% = 82.6). ► U-238 in Kurun phosphate shows lower degree of mobility (CV% = 64.7). ► The radioactive variables did not follow a normal distribution. ► The ratio of Th

Evaluation on prognosis of esophageal squamous cell carcinoma patients after three-dimensional conformal radiotherapy with different clinical stage system

International Nuclear Information System (INIS)

Wang Yuxiang; Zhu Shuchai; Qiu Rong; Liu Zhikun; Shen Wenbin

2011-01-01

Objective: To evaluate the prognostic significance of 3 clinical stage system in 3-dimensional conformal radiotherapy (3DCRT) for esophageal squamous cell carcinoma. Methods: From January 2004 to August 2007, 179 cases of esophageal squamous cell carcinoma were treated with 3DCRT. Before radiation, each patient was staged with UICC 2003 TNM stage, stage of Chinese esophageal cancer cooperation group (cooperation group' stage), and Zhu's clinical stage respectively. Concordance of each clinical stage and prognosis was analyzed with SPSS 11.5. Results In 179 cases of esophageal cancer, Concordance was better in T stage (Kappa = 0.271) than in TNM stage (Kappa = 0.167) between cooperation group' stage and Zhu's stage. Among them, 98 cases was staged with UICC stage, concordance of T stage was better between UICC-T and cooperation group' T stage (Kappa =0.261) than between UICCT and Zhu's T stage (Kappa = 0.045) ;concordance of TNM stage was better between UICC-TNM and Zhu's TNM stage (Kappa = 0.597) than between UICC-TNM and cooperation group' TNM stage (Kappa =0.299). With multivariate analysis, T (χ 2 value is 11.58, 26.00 and 51.05, all P 2 value is 15.28, 16.10 and 16.10, all P 2 value is 5.59, 27.78 and 27.78, all P 2 value is 15.77, 34, 35 and 51.10, all P 1 - T 3 was difficult to definite and the prognosis was not significantly different in T 1 - T 3 stage. Conclusions: In this study, 3 kinds of clinical stage could evaluate prognosis of esophageal cancer after radiotherapy; cooperation group' stage and Zhu's stage need further application, with further accuracy needed. (authors)

Downregulation of tumor suppressor QKI in gastric cancer and its implication in cancer prognosis

International Nuclear Information System (INIS)

Bian, Yongqian; Wang, Li; Lu, Huanyu; Yang, Guodong; Zhang, Zhang; Fu, Haiyan; Lu, Xiaozhao; Wei, Mengying; Sun, Jianyong; Zhao, Qingchuan; Dong, Guanglong; Lu, Zifan

2012-01-01

Highlights: ► QKI expression is decreased in gastric cancer samples. ► Promoter hyper methylation contributes to the downregulation of QKI. ► QKI inhibits the growth of gastric cancer cells. ► Decreased QKI expression predicts poor survival. -- Abstract: Gastric cancer (GC) is the fourth most common cancer and second leading cause of cancer-related death worldwide. RNA-binding protein Quaking (QKI) is a newly identified tumor suppressor in multiple cancers, while its role in GC is largely unknown. Our study here aimed to clarify the relationship between QKI expression with the clinicopathologic characteristics and the prognosis of GC. In the 222 GC patients’ specimens, QKI expression was found to be significantly decreased in most of the GC tissues, which was largely due to promoter hypermethylation. QKI overexpression reduced the proliferation ability of GC cell line in vitro study. In addition, the reduced QKI expression correlated well with poor differentiation status, depth of invasion, gastric lymph node metastasis, distant metastasis, advanced TNM stage, and poor survival. Multivariate analysis showed QKI expression was an independent prognostic factor for patient survival.

The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

Science.gov (United States)

Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

2017-02-01

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

Transcription Factor EB Expression in Early Breast Cancer Relates to Lysosomal/Autophagosomal Markers and Prognosis.

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Giatromanolaki, Alexandra; Sivridis, Efthimios; Kalamida, Dimitra; Koukourakis, Michael I

2017-06-01

Disrupting the autophagic balance to trigger autophagic death may open new strategies for cancer therapy. Transcription factor EB (TFEB) is a master regulator of lysosomal biogenesis and may play a role in cancer biology and clinical behavior. The expression of TFEB and the lysosomal cancer cell content (expression of lysosomal associated membrane protein 2a [LAMP2a] and cathepsin D) was studied in a series of 100 T1-stage breast carcinomas. Expression patterns were correlated with autophagy/hypoxia-related proteins, angiogenesis, and clinical outcome. The effect of hypoxic/acidic conditions on TFEB kinetics was studied in the MCF-7 cancer cell line. Overexpression of TFEB in cancer cell cytoplasm and the perinuclear/nuclear area was noted in 23 (23%) of 100 cases. High LAMP2a and cathepsin D expression was noted in 30 (30%) of 100 and 28 (28%) of 100 cases, respectively. TFEB expression was directly linked with LAMP2a (P factor 2-alpha (HIF-2α) (P = .01, r = 0.25) expression and inversely with progesterone receptor (P = .01, r = 0.22). High vascular density was directly linked with LAMP2a (P = .05, r = 0.18) and cathepsin D (P = .005, r = 0.28). In Kaplan-Meier survival analysis, TFEB and cathepsin D expression were related to an ominous prognosis (P = .001 and P = .03, respectively). In multivariate analysis, TFEB expression sustained its independent prognostic significance (P = .05, hazard ratio 2.1). In in vitro experiments, acidity triggered overexpression of TFEB and nuclear translocation. Intense TFEB expression and lysosomal biogenesis, evident in one fourth of early breast carcinomas, define poor prognosis. Tumor acidity is among the microenvironmental conditions that trigger TFEB overactivity. TFEB is a sound target for the development of lysosomal targeting therapies. Copyright © 2016 Elsevier Inc. All rights reserved.

Squamous cell carcinoma of the tongue: clinical and morphological analysis of 57 cases and correlation with prognosis

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Marcelo Gadelha Vasconcelos

2014-10-01

Full Text Available Introduction: Oral squamous cell carcinoma (OSCC, which represents more than 90% of head and neck malignant neoplasms, has a poor prognosis due to its high frequency of lymph node metastasis and local invasion. Previous studies have investigated parameters related to the biological behavior of OSCC and its correlation with disease outcome (DO. Objective: To evaluate clinical and morphological data in cases of tongue squamous cell carcinoma (TSCC, correlating these findings with prognosis. Material and methods: Fifty-seven specimens of TSCC were obtained from patients undergoing surgical excision at a referral hospital in Natal, Brazil. Clinical data, such as tumor-node-metastasis (TNM stage and DO, were collected from medical records. Hematoxylin and eosin-stained sections were analyzed regarding histological grade of malignancy (HGM, based on the system proposed by Bryne (1998 Results: The majority of patients (38.6% were diagnosed as TNM stage III, and 57.9% developed metastases. Remission of the tumor occurred in 77.2% of the cases. The parameter “metastasis” exhibited a significant association with DO (p = 0 and TNM stage (p = 0.001, thus constituting a good indicator of tumor progression. Correlation of HGM and TNM stage with DO was not evidenced. Nevertheless, statistical analysis showed a significant association between HGM and TNM stage (p = 0.006. Conclusion: TNM clinical staging and HGM, evaluated in association, may be useful to estimate the prognosis of TSCC.

An uncertain journey around the tails of multivariate hydrological distributions

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Serinaldi, Francesco

2013-10-01

Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

Intelligence after traumatic brain injury: meta-analysis of outcomes and prognosis.

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Königs, M; Engenhorst, P J; Oosterlaan, J

2016-01-01

Worldwide, 54-60 million individuals sustain traumatic brain injury (TBI) each year. This meta-analysis aimed to quantify intelligence impairments after TBI and to determine the value of age and injury severity in the prognosis of TBI. An electronic database search identified 81 relevant peer-reviewed articles encompassing 3890 patients. Full-scale IQ (FSIQ), performance IQ (PIQ) and verbal IQ (VIQ) impairments were quantified (Cohen's d) for patients with mild, moderate and severe TBI in the subacute phase of recovery and the chronic phase. Meta-regressions explored prognostic values of age and injury severity measures for intelligence impairments. The results showed that, in the subacute phase, FSIQ impairments were absent for patients with mild TBI, medium-sized for patients with moderate TBI (d = -0.61, P intelligence impairments, where children may have better recovery from mild TBI and poorer recovery from severe TBI than adults. Injury severity measures predict intelligence impairments and do not outperform one another. © 2015 EAN.

Significcance of cranial nerve involvement shown by the prognosis of nasopharyngeal carcinoma

International Nuclear Information System (INIS)

Hui Zhouguang; Gao Li; Yi Junlin; Li Suyan; Jin Jing; Huang Xiaodong; Luo Jingwei; Xu Guozhen

2006-01-01

Objective: To analyze the cranial nerve involvement in nasophryngeal carcinoma and its relationship with the prognosis with the optimal treatment for such patients studied also. Methods: 935 untreated nasopharyngeal carcinoma patients, admitted into our hospital from January 1990 to June 1999, were analyzed retrospectively. These patients were divided into cranial nerve involved group and cranial nerve un- involved group by patients symptoms signs and/or images before the treatment. SPSS10.0 soft package was used to analyze the effect of cranial nerve involvement on the prognosis. Results: The overall percentage of cranial nerve involvement was 20.0%, of which the trigeminal nerve was most common . The 5-year local recurrence rate was 20.1% and 16.8% (P=0.465) in cranial nerve involved group and un-involved group, respectively. In the patients with cranial nerve involved, the 5-year local recurrence rates of patients who received boost skull base irradiation dose <70, 70-79 and ≥80 Gy was 38.1%, 24.5% and 16.0% (P =0.082), respectively. The 5-year distant metastasis rate was 31.6% and 19.5% (P=0.020) in cranial nerve involved group and un-involved group. The corresponding overall survival rates and disease-free survival rate was 62.2% and 78.1% (P=O.000) and 43.2%, 62.4% (P=0.000), respectively. By multivariate analysis, cranial nerve involvement was an independent factor both in overall survival (RR 1.62, P=0.001 ) and disease-free survival (RR=1.40, P=0.020). Conclusions: There are more distant metastasis, worse overall survival and disease-free survival in patients with cranial nerve involved. Boost irradiation to the involved skull base may improve the local control. Radiotherapy combined with chemotherapy for these patients may also have brighter future. (authors)

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

Energy Technology Data Exchange (ETDEWEB)

Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

Multivariate Sensitivity Analysis of Time-of-Flight Sensor Fusion

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Schwarz, Sebastian; Sjöström, Mårten; Olsson, Roger

2014-09-01

Obtaining three-dimensional scenery data is an essential task in computer vision, with diverse applications in various areas such as manufacturing and quality control, security and surveillance, or user interaction and entertainment. Dedicated Time-of-Flight sensors can provide detailed scenery depth in real-time and overcome short-comings of traditional stereo analysis. Nonetheless, they do not provide texture information and have limited spatial resolution. Therefore such sensors are typically combined with high resolution video sensors. Time-of-Flight Sensor Fusion is a highly active field of research. Over the recent years, there have been multiple proposals addressing important topics such as texture-guided depth upsampling and depth data denoising. In this article we take a step back and look at the underlying principles of ToF sensor fusion. We derive the ToF sensor fusion error model and evaluate its sensitivity to inaccuracies in camera calibration and depth measurements. In accordance with our findings, we propose certain courses of action to ensure high quality fusion results. With this multivariate sensitivity analysis of the ToF sensor fusion model, we provide an important guideline for designing, calibrating and running a sophisticated Time-of-Flight sensor fusion capture systems.

Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics.

Science.gov (United States)

Gatley-Montross, Caitlyn M; Finlay, John A; Aldred, Nick; Cassady, Harrison; Destino, Joel F; Orihuela, Beatriz; Hickner, Michael A; Clare, Anthony S; Rittschof, Daniel; Holm, Eric R; Detty, Michael R

2017-12-29

Multivariate analyses were used to investigate the influence of selected surface properties (Owens-Wendt surface energy and its dispersive and polar components, static water contact angle, conceptual sign of the surface charge, zeta potentials) on the attachment patterns of five biofouling organisms (Amphibalanus amphitrite, Amphibalanus improvisus, Bugula neritina, Ulva linza, and Navicula incerta) to better understand what surface properties drive attachment across multiple fouling organisms. A library of ten xerogel coatings and a glass standard provided a range of values for the selected surface properties to compare to biofouling attachment patterns. Results from the surface characterization and biological assays were analyzed separately and in combination using multivariate statistical methods. Principal coordinate analysis of the surface property characterization and the biological assays resulted in different groupings of the xerogel coatings. In particular, the biofouling organisms were able to distinguish four coatings that were not distinguishable by the surface properties of this study. The authors used canonical analysis of principal coordinates (CAP) to identify surface properties governing attachment across all five biofouling species. The CAP pointed to surface energy and surface charge as important drivers of patterns in biological attachment, but also suggested that differentiation of the surfaces was influenced to a comparable or greater extent by the dispersive component of surface energy.

Expression of human Piwi-like genes is associated with prognosis for soft tissue sarcoma patients

International Nuclear Information System (INIS)

Greither, Thomas; Taubert, Helge; Koser, Franziska; Kappler, Matthias; Bache, Matthias; Lautenschläger, Christine; Göbel, Steffen; Holzhausen, Hans-Jürgen; Wach, Sven; Würl, Peter

2012-01-01

Argonaute genes are essential for RNA interference, stem cell maintenance and differentiation. The Piwi-like genes, a subclass of the Argonaute genes, are expressed mainly in the germline. These genes may be re-expressed in tumors, and expression of the Piwi-like genes is associated with prognosis in several types of tumors. We measured the expression of Piwi-like mRNAs (Piwi-like 2–4) in 125 soft tissue sarcoma (STS) samples by qPCRs. Statistical tests were applied to study the correlation of expression levels with tumor-specific survival for STS patients. In multivariate Cox’s regression analyses, we showed that low Piwi-like 2 and Piwi-like 4 mRNA expression were significantly associated with a worse prognosis (RR = 1.87; p = 0.032 and RR = 1.82; p = 0.039). Low expression of both genes was associated with a 2.58-fold increased risk of tumor-related death (p = 0.01). Piwi-like 4 and combined Piwi-like 2 and 4 mRNA levels correlated significantly with prognosis (RR = 3.53; p = 0.002 and RR = 5.23; p = 0.004) only for female but not for male patients. However, combined low Piwi-like 2 and 3 transcript levels were associated with worse survival (RR = 5.90; p = 0.02) for male patients. In this study, we identified a significant association between the expression of Piwi-like 2 and 4 mRNAs and the tumor-specific survival of soft tissue sarcoma patients. Furthermore, a connection between sex and the impact of Piwi-like mRNA expressions on STS patients’ prognosis was shown for the first time

Multivariate genetic analysis of brain structure in an extended twin design

DEFF Research Database (Denmark)

Posthuma, D; de Geus, E.J.; Neale, M.C.

2000-01-01

quantitative scale and thus can be assessed in affected and unaffected individuals. Continuous measures increase the statistical power to detect genetic effects (Neale et al., 1994), and allow studies to be designed to collect data from informative subjects such as extreme concordant or discordant pairs....... Intermediate phenotypes for discrete traits, such as psychiatric disorders, can be neurotransmitter levels, brain function, or structure. In this paper we conduct a multivariate analysis of data from 111 twin pairs and 34 additional siblings on cerebellar volume, intracranial space, and body height....... The analysis is carried out on the raw data and specifies a model for the mean and the covariance structure. Results suggest that cerebellar volume and intracranial space vary with age and sex. Brain volumes tend to decrease slightly with age, and males generally have a larger brain volume than females...

Multivariate analysis of microarray data: differential expression and differential connection

Directory of Open Access Journals (Sweden)

Kiiveri Harri T

2011-02-01

Full Text Available Abstract Background Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. Results We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. Conclusion The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

Symptoms, location and prognosis of pulmonary embolism

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M.T. García-Sanz

2014-07-01

Full Text Available Background and objective: Pulmonary embolism (PE is a common disease with variable symptoms and high overall mortality. The clinical relevance of the extent of PE is still debatable, and the role of anticoagulation in patients with subsegmental involvement has been contested. Our objective is to describe the clinical details of patients with PE in our hospital and to analyze their prognosis based on the extent of the disease. Materials and methods: Retrospective study of 313 patients diagnosed with PE by chest computed tomography (CT scan at the Hospital Complex of Pontevedra in Spain for six years. Predictors of mortality were determined by multivariate analysis. Results: Women accounted for 56% of patients, and patient median age was 70 years (interquartile range 53–78 years. Subsegmental PE accounted for 7% of all cases; these patients were younger and had lower comorbidity; they reported chest pain more often, performed better in blood gas analysis and none of them had proximal deep vein thrombosis (DVT. Patients with subsegmental PE had a higher survival rate. Factors independently associated with mortality were cancer diagnosis and higher comorbidity. Conclusions: Patients with subsegmental PE clinically differ from those with more proximal PE. Underlying diseases have more influence on the prognosis than the extent of the disease. Resumo: Contexto e objectivo: A embolia pulmonar (PE é uma doença comum com sintomas variáveis e uma elevada taxa de mortalidade global. A relevância clínica da extensão da PE é ainda fonte de debate, e o papel da anticoagulação em pacientes com envolvimento de sub-segmentos foi contestado. O nosso objectivo é descrever os dados clínicos de doentes com PE no nosso hospital e analisar o seu prognóstico, com base na extensão da doença. Materiais e métodos: Estudo retrospectivo de 313 doentes, diagnosticados com PE, através de uma tomografia computadorizada de t

Predictive factors and prognosis for recurrent laryngeal nerve invasion in papillary thyroid carcinoma

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Chen W

2017-09-01

Full Text Available Wenjie Chen,1 Jianyong Lei,1 Jiaying You,2 Yali Lei,3 Zhihui Li,1 Rixiang Gong,1 Huairong Tang,3 Jingqiang Zhu1 1Thyroid and Parathyroid Surgery Center, 2West China School of Clinical Medicine, 3Health and Management Center, West China Hospital of Sichuan University, Chengdu, People’s Republic of China Background: Recurrent laryngeal nerve (RLN invasion in papillary thyroid carcinoma (PTC is one of the main predictors of poor prognosis. The present study investigated the risk factors for RLN invasion in PTC patients.Methods: A total of 3,236 patients who received thyroidectomy due to PTC in Thyroid and Parathyroid Surgery Center of West China Hospital of Sichuan University were reviewed. Demographics and clinical factors, imaging examination (ultrasonography characteristics, surgical details, postoperative pathological details, recurrence, and postoperative complications were recorded. Univariate and multivariate analyses were used to study the risk factors of RLN invasion, Kaplan–Meier method was performed to compare the outcomes of tumor recurrence.Results: Patients with RLN invasion had a higher recurrence rate than those in the control group (p<0.001. Multivariate analyses showed that age greater than 45 years (p<0.001, a largest tumor size bigger than 10 mm (p<0.001, clinical lymph node metastasis (cN1 (p<0.001, posterior focus (p<0.001, extrathyroidal extension (p<0.001, esophageal extension (p<0.001, tracheal extension (p<0.001, and preoperative vocal cord paralysis (p<0.001 were independent predictors for RLN invasion.Conclusion: PTC patients with RLN invasion have a negative prognosis and a higher recurrence rate. Meticulous operation and careful follow-up of patients with the above factors is recommended. Keywords: papillary thyroid carcinoma, recurrent laryngeal nerve invasion, predictive factors, lymph node metastases, Hashimoto’s thyroiditis

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

Science.gov (United States)

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Multivariate statistical analysis of a multi-step industrial processes

DEFF Research Database (Denmark)

Reinikainen, S.P.; Høskuldsson, Agnar

2007-01-01

Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized...... efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse...... multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors...

Radiation treatment of glottic squamous cell carcinoma, Stage I and II: analysis of factors affecting prognosis

International Nuclear Information System (INIS)

Franchin, Giovanni; Minatel, Emilio; Gobitti, Carlo; Talamini, Renato; Sartor, Giovanna; Caruso, Giuseppe; Grando, Giuseppe; Politi, Doriano; Gigante, Marco; Toffoli, Giuseppe; Trovo, Mauro G.; Barzan, Luigi

1998-01-01

Purpose: At least in some European Countries, there is still considerable controversy regarding the choice between surgery and radiotherapy for the treatment of patients with early laryngeal-glottic carcinoma. Methods and Materials: Two hundred and forty-six patients with laryngeal-glottic neoplasms, Stage I-II, were treated with radical radiotherapy. Before radiotherapy the patients were evaluated to determine the surgical procedure of choice. Either 66-68.4 Gy (33-38 fractions) or 63-65 Gy (28-29 fractions) of radiation therapy (RT) were administered. The overall disease free survival was determined for each subgroup of patients. Univariate and multivariate analyses were performed to determine significant prognostic variables. Results: Five- and 10-year overall survival rates were 83 and 72%, respectively. At a median follow-up of 6 years 204 patients are alive and disease free. No patient developed distant metastases. One patient died of a large local recurrence, 38 patients died of causes unrelated to their tumor, and 3 patients were lost to follow-up. The multivariate analysis confirmed that performance status (PS), macroscopic presentation of the lesion, and persistence of dysphonia after radiotherapy are significant prognostic factors. Conclusions: According to the multivariate analysis, the patients with PS >80 and with exophytic lesions are eligible for radical RT. The surgical procedure proposed for each patient was not found to be an independent prognostic factor

Intelligent multivariate process supervision

International Nuclear Information System (INIS)

Visuri, Pertti.

1986-01-01

This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

Evaluation of clinical characteristics and prognosis of chronic pulmonary aspergillosis depending on the underlying lung diseases: Emphysema vs prior tuberculosis.

