Risk Factors of Acute Behavioral Regression in Psychiatrically Hospitalized Adolescents with Autism
Périsse, Didier; Amiet, Claire; Consoli, Angèle; Thorel, Marie-Vincente; Gourfinkel-An, Isabelle; Bodeau, Nicolas; Guinchat, Vincent; Barthélémy, Catherine; Cohen, David
2010-01-01
Aim: During adolescence, some individuals with autism engage in severe disruptive behaviors, such as violence, agitation, tantrums, or self-injurious behaviors. We aimed to assess risk factors associated with very acute states and regression in adolescents with autism in an inpatient population. Method: Between 2001 and 2005, we reviewed the charts of all adolescents with autism (N=29, mean age=14.8 years, 79% male) hospitalized for severe disruptive behaviors in a psychiatric intensive care unit. We systematically collected data describing socio-demographic characteristics, clinical variables (severity, presence of language, cognitive level), associated organic conditions, etiologic diagnosis of the episode, and treatments. Results: All patients exhibited severe autistic symptoms and intellectual disability, and two-thirds had no functional verbal language. Fifteen subjects exhibited epilepsy, including three cases in which epilepsy was unknown before the acute episode. For six (21%) of the subjects, uncontrolled seizures were considered the main cause of the disruptive behaviors. Other suspected risk factors associated with disruptive behavior disorders included adjustment disorder (N=7), lack of adequate therapeutic or educational management (N=6), depression (N=2), catatonia (N=2), and painful comorbid organic conditions (N=3). Conclusion: Disruptive behaviors among adolescents with autism may stem from diverse risk factors, including environmental problems, comorbid acute psychiatric conditions, or somatic diseases such as epilepsy. The management of these behavioral changes requires a multidisciplinary functional approach. PMID:20467546
Abnormal behavior of the least squares estimate of multiple regression
陈希孺; 安鸿志
1997-01-01
An example is given to reveal the abnormal behavior of the least squares estimate of multiple regression. It is shown that the least squares estimate of the multiple linear regression may be "improved in the sense of weak consistency when nuisance parameters are introduced into the model. A discussion on the implications of this finding is given.
A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior
Bin Lu
2015-01-01
Full Text Available This paper presents a Support Vector Regression (SVR approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.
[Regression of acute Chagas cardiopathy in an infant with a suspected transfusion infection].
Gónzalez-Zambrano, H; Amador Mena, J E; Delgadillo Jaime, C B
1999-01-01
Chagas disease was described in Mexico by Mazzotti in 1940. Post-transfusional cases have not been described. We report proved case of acute chagasic cardiopathy in a nine months old infant with suspected transfusional infection during neonatal period. She was treated with nifurtimox with disappearance of parasites and regression of cardiopathy. She is asymptomatic nine years afterwards with normal growth and negative parasitology and serology.
2009-01-01
In this paper, we study the local asymptotic behavior of the regression spline estimator in the framework of marginal semiparametric model. Similarly to Zhu, Fung and He (2008), we give explicit expression for the asymptotic bias of regression spline estimator for nonparametric function f. Our results also show that the asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, which distinguishes the regression splines from the smoothing splines and the seemingly unrelated kernel. To understand the local bias result of the regression spline estimator, we show that the regression spline estimator can be obtained iteratively by applying the standard weighted least squares regression spline estimator to pseudo-observations. At each iteration, the bias of the estimator is unchanged and only the variance is updated.
Chang-zhi CHENG
2011-06-01
Full Text Available Objective To explore the risk factors of complication of acute renal failure(ARF in war injuries of limbs.Methods The clinical data of 352 patients with limb injuries admitted to 303 Hospital of PLA from 1968 to 2002 were retrospectively analyzed.The patients were divided into ARF group(n=9 and non-ARF group(n=343 according to the occurrence of ARF,and the case-control study was carried out.Ten factors which might lead to death were analyzed by logistic regression to screen the risk factors for ARF,including causes of trauma,shock after injury,time of admission to hospital after injury,injured sites,combined trauma,number of surgical procedures,presence of foreign matters,features of fractures,amputation,and tourniquet time.Results Fifteen of the 352 patients died(4.3%,among them 7 patients(46.7% died of ARF,3(20.0% of pulmonary embolism,3(20.0% of gas gangrene,and 2(13.3% of multiple organ failure.Univariate analysis revealed that the shock,time before admitted to hospital,amputation and tourniquet time were the risk factors for ARF in the wounded with limb injuries,while the logistic regression analysis showed only amputation was the risk factor for ARF(P < 0.05.Conclusion ARF is the primary cause-of-death in the wounded with limb injury.Prompt and accurate treatment and optimal time for amputation may be beneficial to decreasing the incidence and mortality of ARF in the wounded with severe limb injury and ischemic necrosis.
Effect of acute hypoxia on cognition: A systematic review and meta-regression analysis.
McMorris, Terry; Hale, Beverley J; Barwood, Martin; Costello, Joseph; Corbett, Jo
2017-03-01
A systematic meta-regression analysis of the effects of acute hypoxia on the performance of central executive and non-executive tasks, and the effects of the moderating variables, arterial partial pressure of oxygen (PaO2) and hypobaric versus normobaric hypoxia, was undertaken. Studies were included if they were performed on healthy humans; within-subject design was used; data were reported giving the PaO2 or that allowed the PaO2 to be estimated (e.g. arterial oxygen saturation and/or altitude); and the duration of being in a hypoxic state prior to cognitive testing was ≤6days. Twenty-two experiments met the criteria for inclusion and demonstrated a moderate, negative mean effect size (g=-0.49, 95% CI -0.64 to -0.34, p<0.001). There were no significant differences between central executive and non-executive, perception/attention and short-term memory, tasks. Low (35-60mmHg) PaO2 was the key predictor of cognitive performance (R(2)=0.45, p<0.001) and this was independent of whether the exposure was in hypobaric hypoxic or normobaric hypoxic conditions.
Regression rate behaviors of HTPB-based propellant combinations for hybrid rocket motor
Sun, Xingliang; Tian, Hui; Li, Yuelong; Yu, Nanjia; Cai, Guobiao
2016-02-01
The purpose of this paper is to characterize the regression rate behavior of hybrid rocket motor propellant combinations, using hydrogen peroxide (HP), gaseous oxygen (GOX), nitrous oxide (N2O) as the oxidizer and hydroxyl-terminated poly-butadiene (HTPB) as the based fuel. In order to complete this research by experiment and simulation, a hybrid rocket motor test system and a numerical simulation model are established. Series of hybrid rocket motor firing tests are conducted burning different propellant combinations, and several of those are used as references for numerical simulations. The numerical simulation model is developed by combining the Navies-Stokes equations with the turbulence model, one-step global reaction model, and solid-gas coupling model. The distribution of regression rate along the axis is determined by applying simulation mode to predict the combustion process and heat transfer inside the hybrid rocket motor. The time-space averaged regression rate has a good agreement between the numerical value and experimental data. The results indicate that the N2O/HTPB and GOX/HTPB propellant combinations have a higher regression rate, since the enhancement effect of latter is significant due to its higher flame temperature. Furthermore, the containing of aluminum (Al) and/or ammonium perchlorate(AP) in the grain does enhance the regression rate, mainly due to the more energy released inside the chamber and heat feedback to the grain surface by the aluminum combustion.
The limiting behavior of the estimated parameters in a misspecified random field regression model
Dahl, Christian Møller; Qin, Yu
, as a consequence the random field model specification introduces non-stationarity and non-ergodicity in the misspecified model and it becomes non-trivial, relative to the existing literature, to establish the limiting behavior of the estimated parameters. The asymptotic results are obtained by applying some...... convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully...
Mogensen, T; Scott, N B; Lund, Claus;
1988-01-01
The purpose of this study was to investigate whether regression of sensory analgesia during constant epidural bupivacaine infusion was different in postoperative patients with acute pain than in patients with chronic nonsurgical pain. Sensory levels of analgesia (to pinprick) and pain (on a five......-point scale) were assessed hourly for 16 hours during continuous epidural infusion of 0.5% plain bupivacaine (8 ml/hr) in 12 patients with chronic nonsurgical pain and in 30 patients after major abdominal surgery performed under combined bupivacaine and halothane--N2O general anesthesia. No opiates were given.......01). Mean duration of sensory blockade was significantly longer (P less than 0.005) in the patients with chronic pain than in surgical patients (13.1 +/- 1.2 and 8.5 +/- 0.7 hours, respectively). Thus, surgical injury hastens regression of sensory analgesia during continuous epidural bupivacaine infusion...
Welten, Carlijn C M; Koeter, Maarten W J; Wohlfarth, Tamar D; Storosum, Jitschak G; van den Brink, Wim; Gispen-de Wied, Christine C; Leufkens, Hubert G M; Denys, Damiaan A J P
2016-02-01
Patients having an acute manic episode of bipolar disorder often lack insight into their condition. Because little is known about the possible effect of insight on treatment efficacy, we examined whether insight at the start of treatment affects the efficacy of antipsychotic treatment in patients with acute mania. We used individual patient data from 7 randomized, double-blind, placebo-controlled registration studies of 4 antipsychotics in patients with acute mania (N = 1904). Insight was measured with item 11 of the Young Mania Rating Scale (YMRS) at baseline and study endpoint 3 weeks later. Treatment outcome was defined by (a) mean change score, (b) response defined as 50% or more improvement on YMRS, and (c) remission defined as YMRS score less than 8 at study endpoint. We used multilevel mixed effect linear (or logistic) regression analyses of individual patient data to assess the interaction between baseline insight and treatment outcomes. At treatment initiation, 1207 (63.5%) patients had impaired or no insight into their condition. Level of insight significantly modified the efficacy of treatment by mean change score (P = 0.039), response rate (P = 0.033), and remission rate (P = 0.043), with greater improvement in patients with more impaired insight. We therefore recommend that patients experiencing acute mania should be treated immediately and not be delayed until patients regain insight.
Arfuso Frank
2008-11-01
Full Text Available Abstract Background Corpus luteum (CL regression is known to occur as two parts; functional regression when steroidogenesis declines and structural regression when apoptosis is induced. Previous studies suggest this process occurs by the production of luteolytic factors, such as tumour necrosis factor-alpha (TNF-alpha. Methods We examined TNF-alpha, TNF-alpha receptors (TNFR1 and 2 and steroidogenic acute regulatory (StAR protein expression during CL regression in albino Wistar rats. CL from Days 16 and 22 of pregnancy and Day 3 post-partum were examined, in addition CL from Day 16 of pregnancy were cultured in vitro to induce apoptosis. mRNA was quantitated by kinetic RT-PCR and protein expression examined by immunohistochemistry and Western blot analyses. Results TNF-alpha mRNA increased on Day 3 post-partum. TNFR were immunolocalized to luteal cells, and an increase in TNFR2 mRNA observed on Day 3 post-partum whilst no change was detected in TNFR1 mRNA relative to Day 16. StAR protein decreased on Day 3 post-partum and following trophic withdrawal but no change was observed following exogenous TNF-alpha treatment. StAR mRNA decreased on Day 3 post-partum; however, it increased following trophic withdrawal and TNF-alpha treatment in vitro. Conclusion These results demonstrate the existence of TNFR1 and TNFR2 in rat CL and suggest the involvement of TNF-alpha in rat CL regression following parturition. Furthermore, decreased StAR expression over the same time points was consistent with the functional regression of the CL.
Maternal heavy alcohol use and toddler behavior problems: a fixed effects regression analysis.
Knudsen, Ann Kristin; Ystrom, Eivind; Skogen, Jens Christoffer; Torgersen, Leila
2015-10-01
Using data from the longitudinal Norwegian Mother and Child Cohort Study, the aims of the current study were to examine associations between postnatal maternal heavy alcohol use and toddler behavior problems, taking both observed and unobserved confounding factors into account by employing fixed effects regression models. Postnatal maternal heavy alcohol use (defined as drinking alcohol 4 or more times a week, or drinking 7 units or more per alcohol use episode) and toddler internalizing and externalizing behavior problems were assessed when the toddlers were aged 18 and 36 months. Maternal psychopathology, civil status and negative life events last year were included as time-variant covariates. Maternal heavy alcohol use was associated with toddler internalizing and externalizing behavior problems (p < 0.001) in the population when examined with generalized estimating equation models. The associations disappeared when observed and unobserved sources of confounding were taken into account in the fixed effects models [(p = 0.909 for externalizing behaviors (b = 0.002, SE = 0.021), p = 0.928 for internalizing behaviors (b = 0.002, SE = 0.023)], with an even further reduction of the estimates with the inclusion of time-variant confounders. No causal effect was found between postnatal maternal heavy alcohol use and toddler behavior problems. Increased levels of behavior problems among toddlers of heavy drinking mothers should therefore be attributed to other adverse characteristics associated with these mothers, toddlers and families. This should be taken into account when interventions aimed at at-risk families identified by maternal heavy alcohol use are planned and conducted.
Yoshizawa Masato
2012-12-01
Full Text Available Abstract Background How and why animals lose eyesight during adaptation to the dark and food-limited cave environment has puzzled biologists since the time of Darwin. More recently, several different adaptive hypotheses have been proposed to explain eye degeneration based on studies in the teleost Astyanax mexicanus, which consists of blind cave-dwelling (cavefish and sighted surface-dwelling (surface fish forms. One of these hypotheses is that eye regression is the result of indirect selection for constructive characters that are negatively linked to eye development through the pleiotropic effects of Sonic Hedgehog (SHH signaling. However, subsequent genetic analyses suggested that other mechanisms also contribute to eye regression in Astyanax cavefish. Here, we introduce a new approach to this problem by investigating the phenotypic and genetic relationships between a suite of non-visual constructive traits and eye regression. Results Using quantitative genetic analysis of crosses between surface fish, the Pachón cavefish population and their hybrid progeny, we show that the adaptive vibration attraction behavior (VAB and its sensory receptors, superficial neuromasts (SN specifically found within the cavefish eye orbit (EO, are genetically correlated with reduced eye size. The quantitative trait loci (QTL for these three traits form two clusters of congruent or overlapping QTL on Astyanax linkage groups (LG 2 and 17, but not at the shh locus on LG 13. Ablation of EO SN in cavefish demonstrated a major role for these sensory receptors in VAB expression. Furthermore, experimental induction of eye regression in surface fish via shh overexpression showed that the absence of eyes was insufficient to promote the appearance of VAB or EO SN. Conclusions We conclude that natural selection for the enhancement of VAB and EO SN indirectly promotes eye regression in the Pachón cavefish population through an antagonistic relationship involving genetic
Yu, Rongqin; Geddes, John R; Fazel, Seena
2012-10-01
The risk of antisocial outcomes in individuals with personality disorder (PD) remains uncertain. The authors synthesize the current evidence on the risks of antisocial behavior, violence, and repeat offending in PD, and they explore sources of heterogeneity in risk estimates through a systematic review and meta-regression analysis of observational studies comparing antisocial outcomes in personality disordered individuals with controls groups. Fourteen studies examined risk of antisocial and violent behavior in 10,007 individuals with PD, compared with over 12 million general population controls. There was a substantially increased risk of violent outcomes in studies with all PDs (random-effects pooled odds ratio [OR] = 3.0, 95% CI = 2.6 to 3.5). Meta-regression revealed that antisocial PD and gender were associated with higher risks (p = .01 and .07, respectively). The odds of all antisocial outcomes were also elevated. Twenty-five studies reported the risk of repeat offending in PD compared with other offenders. The risk of a repeat offense was also increased (fixed-effects pooled OR = 2.4, 95% CI = 2.2 to 2.7) in offenders with PD. The authors conclude that although PD is associated with antisocial outcomes and repeat offending, the risk appears to differ by PD category, gender, and whether individuals are offenders or not.
A comparative study of regression of jaundice in patients of malaria and acute viral hepatitis
D.K. Kochar, K. Kaswan, S.K. Kochar, P. Sirohi, M. Pala, A. Kochar , R.P. Agrawal , A. Das
2006-09-01
Full Text Available Background & objectives: Jaundice is one of the common manifestations of severe malaria in adults.The purpose of this study is to compare the pattern of clinical and biochemical parameters such asserum bilirubin and liver enzyme levels in patients of malaria with jaundice and acute viral hepatitis.Methodology: The present study was conducted on 34 patients of malaria with jaundice and 15patients of acute viral hepatitis. Estimation of serum bilirubin, aspartate amino transferase (AST,alanine amino transferase (ALT and alkaline phosphatase was done daily using standard proceduresin malaria patients and weekly in acute viral hepatitis patients.Results: Mean level of serum bilirubin on first day in malaria and acute viral hepatitis patients was7.07 ± 3.94 and 10.38 ± 7.87 mg%, whereas on Day 8 it was 1.19 ± 1.43 and 7.88 ± 7.02 mg%respectively. Mean level of AST on Day 1 in malaria and acute viral hepatitis patients was 158.47 ±120.35 and 1418.6 ± 834.11 IU/L, whereas on Day 8 it was 41 ± 28.33 and 775.3 ± 399.01IU/L respectively. Mean level of ALT on Day 1 in malaria and acute viral hepatitis patients was220.14 ± 145.61 and 1666.67 ± 1112.77 IU/L, whereas on Day 8 it was 50.85 ± 37.31 and 823.8 ±475.06 IU/L respectively. Mean level of serum alkaline phosphatase on Day 1 in malaria and acuteviral hepatitis patients was 394.74 ± 267.78 and 513.4 ± 324.7 IU/L, whereas on Day 8 it was84.76 ± 68.50 and 369.27 ± 207.75 IU/L respectively.Interpretation & conclusion: We observed that resolution of jaundice in malaria took 1–2 weeks incontrast 6 to 8 weeks in viral hepatitis. This difference in duration was statistically significant. Thus,jaundice not resolving in 1–2 weeks time in a patient of malaria requires serious consideration forpresence of other concomitant diseases including viral hepatitis.
Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H
2017-02-01
At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification.
Sullivan, Paul
2017-01-01
Objectives Previous studies found that hospital and specialty have limited influence on patient experience scores, and patient level factors are more important. This could be due to heterogeneity of experience delivery across subunits within organisations. We aimed to determine whether organisation level factors have greater impact if scores for the same subspecialty microsystem are analysed in each hospital. Setting Acute medical admission units in all NHS Acute Trusts in England. Participants We analysed patient experience data from the English Adult Inpatient Survey which is administered to 850 patients annually in each acute NHS Trusts in England. We selected all 8753 patients who returned the survey and who were emergency medical admissions and stayed in their admission unit for 1–2 nights, so as to isolate the experience delivered during the acute admission process. Primary and secondary outcome measures We used multilevel logistic regression to determine the apportioned influence of host organisation and of organisation level factors (size and teaching status), and patient level factors (demographics, presence of long-term conditions and disabilities). We selected ‘being treated with respect and dignity’ and ‘pain control’ as primary outcome parameters. Other Picker Domain question scores were analysed as secondary parameters. Results The proportion of overall variance attributable at organisational level was small; 0.5% (NS) for respect and dignity, 0.4% (NS) for pain control. Long-standing conditions and consequent disabilities were associated with low scores. Other item scores also showed that most influence was from patient level factors. Conclusions When a single microsystem, the acute medical admission process, is isolated, variance in experience scores is mainly explainable by patient level factors with limited organisational level influence. This has implications for the use of generic patient experience surveys for comparison between
Dennis, C L [Materials Science and Engineering Laboratory, NIST, Gaithersburg, MD 20899-8552 (United States); Jackson, A J; Borchers, J A [NIST Center for Neutron Research, Gaithersburg, MD 20899-8562 (United States); Hoopes, P J; Strawbridge, R [Dartmouth College, Hanover, NH 03755 (United States); Foreman, A R; Ivkov, R [Triton BioSystems, Incorporated, Chelmsford, MA 01824 (United States); Van Lierop, J [Department of Physics and Astronomy, University of Manitoba, Winnipeg, MB, R3T 2N2 (Canada); Gruettner, C, E-mail: cindi.dennis@nist.go, E-mail: rivkov@jhmi.ed [Mic romod Partikeltechnologie GmbH, 18119 Rostock-Warnemuende (Germany)
2009-09-30
One potential cancer treatment selectively deposits heat to the tumor through activation of magnetic nanoparticles inside the tumor. This can damage or kill the cancer cells without harming the surrounding healthy tissue. The properties assumed to be most important for this heat generation (saturation magnetization, amplitude and frequency of external magnetic field) originate from theoretical models that assume non-interacting nanoparticles. Although these factors certainly contribute, the fundamental assumption of 'no interaction' is flawed and consequently fails to anticipate their interactions with biological systems and the resulting heat deposition. Experimental evidence demonstrates that for interacting magnetite nanoparticles, determined by their spacing and anisotropy, the resulting collective behavior in the kilohertz frequency regime generates significant heat, leading to nearly complete regression of aggressive mammary tumors in mice.
Byers, John A
2013-08-01
Dose-response curves of the effects of semiochemicals on neurophysiology and behavior are reported in many articles in insect chemical ecology. Most curves are shown in figures representing points connected by straight lines, in which the x-axis has order of magnitude increases in dosage vs. responses on the y-axis. The lack of regression curves indicates that the nature of the dose-response relationship is not well understood. Thus, a computer model was developed to simulate a flux of various numbers of pheromone molecules (10(3) to 5 × 10(6)) passing by 10(4) receptors distributed among 10(6) positions along an insect antenna. Each receptor was depolarized by at least one strike by a molecule, and subsequent strikes had no additional effect. The simulations showed that with an increase in pheromone release rate, the antennal response would increase in a convex fashion and not in a logarithmic relation as suggested previously. Non-linear regression showed that a family of kinetic formation functions fit the simulated data nearly perfectly (R(2) >0.999). This is reasonable because olfactory receptors have proteins that bind to the pheromone molecule and are expected to exhibit enzyme kinetics. Over 90 dose-response relationships reported in the literature of electroantennographic and behavioral bioassays in the laboratory and field were analyzed by the logarithmic and kinetic formation functions. This analysis showed that in 95% of the cases, the kinetic functions explained the relationships better than the logarithmic (mean of about 20% better). The kinetic curves become sigmoid when graphed on a log scale on the x-axis. Dose-catch relationships in the field are similar to dose-EAR (effective attraction radius, in which a spherical radius indicates the trapping effect of a lure) and the circular EARc in two dimensions used in mass trapping models. The use of kinetic formation functions for dose-response curves of attractants, and kinetic decay curves for
Acute Oral Toxicity and Kinetic Behaviors of Inorganic Layered Nanoparticles
Jin Yu
2013-01-01
Full Text Available Layered double hydroxide (LDH nanoparticles, also known as anionic clays, have attracted a great deal of interest for their potential as delivery carriers. Recent studies showed that LDH nanoparticles can efficiently deliver drugs or bioactive molecules into cells, which are highly related to their endocytic pathway. However, the efficient cell permeation capacity of LDH may also raise concern about their toxicity potential. In this study, the acute oral toxicity of LDH nanoparticles was assessed, and their kinetic behaviors, such as plasma concentration-time curve, tissue distribution, and excretion, were also evaluated in mice. No significant effects of oral LDH nanoparticles on behaviors, body weight gain, survival rate, and organosomatic index were observed up to the dose of 2000 mg/kg for 14 days. Serum biochemical parameters did not significantly increase, indicating that LDH nanoparticles did not cause acute liver or kidney injury. Plasma concentration of LDH nanoparticles rapidly decreased within 30 min depending on exposure doses, but they did not accumulate in any specific organ. Their excretion via urine and feces was observed within 24 h. These findings suggest that LDH nanoparticles do not exhibit acute oral toxicity and favorable kinetic behaviors in mice and, therefore, will be promising candidates for biological and pharmaceutical applications.
Fluoxetine and diazepam acutely modulate stress induced-behavior.