Science.gov (United States)

Koyama, Kazuya; Ohshima, Nobuharu; Suzuki, Junko; Kawashima, Masahiro; Okuda, Kenichi; Sato, Ryota; Suzukawa, Maho; Nagai, Hideaki; Matsui, Hirotoshi; Ohta, Ken

2015-11-01

There have been scarce data evaluating the differences of clinical characteristics and prognosis of chronic pulmonary aspergillosis (CPA) depending on underlying pulmonary diseases. We tried to clarify them in CPA patients who had pulmonary emphysema or previous pulmonary tuberculosis. We reviewed and evaluated CPA patients diagnosed between 2007 and 2013 with pulmonary emphysema (PE group; n = 29), with previous pulmonary tuberculosis (PT group; n = 47) and with combination of these 2 underlying conditions (CTE group; n = 24). In CT findings, fungus balls were rare in PE group (7% in PE group and 36% in PT group; p = 0.006). Compared with PT group, PE group patients exhibited more frequent preceding antibiotics administration (45% vs 11%; p = 0.002) and fever (52% vs 17%; p = 0.002), less frequent hemosputum (24% vs 57%; p = 0.008), and more frequent consolidations in imaging (79% vs 38%; p = 0.001) and respiratory failure (34% vs 13%; p = 0.020), possibly suggesting more acute clinical manifestations of CPA in emphysematous patients. Trend of the differences between PT and PE group was not changed when patients with fungal balls were excluded. Multivariate Cox regression analysis of risks for all-cause mortality revealed age (HR, 1.079; p = 0.002) and emphysema (HR, 2.45; p = 0.040) as risk factors. Assessment of underlying lung diseases is needed when we estimate prognosis and consider treatment of CPA patients. Particularly, emphysematous patients can be presented as refractory pneumonia and show poor prognosis. Copyright © 2015 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

KAUST Repository

Migliorati, Giovanni

2016-01-05

We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low-discrepancy point sets, and noisy evaluations at random points.

Use of multivariate analysis to research career advancement of academic librarians

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Filiberto Felipe Martínez Arellano

2004-01-01

Full Text Available Diverse variables dealing with credential factors, bureaucratiuc factors, organizational and disciplinary achievements, academic culture factors, social ascribed factors, and institutional factors were stated as explanatory elements of promotion, tenure status, and earnings. A survey was the research instrument for collecting data to test diverse variables dealing with academic librarians rewards and earnings. Since the study attempted to analyze variables in a multivariate context, variable interactions were tested using multiple regression analysis. Findings of this study contribute to a better understanding of those factors influencing career advancement of academic librarians. Likewise, research methodology of this study could be used in Library and Information Science(LIS research.

ColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients

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Mingguang Shi

2016-03-01

Full Text Available Colorectal cancer (CRC is a heterogeneous disease with a high mortality rate and is still lacking an effective treatment. Our goal is to develop a robust prognosis model for predicting the prognosis in CRC patients. In this study, 871 stage II and III CRC samples were collected from six gene expression profilings. ColoFinder was developed using a 9-gene signature based Random Survival Forest (RSF prognosis model. The 9-gene signature recurrence score was derived with a 5-fold cross validation to test the association with relapse-free survival, and the value of AUC was gained with 0.87 in GSE39582(95% CI [0.83–0.91]. The low-risk group had a significantly better relapse-free survival (HR, 14.8; 95% CI [8.17–26.8]; P < 0.001 than the high-risk group. We also found that the 9-gene signature recurrence score contributed more information about recurrence than standard clinical and pathological variables in univariate and multivariate Cox analyses when applied to GSE17536(p = 0.03 and p = 0.01 respectively. Furthermore, ColoFinder improved the predictive ability and better stratified the risk subgroups when applied to CRC gene expression datasets GSE14333, GSE17537, GSE12945and GSE24551. In summary, ColoFinder significantly improves the risk assessment in stage II and III CRC patients. The 9-gene prognostic classifier informs patient prognosis and treatment response.

The expression of epidermal growth factor receptor results in a worse prognosis for patients with rectal cancer treated with preoperative radiotherapy: a multicenter, retrospective analysis

International Nuclear Information System (INIS)

Giralt, Jordi; Heras, Manuel de las; Cerezo, Laura; Eraso, Aranzazu; Hermosilla, Edurado; Velez, Dolores; Lujan, Juan; Espin, Eloi; Rossello, Jose; Majo, Joaquin; Benavente, Sergi; Armengol, Manel; Torres, I. de

2005-01-01

Background and purpose: Expression of epidermal growth factor receptor (EGFR) is observed in 50-70% of colorectal carcinoma and is associated with poor prognosis. The aim of this study was to determine the prognostic value of EGFR status before radiotherapy in a group of patients with locally advanced rectal cancer treated with preoperative radiotherapy. Patients and methods: Eighty-seven patients were studied retrospectively. Treatment consisted of pelvic radiotherapy, in 50 patients with concomitant chemotherapy and surgical resection. Immunohistochemistry for EGFR was determined at the preradiation biopsy and in the resected specimens. Immunohistochemical analysis for EGFR expression was evaluated according to extension and staining intensity. We defined positive staining (EGFR positive), when extension was 5% or more. Results: A total of 52 of 87 tumors showed EGFR positive status at biopsy (60%) and EGFR expression was associated neither with clinical tumor stage nor with clinical nodal stage. EGFR positive expression was linked to a lack of pathologic complete response to preoperative radiotherapy (P=0.006). Disease-free survival was lower among patients with EGFR positive status before radiotherapy (P=0.003). In a multivariate analysis EGFR expression at biopsy was a statistically significant predictor of disease-free survival, RR=2.88 (1.1-7.8), P=0.036. Conclusions: EGFR is expressed in a significant number of rectal tumors. EGFR-positive expression before radiotherapy is an indicator for poor response and low disease-free survival

Multivariate analysis of TOF-SIMS spectra of monolayers on scribed silicon.

Science.gov (United States)

Yang, Li; Lua, Yit-Yian; Jiang, Guilin; Tyler, Bonnie J; Linford, Matthew R

2005-07-15

Static time-of-flight secondary ion mass spectrometry (TOF-SIMS) was performed on monolayers on scribed silicon (Si(scr)) derived from 1-alkenes, 1-alkynes, 1-holoalkanes, aldehydes, and acid chlorides. To rapidly determine the variation in the data without introducing user bias, a multivariate analysis was performed. First, principal components analysis (PCA) was done on data obtained from silicon scribed with homologous series of aldehydes and acid chlorides. For this study, the positive ion spectra, the negative ion spectra, and the concatentated (linked) positive and negative ion spectra were preprocessed by normalization, mean centering, and autoscaling. The mean centered data consistently showed the best correlations between the scores on PC1 and the number of carbon atoms in the adsorbate. These correlations were not as strong for the normalized and autoscaled data. After reviewing these methods, it was concluded that mean centering is the best preprocessing method for TOF-SIMS spectra of monolayers on Si(scr). A PCA analysis of all of the positive ion spectra revealed a good correlation between the number of carbon atoms in all of the adsorbates and the scores on PC1. PCA of all of the negative ion spectra and the concatenated positive and negative ion spectra showed a correlation based on the number of carbon atoms in the adsorbate and the class of the adsorbate. These results imply that the positive ion spectra are most sensitive to monolayer thickness, while the negative ion spectra are sensitive to the nature of the substrate-monolayer interface and the monolayer thickness. Loadings show an inverse relationship between (inorganic) fragments that are expected from the substrate and (organic) fragments expected from the monolayer. Multivariate peak intensity ratios were derived. It is also suggested that PCA can be used to detect outlier surfaces. Partial least squares showed a strong correlation between the number of carbon atoms in the adsorbate and the

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

Science.gov (United States)

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

SOXs in human prostate cancer: implication as progression and prognosis factors

International Nuclear Information System (INIS)

Zhong, Wei-de; Chen, Xi-bin; Lin, Zhuo-yuan; Deng, Ye-han; Wu, Shu-lin; He, Hui-chan; Wu, Chin-lee; Qin, Guo-qiang; Dai, Qi-shan; Han, Zhao-dong; Chen, Shan-ming; Ling, Xiao-hui; Fu, Xin; Cai, Chao; Chen, Jia-hong

2012-01-01

SOX genes play an important role in a number of developmental processes. Potential roles of SOXs have been demonstrated in various neoplastic tissues as tumor suppressors or promoters depending on tumor status and types. The aim of this study was to investigate the involvement of SOXs in the progression and prognosis of human prostate cancer (PCa). The gene expression changes of SOXs in human PCa tissues compared with non-cancerous prostate tissues was detected using gene expression microarray, and confirmed by real-time quantitative reverse transcriptase-polymerase chain reaction (QRT-PCR) analysis and immunohositochemistry. The roles of these genes in castration resistance were investigated in LNCaP xenograft model of PCa. The microarray analysis identified three genes (SOX7, SOX9 and SOX10) of SOX family that were significantly dis-regulated in common among four PCa specimens. Consistent with the results of the microarray, differential mRNA and protein levels of three selected genes were found in PCa tissues by QRT-PCR analysis and immunohistochemistry. Additionally, we found that the immunohistochemical staining scores of SOX7 in PCa tissues with higher serum PSA level (P = 0.02) and metastasis (P = 0.03) were significantly lower than those with lower serum PSA level and without metastasis; the increased SOX9 protein expression was frequently found in PCa tissues with higher Gleason score (P = 0.02) and higher clinical stage (P < 0.0001); the down-regulation of SOX10 tend to be found in PCa tissues with higher serum PSA levels (P = 0.03) and advanced pathological stage (P = 0.01). Moreover, both univariate and multivariate analyses showed that the down-regulation of SOX7 and the up-regulation of SOX9 were independent predictors of shorter biochemical recurrence-free survival. Furthermore, we discovered that SOX7 was significantly down-regulated and SOX9 was significantly up-regulated during the progression to castration resistance. Our data offer the convince

Protein Z efficiently depletes thrombin generation in disseminated intravascular coagulation with poor prognosis.

Science.gov (United States)

Lee, Nuri; Kim, Ji-Eun; Gu, Ja-Yoon; Yoo, Hyun Ju; Kim, Inho; Yoon, Sung-Soo; Park, Seonyang; Han, Kyou-Sup; Kim, Hyun Kyung

2016-01-01

Disseminated intravascular coagulation (DIC) is characterized by consumption of coagulation factors and anticoagulants. Thrombin generation assay (TGA) gives useful information about global hemostatic status. We developed a new TGA system that anticoagulant addition can deplete thrombin generation in plasma, which may reflect defective anticoagulant system in DIC. TGAs were measured on the calibrated automated thrombogram with and without thrombomodulin or protein Z in 152 patients who were suspected of having DIC, yielding four parameters including lag time, endogenous thrombin potential, peak thrombin and time-to-peak in each experiment. Nonsurvivors showed significantly prolonged lag time and time-to-peak in TGA-protein Z system, which was performed with added protein Z. In multivariate Cox regression analysis, lag time and time-to-peak in TGA system were significant independent prognostic factors. In TGA-protein Z system, lag time and time-to-peak were revealed as independent prognostic factors of DIC. Protein Z addition could potentiate its anticoagulant effect in DIC with poor prognosis, suggesting the presence of defective protein Z system. The prolonged lag time and time-to-peak in both TGA and TGA-protein Z systems are expected to be used as independent prognostic factors of DIC.

Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

Science.gov (United States)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.