Giacomini, Ana Cristina V V; Abreu, Murilo S; Giacomini, Luidia V; Siebel, Anna M; Zimerman, Fernanda F; Rambo, Cassiano L; Mocelin, Ricieri; Bonan, Carla D; Piato, Angelo L; Barcellos, Leonardo J G
2016-01-01
Drug residue contamination in aquatic ecosystems has been studied extensively, but the behavioral effects exerted by the presence of these drugs are not well known. Here, we investigated the effects of acute stress on anxiety, memory, social interaction, and aggressiveness in zebrafish exposed to fluoxetine and diazepam at concentrations that disrupt the hypothalamic-pituitary-interrenal (HPI) axis. Stress increased the locomotor activity and time spent in the bottom area of the tank (novel tank). Fluoxetine and diazepam prevented these behaviors. We also observed that stress and fluoxetine and diazepam exposures decreased social interaction. Stress also increased aggressive behavior, which was not reversed by fluoxetine or diazepam. These data suggest that the presence of these drugs in aquatic ecosystems causes significant behavioral alterations in fish.
Wolff, Hendrik Andreas; Herrmann, Markus Karl Alfred; Hennies, Steffen; Rave-Fraenk, Margret; Hess, Clemens Friedrich; Christiansen, Hans [Dept. of Radiotherapy and Radiooncology, Univ. Medicine Goettingen (Germany); Gaedcke, Jochen; Liersch, Torsten [Dept. of Surgery, Univ. Medicine Goettingen (Germany); Jung, Klaus [Dept. of Medical Statistics, Univ. Medicine Goettingen (Germany); Hermann, Robert Michael [Dept. of Radiotherapy and Radiooncology, Univ. Medicine Goettingen (Germany); Dept. of Radiotherapy and Radiooncology, Aerztehaus am Diako, Bremen (Germany); Rothe, Hilka [Dept. of Pathology, Univ. Medicine Goettingen (Germany); Schirmer, Markus [Dept. of Clinical Pharmacology, Univ. Medicine Goettingen (Germany)
2010-01-15
Purpose: To test for a possible correlation between high-grade acute organ toxicity during preoperative radiochemotherapy and complete tumor regression after total mesorectal excision in multimodal treatment of locally advanced rectal cancer. Patients and Methods: From 2001 to 2008, 120 patients were treated. Preoperative treatment consisted of normofractionated radiotherapy at a total dose of 50.4 Gy, and either two cycles of 5-fluorouracil (5-FU) or two cycles of 5-FU and oxaliplatin. Toxicity during treatment was monitored weekly, and any toxicity CTC (Common Toxicity Criteria) {>=} grade 2 of enteritis, proctitis or cystitis was assessed as high-grade organ toxicity for later analysis. Complete histopathologic tumor regression (TRG4) was defined as the absence of any viable tumor cells. Results: A significant coherency between high-grade acute organ toxicity and complete histopathologic tumor regression was found, which was independent of other factors like the preoperative chemotherapy schedule. The probability of patients with acute organ toxicity {>=} grade 2 to achieve TRG4 after neoadjuvant treatment was more than three times higher than for patients without toxicity (odds ratio: 3.29, 95% confidence interval: [1.01, 10.96]). Conclusion: Acute organ toxicity during preoperative radiochemotherapy in rectal cancer could be an early predictor of treatment response in terms of complete tumor regression. Its possible impact on local control and survival is under further prospective evaluation by the authors' working group. (orig.)
Behavioral Cues in the Judgment of Marital Satisfaction: A Linear Regression Analysis
Royce, W. Stephen; Weiss, Robert L.
1975-01-01
Forty undergraduate judges watched videotaped interactions of couples and rated their marital satisfaction based on certain behavioral cues. Results indicate: untrained judges were able to discriminate marital satisfaction/distress with significant validity; judges' ratings were correlated with couples' aversive behavior; and the actuarial…
2012-01-01
Abstract Background How and why animals lose eyesight during adaptation to the dark and food-limited cave environment has puzzled biologists since the time of Darwin. More recently, several different adaptive hypotheses have been proposed to explain eye degeneration based on studies in the teleost Astyanax mexicanus, which consists of blind cave-dwelling (cavefish) and sighted surface-dwelling (surface fish) forms. One of these hypotheses is that eye regression is the result of indirect selec...
Acute behavioral toxicity of carbaryl and propoxur in adult rats.
Ruppert, P H; Cook, L L; Dean, K F; Reiter, L W
1983-04-01
Motor activity and neuromotor function were examined in adult CD rats exposed to either carbaryl or propoxur, and behavioral effects were compared with the time course of cholinesterase inhibition. Rats received an IP injection of either 0, 2, 4, 6 or 8 mg/kg propoxur or 0, 4, 8, 16 or 28 mg/kg carbaryl in corn oil 20 min before testing. All doses of propoxur reduced 2 hr activity in a figure-eight maze, and crossovers and rears in an open field. For carbaryl, dosages of 8, 16 and 28 mg/kg decreased maze activity whereas 16 and 28 mg/kg reduced open field activity. In order to determine the time course of effects, rats received a single IP injection of either corn oil, 2 mg/kg propoxur or 16 mg/kg carbaryl, and were tested for 5 min in a figure-eight maze either 15, 30, 60, 120 or 240 min post-injection. Immediately after testing, animals were sacrificed and total cholinesterase was measured. Maximum effects of propoxur and carbaryl on blood and brain cholinesterase and motor activity were seen within 15 min. Maze activity had returned to control levels within 30 and 60 min whereas cholinesterase levels remained depressed for 120 and 240 min for propoxur and carbaryl, respectively. These results indicate that both carbamates decrease motor activity, but behavioral recovery occurs prior to that of cholinesterase following acute exposure.
Dose-response curves with semiochemicals are reported in many articles in insect chemical ecology regarding neurophysiology and behavioral bioassays. Most such curves are shown in figures where the x-axis has order of magnitude increases in dosages versus responses on the y-axis represented by point...
The objective of this study was to investigate and model the behavior of Salmonella on different types of chicken meat during frozen and refrigerated storage. Portions (0.69 to 0.83 g) of chicken meat (breast, skin, or thigh) were inoculated with a single strain (ATCC 700408) of Salmonella Typhimur...
Selma KARABAŞ
2012-06-01
Full Text Available The purpose of this study is to determine consumer behavior towards organic products in Samsun city center and to determine the factors affacting their preferences of organic products. The study was interviewed with 478 consumers living the city center of Samsun. According of the findings, ease of accesibility of organic products, spouse’s educational level, paying extra for the organic produce, aware of food health benefits, considering the harmful effects of conventional produce, having complete knowledge of organic farming and one unit increase household number income to result increase consumption of organic produce. Consumers were not aware of the certification and control process. As a result, easy of accesibility of organic produces in the supermarkets need to be improved.
Mahani, Mohamad Khayatzadeh; Chaloosi, Marzieh; Maragheh, Mohamad Ghanadi; Khanchi, Ali Reza; Afzali, Daryoush
2007-09-01
The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.
Effects of Acute Tryptophan Depletion on Three Different Types of Behavioral Impulsivity
2010-01-01
Introduction: While central nervous system serotonin has been implicated in a variety of problematic impulsive behaviors, biological manipulation of brain serotonin using acute tryptophan depletion for studying changes in impulsive behavior has received little attention. Methods: Using identical treatment conditions, we examined the effects of reduced serotonin synthesis for each of three matched groups using acute tryptophan depletion. Thirty healthy men and women (ages 18–45) were assigned ...
McCarthy, Maria C; Bastiani, Jessica; Williams, Lauren K
2016-07-01
Sleep disturbance is a recognized common side effect in children treated for acute lymphoblastic leukemia (ALL). Although associated with treatment factors such as hospitalization and corticosteroids, sleep problems may also be influenced by modifiable environmental factors such as parenting behaviors. The purpose of this study was to examine sleep problems in children undergoing treatment for ALL compared to healthy children and whether parenting practices are associated with sleep difficulties. Parents of 73 children aged 2-6 years who were (1) in the maintenance phase of ALL treatment (ALL group, n = 43) or (2) had no major medical illness (healthy control group, n = 30) participated in the study. Parents completed questionnaires measuring their child's sleep behavior and their own parenting practices. Parents of children undergoing ALL treatment reported significantly more child sleep problems; 48% of children with ALL compared to 23% of healthy children had clinical levels of sleep disturbance. Parents of the ALL group also reported significantly more lax parenting practices and strategies associated with their child's sleep including co-sleeping, comforting activities, and offering food and drink in the bedroom. Results of multivariate regression analysis indicated that, after controlling for illness status, parent-child co-sleeping was significantly associated with child sleep difficulties. Strategies employed by parents during ALL treatment may be a potential modifiable intervention target that could result in improved child sleep behaviors. Future research aimed at developing and testing parenting interventions aimed to improve child sleep in the context of oncology treatment is warranted.
Zhang, Xiaona; Sun, Xiaoxuan; Wang, Junhong; Tang, Liou; Xie, Anmu
2017-01-01
Rapid eye movement sleep behavior disorder (RBD) is thought to be one of the most frequent preceding symptoms of Parkinson's disease (PD). However, the prevalence of RBD in PD stated in the published studies is still inconsistent. We conducted a meta and meta-regression analysis in this paper to estimate the pooled prevalence. We searched the electronic databases of PubMed, ScienceDirect, EMBASE and EBSCO up to June 2016 for related articles. STATA 12.0 statistics software was used to calculate the available data from each research. The prevalence of RBD in PD patients in each study was combined to a pooled prevalence with a 95 % confidence interval (CI). Subgroup analysis and meta-regression analysis were performed to search for the causes of the heterogeneity. A total of 28 studies with 6869 PD cases were deemed eligible and included in our meta-analysis based on the inclusion and exclusion criteria. The pooled prevalence of RBD in PD was 42.3 % (95 % CI 37.4-47.1 %). In subgroup analysis and meta-regression analysis, we found that the important causes of heterogeneity were the diagnosis criteria of RBD and age of PD patients (P = 0.016, P = 0.019, respectively). The results indicate that nearly half of the PD patients are suffering from RBD. Older age and longer duration are risk factors for RBD in PD. We can use the minimal diagnosis criteria for RBD according to the International Classification of Sleep Disorders to diagnose RBD patients in our daily work if polysomnography is not necessary.
Acute behavioral responses to pheromones in C. elegans (adult behaviors: attraction, repulsion).
Jang, Heeun; Bargmann, Cornelia I
2013-01-01
The pheromone drop test is a simple and robust behavioral assay to quantify acute avoidance of pheromones in C. elegans, and the suppression of avoidance by attractive pheromones. In the pheromone drop test, water-soluble C. elegans pheromones are individually applied to animals that are freely moving on a large plate. Upon encountering a repellent, each C. elegans animal may or may not try to escape by making a long reversal. The fraction of animals that make a long reversal response indicates the repulsiveness of a given pheromone to a specific genotype/strain of C. elegans. Performing the drop test in the presence of bacterial food enhances the avoidance response to pheromones. Attraction to pheromones can be assayed by the suppression of reversals to repulsive pheromones or by the suppression of the basal reversal rate to buffer.
Ibinson, James W; Vogt, Keith M; Taylor, Kevin B; Dua, Shiv B; Becker, Christopher J; Loggia, Marco; Wasan, Ajay D
2015-12-01
The insula is uniquely located between the temporal and parietal cortices, making it anatomically well-positioned to act as an integrating center between the sensory and affective domains for the processing of painful stimulation. This can be studied through resting-state functional connectivity (fcMRI) imaging; however, the lack of a clear methodology for the analysis of fcMRI complicates the interpretation of these data during acute pain. Detected connectivity changes may reflect actual alterations in low-frequency synchronous neuronal activity related to pain, may be due to changes in global cerebral blood flow or the superimposed task-induced neuronal activity. The primary goal of this study was to investigate the effects of global signal regression (GSR) and task paradigm regression (TPR) on the changes in functional connectivity of the left (contralateral) insula in healthy subjects at rest and during acute painful electric nerve stimulation of the right hand. The use of GSR reduced the size and statistical significance of connectivity clusters and created negative correlation coefficients for some connectivity clusters. TPR with cyclic stimulation gave task versus rest connectivity differences similar to those with a constant task, suggesting that analysis which includes TPR is more accurately reflective of low-frequency neuronal activity. Both GSR and TPR have been inconsistently applied to fcMRI analysis. Based on these results, investigators need to consider the impact GSR and TPR have on connectivity during task performance when attempting to synthesize the literature.
Alavi, Seyyed Salman; Mohammadi, Mohammad Reza; Souri, Hamid; Mohammadi Kalhori, Soroush; Jannatifard, Fereshteh; Sepahbodi, Ghazal
2017-01-01
Background: The aim of this study was to evaluate the effect of variables such as personality traits, driving behavior and mental illness on road traffic accidents among the drivers with accidents and those without road crash. Methods: In this cohort study, 800 bus and truck drivers were recruited. Participants were selected among drivers who referred to Imam Sajjad Hospital (Tehran, Iran) during 2013-2015. The Manchester driving behavior questionnaire (MDBQ), big five personality test (NEO personality inventory) and semi-structured interview (schizophrenia and affective disorders scale) were used. After two years, we surveyed all accidents due to human factors that involved the recruited drivers. The data were analyzed using the SPSS software by performing the descriptive statistics, t-test, and multiple logistic regression analysis methods. P values less than 0.05 were considered statistically significant. Results: In terms of controlling the effective and demographic variables, the findings revealed significant differences between the two groups of drivers that were and were not involved in road accidents. In addition, it was found that depression and anxiety could increase the odds ratio (OR) of road accidents by 2.4- and 2.7-folds, respectively (P=0.04, P=0.004). It is noteworthy to mention that neuroticism alone can increase the odds of road accidents by 1.1-fold (P=0.009), but other personality factors did not have a significant effect on the equation. Conclusion: The results revealed that some mental disorders affect the incidence of road collisions. Considering the importance and sensitivity of driving behavior, it is necessary to evaluate multiple psychological factors influencing drivers before and after receiving or renewing their driver’s license. PMID:28293047
Seyyed Salman Alavi
2017-01-01
Full Text Available Background: The aim of this study was to evaluate the effect of variables such as personality traits, driving behavior and mental illness on road traffic accidents among the drivers with accidents and those without road crash. Methods: In this cohort study, 800 bus and truck drivers were recruited. Participants were selected among drivers who referred to Imam Sajjad Hospital (Tehran, Iran during 2013-2015. The Manchester driving behavior questionnaire (MDBQ, big five personality test (NEO personality inventory and semi-structured interview (SADS were used. After two years, we surveyed all accidents due to human factors that involved the recruited drivers. The data were analyzed using the SPSS software by performing the descriptive statistics, t-test, and multiple logistic regression analysis methods. P values less than 0.05 were considered statistically significant. Results: In terms of controlling the effective and demographic variables, the findings revealed significant differences between the two groups of drivers that were and were not involved in road accidents. In addition, it was found that depression and anxiety could increase the odds ratio (OR of road accidents by 2.4- and 2.7-folds, respectively (P=0.04, P=0.004. It is noteworthy to mention that neuroticism alone can increase the odds of road accidents by 1.1-fold (P=0.009, but other personality factors did not have a significant effect on the equation. Conclusion: The results revealed that some mental disorders affect the incidence of road collisions. Considering the importance and sensitivity of driving behavior, it is necessary to evaluate multiple psychological factors influencing drivers before and after receiving or renewing their driver’s license.
Hughes, James P.; Haley, Danielle F.; Frew, Paula M.; Golin, Carol E.; Adimora, Adaora A; Kuo, Irene; Justman, Jessica; Soto-Torres, Lydia; Wang, Jing; Hodder, Sally
2015-01-01
Purpose Reductions in risk behaviors are common following enrollment in HIV prevention studies. We develop methods to quantify the proportion of change in risk behaviors that can be attributed to regression to the mean versus study participation and other factors. Methods A novel model that incorporates both regression to the mean and study participation effects is developed for binary measures. The model is used to estimate the proportion of change in the prevalence of “unprotected sex in the past 6 months” that can be attributed to study participation versus regression to the mean in a longitudinal cohort of women at risk for HIV infection who were recruited from ten US communities with high rates of HIV and poverty. HIV risk behaviors were evaluated using audio computer-assisted self-interviews at baseline and every 6 months for up to 12 months. Results The prevalence of “unprotected sex in the past 6 months” declined from 96% at baseline to 77% at 12 months. However, this change could be almost completely explained by regression to the mean. Conclusions Analyses that examine changes over time in cohorts selected for high or low risk behaviors should account for regression to the mean effects. PMID:25883065
Understanding Nurses’ Information Needs and Searching Behavior in Acute Care Settings
2005-01-01
We report the results of a pilot study designed to describe nurses’ information needs and searching behavior in acute care settings. Several studies have indicated that nurses have unmet information needs while delivering care to patients. AIM: Identify the information needs of nurses in acute care settings. METHODS: Nurses at three hospitals were asked to use an information retrieval tool (CPG Viewer). A detailed log of their interactions with the tool was generated. RESULT...
Dinardi, Graciela; Canevari, Cecilia; Torabi, Nahal
2016-01-01
Chagas disease (CD) is a tropical parasitic disease largely underdiagnosed and mostly asymptomatic affecting marginalized rural populations. Argentina regularly reports acute cases of CD, mostly young individuals under 14 years old. There is a void of knowledge of health care seeking behavior in subjects experiencing a CD acute condition. Early treatment of the acute case is crucial to limit subsequent development of disease. The article explores how the health outcome of persons with acute CD may be conditioned by their health care seeking behavior. The study, with a qualitative approach, was carried out in rural areas of Santiago del Estero Province, a high risk endemic region for vector transmission of CD. Narratives of 25 in-depth interviews carried out in 2005 and 2006 are analyzed identifying patterns of health care seeking behavior followed by acute cases. Through the retrospective recall of paths for diagnoses, weaknesses of disease information, knowledge at the household level, and underperformance at the provincial health care system level are detected. The misdiagnoses were a major factor in delaying a health care response. The study results expose lost opportunities for the health care system to effectively record CD acute cases. PMID:27829843
Jaime Lynn Speiser
Full Text Available Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF patients often presents significant challenges. King's College (KCC has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART models.CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission and post-admission (days 3-7 for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC, sensitivity (SN, specificity (SP, and area under receiver-operating curve (AUROC were compared between 3 models: KCC (INR, creatinine, coma grade, pH, CART analysis using only KCC variables (KCC-CART and a CART model using new variables (NEW-CART.Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease, lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.• Prognostication in acetaminophen-induced acute liver failure (APAP-ALF is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART methodology allows the
Martinez, Sarah; Davalos, Deana
2016-01-01
Objective: Executive dysfunction in college students who have had an acute traumatic brain injury (TBI) was investigated. The cognitive, behavioral, and metacognitive effects on college students who endorsed experiencing a brain injury were specifically explored. Participants: Participants were 121 college students who endorsed a mild TBI, and 121…
The Additive Benefit of Hypnosis and Cognitive-Behavioral Therapy in Treating Acute Stress Disorder
Bryant, Richard A.; Moulds, Michelle L.; Guthrie, Rachel M.; Nixon, Reginald D. V.
2005-01-01
This research represents the first controlled treatment study of hypnosis and cognitive- behavioral therapy (CBT) of acute stress disorder (ASD). Civilian trauma survivors (N = 87) who met criteria for ASD were randomly allocated to 6 sessions of CBT, CBT combined with hypnosis (CBT-hypnosis), or supportive counseling (SC). CBT comprised exposure,…
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
OSO paradigm--A rapid behavioral screening method for acute psychosocial stress reactivity in mice.
Brzózka, M M; Unterbarnscheidt, T; Schwab, M H; Rossner, M J
2016-02-01
Chronic psychosocial stress is an important environmental risk factor for the development of psychiatric diseases. However, studying the impact of chronic psychosocial stress in mice is time consuming and thus not optimally suited to 'screen' increasing numbers of genetically manipulated mouse models for psychiatric endophenotypes. Moreover, many studies focus on restraint stress, a strong physical stressor with limited relevance for psychiatric disorders. Here, we describe a simple and a rapid method based on the resident-intruder paradigm to examine acute effects of mild psychosocial stress in mice. The OSO paradigm (open field--social defeat--open field) compares behavioral consequences on locomotor activity, anxiety and curiosity before and after exposure to acute social defeat stress. We first evaluated OSO in male C57Bl/6 wildtype mice where a single episode of social defeat reduced locomotor activity, increased anxiety and diminished exploratory behavior. Subsequently, we applied the OSO paradigm to mouse models of two schizophrenia (SZ) risk genes. Transgenic mice with neuronal overexpression of Neuregulin-1 (Nrg1) type III showed increased risk-taking behavior after acute stress exposure suggesting that NRG1 dysfunction is associated with altered affective behavior. In contrast, Tcf4 transgenic mice displayed a normal stress response which is in line with the postulated predominant contribution of TCF4 to cognitive deficits of SZ. In conclusion, the OSO paradigm allows for rapid screening of selected psychosocial stress-induced behavioral endophenotypes in mouse models of psychiatric diseases.
Cannabinoids & Stress: impact of HU-210 on behavioral tests of anxiety in acutely stressed mice.
Kinden, Renee; Zhang, Xia
2015-05-01
Anxiety disorders are one of the most prevalent classes of mental disorders affecting the general population, but current treatment strategies are restricted by their limited efficacy and side effect profiles. Although the cannabinoid system is speculated to be a key player in the modulation of stress responses and emotionality, the vast majority of current research initiatives had not incorporated stress exposure into their experimental designs. This study was the first to investigate the impact of exogenous cannabinoid administration in an acutely stressed mouse model, where CD1 mice were pre-treated with HU-210, a potent CB1R agonist, prior to acute stress exposure and subsequent behavioral testing. Exogenous cannabinoid administration induced distinct behavioral phenotypes in stressed and unstressed mice. While low doses of HU-210 were anxiolytic in unstressed subjects, this effect was abolished when mice were exposed to an acute stressor. The administration of higher HU-210 doses in combination with acute stress exposure led to severe locomotor deficits that were not previously observed at the same dose in unstressed subjects. These findings suggest that exogenous cannabinoids and acute stress act synergistically in an anxiogenic manner. This study underlies the importance of including stress exposure into future anxiety-cannabinoid research due to the differential impact of cannabinoid administration on stressed and unstressed subjects.