Multivariate analysis of factors influencing the effect of radiosynovectomy; Multivariate Analyse der Einflussfaktoren auf die Wirkung der Radiosynoviorthese bei entzuendlichen Gelenkerkrankungen

Energy Technology Data Exchange (ETDEWEB)

Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr. [Wuerzburg Univ. (Germany). Klinik fuer Nuklearmedizin; Wendler, J. [Erlangen-Nuernberg Univ. (Germany). Klinik fuer Innere Medizin III; Kenn, W. [Wuerzburg Univ. (Germany). Inst. fuer Roentgendiagnostik; Reidemeister, C. [Wuerzburg Univ. (Germany). Klinik fuer Innere Medizin

2002-04-01

Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [German] Ziel: In dieser prospektiven Studie wurde die Zeit bis zur Remission nach einer Radiosynoviorthese (RSO) untersucht. Ebenso wurde der Einfluss von Alter, Geschlecht, Grunderkrankung, Gelenktyp und Erkrankungsdauer auf die Erfolgsrate der RSO ermittelt. Methodik: Bei insgesamt 57 Patienten mit rheumatoider Arthritis (n = 33) und Arthritis bei aktivierter Arthrose (n = 24) wurden 130 Gelenke (36 Kniegelenke, 66 kleine und 28 mittelgrosse Gelenke) behandelt. Die Patienten wurden unter Verwendung so genannter visueller Analogskalen eine Woche vor RSO und vier bis sechs Monate danach 3-mal taeglich zur Schmerzintensitaet des

Chalkley estimates of angiogenesis in early breast cancer--relevance to prognosis

DEFF Research Database (Denmark)

Offersen, Birgitte V; Sørensen, Flemming Brandt; Yilmaz, Mette

2002-01-01

The aim of this study was to investigate whether Chalkley estimates of angiogenesis add new knowledge regarding prediction of prognosis in 455 consecutive early breast carcinomas, both node-positive (52%) and node-negative (48%). Median follow-up was 101 months. Intense vascularization indicated......, high malignancy grade, negative oestrogen receptor, and increasing Chalkley counts (both tertiles and continuous) were independent markers of disease-specific death. Thus, in a univariate analysis it was found that high Chalkley estimates of angiogenesis indicated a poor prognosis, but high Chalkley...

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis.

Science.gov (United States)

Zhao, Hongying; Yuan, Huating; Hu, Jing; Xu, Chaohan; Liao, Gaoming; Yin, Wenkang; Xu, Liwen; Wang, Li; Zhang, Xinxin; Shi, Aiai; Li, Jing; Xiao, Yun

2017-12-12

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as 'cell adhesion' and 'cell migration'. Furthermore, they are most significantly correlated with 'tissue invasion and metastasis', a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.

SPICE: exploration and analysis of post-cytometric complex multivariate datasets.

Science.gov (United States)

Roederer, Mario; Nozzi, Joshua L; Nason, Martha C

2011-02-01

Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley-Liss, Inc.

Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer.

Science.gov (United States)

Shukla, Hem D

2017-10-25

During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein-protein interaction, and pharmacogenomics

Predictors of Lymph Node Metastasis and Prognosis in pT1 Colorectal Cancer Patients with Signet-Ring Cell and Mucinous Adenocarcinomas

Directory of Open Access Journals (Sweden)

Bao-Rong Song

2017-03-01

Full Text Available Background/Aims: The local excision of early colorectal cancer is limited by the presence of lymph node metastasis (LNM. Signet-ring cell carcinomas (SRC and mucinous adenocarcinomas (MAC are two relatively infrequent histological subtypes. However, little is known about the predictors of LNM and prognosis to support the feasibility of local excision in early-stage SRC and MAC. Methods: The Surveillance Epidemiology and End Results Database were used to identify all patients with pT1 adenocarcinomas, including conventional adenocarcinoma (AC, MAC, and SRC. The prevalence of LNM was assessed, and the long-term survival rate in the above three types of colorectal cancer was calculated. Results: SRC accounted for 0.3% and MAC accounted for 4.4% of the entire cohort of colorectal adenocarcinomas. Compared to AC, MRC and SRC were more often located in the proximal colon, and exhibited a higher grade. The incidence of LNM in AC, MAC, and SRC was 10.6%, 17.2%, and 33.3% for colon cancers and 14.8%, 25.9%, and 46.2% for rectal cancers, respectively. In patients with lymph nodes resected no less than 12, incidence of LNM in AC, MRC, and SRC was 12%, 21%, and 44% for colon tumors and 17%, 30%, and 14% for rectal tumors, respectively. Although, colon patients MAC showed an entirely worse survival rate than AC, rectum patients MAC showed a similar prognosis to AC. We found that in patients with rectal tumors, SRC had a worse 3 and 5-year prognosis than AC. However, for colon cancers, the prognosis of SRC was similar to that of AC. Histology was not found to be an independent prognostic factor in multivariate survival analysis. Conclusions: MAC and SRC are two distinct subtypes of colorectal cancer that require special attention despite their relatively rare prevalence. pT1 patients with SRC of the rectum and patients with MAC of the colon have higher incidences of LNM, and with these adverse outcomes, local excision is not recommended. AlthoughMAC of the

The prognosis and prognostic risk factors of patients with hepatic artery complications after liver transplantation treated with the interventional techniques

International Nuclear Information System (INIS)

Shan Hong; Huang Mingsheng; Jiang Zaipo; Zhu Kangshun; Yang Yang; Chen Guihua

2008-01-01

Objective: To investigate the prognosis and prognostic risk factors of hepatic artery complications after orthotopic liver transplantation (OLT) treated with the interventional techniques. Methods: The clinical data of 21 patients with hepatic artery complication after liver transplantation receiving thrombolysis, PTA, and stent placement in our institute from November 2003 to April 2007 were retrospectively analyzed. Based on the prognosis of grafts, 21 patients were divided into poor-prognosis group and non-poor-prognosis group. Fifteen variables (including biliary complication, hepatic artery restenosis, early or late artery complication, and so on) were analyzed in both groups with binary logistic regression analysis to screen out the risk factors related to prognosis of pereutaneous interventional treatment for hepatic artery complications after OLT. Results: Twenty-one patients were followed for mean 436 days, median 464 days (3-1037 days). The poor-prognosis group included 11 patients (5 cases received retransplantation, and 6 died). The mean survival time of grafts in poor-prognosis group was 191 days, and median survival time was 73 days (3-616 days). The mean survival time of grafts in non-poor-prognosis group which included 10 patients was 706 days, and median survival time was 692 days (245-1037 days). Univariate analysis showed there were significant difference in biliary complication, total bilimbin and indirect bilirubin between the two groups. The binary, logistic regression analysis showed the risk factor related to prognosis was with biliary complication before the interventional management (P=0.027, OR=22.818). Conclusion: Biliary complication before interventional management is the risk factor related to poor prognosis of patients with hepatic artery stenosis or thrombosis receiving interventional treatment. (authors)

Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.

Science.gov (United States)

Rathore, Anurag S; Pathak, Mili; Jain, Renu; Jadaun, Gaurav Pratap Singh

2016-07-01

Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Science.gov (United States)

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Stability analysis of type 2 diabetes mellitus prognosis model with obesity as a trigger factor and metabolic syndrome as a risk factor

Science.gov (United States)

Jaya, A. I.; Lestari, A. D.; Ratianingsih, R.; Puspitasari, J. W.

2018-03-01

Obesity is found in 90% of the world's patients with a type 2 diabetes mellitus (DM) diagnosis. If it is not being treatment, the disease advances to a metabolic syndrome related to some atherosclerotic cardiovascular diseases. In this study, a mathematical model was constructed that represent the prognosis of type 2 DM. The prognosis is started from the transition of vulnerable people to overweight and obese. The advanced prognosis makes the type 2 DM sufferer become a metabolic syndrome. The model has no disease-free critical point, while the implicit endemic critical point is guaranteed for some requirements. The analysis of the critical point stability, by Jacobian matrix and Routh Hurwitz criteria, requires a parameter interval that identified from the characteristic polynomial. The requirements show that we have to pay attention to the transition rate of overweight to obese, more over the transition rate of obese to type 2 DM. The simulations show that the unstable condition of type 2 DM is easier to achieve because of the tightness of the parameter stability interval.

Pain in diagnostic hysteroscopy: a multivariate analysis after a randomized, controlled trial.

Science.gov (United States)

Mazzon, Ivan; Favilli, Alessandro; Grasso, Mario; Horvath, Stefano; Bini, Vittorio; Di Renzo, Gian Carlo; Gerli, Sandro

2014-11-01

To study which variables are able to influence women's experience of pain during diagnostic hysteroscopy. Multivariate analysis (phase II) after a randomized, controlled trial (phase I). Endoscopic gynecologic center. In phase I, 392 patients were analyzed. Group A: 197 women with carbon dioxide (CO2); group B: 195 women with normal saline. In phase II, 392 patients were assigned to two different groups according to their pain experience as measured by a visual analogue scale (VAS): group VAS>3 (170 patients); group VAS≤3 (222 patients). Free-anesthesia diagnostic hysteroscopy performed using CO2 or normal saline as distension media. Procedure time, VAS score, image quality, and side effects during and after diagnostic hysteroscopy. In phase I the median pain score in group A was 2, whereas in group B it was 3. In phase II the duration of the procedure, nulliparity, and the use of normal saline were significantly correlated with VAS>3. A higher presence of cervical synechiae was observed in the group VAS>3. The multivariate analysis revealed an inverse correlation between parity and a VAS>3, whereas the use of normal saline, the presence of synechiae in the cervical canal, and the duration of the hysteroscopy were all directly correlated to a VAS score>3. Pain in hysteroscopy is significantly related to the presence of cervical synechiae, to the duration of the procedure, and to the use of normal saline; conversely, parity seems to have a protective role. NCT01873391. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Long-term Prognosis of Anti-Neutrophil Cytoplasmic Antibody-Negative Renal Vasculitis: Cohort Study in Korea.

Science.gov (United States)

Lee, Sung Woo; Yu, Mi-Yeon; Baek, Seon Ha; Ahn, Shin-Young; Kim, Sejoong; Na, Ki Young; Chae, Dong-Wan; Chin, Ho Jun

2016-04-01

Few studies have reported on the long-term prognosis of anti-neutrophil cytoplasmic antibody (ANCA)-negative renal vasculitis. Between April 2003 and December 2013, 48 patients were diagnosed with renal vasculitis. Their ANCA status was tested using indirect immunofluorescence and enzyme-linked immunosorbent assays. During a median (interquartile range) follow-up duration of 933.5 (257.5-2,079.0) days, 41.7% of patients progressed to end stage renal disease (ESRD) and 43.8% died from any cause. Of 48 patients, 6 and 42 were ANCA-negative and positive, respectively. The rate of ESRD within 3 months was higher in ANCA-negative patients than in ANCA-positive patients (P = 0.038). In Kaplan-Meier survival analysis, ANCA-negative patients showed shorter renal survival than did ANCA-positive patients (log-rank P = 0.033). In univariate Cox-proportional hazard regression analysis, ANCA-negative patients showed increased risk of ESRD, with a hazard ratio 3.190 (95% confidence interval, 1.028-9.895, P = 0.045). However, the effect of ANCA status on renal survival was not statistically significant in multivariate analysis. Finally, ANCA status did not significantly affect patient survival. In conclusion, long-term patient and renal survival of ANCA-negative renal vasculitis patients did not differ from those of ANCA-positive renal vasculitis patients. Therefore, different treatment strategy depending on ANCA status might be unnecessary.

Vocal fold motion outcome based on excellent prognosis with laryngeal electromyography.

Science.gov (United States)

Smith, Libby J; Rosen, Clark A; Munin, Michael C

2016-10-01

As laryngeal electromyography (LEMG) becomes more refined, accurate predictions of vocal fold motion recovery are possible. Focus has been on outcomes for patients with poor prognosis for vocal fold motion recovery. Limited information is available regarding the expected rate of purposeful vocal fold motion recovery when there is good to normal motor recruitment, no signs of denervation, and no signs of synkinetic activity with LEMG, termed excellent prognosis. The objective of this study is to determine the rate of vocal fold motion recovery with excellent prognosis findings on LEMG after acute recurrent laryngeal nerve injury. Retrospective review. Patients undergoing a standardized LEMG protocol, consisting of qualitative (evaluation of motor recruitment, motor unit configuration, detection of fibrillations, presence of synkinesis) and quantitative (turns analysis) measurements were evaluated for purposeful vocal-fold motion recovery, calculated after at least 6 months since onset of injury. Twenty-three patients who underwent LEMG for acute vocal fold paralysis met the inclusion criteria of excellent prognosis. Eighteen patients (78.3%) recovered vocal fold motion, as determined by flexible laryngoscopy. Nearly 80% of patients determined to have excellent prognosis for vocal fold motion recovery experienced return of vocal fold motion. This information will help clinicians not only counsel their patients on expectations but will also help guide treatment. 4. Laryngoscope, 126:2310-2314, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Energy Technology Data Exchange (ETDEWEB)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

2015-05-15

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Science.gov (United States)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

International Nuclear Information System (INIS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-01-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

Understanding Cancer Prognosis

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Full Text Available ... and Prevention Risk Factors Genetics Cancer Prevention Overview Research Cancer Screening Cancer Screening Overview Screening Tests Research Diagnosis and Staging Symptoms Diagnosis Staging Prognosis Questions ...

Understanding Cancer Prognosis

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Full Text Available ... our information on Coping With Cancer helpful. Understanding Statistics About Survival Doctors estimate prognosis by using statistics that researchers have collected over many years about ...

Systematic Review and Individual Patient Data Meta-Analysis of Sex Differences in Depression and Prognosis in Persons With Myocardial Infarction: A MINDMAPS Study

NARCIS (Netherlands)

Doyle, F.; McGee, H.; Conroy, R.; Conradi, H.J.; Meijer, E.; Steeds, A; Sato, H.; Stewart, D.; Parakh, K.; Carney, R.; Freedland, F.; Anselmino, M.; Pelletier, R.; Bos, E.; de Jonge, P.

2015-01-01

Objective: Using combined individual patient data from prospective studies, we explored sex differences in depression and prognosis post-myocardial infarction (MI) and determined whether disease indices could account for found differences. Methods: Individual patient data analysis of 10,175 MI

Systematic Review and Individual Patient Data Meta-Analysis of Sex Differences in Depression and Prognosis in Persons With Myocardial Infarction : A MINDMAPS Study

NARCIS (Netherlands)

Doyle, Frank; Mcgee, Hannah; Conroy, Ronn; Conradi, Henk Jan; Meijer, Anna; Steeds, Richard; Sato, Hiroshi; Stewart, Donna E.; Parakh, Kapil; Carney, Robert; Freedland, Kenneth; Anselmino, Matteo; Pelletier, Roxanne; Bos, Elisabeth H.; de Jonge, Peter

Objective: Using combined individual patient data from prospective studies, we explored sex differences in depression and prognosis post-myocardial infarction (MI) and determined whether disease indices could account for found differences. Methods: Individual patient data analysis of 10,175 MI

Multivariate statistical analysis - an application to lunar materials

International Nuclear Information System (INIS)

Deb, M.

1978-01-01

The compositional characteristics of clinopyroxenes and spinels - two minerals considered to be very useful in deciphering lunar history, have been studied using the multivariate statistical method of principal component analysis. The mineral-chemical data used are from certain lunar rocks and fines collected by Apollo 11, 12, 14 and 15 and Luna 16 and 20 missions, representing mainly the mare basalts and also non-mare basalts, breccia and rock fragments from the highland regions, in which a large number of these minerals have been analyzed. The correlations noted in the mineral compositions, indicating substitutional relationships, have been interpreted on the basis of available crystal-chemical and petrological informations. Compositional trends for individual specimens have been delineated and compared by producing ''principal latent vector diagrams''. The percent variance of the principal components denoted by the eigenvalues, have been evaluated in terms of the crystallization history of the samples. Some of the major petrogenetic implications of this study concern the role of early formed cumulate phases in the near-surface fractionation of mare basalts, mixing of mineral compositions in the highland regolith and the subsolidus reduction trends in lunar spinels. (auth.)

Long-term prognosis of endodontically treated teeth: a retrospective analysis of preoperative factors in molars.

Science.gov (United States)

Setzer, Frank C; Boyer, Keith R; Jeppson, Joshua R; Karabucak, Bekir; Kim, Syngcuk

2011-01-01

Long-term predictability of restored endodontically treated teeth is important for the decision of tooth retention versus extraction and implant placement. The purpose of this study was to validate the hypothesis that preoperative factors can predict the long-term prognosis of molars requiring endodontic and restorative treatment for future prognostic investigations. A clinical database was searched for molar endodontic treatments with crown placement and a minimum of 4-year follow-up. Charts of 42 patients with 50 individual treatments were randomly selected. Information concerning crown lengthening; periodontal diagnosis; attachment loss; furcation involvement; mobility; and internal, external, or periradicular resorption was recorded. Radiographs from treatment initiation and follow-up were digitalized. The presence of apical periodontitis was evaluated. Available ferrule was calculated from bitewing radiographs using CAD software (AutoCAD; Autodesk, Cupertino, CA). The resulting data, age, sex, and times of restoration and follow-up were analyzed for correlation with the presence of apical radiolucency at follow-up and the following four possible outcome scenarios: "no event," "nonsurgical retreatment," "surgical retreatment," or "extraction" using Spearman rank order correlation analysis. Patients' ages ranged from 19 to 87 years, 22 were male and 20 female, and 48 teeth (96.0%) were retained at follow-up. Of those, 44 (88.0%) were without intervention ("no event"), and four (8.0%) underwent surgical or nonsurgical retreatment. Two teeth (4.0%) had been extracted. Significant positive correlations existed between "untoward events" (any form of retreatment or extraction) and "prognostic value according to periodontal status" (p = 0.047) and "attachment loss" (p = 0.042). The only preoperative factors significant for the prognosis of restored endodontically treated molars were related to periodontal prognostic value and attachment loss. It can be concluded that

Communicating prognosis with parents of critically ill infants: direct observation of clinician behaviors.

Science.gov (United States)

Boss, R D; Lemmon, M E; Arnold, R M; Donohue, P K

2017-11-01

Delivering prognostic information to families requires clinicians to forecast an infant's illness course and future. We lack robust empirical data about how prognosis is shared and how that affects clinician-family concordance regarding infant outcomes. Prospective audiorecording of neonatal intensive care unit family conferences, immediately followed by parent/clinician surveys. Existing qualitative analysis frameworks were applied. We analyzed 19 conferences. Most prognostic discussion targeted predicted infant functional needs, for example, medications or feeding. There was little discussion of how infant prognosis would affect infant/family quality of life. Prognostic framing was typically optimistic. Most parents left the conference believing their infant's prognosis to be more optimistic than did clinicians. Clinician approach to prognostic disclosure in these audiotaped family conferences tended to be broad and optimistic, without detail regarding implications of infant health for infant/family quality of life. Families and clinicians left these conversations with little consensus about infant prognosis.

Is the prognostic significance of O6-methylguanine- DNA methyltransferase promoter methylation equally important in glioblastomas of patients from different continents? A systematic review with meta-analysis.

Science.gov (United States)

Meng, Wei; Jiang, Yangyang; Ma, Jie

2017-01-01

O6-methylguanine-DNA methyltransferase (MGMT) is an independent predictor of therapeutic response and potential prognosis in patients with glioblastoma multiforme (GBM). However, its significance of clinical prognosis in different continents still needs to be explored. To explore the effects of MGMT promoter methylation on both progression-free survival (PFS) and overall survival (OS) among GBM patients from different continents, a systematic review of published studies was conducted. A total of 5103 patients from 53 studies were involved in the systematic review and the total percentage of MGMT promoter methylation was 45.53%. Of these studies, 16 studies performed univariate analyses and 17 performed multivariate analyses of MGMT promoter methylation on PFS. The pooled hazard ratio (HR) estimated for PFS was 0.55 (95% CI 0.50, 0.60) by univariate analysis and 0.43 (95% CI 0.38, 0.48) by multivariate analysis. The effect of MGMT promoter methylation on OS was explored in 30 studies by univariate analysis and in 30 studies by multivariate analysis. The combined HR was 0.48 (95% CI 0.44, 0.52) and 0.42 (95% CI 0.38, 0.45), respectively. In each subgroup divided by areas, the prognostic significance still remained highly significant. The proportion of methylation in each group was in inverse proportion to the corresponding HR in the univariate and multivariate analyses of PFS. However, from the perspective of OS, compared with data from Europe and the US, higher methylation rates in Asia did not bring better returns.

Multivariate regression analysis for determining short-term values of radon and its decay products from filter measurements

International Nuclear Information System (INIS)

Kraut, W.; Schwarz, W.; Wilhelm, A.