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
HE Wei-ping; HU Jin-hua; ZHAO Jun; TONG Jing-jing; DING Jin-biao; LIN Fang; WANG Hui-fen
2012-01-01
Background Acute-on-chronic hepatitis B liver failure (ACLF-HBV) is a clinically severe disease associated with major life-threatening complications including hepatic encephalopathy and hepatorenal syndrome.The aim of this study was to evaluate the short-term prognostic predictability of the model for end-stage liver disease (MELD),MELD-based indices,and their dynamic changes in patients with ACLF-HBV,and to establish a new model for predicting the prognosis of ACLF-HBV.Methods A total of 172 patients with ACLF-HBV who stayed in the hospital for more than 2 weeks were retrospectively recruited.The predictive accuracy of MELD,MELD-based indices,and their dynamic change (△) were compared using the area under the receiver operating characteristic curve method.The associations between mortality and patient characteristics were studied by univariate and multivariate analyses.Results The 3-month mortality was 43.6％.The largest concordance (c) statistic predicting 3-month mortality was the MELD score at the end of 2 weeks of admission (0.8),followed by the MELD:sodium ratio (MESO) (0.796) and integrated MELD (iMELD) (0.758) scores,△MELD (0.752),△MESO (0.729),and MELD plus sodium (MELD-Na) (0.728) scores.In multivariate Logistic regression analysis,the independent factors predicting prognosis were hepatic encephalopathy (OR=-3.466),serum creatinine,international normalized ratio (INR),and total bilirubin at the end of 2 weeks of admission (OR=10.302,6.063,5.208,respectively),and cholinesterase on admission (OR=0.255).This regression model had a greater prognostic value (c=0.85,95％ Cl 0.791-0.909) compared to the MELD score at the end of 2 weeks of admission (Z=4.9851,P=-0.0256).Conclusions MELD score at the end of 2 weeks of admission is a useful predictor for 3-month mortality in ACLF-HBV patients.Hepatic encephalopathy,serum creatinine,international normalized ratio,and total bilirubin at the end of 2 weeks of admission and cholinesterase on admission are
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Haverroth, Gabriela M B; Welang, Chariane; Mocelin, Riciéri N; Postay, Daniela; Bertoncello, Kanandra T; Franscescon, Francini; Rosemberg, Denis B; Dal Magro, Jacir; Dalla Corte, Cristiane L
2015-12-01
Copper is a heavy metal found at relatively high concentrations in surface waters around the world. Copper is a micronutrient at low concentrations and is essential to several organisms. At higher concentrations copper can become toxic, which reveal the importance of studying the toxic effects of this metal on the aquatic life. Thus, the objective of this study was to evaluate the toxic effects of copper on the behavior and biochemical parameters of zebrafish (Danio rerio). Zebrafish were exposed for 24h at a concentration of 0.006 mg/L Cu. After the exposure period, behavioral profile of animals was recorded through 6 min using two different apparatuses tests: the Novel Tank and the Light-Dark test. After behavioral testing, animals were euthanized with a solution of 250 mg/L of tricaine (MS-222). Brain, muscle, liver and gills were extracted for analysis of parameters related to oxidative stress and accumulation of copper in these tissues. Acetylcholinesterase (AChE) activity was determined in brain and muscle. Results showed acute exposure to copper induces significant changes in behavioral profile of zebrafish by changing locomotion and natural tendency to avoid brightly lit area. On the other hand, there were no significant effects on parameters related to oxidative stress. AChE activity decreased significantly in zebrafish muscle, but there were no significant changes in cerebral AChE activity. Copper levels in tissues did not increase significantly compared to the controls. Taken together, these results indicate that a low concentration of copper can acutely affect behavioral profile of adult zebrafish which could be partially related to an inhibition on muscle AChE activity. These results reinforce the need of additional tests to establishment of safe copper concentrations to aquatic organisms and the importance of behavioral parameters in ecotoxicological studies.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Horner, Stacy B; Fireman, Gary D; Wang, Eugene W
2010-04-01
Peer nominations and demographic information were collected from a diverse sample of 1493 elementary school participants to examine behavior (overt and relational aggression, impulsivity, and prosociality), context (peer status), and demographic characteristics (race and gender) as predictors of teacher and administrator decisions about discipline. Exploratory results using classification tree analyses indicated students nominated as average or highly overtly aggressive were more likely to be disciplined than others. Among these students, race was the most significant predictor, with African American students more likely to be disciplined than Caucasians, Hispanics, or Others. Among the students nominated as low in overt aggression, a lack of prosocial behavior was the most significant predictor. Confirmatory analysis using hierarchical logistic regression supported the exploratory results. Similarities with other biased referral patterns, proactive classroom management strategies, and culturally sensitive recommendations are discussed.
Petersen, M B; Tolver, A; Husted, L; Tølbøll, T H; Pihl, T H
2016-07-01
The objective of this study was to investigate the prognostic value of single and repeated measurements of blood l-lactate (Lac) and ionised calcium (iCa) concentrations, packed cell volume (PCV) and plasma total protein (TP) concentration in horses with acute colitis. A total of 66 adult horses admitted with acute colitis (2 mmol/L (sensitivity, 0.72; specificity, 0.8). In conclusion, blood lactate concentration measured at admission and repeated 6 h later aided the prognostic evaluation of horses with acute colitis in this population with a very high mortality rate. This should allow clinicians to give a more reliable prognosis for the horse.
Pankaj Gupta
2014-01-01
Full Text Available Objectives: The objective of the present study was to assess locomotor behavior of adult zebrafish after acute exposure to different pharmacological reference compounds. Materials and Methods: Adult zebrafish of 4-5-months-old were exposed to different concentrations of known reference compounds for 15 min. The test was conducted separately for each drug concentration as well as control. Locomotor activity parameters viz. distance travelled, speed, total mobile time, and total immobile time were recorded for each animal during the exposure period. Results: Out of 11 compounds tested, nine compounds showed decrease in locomotor behavior with significant changes in distance travelled, speed, total mobile time, and total immobile time. Caffeine exhibited biphasic response in locomotion behavior, while scopolamine failed to induce any significant changes. Conclusion: In view of the above findings, these results suggested that exposure of adult zebrafish with different known compounds produce the expected changes in the locomotion behavior; therefore, adult zebrafish can be used an alternative approach for the assessment of new chemical entities for their effect on locomotor behavior.
Campana, Marion; Bonin-Guillaume, Sylvie; Yagoubi, Ramzi; Berbis, Julie; Franqui, Caroline
2016-06-01
Alzheimer diseases and related disorders (ADRD) remain a major public health issue. The progression of the disease is dominated by behavioral and psychological symptoms of dementia (BPSD) which are frequent and burdensome for caregivers. The aim of our survey was to study how the general practionner managed these behavioral disturbances (particularly agitation and aggressiveness) in community living patients with ADRD and support of their main caregivers. We based our study on a medical survey sent to all general practitioners (GP) practicing in four districts in Marseille near from a secure unit. Ninety five out of 260 answered to the survey and 57 had already been exposed to patients' behavioral decompensation. For these BPSD management, atypical neuroleptics and benzodiazepines were mostly prescribed, and according to the literature and guidelines. Half of the GP's recognized the weak effectiveness of this strategy. Almost all of them are interested in having a document summarizing the main strategy to be set up or a possibility to call a specialized mobile team with doctors and professionals caregivers. A few dedicated consultations were devoted to informal caregivers whereas GP were aware of negative effects of these decompensations on them. This study point out difficulties for GP to provide appropriate management for their patients with ADRD living at home and for their informal caregivers, particularly during acute behavioral disturbance, despite their practical knowledges.
Farmer, William H.; Over, Thomas M.; Vogel, Richard M.
2015-01-01
Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities.
Niedobitek Gerald
2010-03-01
Full Text Available Abstract Epstein-Barr virus (EBV-associated B-cell post-transplantation lymphoproliferative disorder (PTLD is a severe complication following stem cell transplantation. This is believed to occur as a result of iatrogenic immunosuppression leading to a relaxation of T-cell control of EBV infection and thus allowing viral reactivation and proliferation of EBV-infected B-lymphocytes. In support of this notion, reduction of immunosuppressive therapy may lead to regression of PTLD. We present a case of an 18-year-old male developing a monomorphic B-cell PTLD 2 months after receiving an allogenic stem cell transplant for acute lymphoblastic leukemia. Reduction of immunosuppressive therapy led to regression of lymphadenopathy. Nevertheless, the patient died 3 months afterwards due to extensive graft-vs.-host-disease and sepsis. As a diagnostic lymph node biopsy was performed only after reduction of immunosuppressive therapy, we are able to study the histopathological changes characterizing PTLD regression. We observed extensive apoptosis of blast cells, accompanied by an abundant infiltrate comprising predominantly CD8-positive, Granzyme B-positive T-cells. This observation supports the idea that regression of PTLD is mediated by cytotoxic T-cells and is in keeping with the observation that T-cell depletion, represents a major risk factor for the development of PTLD.
XU Jian-yu; MIAO Xiang-wen; LIU Ying; CUI Shao-rong
2005-01-01
The behavioral responses of a tilapia (Oreochromis niloticus) school to low (0.13 mg/L), moderate (0.79 mg/L) and high (2.65 mg/L) levels of unionized ammonia (UIA) concentration were monitored using a computer vision system. The swimming activity and geometrical parameters such as location of the gravity center and distribution of the fish school were calculated continuously. These behavioral parameters of tilapia school responded sensitively to moderate and high UIA concentration. Under high UIA concentration the fish activity showed a significant increase (P＜0.05), exhibiting an avoidance reaction to high ammonia condition, and then decreased gradually. Under moderate and high UIA concentration the school's vertical location had significantly large fluctuation (P＜0.05) with the school moving up to the water surface then down to the bottom of the aquarium alternately and tending to crowd together. After several hours' exposure to high UIA level, the school finally stayed at the aquarium bottom. These observations indicate that alterations in fish behavior under acute stress can provide important information useful in predicting the stress.
Trusz, Sarah Geiss; Wagner, Amy W; Russo, Joan; Love, Jeff; Zatzick, Douglas F
2011-01-01
Cognitive Behavioral Therapy (CBT) interventions are efficacious in reducing posttraumatic stress disorder (PTSD) but are challenging to implement in acute care and other non-specialty mental health settings. This investigation identified barriers impacting CBT delivery through a content analysis of interventionist chart notes from an acute care PTSD prevention trial. Only 8.5% of all intervention patients were able to complete CBT. Lack of engagement, clinical and logistical barriers had the greatest impact on CBT entry. Treatment preferences and stigma only prevented entry when more primary barriers resolved. Patients with prior diagnosis of alcohol abuse or dependence were able to enter CBT after six months of sobriety. Based on the first trial, we developed a CBT readiness assessment tool. We implemented and evaluated the tool in a second early intervention trial. Lack of engagement emerged again as the primary impediment to CBT entry. Patients who were willing to enter CBT treatment but demonstrated high rates of past trauma or diagnosis of PTSD were also the least likely to engage in any PTSD treatment one month post-discharge. Findings support the need for additional investigations into engagement and alternative delivery strategies, including those which dismantle traditional office-based, multi-session CBT into stepped, deliverable components.
Freedland, Kenneth E.; Carney, Robert M.; Hayano, Junichiro; Steinmeyer, Brian C.; Reese, Rebecca L.; Roest, Annelieke M.
2012-01-01
Objective: To determine whether obstructive sleep apnea (OSA) interferes with cognitive behavior therapy (CBT) for depression in patients with coronary heart disease. Methods: Patients who were depressed within 28 days after an acute myocardial infarction (MI) were enrolled in the Enhancing Recovery
SONG Li; YAN Hong-bing; YANG Jin-gang; SUN Yi-hong; HU Da-yi
2010-01-01
Background Delay in seeking medical care in patients with acute myocardial infarction (AMI) is receiving increasing attention. This study aimed to examine the association between expected symptoms and experienced symptoms of AMI and its effects on care-seeking behaviors of patients with AMI.Methods Between November 1, 2005 and December 31, 2006, a cross-sectional and multicenter survey was conducted in 19 hospitals in Beijing and included 799 patients with ST-elevation myocardial infarction (STEMI) admitted within 24 hours after onset of symptoms. Data were collected by structured interviews and medical record review.Results The median (25%, 75%) prehospital delay was 140 (75, 300) minutes. Only 264 (33.0%) arrived at the hospital by ambulance. The most common symptoms expected by patients with STEMI were central or left chest pain (71.4%),radiating arm or shoulder pain (68.7%), shortness of breath or dyspnea (65.5%), and loss of consciousness (52.1%). The most common symptoms experienced were central or left chest pain (82.1%), sweats (71.8%), shortness of breath or dyspnea (43.7%), nausea or vomiting (32.3%), and radiating pain (29.4%). A mismatch between symptoms experienced and those expected occurred in 41.8% of patients. Patients who interpreted their symptoms as noncardiac in origin were more likely to arrive at the hospital by self-transport (86.5% vs. 52.9%, P <0.001) and had longer prehospital delays (medians, 180 vs. 120 minutes, P <0.001) compared to those who interpreted their symptoms as cardiac in origin.Conclusions Symptom interpretation influenced the care-seeking behaviors of patients with STEMI in Beijing. A mismatch between expectation and actual symptoms was associated with longer prehospital delay and decreased use of emergency medical service (EMS).
White, Melanie J; Lawford, Bruce R; Morris, C Phillip; Young, Ross McD
2009-05-01
The dopamine D2 receptor (DRD2) C957T polymorphism CC genotype is associated with decreased striatal binding of DRD2 and executive function and working memory impairments in healthy adults. We investigated the relationships between C957T and acute stress with behavioral phenotypes of impulsivity in 72 young adults randomly allocated to either an acute psychosocial stress or relaxation induction condition. Homozygotes for 957C showed increased reward responsiveness after stress induction. They were also quicker when making immediate choices on the delay discounting task when stressed, compared with homozygotes who were not stressed. No effects were found for response inhibition, a dimension of impulsivity not related to extrinsic rewards. These data suggest that C957T is associated with a reward-related impulsivity endophenotype in response to acute psychosocial stress. Future studies should examine whether the greater sensitivity of 957C homozygotes to the effects of stress is mediated through dopamine release.
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Saito, Luis P; Fukushiro, Daniela F; Hollais, André W; Mári-Kawamoto, Elisa; Costa, Jacqueline M; Berro, Laís F; Aramini, Tatiana C F; Wuo-Silva, Raphael; Andersen, Monica L; Tufik, Sergio; Frussa-Filho, Roberto
2014-02-01
It has been demonstrated that a prolonged period (48 h) of paradoxical sleep deprivation (PSD) potentiates amphetamine (AMP)-induced behavioral sensitization, an animal model of addiction-related neuroadaptations. In the present study, we examined the effects of an acute short-term deprivation of total sleep (TSD) (6h) on AMP-induced behavioral sensitization in mice and compared them to the effects of short-term PSD (6 h). Three-month-old male C57BL/6J mice underwent TSD (experiment 1-gentle handling method) or PSD (experiment 2-multiple platforms method) for 6 h. Immediately after the sleep deprivation period, mice were tested in the open field for 10 min under the effects of saline or 2.0 mg/kg AMP. Seven days later, to assess behavioral sensitization, all of the mice received a challenge injection of 2.0 mg/kg AMP and were tested in the open field for 10 min. Total, peripheral, and central locomotion, and grooming duration were measured. TSD, but not PSD, potentiated the hyperlocomotion induced by an acute injection of AMP and this effect was due to an increased locomotion in the central squares of the apparatus. Similarly, TSD facilitated the development of AMP-induced sensitization, but only in the central locomotion parameter. The data indicate that an acute period of TSD may exacerbate the behavioral effects of AMP in mice. Because sleep architecture is composed of paradoxical and slow wave sleep, and 6-h PSD had no effects on AMP-induced hyperlocomotion or sensitization, our data suggest that the deprivation of slow wave sleep plays a critical role in the mechanisms that underlie the potentiating effects of TSD on both the acute and sensitized addiction-related responses to AMP.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
WANG Dayong; XING Xiaojuan
2008-01-01
Locomotion behaviors are susceptible to disruption by a broad spectrum of chemicals and environmental stresses. However, no systematic testing of locomotion behavior defects induced by metal exposure has been conducted in the model organism of nematode Caenorhabditis elegans. In this study, the acute toxicity from heavy metal exposure on the locomotion behaviors was analyzed in nematodes. Endpoints of head thrash, body bend, forward turn, backward turn, and Omega/U turn were chosen to evaluate the locomotion behavioral defects. Our data suggest that the endpoints of head thrash, body bend, and forward turn will be useful for the evaluation of heavy metal toxicity in nematodes. The endpoint of head thrash could detect the toxicity from Cd, Co, Cr, Cu, Hg, and Pb exposures at a low concentration (2.5 μmol/L). The endpoint of body bend could be explored to evaluate the toxicity from all assayed heavy metal exposures at different concentrations, whereas the endpoint of forward turn will be more useful for the evaluation of heavy metal toxicity at high concentrations. Thus, endpoints of these locomotion behaviors establish a fast and economic way to assess the presence of acute toxicity from heavy metal exposure in nematode C. elegans.
Greenwald, Mark K
2005-02-01
This study examined whether acute opioid withdrawal and drug reinforcement opportunity increase opioid craving and seeking behavior. The author used a 3 x 2 within-subject randomized crossover design to assess craving and operant behavioral effects of 3 pretreatments (naloxone 0.1 mg/70 kg, fentanyl 0.75 mg/70 kg, or saline iv) and drug or money reinforcement opportunity in 8 methadone-maintained volunteers. Each pretreatment was paired with response-contingent (15 x fixed-ratio 100) delivery of drug (fentanyl 1.5 mg/70 kg iv) and money (rated equivalent of fentanyl) in different sessions. Naloxone significantly increased opioid craving, withdrawal signs, and symptoms, but not operant behavior, relative to saline and fentanyl pretreatment. However, drug versus money reinforcement opportunity did not significantly increase opioid craving or seeking behavior.
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
Bettina Karin Wölnerhanssen
Full Text Available Previous research has revealed that glucose and fructose ingestion differentially modulate release of satiation hormones. Recent studies have begun to elucidate brain-gut interactions with neuroimaging approaches such as magnetic resonance imaging (MRI, but the neural mechanism underlying different behavioral and physiological effects of glucose and fructose are unclear. In this paper, we have used resting state functional MRI to explore whether acute glucose and fructose ingestion also induced dissociable effects in the neural system. Using a cross-over, double-blind, placebo-controlled design, we compared resting state functional connectivity (rsFC strengths within the basal ganglia/limbic network in 12 healthy lean males. Each subject was administered fructose, glucose and placebo on three separate occasions. Subsequent correlation analysis was used to examine relations between rsFC findings and plasma concentrations of satiation hormones and subjective feelings of appetite. Glucose ingestion induced significantly greater elevations in plasma glucose, insulin, GLP-1 and GIP, while feelings of fullness increased and prospective food consumption decreased relative to fructose. Furthermore, glucose increased rsFC of the left caudatus and putamen, precuneus and lingual gyrus more than fructose, whereas within the basal ganglia/limbic network, fructose increased rsFC of the left amygdala, left hippocampus, right parahippocampus, orbitofrontal cortex and precentral gyrus more than glucose. Moreover, compared to fructose, the increased rsFC after glucose positively correlated with the glucose-induced increase in insulin. Our findings suggest that glucose and fructose induce dissociable effects on rsFC within the basal ganglia/limbic network, which are probably mediated by different insulin levels. A larger study would be recommended in order to confirm these findings.
Calfee, Robin D.; Puglis, Holly J.; Little, Edward E.; Brumbaugh, William G.; Mebane, Christopher A.
2016-01-01
Behavioral responses of aquatic organisms to environmental contaminants can be precursors of other effects such as survival, growth, or reproduction. However, these responses may be subtle, and measurement can be challenging. Using juvenile white sturgeon (Acipenser transmontanus) with copper exposures, this paper illustrates techniques used for quantifying behavioral responses using computer assisted video and digital image analysis. In previous studies severe impairments in swimming behavior were observed among early life stage white sturgeon during acute and chronic exposures to copper. Sturgeon behavior was rapidly impaired and to the extent that survival in the field would be jeopardized, as fish would be swept downstream, or readily captured by predators. The objectives of this investigation were to illustrate protocols to quantify swimming activity during a series of acute copper exposures to determine time to effect during early lifestage development, and to understand the significance of these responses relative to survival of these vulnerable early lifestage fish. With mortality being on a time continuum, determining when copper first affects swimming ability helps us to understand the implications for population level effects. The techniques used are readily adaptable to experimental designs with other organisms and stressors.
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...... eigenvalues and eigenvectors. We give a number of different applications to regression and time series analysis, and show how the reduced rank regression estimator can be derived as a Gaussian maximum likelihood estimator. We briefly mention asymptotic results...
Denesiuk, E V
2015-01-01
The study involved 23 men after acute myocardial infarction (AMI) with comorbid arterial hypertension (AH). Mean age of patients was 56.7 years. Recurrent myocardial infarction was determined in 38.4%, cardiac failure I-III functional classes--100% of the cases. All patients underwent clinical examination, electrocardiography and echocardiography, blood lipid profile. Standard comprehensive treatment for two years included an perindopril 5-10 mg/day, beta-blocker bisoprolol--5-10 mg/day, antisclerotic drug atorvastatin--20 mg/day and aspirin--75 mg/day. The patients after treatment was determined by a gradual increase towards the target of AT at 3, 6 and 12 to 24 months. Concentric left ventricular hypertrophy (LVH) before treatment was determined in 47.8%, eccentric--in 52.2% of patients. In the study of degrees of LVH I (initial) the extent to treatment was determined by 4.3%, II (moderate)--26.1%, III (large)--at 69.6%, indicating the development of cardiac remodeling. After the treatment was determined by marked reduction III (large) degree and transfer it in the II (moderate) and I (small) degree of left ventricular hypertrophy due to more or less pronounced changes remodeling left ventricular. The obtained data allow a more detailed and adequately assess the structural and functional outcome variables and determine the regression of myocardial hypertrophy in the background to achieve target blood pressure, which is important in practical cardiology.
Park, Moonkyoung; Song, Rhayun; Jeong, Jin-Ok
2017-02-24
Effect of goal-attainment-theory-based education program on cardiovascular risks, behavioral modification, and quality of life among patients with first episode of acute myocardial infarction: randomized study BACKGROUND: The behavioral modification strategies should be explored at the time of admission to lead the maximum effect of cardiovascular risk management.
Schiørring, E; Hecht, A
1979-06-28
Groups of eight rats were treated with low, acute doses of morphine (2, 3.5, and 5 mg/kg body weight) or a corresponding volume of isotonic NaCl solution. The formation of groups, certain other features of social interaction, plus some individual items were recorded. Morphine induced an increase in the frequency of group formations without disruption of grooming and rearing patterns. The total picture of morphine-induced behavior changes at the dose levels used might be characterized as a polyactivation (or a varied stimulation); different from the selective stimulation reported for d-amphetamine.
Rasmussen, Rune Skovgaard; Overgaard, Karsten; Kristiansen, Uffe;
2011-01-01
OBJECTIVES: The objective of this study was to examine the effects of d-amphetamine (amph) upon recovery after embolic stroke in rats. METHODS: Ninety-three rats were embolized in the right middle cerebral artery and assigned to: (1) controls; (2) combination (acute amph and later amph-facilitate...
Effects of an acute and a sub-chronic 900 MHz GSM exposure on brain activity and behaviors of rats
Elsa Brillaud; Aleksandra Piotrowski; Anthony Lecomte; Franck Robidel; Rene de Seze [Toxicology Unit, INERIS, Verneuil en Halatte (France)
2006-07-01
Radio frequencies are suspected to produce health effects. Concerning the mobile phone technology, according to position during use (close to the head), possible effects of radio frequencies on the central nervous system have to be evaluated. Previous works showed contradictory results, possibly due to experimental design diversity. In the framework of R.A.M.P. 2001 project, we evaluated possible effect of a 900 MHz GSM exposure on the central nervous system of rat at a structural, a functional and a behavioral level after acute or sub-chronic exposures. Rats were exposed using a loop antenna system to different S.A.R. levels and durations, according to results of the French C.O.M.O.B.I.O. 2001 project. A functional effect was found (modification of the cerebral activity and increase of the glia surface) after an acute exposure, even at a low level of brain averaged S.A.R. (1.5 W/kg). No cumulative effect was observed after a sub-chronic exposure (same amplitude of the effect). No structural or behavioral consequence was noted. We do not conclude on the neurotoxicity of the 900 MHz GSM exposure on the rat brain. Our results do not indicate any health risk. (authors)
Deviche, Pierre; Bittner, Stephanie; Davies, Scott; Valle, Shelley; Gao, Sisi; Carpentier, Elodie
2016-08-01
Acute stress in vertebrates generally stimulates the hypothalamo-pituitary-adrenal axis and is often associated with multiple metabolic changes, such as increased gluconeogenesis, and with behavioral alterations. Little information is available, especially in free-ranging organisms, on the duration of these reversible effects once animals are no longer exposed to the stressor. To investigate this question, we exposed free-ranging adult male Rufous-winged Sparrows, Peucaea carpalis, in breeding condition to a standard protocol consisting of a social challenge (conspecific song playback) followed with capture and restraint for 30min, after which birds were released on site. Capture and restraint increased plasma corticosterone (CORT) and decreased plasma testosterone (T), glucose (GLU), and uric acid (UA). In birds that we recaptured the next day after exposure to conspecific song playback, plasma CORT and UA levels no longer differed from levels immediately after capture the preceding day. However, plasma T was similar to that measured after stress exposure the preceding day, and plasma GLU was markedly elevated. Thus, exposure to social challenge and acute stress resulted in persistent (⩾24h) parameter-specific effects. In recaptured sparrows, the territorial aggressive response to conspecific song playback, as measured by song rate and the number of flights over the song-broadcasting speakers, did not, however, differ between the first capture and the recapture, suggesting no proximate functional association between plasma T and conspecific territorial aggression. The study is the first in free-ranging birds to report the endocrine, metabolic, and behavioral recovery from the effects of combined social challenge and acute stress.