1994-01-01

A multivariate regression analysis is applied to decay measurements of α-resp. β-filter activcity. Activity concentrations for Po-218, Pb-214 and Bi-214, resp. for the Rn-222 equilibrium equivalent concentration are obtained explicitly. The regression analysis takes into account properly the variances of the measured count rates and their influence on the resulting activity concentrations. (orig.) [de

Phosphoglycerate dehydrogenase is a novel predictor for poor prognosis in gastric cancer

Directory of Open Access Journals (Sweden)

Xian Y

2016-09-01

Full Text Available Yun Xian,1,* Shu Zhang,2,* Xudong Wang,3 Jin Qin,2 Wei Wang,2 Han Wu4 1School of Public Health, Nantong University, 2Department of Pathology, 3Department of Laboratory Medicine, 4Department of General Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China *These authors contributed equally to this work Purpose: Phosphoglycerate dehydrogenase (PHGDH acts as a key metabolic enzyme in the rate-limiting step in serine biosynthesis and plays an important role in metastasis of several cancers. The aim of this study was to investigate the prognostic value of PHGDH in gastric cancer (GC. Methods: The messenger RNA expression of PHGDH was determined in 20 pairs of cancerous and adjacent nontumor tissues by real-time polymerase chain reaction. Immunohistochemistry of PHGDH was performed on tissue microarray, composed of 482 GC and 64 matched adjacent nontumor tissues acquired from surgery, 20 chronic gastritis, 18 intestinal metaplasia, and 31 low-grade and 66 high-grade intraepithelial neoplasias acquired through gastric endoscopic biopsy. Univariate and multivariate Cox proportional hazard models were used to perform survival analyses. Results: Both PHGDH messenger RNA and protein product exhibited GC tissue-preferred expression, when compared with benign tissues. The high PHGDH expression was significantly correlated with histological type (P=0.011, tumor stage (P=0.014, and preoperative carcinoembryonic antigen (P<0.001. A negative correlation was found between PHGDH expression and the 5-year survival rate of patients with GC. Furthermore, multivariate analysis indicated that PHGDH was an independent prognostic factor for outcome in GC. Conclusion: PHGDH is important in predicting patient outcomes and is a potential target for the development of therapeutic approaches to GC. Keywords: metabolism, gastric cancer, prognosis, serine biosynthesis

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

Science.gov (United States)

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

Analysis of prognosis of thymoma from long-term follow-up

Energy Technology Data Exchange (ETDEWEB)

Ikeda, Hiroshi; Masaki, Norie; Nishiyama, Kinji; Inoue, Takehiro; Matayoshi, Yoshinobu; Kozuka, Takahiro; Nakahara, Kazuya; Hashimoto, Jumpei

1988-03-01

In order to determine the prognosis and the mode of recurrence, a total of 108 cases of thymoma which were treated at the Department of Radiology and First Department of Surgery, Osaka University Hospital, were analyzed. Of the cases with complete resection followed by postoperative radiotherapy by 40 Gy/4 weeks (76 cases), ten-year survival of 88 % was obtained, which was in contrast to that with incomplete resection. Of the cases with Stages III and IVa with incomplete resection, recurrence-free survival was 62 % at 10 years, and recurrence occurred even 12 years after initial therapy. Local recurrence was found most frequently and was in 9 out of 10 recurred cases from invasive thymoma. Most recurrent cases led to fatal due to tumor. Lower neck was involved in only 3 cases through the entire course and distant metastasis was rather frequent at terminal stage (10 out of 13), sites being lung parenchyma (7), bone (4), liver (2) and brain (2), in order. Radiotherapy to hemithorax had been withheld in a case with Stage IVa, until pleural recurrence was clinically evident 10 years later. Myasthenia gravis was seen in 62 % of the cases, and was even higher (77 %) in encapsulated cases. Prognosis did not differ from the cases without myasthenia gravis under the same surgical stage. No apparent dose-response relationship was found in the radiotherapy of thymoma.

Analysis of prognosis of thymoma from long-term follow-up

International Nuclear Information System (INIS)

Ikeda, Hiroshi; Masaki, Norie; Nishiyama, Kinji; Inoue, Takehiro; Matayoshi, Yoshinobu; Kozuka, Takahiro; Nakahara, Kazuya; Hashimoto, Jumpei

1988-01-01

In order to determine the prognosis and the mode of recurrence, a total of 108 cases of thymoma which were treated at the Department of Radiology and First Department of Surgery, Osaka University Hospital, were analyzed. Of the cases with complete resection followed by postoperative radiotherapy by 40 Gy/4 weeks (76 cases), ten-year survival of 88 % was obtained, which was in contrast to that with incomplete resection. Of the cases with Stages III and IVa with incomplete resection, recurrence-free survival was 62 % at 10 years, and recurrence occurred even 12 years after initial therapy. Local recurrence was found most frequently and was in 9 out of 10 recurred cases from invasive thymoma. Most recurrent cases led to fatal due to tumor. Lower neck was involved in only 3 cases through the entire course and distant metastasis was rather frequent at terminal stage (10 out of 13), sites being lung parenchyma (7), bone (4), liver (2) and brain (2), in order. Radiotherapy to hemithorax had been withheld in a case with Stage IVa, until pleural recurrence was clinically evident 10 years later. Myasthenia gravis was seen in 62 % of the cases, and was even higher (77 %) in encapsulated cases. Prognosis did not differ from the cases without myasthenia gravis under the same surgical stage. No apparent dose-response relationship was found in the radiotherapy of thymoma. (author)

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma.

Science.gov (United States)

Giridhar, Karthik V; Sosa, Carlos P; Hillman, David W; Sanhueza, Cristobal; Dalpiaz, Candace L; Costello, Brian A; Quevedo, Fernando J; Pitot, Henry C; Dronca, Roxana S; Ertz, Donna; Cheville, John C; Donkena, Krishna Vanaja; Kohli, Manish

2017-11-03

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04-0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05-0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only ( p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies.

Mulch materials in processing tomato: a multivariate approach

Directory of Open Access Journals (Sweden)

Marta María Moreno

2013-08-01

Full Text Available Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L. crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2, one oxo-biodegradable material (OB, two types of paper (PP1, PP2, and one barley straw cover (BS were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]. A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

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Fei Liu

2018-02-01

Full Text Available Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS, coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice. For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV. Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Clinical value of liver and spleen shear wave velocity in predicting the prognosis of patients with portal hypertension.

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Zhang, Yan; Mao, Da-Feng; Zhang, Mei-Wu; Fan, Xiao-Xiang

2017-12-07

To explore the relationship of liver and spleen shear wave velocity in patients with liver cirrhosis combined with portal hypertension, and assess the value of liver and spleen shear wave velocity in predicting the prognosis of patients with portal hypertension. All 67 patients with liver cirrhosis diagnosed as portal hypertension by hepatic venous pressure gradient in our hospital from June 2014 to December 2014 were enrolled into this study. The baseline information of these patients was recorded. Furthermore, 67 patients were followed-up at 20 mo after treatment, and liver and spleen shear wave velocity were measured by acoustic radiation force impulse at the 1 st week, 3 rd month and 9 th month after treatment. Patients with favorable prognosis were assigned into the favorable prognosis group, while patients with unfavorable prognosis were assigned into the unfavorable prognosis group. The variation and difference in liver and spleen shear wave velocity in these two groups were analyzed by repeated measurement analysis of variance. Meanwhile, in order to evaluate the effect of liver and spleen shear wave velocity on the prognosis of patients with portal hypertension, Cox's proportional hazard regression model analysis was applied. The ability of those factors in predicting the prognosis of patients with portal hypertension was calculated through receiver operating characteristic (ROC) curves. The liver and spleen shear wave velocity in the favorable prognosis group revealed a clear decline, while those in the unfavorable prognosis group revealed an increasing tendency at different time points. Furthermore, liver and spleen shear wave velocity was higher in the unfavorable prognosis group, compared with the favorable prognosis group; the differences were statistically significant ( P portal hypertension was significantly affected by spleen hardness at the 3 rd month after treatment [relative risk (RR) = 3.481]. At the 9 th month after treatment, the prognosis

Multivariate hydrological frequency analysis for extreme events using Archimedean copula. Case study: Lower Tunjuelo River basin (Colombia)

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Gómez, Wilmar

2017-04-01

By analyzing the spatial and temporal variability of extreme precipitation events we can prevent or reduce the threat and risk. Many water resources projects require joint probability distributions of random variables such as precipitation intensity and duration, which can not be independent with each other. The problem of defining a probability model for observations of several dependent variables is greatly simplified by the joint distribution in terms of their marginal by taking copulas. This document presents a general framework set frequency analysis bivariate and multivariate using Archimedean copulas for extreme events of hydroclimatological nature such as severe storms. This analysis was conducted in the lower Tunjuelo River basin in Colombia for precipitation events. The results obtained show that for a joint study of the intensity-duration-frequency, IDF curves can be obtained through copulas and thus establish more accurate and reliable information from design storms and associated risks. It shows how the use of copulas greatly simplifies the study of multivariate distributions that introduce the concept of joint return period used to represent the needs of hydrological designs properly in frequency analysis.

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study

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Tania Dehesh

2015-01-01

Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

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Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

KAUST Repository

Malvaso, Federica

2015-01-01

. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (h

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

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Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

Science.gov (United States)

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Impact of serum SP-A and SP-D levels on comparison and prognosis of idiopathic pulmonary fibrosis

OpenAIRE

Wang, Kai; Ju, Qing; Cao, Jing; Tang, Wenze; Zhang, Jian

2017-01-01

Abstract Background and objective: Idiopathic pulmonary fibrosis (IPF) has a poor prognosis in general; however, it is heterogeneous to detect relative biomarkers for predicting the disease progression. Serum biomarkers can be conveniently collected to detect and help to differentially diagnose IPF and predict IPF prognosis. This meta-analysis aimed to evaluate the use of serum surfactant proteins A and D (SP-A and SP-D) for differential diagnosis and prognosis of IPF. Methods: Relevant artic...

Understanding Cancer Prognosis

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Full Text Available ... how to discuss prognosis with their patients. Good communication, he says, is part of providing good ... Please note that blog posts that are written by individuals from outside the ...

Network structure of multivariate time series.

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Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Multivariate methods and forecasting with IBM SPSS statistics

CERN Document Server

Aljandali, Abdulkader

2017-01-01

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...

Value of neutrophil-to-lymphocyte ratio for predicting lung cancer prognosis: A meta-analysis of 7,219 patients.

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Yu, Yu; Qian, Lei; Cui, Jiuwei

2017-09-01

Current evidence suggests that the neutrophil-to-lymphocyte ratio (NLR) may be a biomarker for poor prognosis in lung cancer, although this association remains controversial. Therefore, a meta-analysis was performed to evaluate the association between NLR and lung cancer outcome. A systematic literature search was performed through the PubMed, Embase and Cochrane Library databases (until July 30, 2016), to identify studies evaluating the association between NLR and overall survival (OS) and/or progression-free survival (PFS) among patients with lung cancer. Based on the results of this search, data from 18 studies involving 7,219 patients with lung cancer were evaluated. The pooled hazard ratio (HR) suggested that elevated pretreatment NLR predicted poor OS [HR=1.46, 95% confidence interval (CI): 1.30-1.64] and poor PFS (HR=1.42, 95% CI: 1.15-1.75) among patients with lung cancer. Subgroup analysis revealed that the prognostic value of NLR for predicting poor OS increased among patients who underwent surgery (HR=1.50, 95% CI: 1.21-1.84) or patients with early-stage disease (HR=1.64, 95% CI: 1.37-1.97). An NLR cut-off value of ≥4 significantly predicted poor OS (HR=1.56, 95% CI: 1.31-1.85) and PFS (HR=1.54, 95% CI: 1.13-1.82), particularly in the cases of small-cell lung cancer. Thus, the results of the present meta-analysis suggested that an elevated pretreatment NLR (e.g., ≥4) may be considered as a biomarker for poor prognosis in patients with lung cancer.

Clinical impact of sarcopenia on prognosis in pancreatic ductal adenocarcinoma: A retrospective cohort study.