Acute maternal alcohol consumption disrupts behavioral state organization in the near-term fetus
Mulder, EJH; Morssink, LP; Van der Schee, T; Visser, GHA
1998-01-01
Disturbed sleep regulation is often observed in neonates of women who drank heavily during pregnancy. It is unknown if (and how) an occasional drink affects fetal sleeping behavior. In 28 near-term pregnant women we examined the effects on fetal behavioral state organization of two glasses of wine (
Acute stress and episodic memory retrieval: neurobiological mechanisms and behavioral consequences.
Gagnon, Stephanie A; Wagner, Anthony D
2016-04-01
Episodic retrieval allows people to access memories from the past to guide current thoughts and decisions. In many real-world situations, retrieval occurs under conditions of acute stress, either elicited by the retrieval task or driven by other, unrelated concerns. Memory under such conditions may be hindered, as acute stress initiates a cascade of neuromodulatory changes that can impair episodic retrieval. Here, we review emerging evidence showing that dissociable stress systems interact over time, influencing neural function. In addition to the adverse effects of stress on hippocampal-dependent retrieval, we consider how stress biases attention and prefrontal cortical function, which could further affect controlled retrieval processes. Finally, we consider recent data indicating that stress at retrieval increases activity in a network of brain regions that enable reflexive, rapid responding to upcoming threats, while transiently taking offline regions supporting flexible, goal-directed thinking. Given the ubiquity of episodic memory retrieval in everyday life, it is critical to understand the theoretical and applied implications of acute stress. The present review highlights the progress that has been made, along with important open questions.
Rigid patterns of effortful choice behavior after acute stress in rats.
Hart, Evan E; Stolyarova, Alexandra; Conoscenti, Michael A; Minor, Thomas R; Izquierdo, Alicia
2017-01-01
Physical effort is a common cost of acquiring rewards, and decreased effort is a feature of many neuropsychiatric disorders. Stress affects performance on several tests of cognition and decision making in both humans and nonhumans. Only a few recent reports show impairing effects of stress in operant tasks involving effort and cognitive flexibility. Brain regions affected by stress, such as the medial prefrontal cortex and amygdala, are also implicated in mediating effortful choices. Here, we assessed effort-based decision making after an acute stress procedure known to induce persistent impairment in shuttle escape and elevated plasma corticosterone. In these animals, we also probed levels of polysialyted neural cell adhesion molecule (PSA-NCAM), a marker of structural plasticity, in medial frontal cortex and amygdala. We found that animals that consistently worked for high magnitude rewards continued to do so, even after acute shock stress. We also found that PSA-NCAM was increased in both regions after effortful choice experience but not after shock stress alone. These findings are discussed with reference to the existing broad literature on cognitive effects of stress and in the context of how acute stress may bias effortful decisions to a rigid pattern of responding.
Pedraza, C; Sánchez-López, J; Castilla-Ortega, E; Rosell-Valle, C; Zambrana-Infantes, E; García-Fernández, M; Rodriguez de Fonseca, F; Chun, J; Santín, L J; Estivill-Torrús, G
2014-09-01
LPA1 receptor is one of the six characterized G protein-coupled receptors (LPA1-6) through which lysophosphatidic acid acts as an intercellular signaling molecule. It has been proposed that this receptor has a role in controlling anxiety-like behaviors and in the detrimental consequences of stress. Here, we sought to establish the involvement of the LPA1 receptor in emotional regulation. To this end, we examined fear extinction in LPA1-null mice, wild-type and LPA1 antagonist-treated animals. In LPA1-null mice we also characterized the morphology and GABAergic properties of the amygdala and the medial prefrontal cortex. Furthermore, the expression of c-Fos protein in the amygdala and the medial prefrontal cortex, and the corticosterone response following acute stress were examined in both genotypes. Our data indicated that the absence of the LPA1 receptor significantly inhibited fear extinction. Treatment of wild-type mice with the LPA1 antagonist Ki16425 mimicked the behavioral phenotype of LPA1-null mice, revealing that the LPA1 receptor was involved in extinction. Immunohistochemistry studies revealed a reduction in the number of neurons, GABA+ cells, calcium-binding proteins and the volume of the amygdala in LPA1-null mice. Following acute stress, LPA1-null mice showed increased corticosterone and c-Fos expression in the amygdala. In conclusion, LPA1 receptor is involved in emotional behaviors and in the anatomical integrity of the corticolimbic circuit, the deregulation of which may be a susceptibility factor for anxiety disorders and a potential therapeutic target for the treatment of these diseases.
Fisher, Derek J; Daniels, Richelle; Jaworska, Natalia; Knobelsdorf, Amy; Knott, Verner J
2012-01-06
Enhancements in working memory (WM) performance have been previously reported following acute smoking/nicotine. Neuroimaging and behavioral assessments of nicotine's effects on WM have yielded inconsistent findings. Few studies, however, have examined the effects of nicotine on WM-related neural activity in non-smokers. The present study examined the effect of acute nicotine gum administration (6 mg) on electroencephalographic (EEG) activity (alpha(1), alpha(2) and theta bands) and performance on the parametrically manipulated N-back task of WM in 20 non-smoking adults. EEG activity varied with WM load (e.g. alpha(1) decreasing and theta increasing). Performance on the N-back was also load-sensitive, with slower reaction times and decreased accuracy associated with increasing memory load. Neither response speed nor accuracy measures were affected by nicotine but EEG was, with the effects varying by load and brain region. Nicotine-induced increases in alpha(2) and theta were observed under lower (0-, 1-back) memory load conditions Additionally, nicotine significantly reduced signal detection sensitivity values and altered response bias toward being more conservative at all levels of the N-back. Taken together, these findings suggest that while nicotine may boost WM neural processes at lower levels of WM load in non-smokers, it also may activate concurrent behavioral inhibition networks that negate any effects on behavioral performance. Additionally, nicotine appears to have no impact, or perhaps a negative impact, on these processes under more demanding (2-back, 3-back) conditions in non-smokers.
Vikrant Sudan
2014-03-01
Full Text Available Toxoplasma gondii, an apicomplexan parasite, is capable of infecting a broad range of intermediate warm-blooded hosts including humans. The parasite seems to be capable of altering the natural behavior of the host to favor its transmission in the environment. The aim of this study was to evaluate the course, alterations in behavior along with normal kinetics of the abnormally induced experimental acute toxoplasmosis in murine models.Ten Swiss albino mice were intraperitoneally inoculated with 100 virulent RH strain tachyzoites and finally, the alterations in behavior were described and compared with other known alterations in humans and animals.The behavior and the other symptoms of the acute toxoplasmosis were recorded. Such mice showed typical symptoms like normal coat, severe ascites with pendulous abdomen and tachypnoea exhibited by resting fore legs either on walls of the cage, or nozzle of water bottle or other resting mice and yielded a creamy colored cloudy natured peritoneal fluid on aspiration.Finally the alterations in behavior were described and compared with other known alterations in humans and animals. The study has generated some important data related to possible causes of behavioral alterations and generation of suitable strategies for control of these alterations in behavior vis-à-vis better understanding of the effect of acute infection of parasite on normal behavior of infected intermediate host.
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Warnock, Fay; Sandrin, Dilma
2004-02-01
One of the most difficult challenges still facing researchers and clinicians is assessing pain in the newborn. Behaviors provide one of the most promising avenues for deepening our fundamental understanding of complex phenomenon like newborn pain, and are key to developing descriptive-level knowledge to further newborn pain assessment efforts. In this ethologically based research, we report on the duration and frequency of neonatal distress behavior to seven distinct noxious and non-noxious but distress-provoking events including baseline (diaper change, post-diaper change, application of arm and leg restraints, post-application of arm and leg restraints, circumcision, post-circumcision) associated with newborn surgical pain. Approximately 67 min of videotaped data, involving four neonates who had undergone newborn male circumcision, were coded at 1-s intervals (4010 s in total). A reliably established coding scheme was used to code behaviors as they were observed on videotape for the duration of the seven designated events. This led to the identification of (1) 40 distress behaviors as they occurred along the continuum of distress, (2) eight distress behaviors specific to surgery, (3) 11 classes of behaviors occurring within the five sub-phases of circumcision, and (4) a description of 25 distinct post-distress behaviors. Findings support the ability to distinguish distress behaviors specific to pain and the ability to detect prolonged distress as well as individual differences in distress-related pain expression. Findings also justify ongoing use of ethological approaches to further newborn pain assessment and to investigate poorly understood topics such as infant self-regulation within the context of pain (pain recovery).
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Crockett, Molly J; Clark, Luke; Robbins, Trevor W
2009-09-23
The neuromodulator serotonin has been implicated in a large number of affective and executive functions, but its precise contribution to motivation remains unclear. One influential hypothesis has implicated serotonin in aversive processing; another has proposed a more general role for serotonin in behavioral inhibition. Because behavioral inhibition is a prepotent reaction to aversive outcomes, it has been a challenge to reconcile these two accounts. Here, we show that serotonin is critical for punishment-induced inhibition but not overall motor response inhibition or reporting aversive outcomes. We used acute tryptophan depletion to temporarily lower brain serotonin in healthy human volunteers as they completed a novel task designed to obtain separate measures of motor response inhibition, punishment-induced inhibition, and sensitivity to aversive outcomes. After a placebo treatment, participants were slower to respond under punishment conditions compared with reward conditions. Tryptophan depletion abolished this punishment-induced inhibition without affecting overall motor response inhibition or the ability to adjust response bias in line with punishment contingencies. The magnitude of reduction in punishment-induced inhibition depended on the degree to which tryptophan depletion reduced plasma tryptophan levels. These findings extend and clarify previous research on the role of serotonin in aversive processing and behavioral inhibition and fit with current theorizing on the involvement of serotonin in predicting aversive outcomes.
Tilman J. Gaber
2015-08-01
Full Text Available Background: Alterations in serotonergic (5-HT neurotransmission are thought to play a decisive role in affective disorders and impulse control. Objective: This study aims to reproduce and extend previous findings on the effects of acute tryptophan depletion (ATD and subsequently diminished central 5-HT synthesis in a reinforced categorization task using a refined body weight–adjusted depletion protocol. Design: Twenty-four young healthy adults (12 females, mean age [SD]=25.3 [2.1] years were subjected to a double-blind within-subject crossover design. Each subject was administered both an ATD challenge and a balanced amino acid load (BAL in two separate sessions in randomized order. Punishment-related behavioral inhibition was assessed using a forced choice go/no-go task that incorporated a variable payoff schedule. Results: Administration of ATD resulted in significant reductions in TRP measured in peripheral blood samples, indicating reductions of TRP influx across the blood–brain barrier and related brain 5-HT synthesis. Overall accuracy and response time performance were improved after ATD administration. The ability to adjust behavioral responses to aversive outcome magnitudes and behavioral adjustments following error contingent punishment remained intact after decreased brain 5-HT synthesis. A previously observed dissociation effect of ATD on punishment-induced inhibition was not observed. Conclusions: Our results suggest that neurodietary challenges with ATD Moja–De have no detrimental effects on task performance and punishment-related inhibition in healthy adults.
Coping with an acute psychosocial challenge: behavioral and physiological responses in young women.
Carolina Villada
Full Text Available Despite the relevance of behavior in understanding individual differences in the strategies used to cope with stressors, behavioral responses and their relationships with psychobiological changes have received little attention. In this study on young women, we aimed at analyzing the associations among different components of the stress response and behavioral coping using a laboratory psychosocial stressor. The Ethological Coding System for Interviews, as well as neuroendocrine, autonomic and mood parameters, were used to measure the stress response in 34 young women (17 free-cycling women in their early follicular phase and 17 oral contraceptive users subjected to the Trier Social Stress Test (TSST and a control condition in a crossover design. No significant differences in cardiac autonomic, negative mood and anxiety responses to the stressor were observed between the two groups of women. However, women in the follicular phase showed a higher cortisol response and a larger decrease in positive mood during the social stress episode, as well as greater anxiety overall. Interestingly, the amount of displacement behavior exhibited during the speaking task of the TSST was positively related to anxiety levels preceding the test, but negatively related to baseline and stress response values of heart rate. Moreover, the amount of submissive behavior was negatively related to basal cortisol levels. Finally, eye contact and low-aggressiveness behaviors were associated with a worsening in mood. Overall, these findings emphasize the close relationship between coping behavior and psychobiological reactions, as well as the role of individual variations in the strategy of coping with a psychosocial stressor.
The behavior of tillage tools with acute and obtuse lift angles
Mohammad Hossein Abbaspour-Fard
2014-01-01
Full Text Available An experimental investigation was conducted to study the trend of draft force against forward speed and working depth for a range of lift angles beyond acute angles for a simple plane tillage tool. The experiments were performed in an indoor soil bin facility equipped with a tool carriage and a soil preparation unit propelled by an integrated hydraulic power system. The system was also equipped with electronic instrumentation including an Extended Octagonal Ring Transducer (EORT and a data logger. The factorial experiment (4×3×3 with three replications was used based on Randomized Complete Block Design (RCBD. The independent variables were lift angle of the blade (45, 70, 90 and 120°, forward speed (2, 4 and 6 km h-1 and working depth (10, 25 and 40 cm. The variance analysis for the draft force shows that all independent variables affect the draft force at 1% level of significance. The trend of the draft force against working depth and forward speed had almost a linear increase. However, the trend of the draft force against the lift angle is reversed for lift angles >90. This finding, conflicts with the results of analytical and numerical studies which extrapolate the results achieved for acute lift angles to obtuse lift angles and have not been reported experimentally.
The behavior of tillage tools with acute and obtuse lift angles
Abbaspour-Fard, M. H.; Hoseini, S. A.; Agkhani, M. H.; Sharifi, A.
2014-06-01
An experimental investigation was conducted to study the trend of draft force against forward speed and working depth for a range of lift angles beyond acute angles for a simple plane tillage tool. The experiments were performed in an indoor soil bin facility equipped with a tool carriage and a soil preparation unit propelled by an integrated hydraulic power system. The system was also equipped with electronic instrumentation including an Extended Octagonal Ring Transducer (EORT) and a data logger. The factorial experiment (4 × 3 × 3) with three replications was used based on Randomized Complete Block Design (RCBD). The independent variables were lift angle of the blade (45, 70, 90 and 120 degree centigrade), forward speed (2, 4 and 6 km h{sup -}1) and working depth (10, 25 and 40 cm). The variance analysis for the draft force shows that all independent variables affect the draft force at 1% level of significance. The trend of the draft force against working depth and forward speed had almost a linear increase. However, the trend of the draft force against the lift angle is reversed for lift angles > 90 degree centigrade. This finding, conflicts with the results of analytical and numerical studies which extrapolate the results achieved for acute lift angles to obtuse lift angles and have not been reported experimentally. (Author)
程昌志; 赵东海; 李全岳; 曲海燕; 陈伯成; 林舟丹
2011-01-01
Objective To explore the risk factors of complication of acute renal failure (ARF) in war injuries of limbs. Methods The clinical data of 352 patients with limb injuries admitted to 303 Hospital of PLA from 1968 to 2002 were retrospectively analyzed. The patients were divided into ARF group (n=9) and non-ARF group ( n=343) according to the occurrence of ARF, and the case-control study was carried out. Ten factors which might lead to death were analyzed by logistic regression to screen the risk factors for ARF,including causes of trauma, shock after injury, time of admission to hospital after injury, injured sites, combined trauma, number of surgical procedures, presence of foreign matters, features of fractures, amputation, and tourniquet time. Results Fifteen of the 352 patients died (4.3％) , among them 7 patients (46.7％) died of ARF, 3 (20.0％) of pulmonary embolism, 3 (20.0 ％) of gas gangrene,and 2 (13.3％) of multiple organ failure. Univariate analysis revealed that the shock, time before admitted to hospital, amputation and tourniquet time were the risk factors for ARF in the wounded with limb injuries, while the logistic regression analysis showed only amputation was the risk factor for ARF ( P＜0.05). Conclusion ARF is the primary cause-of-death in the wounded with limb injury.Prompt and accurate treatment and optimal time for amputation may be beneficial to decreasing the incidence and mortality of ARF in the wounded with severe limb injury and ischemic necrosis.%目的 探讨四肢战创伤并发急性肾衰竭(ARF)的危险因素.方法 回顾性分析1968-2002年收治的352例四肢战创伤患者,根据是否发生ARF将患者分为ARF组(9例)和非ARF组(343例)并进行病例对照研究,选择可能影响患者死亡的10个因素(致伤物、伤后是否休克、伤后入院时间、受伤部位、有无合并伤、手术次数、有否异物存留、骨折性质、是否截肢、止血带时间)进行logistic回归分析,筛
Freitas, Kelen C; Carroll, F Ivy; Negus, S Stevens
2015-11-01
Agonists at nicotinic acetylcholine receptors (nAChRs) constitute one drug class being evaluated as candidate analgesics. Previous preclinical studies have implicated α4β2 and α7 nAChRs as potential mediators of the antinociceptive effects of (–)-nicotine hydrogen tartrate (nicotine) and other nAChR agonists; however, these studies have relied exclusively on measures of pain-stimulated behavior, which can be defined as behaviors that increase in frequency, rate, or intensity after presentation of a noxious stimulus. Pain is also associated with depression of many behaviors, and drug effects can differ in assays of pain-stimulated versus pain-depressed behavior. Accordingly, this study compared the effects of nicotine, the selective α4/6β2 agonist 5-(123I)iodo-3-[2(S)-2-azetidinylmethoxy]pyridine (5-I-A-85380), and the selective α7 agonist N-(3R)-1-azabicyclo(2.2.2)oct-3-yl-4-chlorobenzamide in assays of pain-stimulated and pain-depressed behavior in male Sprague-Dawley rats. Intraperitoneal injection of dilute lactic acid served as an acute noxious stimulus to either stimulate a stretching response or depress the operant responding, which is maintained by electrical brain stimulation in an intracranial self-stimulation (ICSS) procedure. Nicotine produced a dose-dependent, time-dependent, and mecamylamine-reversible blockade of both acid-stimulated stretching and acid-induced depression of ICSS. 5-I-A-85380 also blocked both acid-stimulated stretching and acid-induced depression of ICSS, whereas N-(3R)-1-azabicyclo(2.2.2)oct-3-yl-4-chlorobenzamide produced no effect in either procedure. Both nicotine and 5-I-A-85380 were ≥10-fold more potent in blocking the acid-induced depression of ICSS than in blocking the acid-induced stimulation of stretching. These results suggest that stimulation of α4β2 and/or α6β2 nAChRs may be especially effective to alleviate the signs of pain-related behavioral depression in rats; however, nonselective behavioral effects
Numerical calculation on behavior of fuel regression in hybrid rocket motor%混合火箭发动机燃料退移特性的数值计算
单繁立; 侯凌云; 朴英
2011-01-01
混合火箭发动机在航天推进领域优势明显,但由于氧化剂和燃料相态不同,燃料退移的机理和特性比较复杂.采用自行编写的混合火箭发动机程序(HRM)模拟了这种发动机的非稳态工作过程.通过该程序实时数值求解了从氧化剂注入端到尾喷管的全部物理化学过程,并基于燃料表面上气固间的质量和能量耦合,运用燃料表面动态退移和两步计算方法,模拟了燃料退移.在与发动机推力和燃料退移量等实验数据对比的基础上,给出了燃料退移速率方程和燃料退移速率随燃烧室直径的变化规律,确定并分析了影响混合火箭发动机尺度效应的因素.%Hybrid rocket motor has many advantages for space propulsion applications. The mechanism and behavior of the fuel regression in the motor are complicated due to different phase states of the oxidizer and the fuel. The hybrid rocket motor code ( HRM) for the unsteady simulation of motor operation has been programmed. This code can calculate physical and chemical processes from the oxidizer injector to the nozzle during motor operation. The mass and energy coupling between gas and solid phases as well as a dynamic fuel surface regression technique with a two-step calculation method is applied to simulate the fuel regression. The calculated motor thrust and fuel regression are compared to the experimental data for the code validation. The fuel regression rate equation and the relation between fuel regression rate and chamber diameter have been derived. The reasons for scale effect in hybrid rocket motor have been determined and analyzed.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Zhenyong Lyu
2016-10-01
Full Text Available Stressors can trigger binge-eating but researchers have yet to consider their effects on both neural responses to food cues and food consumption among those at risk. In this experiment, we examined the impact of acute stressors on neural activation to food images and subsequent food consumption within binge-eating disorder (BED and non-eating disordered control groups. Eighteen women meeting DSM-IV BED criteria and 26 women serving as non-eating disordered controls were randomly assigned to unpleasant stressor (painful cold pressor test followed by negative performance feedback or less unpleasant stressor (non-painful sensory discrimination task followed by positive performance feedback conditions. Subsequently, they were scanned with functional magnetic resonance imaging (fMRI while viewing food and neutral images. After the scans, participants completed a self-report battery in an environment conducive to snacking. During exposure to food images, BED-symptomatic women in the unpleasant stressor condition reported more liking of high calorie food images and showed less activation in one inhibitory area, the hippocampus, compared to controls in this condition. BED-symptomatic women exposed to unpleasant stressors also consumed more chocolate than any other group during the post-scan questionnaire completion. Crucially, reduced hippocampal activation to high calorie food images predicted more chocolate consumption following fMRI scans within the entire sample. This experiment provides initial evidence suggesting unpleasant acute stressors contribute to reduced inhibitory region responsiveness in relation to external food cues and later food consumption among BED-symptomatic women.
Behavioral changes and cholinesterase activity of rats acutely treated with propoxur.
Thiesen, F V; Barros, H M; Tannhauser, M; Tannhauser, S L
1999-01-01
Early assessment of neurological and behavioral effects is extremely valuable for early identification of intoxications because preventive measures can be taken against more severe or chronic toxic consequences. The time course of the effects of an oral dose of the anticholinesterase agent propoxur (8.3 mg/kg) was determined on behaviors displayed in the open-field and during an active avoidance task by rats and on blood and brain cholinesterase activity. Maximum inhibition of blood cholinesterase was observed within 30 min after administration of propoxur. The half-life of enzyme-activity recovery was estimated to be 208.6 min. Peak brain cholinesterase inhibition was also detected between 5 and 30 min of the pesticide administration, but the half-life for enzyme activity recovery was much shorter, in the range of 85 min. Within this same time interval of the enzyme effects, diminished motor and exploratory activities and decreased performance of animals in the active avoidance task were observed. Likewise, behavioral normalization after propoxur followed a time frame similar to that of brain cholinesterase. These data indicate that behavioral changes that occur during intoxication with low oral doses of propoxur may be dissociated from signs characteristic of cholinergic over-stimulation but accompany brain cholinesterase activity inhibition.
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Cardel, M I; Johnson, S L; Beck, J; Dhurandhar, E; Keita, A D; Tomczik, A C; Pavela, G; Huo, T; Janicke, D M; Muller, K; Piff, P K; Peters, J C; Hill, J O; Allison, D B
2016-08-01
Both subjective and objectively measured social status has been associated with multiple health outcomes, including weight status, but the mechanism for this relationship remains unclear. Experimental studies may help identify the causal mechanisms underlying low social standing as a pathway for obesity. Our objective was to investigate the effects of experimentally manipulated social status on ad libitum acute dietary intakes and stress-related outcomes as potential mechanisms relating social status and weight. This was a pilot feasibility, randomized, crossover study in Hispanic young adults (n=9; age 19-25; 67% female; BMI ≥18.5 and ≤30kg/m(2)). At visit 1, participants consumed a standardized breakfast and were randomized to a high social status position (HIGH) or low social status position (LOW) in a rigged game of Monopoly™. The rules for the game differed substantially in terms of degree of 'privilege' depending on randomization to HIGH or LOW. Following Monopoly™, participants were given an ad libitum buffet meal and energy intakes (kcal) were estimated by pre- and post-weighing foods consumed. Stress-related markers were measured at baseline, after the game of Monopoly™, and after lunch. Visit 2 used the same standardized protocol; however, participants were exposed to the opposite social status condition. When compared to HIGH, participants in LOW consumed 130 more calories (p=0.07) and a significantly higher proportion of their daily calorie needs in the ad libitum buffet meal (39% in LOW versus 31% in HIGH; p=0.04). In LOW, participants reported decreased feelings of pride and powerfulness following Monopoly™ (p=0.05) and after their lunch meal (p=0.08). Relative to HIGH, participants in LOW demonstrated higher heart rates following Monopoly™ (p=0.06), but this relationship was not significant once lunch was consumed (p=0.31). Our pilot data suggest a possible causal relationship between experimentally manipulated low social status and
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Acute-phase protein behavior in dairy cattle herd naturally infected with Trypanosoma vivax.