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Ninomiya, Go; Fujii, Tsutomu; Yamada, Suguru; Yabusaki, Norimitsu; Suzuki, Kojiro; Iwata, Naoki; Kanda, Mitsuro; Hayashi, Masamichi; Tanaka, Chie; Nakayama, Goro; Sugimoto, Hiroyuki; Koike, Masahiko; Fujiwara, Michitaka; Kodera, Yasuhiro

2017-03-01

To investigate the impact of the body composition such as skeletal muscle, visceral fat and body mass index (BMI) on patients with resected pancreatic ductal adenocarcinoma (PDAC). A total of 265 patients who underwent curative surgery for PDAC were examined in this study. The total skeletal muscle and fat tissue areas were evaluated in a single image obtained at the third lumber vertebra during a preoperative computed tomography (CT) scan. The patients were assigned to either the sarcopenia or non-sarcopenia group based on their skeletal muscle index (SMI) and classified into high visceral fat area (H-VFA) or low VFA (L-VFA) groups. The association of clinicopathological features and prognosis with the body composition were statistically analyzed. There were 170 patients (64.2%) with sarcopenia. The median survival time (MST) was 23.7 months for sarcopenia patients and 25.8 months for patients without sarcopenia. The MST was 24.4 months for H-VFA patients and 25.8 months for L-VFA patients. However, sarcopenia patients with BMI ≥22 exhibited significantly poorer survival than patients without sarcopenia (MST: 19.2 vs. 35.4 months, P = 0.025). There was a significant difference between patients with and without sarcopenia who did not receive chemotherapy (5-year survival rate: 0% vs. 68.3%, P = 0.003). The multivariate analysis revealed that tumor size, positive dissected peripancreatic tissue margin, and sarcopenia were independent prognostic factors. Sarcopenia is an independent prognostic factor in PDAC patients with a BMI ≥22. Therefore, evaluating skeletal muscle mass may be a simple and useful approach for predicting patient prognosis. Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Symptomatic and Asymptomatic Neurological Complications of Infective Endocarditis: Impact on Surgical Management and Prognosis

Science.gov (United States)

Delahaye, François; Tattevin, Pierre; Federspiel, Claire; Le Moing, Vincent; Chirouze, Catherine; Nazeyrollas, Pierre; Vernet-Garnier, Véronique; Bernard, Yvette; Chocron, Sidney; Obadia, Jean-François; Alla, François; Hoen, Bruno; Duval, Xavier

2016-01-01

Objectives Symptomatic neurological complications (NC) are a major cause of mortality in infective endocarditis (IE) but the impact of asymptomatic complications is unknown. We aimed to assess the impact of asymptomatic NC (AsNC) on the management and prognosis of IE. Methods From the database of cases collected for a population-based study on IE, we selected 283 patients with definite left-sided IE who had undergone at least one neuroimaging procedure (cerebral CT scan and/or MRI) performed as part of initial evaluation. Results Among those 283 patients, 100 had symptomatic neurological complications (SNC) prior to the investigation, 35 had an asymptomatic neurological complications (AsNC), and 148 had a normal cerebral imaging (NoNC). The rate of valve surgery was 43% in the 100 patients with SNC, 77% in the 35 with AsNC, and 54% in the 148 with NoNC (p<0.001). In-hospital mortality was 42% in patients with SNC, 8.6% in patients with AsNC, and 16.9% in patients with NoNC (p<0.001). Among the 135 patients with NC, 95 had an indication for valve surgery (71%), which was performed in 70 of them (mortality 20%) and not performed in 25 (mortality 68%). In a multivariate adjusted analysis of the 135 patients with NC, age, renal failure, septic shock, and IE caused by S. aureus were independently associated with in-hospital and 1-year mortality. In addition SNC was an independent predictor of 1-year mortality. Conclusions The presence of NC was associated with a poorer prognosis when symptomatic. Patients with AsNC had the highest rate of valve surgery and the lowest mortality rate, which suggests a protective role of surgery guided by systematic neuroimaging results. PMID:27400273

Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy

OpenAIRE

Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto

2008-01-01

In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...

Using an Agent-oriented Framework for Supervision, Diagnosis and Prognosis Applications in Advanced Automation Environments

DEFF Research Database (Denmark)

Thunem, Harald P-J; Thunem, Atoosa P-J; Lind, Morten

2011-01-01

This paper demonstrates how a generic agent-oriented framework can be used in advanced automation environments, for systems analysis in general and supervision, diagnosis and prognosis purposes in particular. The framework’s background and main application areas are briefly described. Next......-oriented supervision, diagnosis and prognosis purposes are equally explained. Finally, the paper sums up by also addressing plans for further enhancement and in that respect integration with other tailor-made tools for joint treatment of various modeling and analysis activities upon advanced automation environments....

Banach frames for multivariate alpha-modulation spaces

DEFF Research Database (Denmark)

Borup, Lasse; Nielsen, Morten

2006-01-01

The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....

The role of pre-existing diabetes mellitus on hepatocellular carcinoma occurrence and prognosis: a meta-analysis of prospective cohort studies.

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Wan-Shui Yang

Full Text Available The impact of pre-existing diabetes mellitus (DM on hepatocellular carcinoma (HCC occurrence and prognosis is complex and unclear. The aim of this meta-analysis is to evaluate the association between pre-existing diabetes mellitus and hepatocellular carcinoma occurrence and prognosis.We searched PubMed, Embase and the Cochrane Library from their inception to January, 2011 for prospective epidemiological studies assessing the effect of pre-existing diabetes mellitus on hepatocellular carcinoma occurrence, mortality outcomes, cancer recurrence, and treatment-related complications. Study-specific risk estimates were combined by using fixed effect or random effect models.The database search generated a total of 28 prospective studies that met the inclusion criteria. Among these studies, 14 reported the risk of HCC incidence and 6 studies reported risk of HCC specific mortality. Six studies provided a total of 8 results for all-cause mortality in HCC patients. Four studies documented HCC recurrence risks and 2 studies reported risks for hepatic decomposition occurrence in HCC patients. Meta-analysis indicated that pre-existing diabetes mellitus (DM was significantly associated with increased risk of HCC incidence [meta-relative risk (RR = 1.87, 95% confidence interval (CI: 1.15-2.27] and HCC-specific mortality (meta-RR = 1.88, 95%CI: 1.39-2.55 compared with their non-DM counterparts. HCC patients with pre-existing DM had a 38% increased (95% CI: 1.13-1.48 risk of death from all-causes and 91% increased (95%CI: 1.41-2.57 risk of hepatic decomposition occurrence compared to those without DM. In DM patients, the meta-RR for HCC recurrence-free survival was 1.93(95%CI: 1.12-3.33 compared with non-diabetic patients.The findings from the current meta-analysis suggest that DM may be both associated with elevated risks of both HCC incidence and mortality. Furthermore, HCC patients with pre-existing diabetes have a poorer prognosis relative to their

A Morphometric Survey among Three Iranian Horse Breeds with Multivariate Analysis

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M. Hosseini

2016-12-01

Full Text Available Three Iranian horse breeds, Turkoman, Caspian, and Kurdish, are the most important Iranian horse breeds which are well known in all around of the world because of their beauty, versatility, great stamina, and  intelligence. Phenotypic characterization was used to identify and document the diversity within and between distinct breeds, based on their observable attributes. Phenotypic characterization and body biometric in 23 traits were measured in 191 purebred horses belonging to three breeds, i.e. Turkoman (70 horses, Kurdish (77 horses, and Caspian (44 horses.  Caspian breed was  sampled from the Provinces of Alborz and Gilan. Kurdish breed was sampled from the Provinces of Kurdistan, Kermanshah, and Hamadan. Turkoman breed was sampled from the Provinces of Golestan, Markazi, and Isfahan. Multivariate analysis of variance (MANOVA was implemented. In addition, Canonical Discriminate Analysis (CDA, Principal Component Analysis (PCA, and Custer analysis were executed for assessing the relationship among the breeds. All statistical analysis was executed by SAS statistical program. The results of our investigation represented the breeds classification into 3 different classes (Caspian, Turkoman, and Kurdish based on different morphometrical traits. Caspian breed with smaller size in most variables was detached clearly from the others with more distance than Kurdish and Turkoman breeds. The result showed that the most variably trait for classification was Hind Hoof Length. Adaptation with different environments causes difference in morphology and difference among breeds. We can identify and classify domestic population using PCA, CDA, and cluster analysis.

Multivariate analysis in provenance studies: Cerrillos obsidians case, Peru

International Nuclear Information System (INIS)

Bustamante, A.; Delgado, M.; Latini, R. M.; Bellido, A. V. B.

2007-01-01

We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800-100 b.c.) using Moessbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Moessbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe 2+ in two different sites (species A and B), one weak Fe 3+ doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Moessbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

Multivariate analysis in provenance studies: Cerrillos obsidians case, Peru

Science.gov (United States)

Bustamante, A.; Delgado, M.; Latini, R. M.; Bellido, A. V. B.

2007-02-01

We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800 100 b.c.) using Mössbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Mössbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe2+ in two different sites (species A and B), one weak Fe3+ doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Mössbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

Impact of Exhaled Breath Acetone in the Prognosis of Patients with Heart Failure with Reduced Ejection Fraction (HFrEF. One Year of Clinical Follow-up.

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Fabiana G Marcondes-Braga

Full Text Available The identification of new biomarkers of heart failure (HF could help in its treatment. Previously, our group studied 89 patients with HF and showed that exhaled breath acetone (EBA is a new noninvasive biomarker of HF diagnosis. However, there is no data about the relevance of EBA as a biomarker of prognosis.To evaluate whether EBA could give prognostic information in patients with heart failure with reduced ejection fraction (HFrEF.After breath collection and analysis by gas chromatography-mass spectrometry and by spectrophotometry, the 89 patients referred before were followed by one year. Study physicians, blind to the results of cardiac biomarker testing, ascertained vital status of each study participant at 12 months.The composite endpoint death and heart transplantation (HT were observed in 35 patients (39.3%: 29 patients (32.6% died and 6 (6.7% were submitted to HT within 12 months after study enrollment. High levels of EBA (≥3.7μg/L, 50th percentile were associated with a progressively worse prognosis in 12-month follow-up (log-rank = 11.06, p = 0.001. Concentrations of EBA above 3.7μg/L increased the risk of death or HT in 3.26 times (HR = 3.26, 95%CI = 1.56-6.80, p = 0.002 within 12 months. In a multivariable cox regression model, the independent predictors of all-cause mortality were systolic blood pressure, respiratory rate and EBA levels.High EBA levels could be associated to poor prognosis in HFrEF patients.

Impact of Exhaled Breath Acetone in the Prognosis of Patients with Heart Failure with Reduced Ejection Fraction (HFrEF). One Year of Clinical Follow-up

Science.gov (United States)

Saldiva, Paulo H. N.; Mangini, Sandrigo; Issa, Victor S.; Ayub-Ferreira, Silvia M.; Bocchi, Edimar A.