Sampaio, Paulo Henrique; Fidelis Junior, Otavio Luiz; Marques, Luiz Carlos; Machado, Rosangela Zacarias; Barnabé, Patrícia de Athayde; André, Marcos Rogério; Balbuena, Tiago Santana; Cadioli, Fabiano Antonio
2015-07-30
Trypanosoma vivax is a hemoprotozoon that causes disease in cattle and is difficult to diagnose. The host-parasite relationship in cattle that are infected by T. vivax has only been poorly studied. In the present study, a total of 429 serum proteinograms were produced from naturally infected animals (NIF) and were compared with 50 samples from control animals (C). The total protein, IgA band, complement C3 β chain band, albumin band, antitrypsin band, IgG band, haptoglobin band, complement C3c α chain band and protein HP-20 band presented higher levels in the serum proteinograms of the NIF group. Inter-alpha-trypsin inhibitor heavy chain H4, α2-macroglobulin, complement C6, ceruloplasmin, transferrin band and apolipoprotein A1 band presented lower levels in this group. There was no significant difference (pNIF and C groups. Acute phase proteins may be useful for understanding the host-parasite relationship, since the antitrypsin band was only present in the NIF group. This can be used as an indicator for infection in cattle that are naturally infected by T. vivax.
Flexible survival regression modelling
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time......-varying effects. We start by considering classical and also more recent goodness-of-fit procedures for the Cox model that will reveal when the Cox model does not capture important aspects of the data, such as time-varying effects. We present recent regression models that are able to deal with and describe...... such time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox's regression model and the need for other supplementary analyses with models such as those we present here....
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
夏斌; 王春丽; 张笋
2013-01-01
Objective To test and verify the regression equation got before for children's dental behavior management problems(BMP).Methods The study group included 279 children aged 2-＜ 8 years who received dental treatment by 16 pediatric dentists in the Department of Pediatric Dentistry,Peking University School of and Hospital of Stomatology.Interviews were conducted with accompanying guardians and children's dental behavior was rated by a modified Venham's clinical anxiety scale and a cooperative behavior rating scale.The variables were put into the regression equation and the results were compared with their dental behavior scale.Results The accuracy rate of regression equation reached 84.2％ (235/279),sensitivity was 0.613(95％CI:0.514-0.712) and specificity was 0.957 (95％CI:0.928-0.986).Conclusions The regression equation is characterized by its accuracy rate at a good level.Younger age,negative guardian expectations of the child's behavior during treatment,anxiety or shyness around strangers,and presence of toothache were four risk factors for children's dental BMP.%目的 检验既往研究获得的口腔诊疗中儿童行为表现预测回归方程的准确性,为该方程在临床的应用提供指导.方法 对北京大学口腔医学院·口腔医院儿童口腔科门诊279名2～＜8岁首次就诊儿童的家长进行问卷调查,并对儿童就诊时的行为表现进行评价记录,将通过问卷调查获得的影响因素代入以往研究所获得的回归方程[logit(P)=-0.884a+1.212b+ 1.063c+0.918d +0.955,P:概率值;a:年龄;b:监护人预测;c:是否存在行为方面的问题;d:是否有牙痛史]进行验证,对得出的预测值与儿童实际行为表现间的异同进行比较.结果 回归方程预测准确率为84.2％(235/279);回归方程的敏感度为0.613(95％ CI:0.514 ～0.712),特异度为0.957(95％ CI:0.928 ～0.986),阳性预测值为0.877(95％CI:0.797 ～0.957),阴性预测值为0.832(95％ CI:0.782 ～0.882).结论 该回归
刘弘; 罗宝章; 吴春峰; 陆冬磊; 邢之慧
2012-01-01
Objective To study acute diarrhea status and risk factors of dietary behavior in Shanghai. Methods A stratified multi-stage cluster random household sampling was used in this cross-sectional survey. Results The incidence rate of acute diarrhea among Shanghai residents was 0. 68 episodes per person-year. It was 0. 75 episodes per person-year for males and 0.62 episodes per person-year for females. It was estimated to be 0.96, 0.54, 0.71, 0.71 and 0.64 episodes per person-year for age groups of 0 - 7 , 8 - 17 , 18 - 33 , 34 - 59 and ≥60, respectively. 24. 34% of respondents suspected their illness was due to contaminated food. 40. 03% of respondents had visited doctor. Single variable and logistic regression analysis showed that odds ratio of acute diarrhea were 1.37 ( 95 % confidence interval, 1. 13 - 1. 67 ) , 0.66 (95% confidence interval, 0.49 -0.88) and 0.76 (95% confidence interval, 0.62 -0.94) for the respondents who had the behavior of eating delicatessen, keeping food at low temperature and salty taste. Conclusion Acute diarrhea is a common illness among residents in Shanghai. The incidence of acute diarrhea was slightly higher in males. After the age of eight, the incidence declined, but increased again in adult and then declined above 60. The behavior of eating delicatessen was a risk factor for acute diarrhea. The habit of keeping the food at low temperature and salty taste might be the protective factors for acute diarrhea.%目的 了解上海市急性腹泻现况及饮食行为危险因素.方法 采用多阶段随机抽样,进行横断面入户问卷调查.结果 上海市居民急性腹泻年发生率0.68次/人年.男性0.75次/人年、女性0.62次/人年,0～7岁、8 ～17岁、18 ～33岁、34 ～59岁、≥60岁分别为0.96、0.54、0.71、0.71、0.64次/人年.有24.34％人自诉有可疑食物史、40.03％的人就诊.单因素及Logistic回归多因素分析显示:食用散装熟食者的急性腹泻OR值为1.37、95％CI (1.13～1.67),
Hung, Chiao-Ling; Huang, Chung-Ju; Tsai, Yu-Jung; Chang, Yu-Kai; Hung, Tsung-Min
2016-01-01
The main purpose of this two-part study was to examine the effects of acute, moderate intensity exercise on task switching in children with attention-deficit/hyperactivity disorder (ADHD). In Study 1, we compared the task switching performance of children with and without ADHD. Twenty children with ADHD and 20 matched controls performed the task switching paradigm, in which the behavioral indices and P3 component of event-related potentials elicited by task-switching were assessed simultaneously. The amplitude and latency of P3 reflected the amount of attention resource allocated to task-relevant stimulus in the environment and the efficiency of stimulus detection and evaluation, respectively. The task switching included two conditions; the pure condition required participants to perform the task on the same rule (e.g., AAAA or BBBB) whereas the mixed condition required participants to perform the task on two alternating rules (e.g., AABBAA…). The results indicated that children with ADHD had significantly longer RTs, less accuracy, and larger global switch cost for accuracy than controls. Additionally, ADHD participants showed smaller amplitudes and longer P3 latencies in global switch effects. In Study 2, we further examined the effects of an acute aerobic exercise session on task switching in children with ADHD. Thirty-four children with ADHD performed a task switching paradigm after 30 min of moderate-intensity aerobic exercise on a treadmill and after control sessions (watching videos while seated). The results revealed that following exercise, children with ADHD exhibited smaller global switch costs in RT compared with after control sessions. The P3 amplitude only increased following exercise in the mixed condition relative to the pure condition, whereas no effects were found in the control session. These findings suggest that single bouts of moderate intensity aerobic exercise may have positive effects on the working memory of children with ADHD. PMID
Crosby, Danielle A; Dowsett, Chantelle J; Gennetian, Lisa A; Huston, Aletha C
2010-09-01
We apply instrumental variables (IV) techniques to a pooled data set of employment-focused experiments to examine the relation between type of preschool childcare and subsequent externalizing problem behavior for a large sample of low-income children. To assess the potential usefulness of this approach for addressing biases that can confound causal inferences in child care research, we compare instrumental variables results with those obtained using ordinary least squares (OLS) regression. We find that our OLS estimates concur with prior studies showing small positive associations between center-based care and later externalizing behavior. By contrast, our IV estimates indicate that preschool-aged children with center care experience are rated by mothers and teachers as having fewer externalizing problems on entering elementary school than their peers who were not in child care as preschoolers. Findings are discussed in relation to the literature on associations between different types of community-based child care and children's social behavior, particularly within low-income populations. Moreover, we use this study to highlight the relative strengths and weaknesses of each analytic method for addressing causal questions in developmental research.
郭倩
2013-01-01
Objective To investigate the risk factors of acute respiratory distress syndrome (ARDS)in full-term newborns,in order to provide references for clinical prevention and treatment .Methods Medical records of 176 full-term newborns with ARDS were discussed and obstetric delivery records of normal full-term newborns were selected randomly with 1∶1 proportion,analysis of factors as-sociated with neonatal ARDS was made .Results ARDS in full-term newborns was associated with the way of giving birth ,cesarean section of social factors,fetal distress and neonatal asphyxia,and amniotic fluid intake( P 0.05).Unconditioned multiariable Logis-tic regression analysis results show that the cesarean section of social factors ,fetal distress ,neonatal asphyxia ,amniotic fluid intake were is independent risk factor of ARDS in full-term newborns( P <0.05 or P <0.01).Conclusion The major risk factors of ARDS in full-term newborns are cesarean section of social factors ,fetal distress,neonatal asphyxia,and amniotic fluid intake.Clinicians should take appropriate measures in prevention and treatment .%目的：探讨引起足月新生儿急性呼吸窘迫综合征（ ARDS）的高危因素，以期为临床防治提供参考。方法收集176例ARDS足月新生儿病历资料，以1∶1比例随机抽取产科分娩足月正常新生儿病历资料，分析引起新生儿ARDS的相关因素。结果足月新生儿ARDS的发生与分娩方式、社会因素剖宫产、胎儿宫内窘迫、新生儿窒息、羊水吸入有关（ P ＜0．05或P ＜0．01），与新生儿性别、分娩孕周、胎位、脐绕颈无关（ P ＞0．05）；非条件多因素Logistic回归分析结果显示，社会因素剖宫产、胎儿宫内窘迫、新生儿窒息、羊水吸入是引起足月新生儿ARDS的独立危险因素（ P ＜0．05或P ＜0．01）。结论引起足月新生儿ARDS的高危因素主要为社会因素剖宫产、胎儿宫内窘迫、新生儿窒息、羊水吸入，应采
Polynomial Regressions and Nonsense Inference
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Danilo F Pereira
2011-02-01
Full Text Available The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.As crescentes demandas e exigências dos mercados consumidores pelo bem-estar das aves nos aviários têm motivado diversas pesquisas científicas a monitorar e a classificar o bem-estar em função do ambiente de criação. Diante da complexidade com que as aves interagem com o ambiente do aviário, a correta interpretação dos comportamentos torna-se uma importante maneira para estimar o bem-estar dessas aves. Este trabalho criou modelos de regressão logística múltipla capazes de estimar o bem-estar de matrizes pesadas em função do ambiente do aviário e dos comportamentos expressos pelas aves. No experimento, foram observados diversos comportamentos expressos por matrizes pesadas alojadas em câmara climática sob três temperaturas controladas e diferentes concentrações de am
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Pase, C S; Roversi, Kr; Trevizol, F; Roversi, K; Kuhn, F T; Schuster, A J; Vey, L T; Dias, V T; Barcelos, R C S; Piccolo, J; Emanuelli, T; Bürger, M E
2013-09-01
Because consumption of processed foods has increased in the last decades and so far its potential influence on emotionality and susceptibility to stress is unknown, we studied the influence of different fatty acids (FA) on behavioral and biochemical parameters after acute restrain stress (AS) exposure. Two sequential generations of female rats were supplemented with soybean oil (control group; C-SO), fish oil (FO) and hydrogenated vegetable fat (HVF) from pregnancy and during lactation. At 41days of age, half the animals of each supplemented group were exposed to AS and observed in open field and elevated plus maze task, followed by euthanasia for biochemical assessments. The HVF-supplemented group showed higher anxiety-like symptoms per se, while the C-SO and FO groups did not show these behaviors. Among groups exposed to AS, HVF showed locomotor restlessness in the open field, while both C-SO and HVF groups showed anxiety-like symptoms in the elevated plus maze, but this was not observed in the FO group. Biochemical evaluations showed higher lipoperoxidation levels and lower cell viability in cortex in the HVF group. In addition, HVF-treated rats showed reduced catalase activity in striatum and hippocampus, as well as increased generation of reactive species in striatum, while FO was associated with increased cell viability in the hippocampus. Among groups exposed to AS, HVF increased reactive species generation in the brain, decreased cell viability in the cortex and striatum, and decreased catalase activity in the striatum and hippocampus. Taken together, our findings show that the type of FA provided during development and growth over two generations is able to modify the brain oxidative status, which was particularly adversely affected by trans fat. In addition, the harmful influence of chronic consumption of trans fats as observed in this study can enhance emotionality and anxiety parameters resulting from stressful situations of everyday life, which can
Praptiningsih, Catharina Y; Lafond, Kathryn E; Wahyuningrum, Yunita; Storms, Aaron D; Mangiri, Amalya; Iuliano, Angela D; Samaan, Gina; Titaley, Christiana R; Yelda, Fitra; Kreslake, Jennifer; Storey, Douglas; Uyeki, Timothy M
2016-06-01
Understanding healthcare-seeking patterns for respiratory illness can help improve estimations of disease burden and inform public health interventions to control acute respiratory disease in Indonesia. The objectives of this study were to describe healthcare-seeking behaviors for respiratory illnesses in one rural and one urban community in Western Java, and to explore the factors that affect care seeking. From February 8, 2012 to March 1, 2012, a survey was conducted in 2520 households in the East Jakarta and Bogor districts to identify reported recent respiratory illnesses, as well as all hospitalizations from the previous 12-month period. We found that 4% (10% of those less than 5years) of people had respiratory disease resulting in a visit to a healthcare provider in the past 2weeks; these episodes were most commonly treated at government (33%) or private (44%) clinics. Forty-five people (0.4% of those surveyed) had respiratory hospitalizations in the past year, and just over half of these (24/45, 53%) occurred at a public hospital. Public health programs targeting respiratory disease in this region should account for care at private hospitals and clinics, as well as illnesses that are treated at home, in order to capture the true burden of illness in these communities.
Multicollinearity in cross-sectional regressions
Lauridsen, Jørgen; Mur, Jesùs
2006-10-01
The paper examines robustness of results from cross-sectional regression paying attention to the impact of multicollinearity. It is well known that the reliability of estimators (least-squares or maximum-likelihood) gets worse as the linear relationships between the regressors become more acute. We resolve the discussion in a spatial context, looking closely into the behaviour shown, under several unfavourable conditions, by the most outstanding misspecification tests when collinear variables are added to the regression. A Monte Carlo simulation is performed. The conclusions point to the fact that these statistics react in different ways to the problems posed.
沈洁; 张朋; 刘福康; 王婷婷; 孙桂菊; 刘江红
2011-01-01
Objective To analyze the effect of behavior characteristics of children on diet behavior, providing scientific knowledge for nutrition instruction. Methods A total of 302 fifth-grade primary school children from Jintan were selected. A questionnaire survey on nutritional behavior and psychological behavior was conducted among them from June to July 2010. The effect factors of diet behavior were analyzed with univariate linear regression. The variable (P < 0.05 in univariate regression model) was selected to establish multivariate regression model. Results The univariate linear regression analysis showed that anxious/depressed, social problems, thought problems, attention problems, aggressive behavior score and total score in boys and girls were negatively correlated with diet behavior score. Multiple linear regressions showed that attention problem scores in boys and thougHt problem scores in girls were negatively correlated with diet behavior score. Conclusion The findings demonstrate that psychological behaviors of school-age children are closely associated with diet behaviors. It is necessary to add health-related curriculum on risk behaviors prevention into quality education, carry out comprehensive behavior surveillance on psychology, nutrition and diet, and conduct early intervention in adolescents.%目的 分析学龄儿童心理行为特点对其饮食行为的影响,为有针对性地对其进行营养教育提供科学依据.方法 选择江苏省金坛市302名五年级儿童作为研究对象,于2010年5月至6月对其进行饮食行为和心理行为问题的问卷调查.利用单因素线性回归分析饮食行为的影响因素,从单因素分析结果中选择P＜ 0.05的变量建立多元线性回归模型.结果 单因素线性回归分析显示,不同性别间焦虑抑郁、社交问题、思维问题、注意缺陷、攻击行为因子分及总分与饮食量表得分负相关；多元线性回归分析显示,男性儿童注意缺陷
基于多元回归模型的青少年交通安全行为分析%Youth Behavior Analysis on Traffic Safety Based on a Regression Model
田晟; 刘尔辉
2015-01-01
In order to improve the traffic safety level of youth in Guangzhou ,China ,protect them in traffic safety , the youth behaviors on traffic safety were studied based on the Guangzhou youth safe behavior survey data in this paper . Four indicators of youth behaviors on traffic safety including education ,awareness ,attitudes and personal factors were i‐dentified by quantitative analysis .The information of traffic safety education ,awareness ,and attitude was collected by the questionnaires .Pearson correlation analysis and multiple regression analysis with SPSS 19 .0 were used to analyze the collected data .The results show that education ,awareness ,attitude and other factors were directly related to the youth behaviors on traffic safety .The significant levels (sig .) of the variables were less than 0 .05 ,which meant that all varia‐bles have significant effects on the youth behaviors on traffic safety ,and they can be well applied in the evaluation of youth behaviors on traffic safety .The results also provide a scientific basis for youth education on traffic safety for par‐ents ,schools ,traffic management authorities to develop relevant policies .%为了提高广州市青少年交通安全水平，保障青少年交通安全。基于广州市青少年安全调查数据对青少年交通安全行为影响因素进行了研究。通过定量分析的方法筛选出了与青少年交通安全最密切相关的4项指标（教育、意识、态度、个人因素），设计并使用调查问卷，获得交通安全教育、意识、态度的评分，运用SPSS19．0采用皮尔逊（Pearson ）相关分析以及多元回归模型分析进行实证分析。结果表明，教育、意识、态度、个人因素能够很好地解释青少年交通安全行为得分，所有变量的显著性水平sig ．均小于0．05，可以认为所有变量均有显著影响，能够很好地应用在青少年交通安全行为的评价之中。
Seyed Ali Akbar Hedayati
2015-09-01
Full Text Available Background: Diazinon is an organophosphorous pesticide which widely found in municipal, agricultural, and urban storm water discharges. The present study was conducted to achieve lethal concentration (LC50 and behavioral changes of Rutilus rutilus caspius and Hypophthal-micthys molitrix after exposure to lethal concentration of diazinon. Methods: The experiment was carried out in static conditions, based on instructions of OECD in 4 days under controlled water physicochemical conditions with pH of 7.2±0.2, oxygen of 7±0.3 mg/l, total hardness of 180 mg CaCo3 and temperature of 24±1 C°. All fishes were accli-matized in 400 L aquaria for 10 days. Treated aquaria had concentrations of 0.5, 1, 2, 4, 8, 10, 20, 40, 60, and 80 ppm of diazinon for H. molitrix, and 1, 2, 4, 8, 10, and 20 for R. rutilus caspi-cus, while there was no toxic concentration for the control group. LC1, LC10, LC30, LC50, LC70, LC90, and LC99 were calculated for 24, 48, 72, and 96 hours. Results: LC50 96h diazinon values were 3.93 and 1.71 ppm for H. molitrix and R. rutilus caspi-cus, respectively. Clinical observation revealed that the poisoned fishes suffered from nerve paralysis syndrome. The fishes exhibited irregular, erratic, and darting swimming movements, severe aching, and collapse to the bottom of the aquarium. Conclusion: These findings suggest that diazinon has medium toxicity at low concentrations for thede two species and causes morbidities.
Aggression and the Risk for Suicidal Behaviors among Children
Greening, Leilani; Stoppelbein, Laura; Luebbe, Aaron; Fite, Paula J.
2010-01-01
Two subtypes of aggression--reactive and proactive--were examined to see how they relate to suicidal behaviors among young children admitted for acute psychiatric inpatient care. The children and their parents completed self-report questionnaires/interviews. Regression analyses revealed that depressed girls who scored higher on reactive aggression…
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Sickmann, Helle Mark; Skoven, Christian; Arentzen, Tina S.
Prenatal maternal stress increases the predisposition for affective disorders. Furthermore, women appear twice as likely as men to develop stress- and depression-related disorders. Comparable behavioral changes characteristic of clinical depression are found in rat offspring following prenatal...... stress (PS). These include increased helplessness, altered anxiety indicators and sleep modifications. Our purpose was to further investigate behavioral depression indices following PS as well as CNS structural changes including sex specificity of these variables. Pregnant Sprague-Dawley rats were...... in locomotor activity, depressive- and anxiety-like behavior as well as sleep architecture. Some animals were analyzed for CNS microstructural changes based on diffusion MRI. Subsets of PS and control rats were exposed to an acute stressor prior to the behavioral tests. Rearing/climbing activity in a familiar...
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
The role of innate immunity in spontaneous regression of cancer
J A Thomas
2011-01-01
Full Text Available Nature has provided us with infections - acute and chronic - and these infections have both harmful and beneficial effects on the human system. Worldwide, a number of chronic infections are associated with a risk of cancer, but it is also known that cancer regresses when associated with acute infections such as bacterial, viral, fungal, protozoal, etc. Acute infections are known to cure chronic diseases since the time of Hippocrates. The benefits of these fever producing acute infections has been applied in cancer vaccinology such as the Coley′s toxins. Immune cells like the natural killer cells, macrophages and dendritic cells have taken greater precedence in cancer immunity than ever before. This review provides an insight into the benefits of fever and its role in prevention of cancer, the significance of common infections in cancer regression, the dual nature of our immune system and the role of the often overlooked primary innate immunity in tumor immunology and spontaneous regression of cancer.
Olga Zhuk
2015-03-01
Full Text Available The pharmacological activities and acute toxicity of the psilocin (PC and dried residues of the crude extracts of psychotropic mushrooms were investigated in mice. The hallucinogenic substances were effectively isolated, by using methanol, from the species of Psilocybe semilanceata and Pholiotina cyanopus, that were collected in the north-east region of Poland. The chemical analysis of these extracts, which was performed by liquid chromatography with mass spectrometry detection (LC-MS, indicated the presence of psilocin and other hallucinogenic substances, including indolealkylamines and their phosphorylated analogues. When the pure psilocin or fungal extracts were used, slight differences in determined LD50 values were observed. However, the application of PC evoked the highest level of toxicity (293.07 mg/kg compared to the activity of extracts from Ph. cyanopus and P. semilanceata, where the level of LD50 was 316.87 mg/kg and 324.37 mg/kg, respectively. Furthermore, the behavioral test, which considered the head-twitching response (HTR, was used to assess the effects of the studied psychotropic factors on the serotonergic system. Both, the fungal extracts and psilocin evoked characteristic serotoninergic effects depending on the dose administered to mice, acting as an agonist/partial agonist on the serotonergic system. A dose of 200 mg/kg 5-hydroxytryptophan (5-HTP induced spontaneous head-twitching in mice (100% effect, as a result of the formation of 5-hydroxytryptamine (5-HT in the brain. Compared to the activity of 5-HTP, the intraperitoneal administration of 1mg/kg of psilocin or hallucinogenic extracts of studied mushrooms (Ph. cyanopus and P. semilanceata reduced the number of head-twitch responses of about 46% and 30%, respectively. In contrast, the administration of PC exhibited a reduction of about 60% in HTR numbers.
Zhuk, Olga; Jasicka-Misiak, Izabela; Poliwoda, Anna; Kazakova, Anastasia; Godovan, Vladlena V.; Halama, Marek; Wieczorek, Piotr P.
2015-01-01
The pharmacological activities and acute toxicity of the psilocin (PC) and dried residues of the crude extracts of psychotropic mushrooms were investigated in mice. The hallucinogenic substances were effectively isolated, by using methanol, from the species of Psilocybe semilanceata and Pholiotina cyanopus, that were collected in the north-east region of Poland. The chemical analysis of these extracts, which was performed by liquid chromatography with mass spectrometry detection (LC-MS), indicated the presence of psilocin and other hallucinogenic substances, including indolealkylamines and their phosphorylated analogues. When the pure psilocin or fungal extracts were used, slight differences in determined LD50 values were observed. However, the application of PC evoked the highest level of toxicity (293.07 mg/kg) compared to the activity of extracts from Ph. cyanopus and P. semilanceata, where the level of LD50 was 316.87 mg/kg and 324.37 mg/kg, respectively. Furthermore, the behavioral test, which considered the head-twitching response (HTR), was used to assess the effects of the studied psychotropic factors on the serotonergic system. Both, the fungal extracts and psilocin evoked characteristic serotoninergic effects depending on the dose administered to mice, acting as an agonist/partial agonist on the serotonergic system. A dose of 200 mg/kg 5-hydroxytryptophan (5-HTP) induced spontaneous head-twitching in mice (100% effect), as a result of the formation of 5-hydroxytryptamine (5-HT) in the brain. Compared to the activity of 5-HTP, the intraperitoneal administration of 1mg/kg of psilocin or hallucinogenic extracts of studied mushrooms (Ph. cyanopus and P. semilanceata) reduced the number of head-twitch responses of about 46% and 30%, respectively. In contrast, the administration of PC exhibited a reduction of about 60% in HTR numbers. PMID:25826052
Zhuk, Olga; Jasicka-Misiak, Izabela; Poliwoda, Anna; Kazakova, Anastasia; Godovan, Vladlena V; Halama, Marek; Wieczorek, Piotr P
2015-03-27
The pharmacological activities and acute toxicity of the psilocin (PC) and dried residues of the crude extracts of psychotropic mushrooms were investigated in mice. The hallucinogenic substances were effectively isolated, by using methanol, from the species of Psilocybe semilanceata and Pholiotina cyanopus, that were collected in the north-east region of Poland. The chemical analysis of these extracts, which was performed by liquid chromatography with mass spectrometry detection (LC-MS), indicated the presence of psilocin and other hallucinogenic substances, including indolealkylamines and their phosphorylated analogues. When the pure psilocin or fungal extracts were used, slight differences in determined LD50 values were observed. However, the application of PC evoked the highest level of toxicity (293.07 mg/kg) compared to the activity of extracts from Ph. cyanopus and P. semilanceata, where the level of LD50 was 316.87 mg/kg and 324.37 mg/kg, respectively. Furthermore, the behavioral test, which considered the head-twitching response (HTR), was used to assess the effects of the studied psychotropic factors on the serotonergic system. Both, the fungal extracts and psilocin evoked characteristic serotoninergic effects depending on the dose administered to mice, acting as an agonist/partial agonist on the serotonergic system. A dose of 200 mg/kg 5-hydroxytryptophan (5-HTP) induced spontaneous head-twitching in mice (100% effect), as a result of the formation of 5-hydroxytryptamine (5-HT) in the brain. Compared to the activity of 5-HTP, the intraperitoneal administration of 1mg/kg of psilocin or hallucinogenic extracts of studied mushrooms (Ph. cyanopus and P. semilanceata) reduced the number of head-twitch responses of about 46% and 30%, respectively. In contrast, the administration of PC exhibited a reduction of about 60% in HTR numbers.
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Eliana Peresi
2008-11-01
Full Text Available OBJETIVO: Analisar o padrão de citocinas pró- e antiinflamatórias e da resposta de fase aguda (RFA como marcadores de resposta ao tratamento da tuberculose pulmonar. MÉTODOS: Determinação dos níveis de interferon-gama (IFN-γ, tumor necrosis factor-alpha (TNF-α, fator de necrose tumoral-alfa, interleucina-10 (IL-10 e transforming growth factor-beta (TGF-β, fator transformador de crescimento-beta, pelo método ELISA, em sobrenadante de cultura de células mononucleares do sangue periférico e monócitos, assim como dos níveis de proteínas totais, albumina, globulinas, alfa-1-glicoproteína ácida (AGA, proteína C reativa (PCR e velocidade de hemossedimentação (VHS em 28 doentes com tuberculose pulmonar, em três tempos: antes (T0, aos três meses (T3 e aos seis meses (T6 de tratamento, em relação aos controles saudáveis, em um único tempo. RESULTADOS: Os pacientes apresentaram valores maiores de citocinas e RFA que os controles em T0, com diminuição em T3 e diminuição (TNF-α, IL-10, TGF-β, AGA e VHS ou normalização (IFN-γ e PCR em T6. CONCLUSÕES: PCR, AGA e VHS são possíveis marcadores para auxiliar no diagnóstico de tuberculose pulmonar e na indicação de tratamento de indivíduos com baciloscopia negativa; PCR (T0 > T3 > T6 = referência pode também ser marcador de resposta ao tratamento. Antes do tratamento, o perfil Th0 (IFN-γ, IL-10, TNF-α e TGF-β, indutor de e protetor contra inflamação, prevaleceu nos pacientes; em T6, prevaleceu o perfil Th2 (IL-10, TNF-α e TGF-β, protetor contra efeito nocivo pró-inflamatório do TNF-α ainda presente. O comportamento do IFN-γ (T0 > T3 > T6 = controle sugere sua utilização como marcador de resposta ao tratamento.OBJECTIVE: To evaluate the pattern of pro-inflammatory cytokines, anti-inflammatory cytokines and the acute phase response (APR as markers of the response to treatment of pulmonary tuberculosis. METHODS: Twenty-eight patients with pulmonary tuberculosis
汪凯; 王国庆; 李钋; 黄建生; 朱迎
2001-01-01
Purpose: To know the relation between chronic diseases and behavior factors in Heishui county. Method: The 1 483 inhabitants over 15in Heishui county, from the stratum sampling,areinterviewed. Resuls:The analysis of Logistic regression shows there are 8 kinds of factors affeting chronic diseases of Heishui inhabitants,and 5 kinds of factors are from behavior. The more important of them are occupation,drinking-alcohol and harmful dietetic habits. In the people of drinking-alcohol,the drinking-alcohol factors,which related with chronic diseases,are kinds of drinking-alcohol, years of drinking-alcohol,and many people drinking Za liquor with same straw and the level of drink-alcohol of 3 days. By the step wise regression,there are 5 kinds of factors on the digestive system diseases and 3 kinds are from behavior,the most important of them are harmful dietetic habits, drinking-alcohol,personal health habits. Conclusion:the behavior factors are the important for chronic diseases in the habitant of Heishui county, especially drink-alcool, harmful.%了解黑水县居民行为因素与慢性病的关系；方法：通过分层整群抽样，访谈调查了黑水县15岁及以上的居民1483人；结果：通过LOGLSTIC回归分析发现影响黑水县居民患慢性病的因素有八类因素，五类为行为因素，其中尤以职业、饮酒和不良饮食习惯因子1的作用明显，其患慢性病的概率和不患慢性病的概率之比分别为1.6627、1.4063和1.3986；与慢性病有关的饮酒因素中有饮酒种类、饮酒年限、共用吸管饮咂酒和3日饮酒量，其中喝咂酒共用吸管的相对危险度1.390为最大；逐步回归分析表明影响消化系统疾病的因素有五类，其中三类为行为因素，不良饮食卫生习惯因子1、饮酒和个人卫生因子2为最重要的三个因素。结论：影响黑水县居民患慢性病的因素是多方面的，其中自创性的行为因素占有重要的位置，尤其是饮酒
Caporino, Nicole E; Herres, Joanna; Kendall, Philip C; Wolk, Courtney Benjamin
2016-08-01
This study evaluated the impact of dysregulation across cognitive, affective, and behavioral domains on acute and 7- to 19-year follow-up outcomes of cognitive-behavioral therapy (CBT) for anxiety, and explored dysregulation as a predictor of psychopathology and impairment in young adulthood among individuals who received anxiety treatment as youth. Participants (N = 64; 50 % female, 83 % non-Hispanic White) from two randomized clinical trials completed a follow-up assessment 7-19 years later. Latent profile analysis identified dysregulation based on Anxious/Depressed, Attention Problems, and Aggressive Behavior scores on the Child Behavior Checklist. Although pretreatment dysregulation was not related to acute or follow-up outcomes for anxiety diagnoses that were the focus of treatment, dysregulation predicted an array of non-targeted psychopathology at follow-up. Among youth with a principal anxiety disorder, the effects of CBT (Coping Cat) appear to be robust against broad impairments in self-regulation. However, youth with a pretreatment dysregulation profile likely need follow-up to monitor for the emergence of other disorders.
Miriam Kron
2014-09-01
Full Text Available Reduced levels of brain-derived neurotrophic factor (BDNF are thought to contribute to the pathophysiology of Rett syndrome (RTT, a severe neurodevelopmental disorder caused by loss-of-function mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2. In Mecp2 mutant mice, BDNF deficits have been associated with breathing abnormalities, a core feature of RTT, as well as with synaptic hyperexcitability within the brainstem respiratory network. Application of BDNF can reverse hyperexcitability in acute brainstem slices from Mecp2-null mice, suggesting that therapies targeting BDNF or its receptor, TrkB, could be effective at acute reversal of respiratory abnormalities in RTT. Therefore, we examined the ability of LM22A-4, a small-molecule BDNF loop-domain mimetic and TrkB partial agonist, to modulate synaptic excitability within respiratory cell groups in the brainstem nucleus tractus solitarius (nTS and to acutely reverse abnormalities in breathing at rest and during behavioral arousal in Mecp2 mutants. Patch-clamp recordings in Mecp2-null brainstem slices demonstrated that LM22A-4 decreases excitability at primary afferent synapses in the nTS by reducing the amplitude of evoked excitatory postsynaptic currents and the frequency of spontaneous and miniature excitatory postsynaptic currents. In vivo, acute treatment of Mecp2-null and -heterozygous mutants with LM22A-4 completely eliminated spontaneous apneas in resting animals, without sedation. Moreover, we demonstrate that respiratory dysregulation during behavioral arousal, a feature of human RTT, is also reversed in Mecp2 mutants by acute treatment with LM22A-4. Together, these data support the hypothesis that reduced BDNF signaling and respiratory dysfunction in RTT are linked, and establish the proof-of-concept that treatment with a small-molecule structural mimetic of a BDNF loop domain and a TrkB partial agonist can acutely reverse abnormal breathing at rest and in response to
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Zhou, ZhiQiang; Zhang, GuangFen; Li, XiaoMin; Liu, XiaoYu; Wang, Nan; Qiu, LiLi; Liu, WenXue; Zuo, ZhiYi; Yang, JianJun
2015-04-01
Accumulating evidence has demonstrated that single subanesthetic dose of ketamine exerts rapid, robust, and lasting antidepressant-like effects. Nevertheless, repeated subanesthetic doses of ketamine produce psychosis-like effects with dysfunction of parvalbumin (PV) interneurons. We hypothesized that PV interneurons play an important role in the antidepressant-like actions of ketamine, and different changes in PV interneurons occur with the antidepressant-like and propsychotic-like effects of ketamine. To test this hypothesis, ketamine's antidepressant-like effects were evaluated by the forced swimming test. Ketamine-induced stereotyped behaviors and hyperactivity actions and the function of PV interneurons were also assessed. We demonstrated that an acute dose of 10 mg/kg ketamine induced significant antidepressant-like effects and reduced the levels of PV and the gamma-aminobutyric acid (GABA)-producing enzyme GAD67 in the rat prefrontal cortex. Moreover, inhibition of ketamine-induced loss of PV by apocynin blocked these antidepressant-like effects. Repeated administration of 30 mg/kg ketamine elicited stereotyped behaviors and hyperactivity actions as well as a longer duration of PV and GAD67 loss, higher brain glutamate levels, and lower brain GABA levels than acute single dose of ketamine. Our results reveal that the loss of phenotype of PV interneurons in the prefrontal cortex contributes to the antidepressant-like actions and is also involved in the propsychotic-like behaviors following acute and repeated ketamine administration, which may be partially mediated by the disinhibition of glutamate signaling. The different degrees and durations of the actions on PV interneurons produced by the two regimens of ketamine may partly underline the behavioral variance between the antidepressant- and propsychotic-like effects.
TWO REGRESSION CREDIBILITY MODELS
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Li, Xiang; Li, Xu; Li, Yi-Xiang; Zhang, Yuan; Chen, Di; Sun, Ming-Zhu; Zhao, Xin; Chen, Dong-Yan; Feng, Xi-Zeng
2015-01-01
We describe an interdisciplinary comparison of the effects of acute and chronic alcohol exposure in terms of their disturbance of light, dark and color preferences and the occurrence of Parkinson-like behavior in zebrafish through computer visual tracking, data mining, and behavioral and physiological analyses. We found that zebrafish in anxiolytic and anxious states, which are induced by acute and chronic repeated alcohol exposure, respectively, display distinct emotional reactions in light/dark preference tests as well as distinct learning and memory abilities in color-enhanced conditional place preference (CPP) tests. Additionally, compared with the chronic alcohol (1.0%) treatment, acute alcohol exposure had a significant, dose-dependent effect on anxiety, learning and memory (color preference) as well as locomotive activities. Acute exposure doses (0.5%, 1.0%, and 1.5%) generated an "inverted V" dose-dependent pattern in all of the behavioral parameters, with 1.0% having the greatest effect, while the chronic treatment had a moderate effect. Furthermore, by measuring locomotive activity, learning and memory performance, the number of dopaminergic neurons, tyrosine hydroxylase expression, and the change in the photoreceptors in the retina, we found that acute and chronic alcohol exposure induced varying degrees of Parkinson-like symptoms in zebrafish. Taken together, these results illuminated the behavioral and physiological mechanisms underlying the changes associated with learning and memory and the cause of potential Parkinson-like behaviors in zebrafish due to acute and chronic alcohol exposure.
Leukemia prediction using sparse logistic regression.
Tapio Manninen
Full Text Available We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
Amin, Shaimaa Nasr; El-Aidi, Ahmed Amro; Ali, Mohamed Mostafa; Attia, Yasser Mahmoud; Rashed, Laila Ahmed
2015-06-01
Stress is any condition that impairs the balance of the organism physiologically or psychologically. The response to stress involves several neurohormonal consequences. Glutamate is the primary excitatory neurotransmitter in the central nervous system, and its release is increased by stress that predisposes to excitotoxicity in the brain. Memantine is an uncompetitive N-methyl D-aspartate glutamatergic receptors antagonist and has shown beneficial effect on cognitive function especially in Alzheimer's disease. The aim of the work was to investigate memantine effect on memory and behavior in animal models of acute and repeated restraint stress with the evaluation of serum markers of stress and the expression of hippocampal markers of synaptic plasticity. Forty-two male rats were divided into seven groups (six rats/group): control, acute restraint stress, acute restraint stress with Memantine, repeated restraint stress, repeated restraint stress with Memantine and Memantine groups (two subgroups as positive control). Spatial working memory and behavior were assessed by performance in Y-maze. We evaluated serum cortisol, tumor necrotic factor, interleukin-6 and hippocampal expression of brain-derived neurotrophic factor, synaptophysin and calcium-/calmodulin-dependent protein kinase II. Our results revealed that Memantine improved spatial working memory in repeated stress, decreased serum level of stress markers and modified the hippocampal synaptic plasticity markers in both patterns of stress exposure; in ARS, Memantine upregulated the expression of synaptophysin and brain-derived neurotrophic factor and downregulated the expression of calcium-/calmodulin-dependent protein kinase II, and in repeated restraint stress, it upregulated the expression of synaptophysin and downregulated calcium-/calmodulin-dependent protein kinase II expression.
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Moore, J.
2011-01-01
Early forms of psychology assumed that mental life was the appropriate subject matter for psychology, and introspection was an appropriate method to engage that subject matter. In 1913, John B. Watson proposed an alternative: classical S-R behaviorism. According to Watson, behavior was a subject matter in its own right, to be studied by the…
Babinski, Dara E; Waxmonsky, James G; Pelham, William E
2014-10-01
This multiple baseline study evaluated the efficacy of behavioral parent training (BPT) for 12 parents (M age = 39.17 years; 91% mothers) and their children (ages 6-12; 83% boys) both with Attention-Deficit/Hyperactivity Disorder (ADHD), and also explored the acute effect of stimulant medication for parents before and after BPT. Parents rated their own and their children's symptoms and impairment and were stabilized on optimally dosed medication. Then, parents discontinued medication and were randomly assigned to a 3, 4, or 5 week baseline (BL), during which they provided twice-weekly ratings of their impairment, parenting, and their child's behavior. Following BL, parents and their children completed two laboratory tasks, once on their optimally dosed medication and once on a placebo to assess observable effects of medication on parent-child behavior, and they completed additional assessments of family functioning. Parents then completed eight BPT sessions, during which they were unmedicated. Twice-weekly ratings of parent and child behavior were collected during BPT and additional ratings were collected upon completing BPT. Two more parent-child tasks with and without parent medication were conducted upon BPT completion to assess the observable effects of BPT and BPT plus medication. Ten (83.33%) parents completed the trial. Improvements in parent and child behavior were observed, and parents reported improved child behavior with BPT. Few benefits of BPT emerged through parent reports of parent functioning, with the exception of inconsistent discipline, and no medication or interaction effects emerged. These results, although preliminary, suggest that some parents with ADHD benefit from BPT. While pharmacological treatment is the most common intervention for adults with ADHD, further examination of psychosocial treatments for adults is needed.
1951-03-01
clasmatodendrosis might be explained as a con- deprived of the oxygen supply, causing the acute sequence of a necrobiosis caused by the arrest onset...odendrosis is a necrobiosis His opinion brief instant anoxia has not a deadly hut a stimo- rust now be modified to allow for the fact that ulative effect...in a strict sense does not exist. It is actually a necrobiosis of Astrocytes within a tissue infiltrate in a case of an the cells, which is
Combining Alphas via Bounded Regression
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
席翼; 周泉; 刘朝霞
2015-01-01
Objective :To grasp the prevalence situation of primary and secondary school students’ obesity of Shenzhen and to study the dangerous factors resulted in obesity in order to provide the basis for further preventing and controlling the students’ obesity .Methods :The“BMI classification reference for screening overweight and obesity in Chinese school‐age chil‐dren and adolescents proposed by Working Group on Obesity in China (WGOC )”was used to screen the 3 833 primary school and secondary school students at the age of 8‐16 years in Shenzhen .Among which 195 obese students were picked out to form an “ obesity group” , while another 195 students were chosen to form the “control group” .The behavior factors of the two groups were investigated by the method of closed questionnaire (48 items ) and two rounds analysis were made by using single factor χ2 test and non conditional logistic regression model .Results and Conclusion :7 important factors which were recognized the main reasons re‐sulted in the primary and secondary school students’ obesity were selected :how to describe one’s body weight ,how to control body weight ,the living condition ,time of community sports activities ,mother’ s education level ,how long snacks before bedtime ,the amount of food to eat .That is the main reason causing the behavior of primary and middle school students obesi‐ty .Among them ,the living conditions ,education level of mother ,and body weight describation have not been reported in the previous studies .%研究目的：掌握深圳市中、小学学生肥胖的流行情况，探讨导致肥胖的行为危险因素，为进一步预防与控制学生肥胖提供依据。研究方法：采用中国肥胖问题工作组推出的“中国学龄儿童青少年超重、肥胖筛查BMI值分类标准”，对深圳3833名8～17岁普通学生筛查，确定肥胖学生195名，并选定对照组195名，对肥胖组和对照组的390人
Ana Belkys Hernández Millán
2015-02-01
Full Text Available Background: acute dentoalveolar abscess is a dental emergency and one of the major condition affecting the population; however, there are few studies on the subject. Objective: to describe the clinical and epidemiological characteristics of acute dentoalveolar abscess in patients of the health area VII in Cienfuegos. Methods: a descriptive study was conducted from January to December 2013 in the Health Area VII, Cienfuegos. The universe consisted of 672 patients and the sample included 374 individuals selected by simple random sampling. Primary data recording and data collection was obtained from medical records prior informed consent of the patients. The main variables were age, sex, pulp irritating agents. Results: females were more affected with 55, 35% and the 19-34 year age group with 33.69%. The main pulp irritator was microbial, 59.36%. Among the iatrogenic factors, the remains of decayed tissues were significant with 32.35%. Conclusions: there is a high number of patients with acute dentoalveolar abscess, thus, as a dental emergency, the dentist should know the characteristics and factors that develop the disease to promote a comprehensive job in terms of health promotion, prevention, treatment and rehabilitation of affected patients.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Leopoldo Soares Piegas
2013-01-01
ão ajudar a promover um melhor planejamento e manejo do atendimento da síndrome coronariana aguda a nível público e privado.BACKGROUND: Brazil lacks published multicenter registries of acute coronary syndrome. OBJECTIVE: The Brazilian Registry of Acute Coronary Syndrome is a multicenter national study aiming at providing data on clinical aspects, management and hospital outcomes of acute coronary syndrome in our country. METHODS: A total of 23 hospitals from 14 cities, participated in this study. Eligible patients were those who came to the emergency wards with suspected acute coronary syndrome within the first 24 hours of symptom onset, associated with compatible electrocardiographic alterations and/or altered necrosis biomarkers. Follow-up lasted until hospital discharge or death, whichever occurred first. RESULTS: Between 2003 and 2008, 2,693 ACS patients were enrolled, of which 864 (32.1% were females. T he final diagnosis was unstable angina in 1,141 patients, (42.4%, with a mortality rate of 3.06%, non-ST elevation acute myocardial infarction (AMI in 529 (19.6%, with mortality of 6.8%, ST-elevation AMI 950 (35.3%, with mortality of 8.1% and non-confirmed diagnosis 73 (2.7%, with mortality of 1.36%. The overall mortality was 5.53%. The multiple logistic regression model identified the following as risk factors for death regarding demographic factors and interventions: female gender (OR=1.45, diabetes mellitus (OR=1.59, body mass index (OR=1.27 and percutaneous coronary intervention (OR=0.70. A second model for death due to major complications identified: cardiogenic shock/acute pulmonary edema (OR=4.57, reinfarction (OR=3.48, stroke (OR=21.56, major bleeding (OR=3.33, cardiopulmonary arrest (OR=40.27 and Killip functional class (OR=3.37. CONCLUSION: The Brazilian Registry of Acute Coronary Syndrome data do not differ from other data collected abroad. The understanding of their findings may help promote better planning and management of acute coronary syndrome care
Zümrüt Başbakkal
2010-02-01
Full Text Available The study is executed with mothers of children aged 3-6 (n=170 whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" with 30 questions were used. Date gathering forms were carried out as pre-test by using face-to-face interview method with mothers of 3-6 aged children who were hospitalized for the first time and were in first 12 hours of hospitalization. "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" was re-carried out with mothers by phone 1 month after children being discharged from hospital.In analyzing of datas statistical programme of SPSS 13.0 for Windows was used. In statistical evaluation; number-percent dispersion, Wilcoxon Sing Rank test and Paired Sample-t test were used.According to the results obtained from the study, 57.6% of children are male with age average of 4,46±1,18 and 52.3% of them were hospitalized due to Gastroentestinal System Illnesses. A significant difference was determined between average points of behavior changes of 3-6 ages group children hospitalized due to an acute illness before hospitalized (10,735±4,882 and after being discharged from hospital (15,0476±4,306. In the study, it is observed that there are some behavioral changes in children after being hospitalized such as being cranky before going to bed and during eating, disquiet, bed-wetting, seperation anxiety, excessive attachment to a parent, to need help even for the things he/she could accomplish, to have fear from new environments, people or objects, bad temper attacks, fear of doctor/nurse and hospital
Hoyt, Richard E.; Speakman, Richard O.; Brown, David R.; Cassis, Lisa A; Silcox, Dennis L.; Anigbogu, Chikodi N.; Randall, David C.