2016-01-01

Background The identification of new biomarkers of heart failure (HF) could help in its treatment. Previously, our group studied 89 patients with HF and showed that exhaled breath acetone (EBA) is a new noninvasive biomarker of HF diagnosis. However, there is no data about the relevance of EBA as a biomarker of prognosis. Objectives To evaluate whether EBA could give prognostic information in patients with heart failure with reduced ejection fraction (HFrEF). Methods After breath collection and analysis by gas chromatography-mass spectrometry and by spectrophotometry, the 89 patients referred before were followed by one year. Study physicians, blind to the results of cardiac biomarker testing, ascertained vital status of each study participant at 12 months. Results The composite endpoint death and heart transplantation (HT) were observed in 35 patients (39.3%): 29 patients (32.6%) died and 6 (6.7%) were submitted to HT within 12 months after study enrollment. High levels of EBA (≥3.7μg/L, 50th percentile) were associated with a progressively worse prognosis in 12-month follow-up (log-rank = 11.06, p = 0.001). Concentrations of EBA above 3.7μg/L increased the risk of death or HT in 3.26 times (HR = 3.26, 95%CI = 1.56–6.80, p = 0.002) within 12 months. In a multivariable cox regression model, the independent predictors of all-cause mortality were systolic blood pressure, respiratory rate and EBA levels. Conclusions High EBA levels could be associated to poor prognosis in HFrEF patients. PMID:28030609

Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

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Hem D. Shukla

2017-10-01

Full Text Available During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA, and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein–protein interaction

Discrimination between Bacillus and Alicyclobacillus isolates in apple juice by Fourier transform infrared spectroscopy and multivariate analysis.

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Al-Holy, Murad A; Lin, Mengshi; Alhaj, Omar A; Abu-Goush, Mahmoud H

2015-02-01

Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera. © 2015 Institute of Food Technologists®

Clinicopathologic Characteristics and Prognosis of Xp11.2 Translocation Renal Cell Carcinoma: Multicenter, Propensity Score Matching Analysis.

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Choo, Min Soo; Jeong, Chang Wook; Song, Cheryn; Jeon, Hwang Gyun; Seo, Seong Il; Hong, Sung Kyu; Byun, Seok-Soo; Chung, Jin Soo; Hong, Sung-Hoo; Hwang, Eu Chang; Kim, Hyeon Hoe; Kwak, Cheol

2017-10-01

We evaluated the clinicopathologic characteristics and prognosis of Xp11.2 translocation (Xp11.2t) renal cell carcinoma (RCC) from a multicenter study and compare them with clear-cell RCC using a propensity score matching analysis. Between 2004 and 2013, 8384 consecutive patients from 7 institutions who were diagnosed with RCC were reviewed, and the pathologically confirmed Xp11.2t cases were enrolled. The oncological outcomes of Xp11.2t were compared with those of clear-cell RCC by selecting matched cases using 1:3 propensity score matching methods in a precollected clear-cell RCC data set from our hospital. The patients were divided into 2 subgroups on the basis of age of onset, either before (early) or after (late) 45 years old. Xp11.2t was found in 61 cases, corresponding to 0.72% of RCC cases for the 10 years. The mean age was 38.2 ± 19.4 years, and the mean tumor size was 6.2 ± 3.9 cm. The Xp11.2t cases were at more advanced stages and showed tendencies to involve lymph nodes at diagnosis. After the matching, there were no significant differences in recurrence-free and overall survival compared with clear-cell RCC. The age of incidence for Xp11.2t had a bimodal distribution, which was most common in the 30s and smaller peak in the 60s. Xp11.2t corresponded to a significantly worse prognosis for overall survival in late onset (after 45 years) subgroup (P = .038; hazard ratio, 3.199; 95% confidence interval, 1.065-9.609). This neoplasm has more aggressive clinicopathologic features at diagnosis. In older patients with onset age > 45 years, Xp11.2t showed a significantly worse prognosis than clear-cell RCC. Copyright © 2017 Elsevier Inc. All rights reserved.

Study on loss detection algorithms for tank monitoring data using multivariate statistical analysis

International Nuclear Information System (INIS)

Suzuki, Mitsutoshi; Burr, Tom

2009-01-01

Evaluation of solution monitoring data to support material balance evaluation was proposed about a decade ago because of concerns regarding the large throughput planned at Rokkasho Reprocessing Plant (RRP). A numerical study using the simulation code (FACSIM) was done and significant increases in the detection probabilities (DP) for certain types of losses were shown. To be accepted internationally, it is very important to verify such claims using real solution monitoring data. However, a demonstrative study with real tank data has not been carried out due to the confidentiality of the tank data. This paper describes an experimental study that has been started using actual data from the Solution Measurement and Monitoring System (SMMS) in the Tokai Reprocessing Plant (TRP) and the Savannah River Site (SRS). Multivariate statistical methods, such as a vector cumulative sum and a multi-scale statistical analysis, have been applied to the real tank data that have superimposed simulated loss. Although quantitative conclusions have not been derived for the moment due to the difficulty of baseline evaluation, the multivariate statistical methods remain promising for abrupt and some types of protracted loss detection. (author)

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

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Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Clinical and prognosis value of the CIMP status combined with MLH1 or p16 INK4a methylation in colorectal cancer.

Science.gov (United States)

Saadallah-Kallel, Amana; Abdelmaksoud-Dammak, Rania; Triki, Mouna; Charfi, Slim; Khabir, Abdelmajid; Sallemi-Boudawara, Tahia; Mokdad-Gargouri, Raja

2017-08-01

Aberrant DNA methylation of CpG islands occurred frequently in CRC and associated with transcriptional silencing of key genes. In this study, the CIMP combined with MLH1 or p16 INK4a methylation status was determined in CRC patients and correlated with clinicopathological parameters and overall survival. Our data showed that CIMP+ CRCs were identified in 32.9% of cases and that CACNAG1 is the most frequently methylated promoter. When we combined the CIMP with the MLH1 or the p16 INK4a methylation status, we found that CIMP-/MLH1-U (37.8%) and CIMP-/p16 INK4a -U (35.4%) tumors were the most frequent among the four subtypes. Statistical analysis showed that tumor location, lymphovascular invasion, TNM stage, and MSI differed among the group of patients. Kaplan-Meier analyses revealed differences in overall survival according to the CIMP combined with MLH1 or p16 INK4a methylation status. In a multivariate analysis, CIMP/MLH1 and CIMP/p16 INK4a methylation statuses were predictive of prognosis, and the OS was longer for patients with tumors CIMP-/MLH1-M, as well as CIMP-/p16 INK4a -M. Furthermore, DNMT1 is significantly overexpressed in tumors than in normal tissues as well as in CIMP+ than CIMP- tumors. Our results suggest that tumor classification based on the CIMP status combined with MLH1 or p16 INK4a methylation is useful to predict prognosis in CRC patients.

Zinc finger AN1-type containing 4 is a novel marker for predicting metastasis and poor prognosis in oral squamous cell carcinoma.

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Kurihara-Shimomura, Miyako; Sasahira, Tomonori; Nakamura, Hiroshi; Nakashima, Chie; Kuniyasu, Hiroki; Kirita, Tadaaki

2018-05-01

Head and neck cancer, including oral squamous cell carcinoma (OSCC), is the sixth most common cancer worldwide and has a high potential for locoregional invasion and nodal metastasis. Therefore, discovery of a useful molecular biomarker capable of predicting tumour progression and metastasis of OSCC is crucial. We have previously reported zinc finger AN1-type containing 4 (ZFAND4) as one of the most upregulated genes in recurrent OSCC using a cDNA microarray analysis. Although ZFAND4 has been shown to promote cell proliferation of gastric cancer, its expression and clinicopathological roles in OSCC remain unclear. In this study, we examined ZFAND4 expression by immunohistochemistry in 214 cases of OSCC. High cytoplasmic expression of ZFAND4 was observed in 45 out of 214 (21%) patients with OSCC. Expression levels of ZFAND4 were strongly associated with metastasis to the lymph nodes (p=0.0429) and distant organs (p=0.0068). Cases with high expression of ZFAND4 had a significantly unfavourable prognosis compared with patients with low expression of ZFAND4 (p<0.0001). Furthermore, ZFAND4 overexpression was an independent poor prognostic factor for OSCC as determined by multivariate analysis using the Cox proportional hazards model (p<0.0001). These results suggest that ZFAND4 is a useful marker for predicting metastasis and poor prognosis in patients with OSCC. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A review of multivariate analyses in imaging genetics

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Jingyu eLiu

2014-03-01

Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and

Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

Directory of Open Access Journals (Sweden)

Isabella Palamara

2012-07-01

Full Text Available An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.

Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Full Text Available ... of survival. The estimate of how the disease will go for you is called prognosis. It can ... they cannot be used to predict exactly what will happen to you. Everyone is different. Treatments and ...

Understanding Cancer Prognosis

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Full Text Available ... Cancer Common Cancer Types Bladder Cancer Breast Cancer Colorectal Cancer Kidney (Renal Cell) Cancer Leukemia Liver Cancer ... need for different kinds of information about her colorectal cancer prognosis. Diving Out of the Dark View ...

Understanding Cancer Prognosis

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Full Text Available ... to know more, the doctor who knows the most about your situation is in the best position ... statistics may be used to estimate prognosis. The most commonly used statistics include: Cancer-specific survival This ...

Understanding Cancer Prognosis

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Full Text Available ... Many people want to know their prognosis. They find it easier to cope when they know more ... this information on your own. Or, you may find statistics confusing and frightening, and think they are ...

Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Full Text Available ... to talk about, even for doctors. Many Factors Can Affect Your Prognosis Some of the factors that ... Understanding your cancer and knowing what to expect can help you and your loved ones make decisions. ...

Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Understanding Cancer Prognosis

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Full Text Available ... spread. Certain traits of the cancer cells Your age and how healthy you were before cancer How ... how to discuss prognosis with their patients. Good communication, he says, is part of providing good care. ...

Understanding Cancer Prognosis

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Full Text Available ... manage treatment side effects How to deal with financial and legal matters Many people want to know ... most about your situation is in the best position to discuss your prognosis and explain what the ...

Understanding Cancer Prognosis

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Full Text Available ... 2 years, 5 years, etc., with 5 years being the time period most often used. Cancer-specific ... a prognosis may not be based on treatments being used today. Still, your doctor may tell you ...

A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan

Energy Technology Data Exchange (ETDEWEB)

Lenzen, M. [University of Sydney (Australia). School of Physics; Wier, M. [Royal Veterinary and Agricultural University, Copenhagen (Denmark). Danish Research Institute of Food Economics; Cohen, C. [Universidade Federal Fluminense, Rio de Janeiro (Brazil). Faculdade de Economia; Hayami, Hitoshi [Keio University, Tokyo (Japan). Keio Economic Observatory; Pachauri, S. [Swiss Federal Institutes of Technology, Zurich (Switzerland). Centre for Energy Policy and Economics; Schaeffer, R. [Universidade Federal do Rio de Janeiro (Brazil). COPPE

2006-03-01

In this paper, we appraise sustainable household consumption from a global perspective. Using per capita energy requirements as an indicator of environmental pressure, we focus on the importance of income growth in a cross-country analysis. Our analysis is supported by a detailed within-country analysis encompassing five countries, in which we assess the importance of various socioeconomic-demographic characteristics of household energy requirements. We bring together family expenditure survey data, input-output tables, and energy statistics in a multivariate analysis. Instead of a uniform Kuznet's curve, we find that the effect of increasing income varies considerably across countries, even when controlling for socioeconomic and demographic variations. The latter variables show similar influences, but differing importance across countries. (author)

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

International Nuclear Information System (INIS)

Theodorakou, Chrysoula; Farquharson, Michael J

2009-01-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

Science.gov (United States)

Gürgey, K.; Canbolat, S.

2017-11-01