2013-01-01
This study examined the effect of two-weeks infusion of angiotensin-II (Ang-II; 175 ng/kg/min) via minipump in rats (n=7) upon the mean arterial blood pressure (mBP) and heart rate (HR) response to an acute stress as compared to rats infused with saline (n=7). The acute stress was produced by a classical aversive conditioning paradigm: a 15 sec. tone (CS+) followed by a half second tail shock. Baseline mBP in Ang-II infused rats (167.7 ± 21.3 mm Hg; mean ± SD) significantly exceeded that of controls (127.6 ± 13.5 mm Hg). Conversely, baseline HR in the Ang-II infused rats (348 ± 33) was significantly lower than controls (384 ± 19 bpm). The magnitude of the mBP increase during CS+ did not differ between groups, but the HR slowing during CS+ in the Ang-II infused rats (−13.2 ± 8.9 bpm) was significantly greater than that seen in controls (−4.2 ± 5.5 bpm). This augmented bradycardia may be inferentially attributed to an accentuated increase in cardiac parasympathetic activity during CS+ in the Ang-II infused rats. The mBP increased above baseline immediately post-shock delivery in controls, but fell in the Ang-II infused rats, perhaps because of a ‘ceiling effect’ in total vascular resistance. This classical conditioning model of ‘acute stress’ differs from most stress paradigms in rats in yielding a HR slowing concomitant with a pressor response, and this slowing is potentiated by Ang-II. PMID:23317537
Time-adaptive quantile regression
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Mohammad Abu Basma Rajeh
2012-01-01
Full Text Available Background: The methanol extract of Euphorbia hirta L (Euphorbiaceae, which is used in traditional medicines, was tested for in vivo toxicity. Materials and Methods: In vivo brine shrimp lethality assay and oral acute toxicity study at single high dose of 5000 mg/kg and observation for 14 days in mice were used to study the toxic effect of E. hirta. Results: Brine shrimp lethality assay was used to calculate the median lethal concentration (LC 50 of E. hirta (for leaves, stems, flowers and roots methanolic extracts at concentrations from 100 to 0.07 mg/ml. The LC 50 values of 1.589, 1.420, 0.206 and 0.0827 mg/ml were obtained for stems, leaves, flowers and roots, respectively. Potassium dichromate (the positive control had LC 50 value of 0.00758 mg/ml. The acute oral toxicity study of the leaf extract resulted in one third mortality and mild behavioral changes among the treated mice. No significant statistical differences found between body weight, relative (% and absolute (g organ weights of treated and untreated groups (P> 0.05. Gross and microscopic examination of the vital organ tissues revealed no differences between control and treated mice. All the tissues appeared normal. Conclusions : E. hirta leaves methanol extract has exhibited mild toxic effects in mice.
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Mazzone, C M; Pati, D; Michaelides, M; DiBerto, J; Fox, J H; Tipton, G; Anderson, C; Duffy, K; McKlveen, J M; Hardaway, J A; Magness, S T; Falls, W A; Hammack, S E; McElligott, Z A; Hurd, Y L; Kash, T L
2016-12-13
The bed nucleus of the stria terminalis (BNST) is a brain region important for regulating anxiety-related behavior in both humans and rodents. Here we used a chemogenetic strategy to investigate how engagement of G protein-coupled receptor (GPCR) signaling cascades in genetically defined GABAergic BNST neurons modulates anxiety-related behavior and downstream circuit function. We saw that stimulation of vesicular γ-aminobutyric acid (GABA) transporter (VGAT)-expressing BNST neurons using hM3Dq, but neither hM4Di nor rM3Ds designer receptors exclusively activated by a designer drug (DREADD), promotes anxiety-like behavior. Further, we identified that activation of hM3Dq receptors in BNST VGAT neurons can induce a long-term depression-like state of glutamatergic synaptic transmission, indicating DREADD-induced changes in synaptic plasticity. Further, we used DREADD-assisted metabolic mapping to profile brain-wide network activity following activation of Gq-mediated signaling in BNST VGAT neurons and saw increased activity within ventral midbrain structures, including the ventral tegmental area and hindbrain structures such as the locus coeruleus and parabrachial nucleus. These results highlight that Gq-mediated signaling in BNST VGAT neurons can drive downstream network activity that correlates with anxiety-like behavior and points to the importance of identifying endogenous GPCRs within genetically defined cell populations. We next used a microfluidics approach to profile the receptorome of single BNST VGAT neurons. This approach yielded multiple Gq-coupled receptors that are associated with anxiety-like behavior and several potential novel candidates for regulation of anxiety-like behavior. From this, we identified that stimulation of the Gq-coupled receptor 5-HT2CR in the BNST is sufficient to elevate anxiety-like behavior in an acoustic startle task. Together, these results provide a novel profile of receptors within genetically defined BNST VGAT neurons that
Kunjbihari Sulakhiya
2016-01-01
Full Text Available Background: Stress plays a significant role in the pathogenesis of neuropsychiatric disorders such as anxiety and depression. Beta vulgaris is commonly known as "beet root" possessing antioxidant, anticancer, hepatoprotective, nephroprotective, wound healing, and anti-inflammatory properties. Objective: To study the protective effect of Beta vulgaris Linn. ethanolic extract (BVEE of leaves against acute restraint stress (ARS-induced anxiety- and depressive-like behavior and oxidative stress in mice. Materials and Methods: Mice (n = 6 were pretreated with BVEE (100 and 200 mg/kg, p. o. for 7 days and subjected to ARS for 6 h to induce behavioral and biochemical changes. Anxiety- and depressive-like behavior were measured by using different behavioral paradigms such as open field test (OFT, elevated plus maze (EPM, forced swim test (FST, and tail suspension test (TST 40 min postARS. Brain homogenate was used to analyze oxidative stress parameters, that is, malondialdehyde (MDA and reduced glutathione (GSH level. Results: BVEE pretreatment significantly (P < 0.05 reversed the ARS-induced reduction in EPM parameters, that is, percentage entries and time spent in open arms and in OFT parameters, that is, line crossings, and rearings in mice. ARS-induced increase in the immobility time in FST and TST was attenuated significantly (P < 0.05 by BVEE pretreatment at both the dosage. An increase in MDA and depletion of GSH level postARS was prevented significantly (P < 0.05 with BVEE pretreatment at both the dosage (100 and 200 mg/kg. Conclusion: BVEE exhibits anxiolytic and antidepressant activity in stressed mice along with good antioxidant property suggesting its therapeutic potential in the treatment of stress-related psychiatric disorders.
Adam Seluzicki
2014-03-01
Full Text Available Molecular circadian clocks are interconnected via neural networks. In Drosophila, PIGMENT-DISPERSING FACTOR (PDF acts as a master network regulator with dual functions in synchronizing molecular oscillations between disparate PDF(+ and PDF(- circadian pacemaker neurons and controlling pacemaker neuron output. Yet the mechanisms by which PDF functions are not clear. We demonstrate that genetic inhibition of protein kinase A (PKA in PDF(- clock neurons can phenocopy PDF mutants while activated PKA can partially rescue PDF receptor mutants. PKA subunit transcripts are also under clock control in non-PDF DN1p neurons. To address the core clock target of PDF, we rescued per in PDF neurons of arrhythmic per⁰¹ mutants. PDF neuron rescue induced high amplitude rhythms in the clock component TIMELESS (TIM in per-less DN1p neurons. Complete loss of PDF or PKA inhibition also results in reduced TIM levels in non-PDF neurons of per⁰¹ flies. To address how PDF impacts pacemaker neuron output, we focally applied PDF to DN1p neurons and found that it acutely depolarizes and increases firing rates of DN1p neurons. Surprisingly, these effects are reduced in the presence of an adenylate cyclase inhibitor, yet persist in the presence of PKA inhibition. We have provided evidence for a signaling mechanism (PKA and a molecular target (TIM by which PDF resets and synchronizes clocks and demonstrates an acute direct excitatory effect of PDF on target neurons to control neuronal output. The identification of TIM as a target of PDF signaling suggests it is a multimodal integrator of cell autonomous clock, environmental light, and neural network signaling. Moreover, these data reveal a bifurcation of PKA-dependent clock effects and PKA-independent output effects. Taken together, our results provide a molecular and cellular basis for the dual functions of PDF in clock resetting and pacemaker output.
van Emmerik, A.A.P.; Kamphuis, J.H.; Emmelkamp, P.M.G.
2008-01-01
Background: Writing assignments have shown promising results in treating traumatic symptomatology. Yet no studies have compared their efficacy to the current treatment of choice, cognitive behavior therapy (CBT). The present study evaluated the efficacy of structured writing therapy (SWT) and CBT as
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Regression Testing Cost Reduction Suite
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
龚顺松; 明炜
2016-01-01
目的：探讨肺挫伤所致急性呼吸窘迫综合征患者病死率密切相关的影响因素。方法112例肺挫伤所致急性呼吸窘迫综合征患者的各项临床指标进行统计分析以明确影响病死率的独立相关因素，根据患者预后，分为生存组和死亡组。研究的因素包括性别、年龄、伤后时间、是否合并多发伤、是否发生低血容量性休克、血小板计数、手术与否、血糖值、有无合并基础疾病、ISS 评分及 TSS 评分。应用单因素、多因素 Lo-gistic 回归分析肺挫伤所导致的急性呼吸窘迫综合征。结果两组间性别、手术与否差异无统计学意义（P ＞0.05），而年龄、伤后时间、低血容量休克、血小板计数、血糖、存在基础疾病、ISS 评分及 TSS 评分差异有统计学意义（P ＜0.05）。多因素 Logistic 分析发现，影响此类患者预后的独立危险因素为年龄≥60岁、伤后时间、合并多发伤、低血容量休克、血小板计数≤80×109／L、有基础疾病、ISS≥30分和 TSS≥15分。结论肺挫伤所致急性呼吸窘迫综合征病死率高，以上独立危险因素可以为正确判断病情提供重要依据。%Objective To investigate the risk factors of mortality in patients with acute respiratory distress syndrome caused by pulmonary contusion.Methods 112 patients with acute respiratory distress syndrome caused by pulmonary contusion were enrolled in the study,and they were divided into the survival group and the death group ac-cording to their prognosis.The factors relevant to outcomes included age,gender,time after injury,multiple inju-ries,hemorrhagic shock,platelet count,operations,blood sugar,basis diseases,ISS and TSS.All these factors were measured and analyzed by univariate and multivariate logistic regression to form a regression model.Results Univa-riate logistical regression analysis showed that ages,time after injury,multiple injuries,hemorrhagic shock
A Matlab program for stepwise regression
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Ruocco, Lucia A; Romano, Emilia; Treno, Concetta; Lacivita, Enza; Arra, Claudio; Gironi-Carnevale, Ugo A; Travaglini, Domenica; Leopoldo, Marcello; Laviola, Giovanni; Sadile, Adolfo G; Adriani, Walter
2014-04-01
We report here the results of studies aimed to investigate the involvement of serotonin receptor 7 subtype (5-HT7-R) in the modulation of emotional response in Naples High-Excitability (NHE) rat, a validated model for hyperactivity and impaired attention. A range of dosages (0.0, 0.125, 0.250, or 0.500 mg/kg) of LP-211, a selective agonist of 5-HT7-Rs, has been evaluated in animals at different age (adolescence and adulthood). Male NHE and random bred (NRB) control rats were tested in an Elevated Zero-Maze (EZM) after LP-211 treatment in two different regimens: at the issue of adolescent, subchronic exposure (14 intraperitoneal [i.p.] injections, once/day, pnd 31-44, tested on pnd 45--Exp. 1) or as adult, acute effect (15 min after i.p. injection--Exp. 2). Adolescent, subchronic LP-211 at 0.500 mg/kg dosage increased the frequency of head-dips only in NHE rats. Drug effect on time spent and entries in open EZM quadrants were revealed with adult, acute administration of 0.125 mg/kg LP-211 (both strains), indicating a tendency toward anxiolytic effects. In conclusion, data demonstrate that subchronic stimulation of 5-HT7-Rs during prepuberal period increases novelty-seeking/risk-taking propensity in NHE adults. These sequels are revealing increased disinhibition and/or motivation to explore in the NHE rats, which are characterized by a hyperactive dopaminergic system. These data may open new perspectives in studying mechanism of risk-seeking behavior.
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
From Rasch scores to regression
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Pérez-Saad, Héctor; Buznego, María T
2008-04-01
Cestrum nocturnum is a garden shrub from the family Solanaceae and is used as a remedy for different health disorders. The aim of the present work was to investigate the potential neuropharmacological action profile of decoctions obtained from dry leaves of the plant. Decoctions were tested in different neuropharmacological models-Irwin test, exploratory behavior, tests for analgesia, isoniazid- and picrotoxin-induced convulsions, and maximal electroshock seizures-in mice, as well as in amphetamine-induced stereotypies and penicillin epileptic foci in rats. Decoctions of 1 and 5% (D1 and D5) induced restlessness, and the 30% decoction (D30) induced passivity. D5 and D30 reduced significantly exploratory behavior and amphetamine-induced stereotypies within a 3-hour observation period. The latter effect was apparent during the second 60 minutes. Decoctions reduced the amount of writhes induced by acetic acid in a dose-dependent manner, but were not effective in the hot plate model. The decoctions were not effective against pharmacologically induced convulsions. However, repeated administration of five doses of D5, at 1-hour intervals, reduced the amplitude of penicillin-induced epileptic spikes in both primary and secondary foci, in curarized rats. Taken together, the results suggest that C. nocturnum possesses active substances with analgesic activity provided through a peripheral action mechanism, in parallel with some psychoactive activity that does not fit well the neuropharmacological action profile of known reference neurotropic drugs.
Khaloo, Pegah; Sadeghi, Banafshe; Ostadhadi, Sattar; Norouzi-Javidan, Abbas; Haj-Mirzaian, Arya; Zolfagharie, Samira; Dehpour, Ahmad-Reza
2016-10-01
Major depressive disorder is disease with high rate of morbidity and mortality. Stressful events lead to depression and they can be used as a model of depression in rodents. In this study we aimed to investigate whether lithium modifies the stressed-induced depression through blockade of opioid receptors in mice. We used foot shock stress as stressor and forced swimming test (FST), tail suspension test (TST) and open field test (OFT) to evaluation the behavioral responses in mice. We also used naltrexone hydrochloride (as opioid receptor antagonist), and morphine (as opioid receptor agonist). Our results displayed that foot-shock stress significantly increased the immobility time in TST and FST but it could not change the locomotor behavior in OFT. When we combined the low concentrations of lithium and naltrexone a significant reduction in immobility time was seen in the FST and TST in comparison with control foot-shock stressed group administered saline only. Despite the fact that our data showed low concentrations of lithium, when administered independently did not significantly affect the immobility time. Also our data indicated that concurrent administration of lithium and naltrexone had no effect on open field test. Further we demonstrated that simultaneous administration of morphine and lithium reverses the antidepressant like effect of active doses of lithium. Our data acclaimed that we lithium can augment stressed-induced depression and opioid pathways are involved in this action.
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Jan eSvoboda
2015-04-01
Full Text Available Patients with schizophrenia often manifest deficits in behavioral flexibility. Non-competitive NMDA receptor antagonists such as MK-801 induce schizophrenia-like symptoms in rodents, including cognitive functions. Despite work exploring flexibility has been done employing behavioral paradigms with simple stimuli, much less is known about what kinds of flexibility are affected in an MK-801 model of schizophrenia-like behavior in the spatial domain. We used a rotating arena-based apparatus (Carousel requiring rats to avoid an unmarked sector defined in either the reference frame of the rotating arena (arena frame task, AF or the stationary room (room frame task, RF. We investigated behavioral flexibility in four conditions involving different cognitive loads. Each condition encompassed an initial (five sessions and a test phase (five sessions in which some aspects of the task were changed to test flexibility in which rats were given saline, 0.05 mg/kg or 0.1 mg/kg MK-801 thirty minutes prior to a session. In the first condition, rats acquired avoidance in RF with clockwise rotation of the arena while in the test phase the arena rotated counterclockwise. In the second condition, rats initially acquired avoidance in RF with the sector on the north and then it was reversed to south (spatial reversal. In the third and fourth conditions, rats initially performed an AF (RF, respectively task, followed by an RF (AF, respectively task, testing the ability of cognitive set-shifting. We found no effect of MK-801 either on simple motor adjustment after reversal of arena rotation or on spatial reversal within the RF. In contrast, administration of MK-801 at a dose of 0.1 mg/kg interfered with set-shifting in both conditions. Furthermore, we observed MK-801 0.1 mg/kg elevated locomotion in all cases. These data suggest that blockade of NMDA receptors by acute system administration of MK-801 preferentially affects set-shifting in the cognitive domain rather
Entrepreneurial intention modeling using hierarchical multiple regression
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Nesrin Şen Celasin
2010-02-01
Full Text Available The study is executed with mothers of children aged 3-6 (n=170 whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" with 30 questions were used. Date gathering forms were carried out as pre-test by using face-to-face interview method with mothers of 3-6 aged children who were hospitalized for the first time and were in first 12 hours of hospitalization. "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" was re-carried out with mothers by phone 1 month after children being discharged from hospital.In analyzing of datas statistical programme of SPSS 13.0 for Windows was used. In statistical evaluation; number-percent dispersion, Wilcoxon Sing Rank test and Paired Sample-t test were used.According to the results obtained from the study, 57.6% of children are male with age average of 4,46±1,18 and 52.3% of them were hospitalized due to Gastroentestinal System Illnesses. A significant difference was determined between average points of behavior changes of 3-6 ages group children hospitalized due to an acute illness before hospitalized (10,735±4,882 and after being discharged from hospital (15,0476±4,306. In the study, it is observed that there are some behavioral changes in children after being hospitalized such as being cranky before going to bed and during eating, disquiet, bed-wetting, seperation anxiety, excessive attachment to a parent, to need help even for the things he/she could accomplish, to have fear from new environments, people or objects, bad temper attacks, fear of doctor/nurse and hospital, fear
... its blood vessels. This problem is called acute pancreatitis. Acute pancreatitis affects men more often than women. Certain ... pancreatitis; Pancreas - inflammation Images Digestive system Endocrine glands Pancreatitis, acute - CT scan Pancreatitis - series References Forsmark CE. Pancreatitis. ...
Uncomplicated urinary tract infection; UTI - acute cystitis; Acute bladder infection; Acute bacterial cystitis ... cause. Menopause also increases the risk for a urinary tract infection. The following also increase your chances of having ...
Hughes, Robert N; Hancock, Nicola J
2016-01-01
To assess the possibility that acute caffeine's behavioral action might depend on rats' strain, effects of 50mg/kg of the drug were observed on activity, anxiety-related behavior and habituation learning in male and female rats from three different strains, namely PVG/c, Long-Evans and Wistar. All subjects were tested in an open field, an elevated plus maze and a light-dark box. For the three strains combined, increased occupancy of the center of the open field and entries of the open plus-maze arms with caffeine suggested caffeine-induced anxiolysis, whereas increased grooming in the open field, decreased rearing in the plus maze and increased risk assessment in the light-dark box were consistent with anxiogenesis. Caffeine also reduced open-field rearing only for PVG/c rats, and entries into and occupation of the light side of the light-dark box only for Long-Evans rats, and increased total defecation in the three types of apparatus for all three strains combined. Overall, caffeine appeared to be mainly anxiogenic. The drug also increased open-field ambulation for PVG/c rats and walking for all rats, but decreased open-field ambulation and entries into the plus maze closed arms for Wistar rats alone. In general, Wistar rats appeared to be the least and Long-Evans the most anxious of the three strains investigated. Caffeine also decreased within-session habituation of open-field ambulation for PVG/c rats alone, thereby suggesting strain-dependent interference with non-associative learning and short-term memory. Several overall sex differences were also observed that supported female rats being more active and less anxious than males.
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
徐冬梅; 文岳中; 李立; 钟旭初
2011-01-01
全氟辛烷磺酰基化合物(PFOS)作为一种新型持久性有机污染物,已经成为环境科学和毒理学的研究热点,其对生态环境的影响值得深入研究.本文采用OECD标准滤纸接触法、人工土壤法及自然土壤法研究了PFOS对蚯蚓急性致死作用及回避行为的影响.结果表明:PFOS对蚯蚓的急性毒性作用与染毒时间和染毒浓度相关,试验求得滤纸法48h、人工土壤法14d和自然土壤法14d的LC50值分别为13.64μg·cm-2、955.28mg·kg-1和542.08mg·kg-1;人工土壤和自然土壤中蚯蚓在PFOS的最大试验浓度组160mg·kg-1,中均表现出显著的回避行为,表明蚯蚓可以明显感知较高浓度PFOS污染土壤并作出回避反应.与急性毒性试验的测试终点LC50,相比,蚯蚓行为测试终点对PFOS的反应更为敏感.自然土壤中PFOS对蚯蚓的急性毒性大于人工土壤,相同浓度PFOS作用下,蚯蚓于自然土壤中的回避行为较人工土壤明显.%As a new kind of persistent organic pollutants, perfluorooctane sulfonate (PFOS) has become a research spot of environmental science and toxicology.Its impacts on ecological environment should be deeply studied.In this paper, standard contact filter paper test of OECD, artificial soil test, and natural soil test were adopted to study the effects of PFOS on the acute lethality and avoidance behavior of earthworm.The results showed that the acute toxicity of PFOS on earthworm was related to the toxicant exposure time and concentration.The LC50,48 h in filter paper test,LC50, 14 d in artificial soil test, and LC50, 14 d in natural soil test were 13.64 μg · cm-2, 955.28 mg· kg-1, and 542.08 mg· kg-1, respectively.At the maximum test concentration of 160 mg·kg-1 , the earthworm in artificial soil and natural soil showed significant avoidance behavior, which proved that earthworm could perceive and avoid the soil contaminated by a higher concentration of PFOS.To assess PFOS-contaminated soils, the
Sheikh, M. A.; Higuchi, T.; Imo, T. S.; Fujimura, H.; Oomori, T.
2007-12-01
Spatial and temporal behavior of the tributyl tin (TBT) were investigated in the coastal areas around Okinawa Island, Japan. A seasonal monitoring study was conducted between February and October 2006. The effects of TBT on the carbon metabolisms (net production and calcification) on the intact coral-alga association Galaxea fascicularis were also investigated. Mean concentration of TBT (2.45 ng/L) found in the Manko estuary waters have exceeded some international permissible targets of waters quality guideline for TBT (1ng/L). The sediments in Manko estuary sediments can be considered lightly contaminated (0-20 ng/g dw) and Okukubi estuary as uncontaminated (below 3ng/g dw) with TBT. The seasonal concentration pattern of TBT at the Manko estuary was autumn > spring > summer > winter. The acute ecotoxicological results show that the photosynthesis rate and calcification rate were significantly reduced by 78 % and 72 % relative to the control (ANOVA, p0.05) were observed when corals were exposed to 1000 ng/LTBT. The present study reports the occurrence and continuous input of TBT in the coastal areas around Okinawa Island, even 16 years after legal restriction of TBT usage in coastal waters was implemented by the Japanese Environmental Authorities. However, the nominal sensitive concentration of TBT that causes alteration of carbon metabolisms of coral within 96 hrs exposure are much higher than those recently found in the coral reef waters and adjacent ecosystems.
Xue, Li-Xia; Zhang, Ting; Zhao, Yu-Wu; Geng, Zhi; Chen, Jing-Jiong; Chen, Hao
2016-05-01
Cerebrolysin and DL-3-n-butylphthalide (NBP) have each shown neuroprotective efficacy in preclinical models of acute ischemic stroke (AIS) and passed clinical trials as therapeutic drugs for AIS. The present study was a clinical trial to assess and compare the efficacy and safety of NBP and Cerebrolysin in the reduction of neurological and behavioral disability following AIS. A randomized, double-blind trial was conducted with enrolment of 60 patients within 12 h of AIS. In addition to routine treatment, patients were randomly assigned to receive a 10-day intravenous administration of NBP, Cerebrolysin or placebo. National Institutes of Health Stroke Scale (NIHSS) and Barthel Index (BI) scores were used to evaluate the efficacy of the treatment in the patients with AIS at 11 and 21 days after the initiation of therapy. Adverse events were also analyzed among the three groups. After 10 days of treatment with NBP or Cerebrolysin, the NIHSS and BI scores at day 21 showed statistical differences compared with those in the placebo group (PCerebrolysin groups were higher than those in the placebo group at days 11 and 21 (PCerebrolysin. The results indicate that NBP may be more effective than Cerebrolysin in improving short-term outcomes following AIS. This trial is registered at ClinicalTrials.gov with clinical trial identifier number NCT02149875.
Inferential Models for Linear Regression
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Simpkins D
2016-10-01
Full Text Available Daniel Simpkins,1 Carmelle Peisah,2,3 Irene Boyatzis1 1Division of Rehabilitation and Aged Care, Hornsby Ku-ring-gai Hospital, 2School of Psychiatry, University of New South Wales, 3Discipline of Psychiatry, University of Sydney, Sydney, NSW, Australia Aim: The management of severely agitated elderly patients is not easy, and limited guidelines are available to assist practitioners. At a Sydney hospital, an Aggression Response Team (ART comprising clinical and security staff can be alerted when a staff member has safety concerns. Our aims were to describe the patient population referred for ART calls, reasons for and interventions during ART calls, and complications following them.Methods: Patients 65 years and older referred for ART calls in the emergency department or wards during 2014 were identified using the Incident Information Management System database and medical records were reviewed. Demographic and clinical data were collected. Results: Of 43 elderly patients with ART calls, 30 had repeat ART calls. Thirty-one patients (72% had underlying dementia, and 22 (51% were agitated at the time of admission. The main reasons for ART calls were wandering and physical aggression. Pharmacological sedation was used in 88% of the ART calls, with a range of psychotropics, doses, and routes of administration, including intravenous (19% and, most commonly, midazolam (53%. Complications were documented in 14% of cases where sedation was used. Conclusion: We observed a high frequency of pharmacological sedation among the severely agitated elderly, with significant variance in the choice and dose of sedation and a high rate of complications arising from sedation, which may be an underestimate given the lack of post-sedation monitoring. We recommend the development of guidelines on the management of behavioral emergency in the elderly patients, including de-escalation strategies and standardized psychotropic guidelines. Keywords: aged, aggression
Al-Ghraibah, Amani
Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Regression of lumbar disk herniation
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Heteroscedasticity checks for regression models
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
... Smoking also slows down the healing process. Acute bronchitis treatment Most cases of acute bronchitis can be treated at home.Drink fluids, but ... bronchial tree. Your doctor will decide if this treatment is right for you. Living with acute bronchitis Most cases of acute bronchitis go away on ...
Patel, Sapan J; Dao, Su; Darie, Costel C; Clarkson, Bayard D
2016-01-01
Quorum sensing (QS) is a generic term used to describe cell-cell communication and collective decision making by bacterial and social insects to regulate the expression of specific genes in controlling cell density and other properties of the populations in response to nutrient supply or changes in the environment. QS mechanisms also have a role in higher organisms in maintaining homeostasis, regulation of the immune system and collective behavior of cancer cell populations. In the present study, we used a p190BCR-ABL driven pre-B acute lymphoblastic leukemia (ALL3) cell line derived from the pleural fluid of a terminally ill patient with ALL to test the QS hypothesis in leukemia. ALL3 cells don’t grow at low density (LD) in liquid media but grow progressively faster at increasingly high cell densities (HD) in contrast to other established leukemic cell lines that grow well at very low starting cell densities. The ALL3 cells at LD are poised to grow but shortly die without additional stimulation. Supernates of ALL3 cells (HDSN) and some other primary cells grown at HD stimulate the growth of the LD ALL3 cells without which they won’t survive. To get further insight into the activation processes we performed microarray analysis of the LD ALL3 cells after stimulation with ALL3 HDSN at days 1, 3, and 6. This screen identified several candidate genes, and we linked them to signaling networks and their functions. We observed that genes involved in lipid, cholesterol, fatty acid metabolism, and B cell activation are most up- or down-regulated upon stimulation of the LD ALL3 cells using HDSN. We also discuss other pathways that are differentially expressed upon stimulation of the LD ALL3 cells. Our findings suggest that the Ph+ ALL population achieves dominance by functioning as a collective aberrant ecosystem subject to defective quorum-sensing regulatory mechanisms. PMID:27429840
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Heteroscedasticity checks for regression models
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
Producing The New Regressive Left
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...... coalition (Iran, Hizbollah, Syria), capitalises on a series of factors that bring them together in spite of their otherwise diverse worldviews and agendas. The New Regressive Left is united by resistance against the growing influence of Saudi Arabia in the religious, cultural, political, economic...
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Mission assurance increased with regression testing
Lang, R.; Spezio, M.
- C based and big endian. The presence of byte swap issues that might not have been identified in the required software changes was very real and can be difficult to find. The ability to have test designs that would exercise all major functions and operations was invaluable to assure that critical operations and tools would operate as they had since first operational use. With the longevity of the mission also came the realization that the original ISAT team would not be the people working on the ISAT regression testing. The ability to have access to all original test designs and test results identified in the regression test suite greatly improved the ability to identify not only the expected system behavior, but also the actual behavior with the old architecture. So in summary, this paper will discuss the importance, practicality, and results achieved by having a well-defined regression test available to assure the New Horizons Mission Operations Control system continues to meet its functional requirements to support the mission objectives.
An Application on Multinomial Logistic Regression Model
Abdalla M El-Habil
2012-03-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children, from a survey of Youth 2003 which was conducted by Palestinian Central Bureau of Statistics (PCBS. Segment of the population of children in the age group (10-14 years for residents in Gaza governorate, size of 66,935 had been selected, and the response variable consisted of four categories. Eighteen of explanatory variables were used for building the primary multinomial logistic regression model. Model had been tested through a set of statistical tests to ensure its appropriateness for the data. Also the model had been tested by selecting randomly of two observations of the data used to predict the position of each observation in any classified group it can be, by knowing the values of the explanatory variables used. We concluded by using the multinomial logistic regression model that we can able to define accurately the relationship between the group of explanatory variables and the response variable, identify the effect of each of the variables, and we can predict the classification of any individual case.
张耀东; 熊昊; 张小玲; 谭利娜; 胡群; 刘双又; 张柳清; 刘爱国; 孙燕
2011-01-01
目的:探讨影响儿童急性淋巴细胞白血病(ALL)首次化疗结果的因素分析为临床治疗提供依据.方法:对华中科技大学同济医学院附属同济医院儿科139例ALL患儿首次化疗的有关指标与化疗效果(完全缓解或非完全缓解)的关系进行单因素和Logistic多因素分析.结果:单因素结果提示除年龄及外周血白细胞计数与首次化疗CR有关,其余各项变量均与首次化疗CR无关.Logistic多因素分析显示,外周血白细胞计数(OR=-0.636,95%CI为0.30～0.95,P=0.003)是导致儿童ALL首次化疗非完全缓解的主要危险因素.结论:对于伴外用血高白细胞的儿童ALL,其首次化疗完全缓解率较低,应引起医师的高度重视.%Objective To investigate the risk factors of complete remission of childhood acute lymphoblastic leukemia (ALL) during the first time chemotherapy, so as to provide theoretical evidence for clinical treatment. Methods Totally 139 children with ALL, who were treated in our hospital during Jan. 2000 to July 2009, were included in the present study.The relations between related indices of the first time chemotherapy and complete remission were analyzed by univariate and multivariate analysis. Results Single-factor analysis showed that the age of patients and peripheral white blood cell count were the risk factors, Multivariate Logistic regression analysis showed peripheral blood white blood cell count was the main risk factor (OR = -0.636, 95% CI = 0.30-0.95 ,P = 0.003). Conclusions High peripheral blood white blood cell count level was a risk factor of complete remission in childhood ALL which should be cared.
Testing for Stock Market Contagion: A Quantile Regression Approach
S.Y. Park (Sung); W. Wang (Wendun); N. Huang (Naijing)
2015-01-01
markdownabstract__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test allows
On Weighted Support Vector Regression
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Leichliter, Jami S.; Lewis, David A.; Paz-Bailey, Gabriela
2017-01-01
Data from baseline surveys and STI/HIV laboratory tests (n=615 men) were used to examine correlates of bacterial ulcers (Treponema pallidum, Haemophilus ducreyi, or Chlamydia trachomatis L1–L3 detected in ulcer) and acute HSV-2 ulcers (HSV-2 positive ulcer specimen, HSV-2 sero-negative, and negative for bacterial pathogens) vs. recurrent HSV-2 ulcers (sero-positive), separately. Compared to men with recurrent HSV-2 ulcers, men with bacterial ulcers had larger ulcers but were less likely to be HIV-positive whereas men with acute HSV-2 ulcers were younger with fewer partners. Acute HIV was higher among men with bacterial and acute HSV-2 ulcers; the difference was not statistically significant. PMID:28217702
Leichliter, Jami S; Lewis, David A; Paz-Bailey, Gabriela
2016-01-01
Data from baseline surveys and STI/HIV laboratory tests (n=615 men) were used to examine correlates of bacterial ulcers (Treponema pallidum, Haemophilus ducreyi, or Chlamydia trachomatis L1-L3 detected in ulcer) and acute HSV-2 ulcers (HSV-2 positive ulcer specimen, HSV-2 sero-negative, and negative for bacterial pathogens) vs. recurrent HSV-2 ulcers (sero-positive), separately. Compared to men with recurrent HSV-2 ulcers, men with bacterial ulcers had larger ulcers but were less likely to be HIV-positive whereas men with acute HSV-2 ulcers were younger with fewer partners. Acute HIV was higher among men with bacterial and acute HSV-2 ulcers; the difference was not statistically significant.
Hybrid rocket fuel combustion and regression rate study
Strand, L. D.; Ray, R. L.; Anderson, F. A.; Cohen, N. S.
1992-01-01
The objectives of this study are to develop hybrid fuels (1) with higher regression rates and reduced dependence on fuel grain geometry and (2) that maximize potential specific impulse using low-cost materials. A hybrid slab window motor system was developed to screen candidate fuels - their combustion behavior and regression rate. Combustion behavior diagnostics consisted of video and high speed motion pictures coverage. The mean fuel regression rates were determined by before and after measurements of the fuel slabs. The fuel for this initial investigation consisted of hydroxyl-terminated polybutadiene binder with coal and aluminum fillers. At low oxidizer flux levels (and corresponding fuel regression rates) the filled-binder fuels burn in a layered fashion, forming an aluminum containing binder/coal surface melt that, in turn, forms into filigrees or flakes that are stripped off by the crossflow. This melt process appears to diminish with increasing oxidizer flux level. Heat transfer by radiation is a significant contributor, producing the desired increase in magnitude and reduction in flow dependency (power law exponent) of the fuel regression rate.
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Nonparametric Regression with Common Shocks
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Lumbar herniated disc: spontaneous regression
Yüksel, Kasım Zafer
2017-01-01
Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Bo-Guang Fan
2010-01-01
Full Text Available Background : Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims : The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods : We reviewed the English-language literature (Medline addressing pancreatitis. Results : Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingestion. There are a number of important issues regarding clinical highlights in the classification, treatment and prognosis of acute pancreatitis, and treatment options for complications of acute pancreatitis including pancreatic pseudocysts. Conclusions : Multidisciplinary approach should be used for the management of the patient with acute pancreatitis.
Bo-Guang Fan
2010-05-01
Full Text Available Background: Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims: The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods: We reviewed the English-language literature (Medline addressing pancreatitis. Results: Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingestion. There are a number of important issues regarding clinical highlights in the classification, treatment and prognosis of acute pancreatitis, and treatment options for complications of acute pancreatitis including pancreatic pseudocysts. Conclusions: Multidisciplinary approach should be used for the management of the patient with acute pancreatitis.
Krishna Tanwar
2014-02-01
Full Text Available Neurosteroids (NS are considered important modulators of brain functions. Lindane a pesticide has been shown to affect the nerv-ous system adversely. The present study was designed to explore the modulation of the effects of lindane on convulsions by Allopregnenalone (AP, and 4′-Chlorodiazepam (4′-CD, in both acute and chronic seizure models using Pentylenetetrazole (PTZ. We used acute and chronic models. In the acute model, seizures were induced by PTZ 90mg/kg, intra-peritoneal (i.p. injection, while in the chronic model, kindling was induced by injecting PTZ 30 mg/kg sub-cutaneous(s.c on alternate days three times in a week. Lindane produced augmented effect on convulsions by decreasing the onset of preclonic convulsions and increased duration of clonic convulsions. AP (2.5mg/kg, i.p and 4′-CD (0.5mg/kg, i.p were able to attenuate the effect of acute as well as chronic exposure of lindane. They significantly increased the onset and decreased the duration of convulsions in lindane-treated rats. These results conclusively demonstrate the efficacy of the neurosteroids in lindane-induced convulsions in both acute as well as chronic models. Thus, NS have a potential role as anticonvulsant in treatment of convulsions produced by pesticides like lindane.
苏丹; 魏璇
2014-01-01
Objective To determine the value of mean platelet volume (MPV) and platelet large cell ratio (P-LCR) in the diagnosis of acute coronary syndrome (ACS) in patients with chest pain. Methods A total of 83 chest pain patients with identified ACS and 56 chest pain patients without cardiovascular disease in our hospital from January to June 2013 were subjected in this study. Their venous blood samples were collected within 6 h of onset for platelet parameters. Mean comparison between the 2 groups and logistic regression analysis were used to find the effective platelet parameters, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic significances for ACS. Results ACS group had significantly lower platelet than the non-cardiac chest pain group [(191.28±67.07)×109 vs (236.75±64.09)×109/L)], and significantly higher MPV [(11.88±1.24) vs (10.73±1.08)fL], platelet distribution width (PDW) [(15.54±1.87) vs (13.40±2.35)fL] and P-LCR [(47.49±9.55)%vs (35.11±10.00)%] (all P0.05). Logistic regression analysis showed that P-LCR and MPV were auxiliary diagnostic indicatos for ACS. ROC curve analysis showed that the cut-off was 0.15µg/L, 38.5%, 11.05fL, and 19.0U/L, respectively, for troponin I (TnI), P-LCR, MPV and creatine kinase-MB (CK-MB), and their areas under the curve were 0.987, 0.817, 0.754 and 0.598, respectively. Their sensitivity was 97.3%, 92.8%, 71.1%and 45.8%respectively, the specificity were 90.3%, 64.3%62.5%, and 73.2%respectively, and the diagnostic accuracy were 100%, 80%, 72.6%, and 67.2%, respectively for TnI, P-LCR, MPV and CK-MB. Conclusion MPV and P-LCR are helpful in the early diagnosis of ACS, and can be seperately used as auxiliary diagnostic indicators of ACS in patients with chest pain. Because of their high diagnostic accuracy, the 2 parameters can be served as a reference for early prediction and differential diagnosis of ACS.%目的：探讨平均血小板体积（MPV）和大型血小板比例（P
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Unbalanced Regressions and the Predictive Equation
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by...
Knowledge and Awareness: Linear Regression
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
Streamflow forecasting using functional regression
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Halpin, Valerie
2014-01-01
Acute cholecystitis causes unremitting right upper quadrant pain, anorexia, nausea, vomiting, and fever, and if untreated can lead to perforations, abscess formation, or fistulae. About 95% of people with acute cholecystitis have gallstones.It is thought that blockage of the cystic duct by a gallstone or local inflammation can lead to acute cholecystitis, but we don't know whether bacterial infection is also necessary.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Assumptions of Multiple Regression: Correcting Two Misconceptions
Matt N. Williams
2013-09-01
Full Text Available In 2002, an article entitled - Four assumptions of multiple regression that researchers should always test- by.Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times.(as of August 2013, and it is one of the first results displayed in a Google search for - regression.assumptions- . While Osborne and Waters' efforts in raising awareness of the need to check assumptions.when using regression are laudable, we note that the original article contained at least two fairly important.misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the.assumption of normally distributed variables; and secondly, that measurement errors necessarily cause.underestimation of simple regression coefficients. In this article, we clarify that multiple regression models.estimated using ordinary least squares require the assumption of normally distributed errors in order for.trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or.predictor variables. Secondly, we point out that regression coefficients in simple regression models will be.biased (toward zero estimates of the relationships between variables of interest when measurement error is.uncorrelated across those variables, but that when correlated measurement error is present, regression.coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of.the assumptions of multiple regression when using ordinary least squares.
Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
2011-01-01
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…
Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression modeling performed on measurements of selected pesticides in composited duplicate diet samples allowed (1) estimation ...
Iuel-Brockdorff, Ann-Sophie Julie D; Ouedraogo, Albertine; Ritz, Christian
2017-01-01
Feeding behaviors have an important impact on children's nutritional status and are essential to consider when implementing nutrition programs. The objective of this study was to explore and compare feeding behaviors related to supplementary feeding with corn-soy blends (CSB) and lipid-based nutr......Feeding behaviors have an important impact on children's nutritional status and are essential to consider when implementing nutrition programs. The objective of this study was to explore and compare feeding behaviors related to supplementary feeding with corn-soy blends (CSB) and lipid...
Using Regression Mixture Analysis in Educational Research
Cody S. Ding
2006-11-01
Full Text Available Conventional regression analysis is typically used in educational research. Usually such an analysis implicitly assumes that a common set of regression parameter estimates captures the population characteristics represented in the sample. In some situations, however, this implicit assumption may not be realistic, and the sample may contain several subpopulations such as high math achievers and low math achievers. In these cases, conventional regression models may provide biased estimates since the parameter estimates are constrained to be the same across subpopulations. This paper advocates the applications of regression mixture models, also known as latent class regression analysis, in educational research. Regression mixture analysis is more flexible than conventional regression analysis in that latent classes in the data can be identified and regression parameter estimates can vary within each latent class. An illustration of regression mixture analysis is provided based on a dataset of authentic data. The strengths and limitations of the regression mixture models are discussed in the context of educational research.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
High-dimensional regression with unknown variance
Giraud, Christophe; Verzelen, Nicolas
2011-01-01
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasize is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso esti- mator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
Logistic Regression for Evolving Data Streams Classification
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
New ridge parameters for ridge regression
A.V. Dorugade
2014-04-01
Full Text Available Hoerl and Kennard (1970a introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation. In this article, a new method for estimating ridge parameters in both situations of ordinary ridge regression (ORR and generalized ridge regression (GRR is proposed. The simulation study evaluates the performance of the proposed estimator based on the mean squared error (MSE criterion and indicates that under certain conditions the proposed estimators perform well compared to OLS and other well-known estimators reviewed in this article.
Enhanced piecewise regression based on deterministic annealing
ZHANG JiangShe; YANG YuQian; CHEN XiaoWen; ZHOU ChengHu
2008-01-01
Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using "migration of prototypes" is proposed to eliminate "empty cell" in the annealing process. Numerical experiments on several benchmark datasets show that the new algo-rithm can remove redundancy and improve generalization of the piecewise regres-sion model.
Geodesic least squares regression on information manifolds
Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, Ghent, Belgium and Laboratory for Plasma Physics, Royal Military Academy, Brussels (Belgium)
2014-12-05
We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion.
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
... out of balance. Acute kidney failure — also called acute renal failure or acute kidney injury — develops rapidly over ... 2015. Palevsky PM. Definition of acute kidney injury (acute renal failure). http://www.uptodate.com/home. Accessed April ...
Acute Pancreatitis and Pregnancy
... Pancreatitis Acute Pancreatitis and Pregnancy Acute Pancreatitis and Pregnancy Timothy Gardner, MD Acute pancreatitis is defined as ... pancreatitis in pregnancy. Reasons for Acute Pancreatitis and Pregnancy While acute pancreatitis is responsible for almost 1 ...
1951-02-01
factitious- ness, it appears reasonable to apply the term mofpbotropic mortal necrobiosis also to ganglion cell changes after acute death« Consequently... necrobiosis can.b^ applied PROJEGT’ÜÜMBER 21-23-W4* REPORTNÜMjBER 1 in the present connection as well» Whenever a new term is introduced, one has to make...Occasionally, one can observe a mörphötropic necrobiosis of intravital origin within a morpbo- trppic mortal necrobiosis . We examined one case, for
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Regression Model With Elliptically Contoured Errors
Arashi, M; Tabatabaey, S M M
2012-01-01
For the regression model where the errors follow the elliptically contoured distribution (ECD), we consider the least squares (LS), restricted LS (RLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for the regression parameters. We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use...
Robust regression for large-scale neuroimaging studies.
Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand
2015-05-01
Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies.
Atherosclerotic plaque regression: fact or fiction?
Shanmugam, Nesan; Román-Rego, Ana; Ong, Peter; Kaski, Juan Carlos
2010-08-01
Coronary artery disease is the major cause of death in the western world. The formation and rapid progression of atheromatous plaques can lead to serious cardiovascular events in patients with atherosclerosis. The better understanding, in recent years, of the mechanisms leading to atheromatous plaque growth and disruption and the availability of powerful HMG CoA-reductase inhibitors (statins) has permitted the consideration of plaque regression as a realistic therapeutic goal. This article reviews the existing evidence underpinning current therapeutic strategies aimed at achieving atherosclerotic plaque regression. In this review we also discuss imaging modalities for the assessment of plaque regression, predictors of regression and whether plaque regression is associated with a survival benefit.
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.
2013-01-01
Background Inappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption that clinicians inappropriately prescribe antibiotics because they are unaware of guideline recommendations for ARIs. If lack of guideline awareness is not the reason for inappropriate prescribing, educational interventions may have limited impact on prescribing rates. Instead, interventions that apply social psychological and behavioral economic principles may be more effective in deterring inappropriate antibiotic prescribing for ARIs by well-informed clinicians. Methods/design The Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections (BEARI) Trial is a multisite, cluster-randomized controlled trial with practice as the unit of randomization. The primary aim is to test the ability of three interventions based on behavioral economic principles to reduce the rate of inappropriate antibiotic prescribing for ARIs. We randomized practices in a 2 × 2 × 2 factorial design to receive up to three interventions for non-antibiotic-appropriate diagnoses: 1) Accountable Justifications: When prescribing an antibiotic for an ARI, clinicians are prompted to record an explicit justification that appears in the patient electronic health record; 2) Suggested Alternatives: Through computerized clinical decision support, clinicians prescribing an antibiotic for an ARI receive a list of non-antibiotic treatment choices (including prescription options) prior to completing the antibiotic prescription; and 3) Peer Comparison: Each provider’s rate of inappropriate antibiotic prescribing relative to top
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Hypotheses testing for fuzzy robust regression parameters
Kula, Kamile Sanli [Ahi Evran University, Department of Mathematics, 40200 Kirsehir (Turkey)], E-mail: sanli2004@hotmail.com; Apaydin, Aysen [Ankara University, Department of Statistics, 06100 Ankara (Turkey)], E-mail: apaydin@science.ankara.edu.tr
2009-11-30
The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression. Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: (http://folk.uib.no/ngbnk/kurs/notes/node38.html); Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters.
Quantile regression applied to spectral distance decay
Rocchini, D.; Cade, B.S.
2008-01-01
Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.
常明; 肖延风; 尹春燕; 易晓青; 徐尔迪
2013-01-01
[Objective]To investigate the correlation between hypertension and physical activity, eating behaviors of children with simple obesity, so as to explore the risk factors to hypertension of children with simple obesity. [ Methods] Cox proportional hazards model was adopted to analyze the hypertension related living risk factors. [ Results] Cox regression analysis showed the relative risk of overeating meat, sweet foods and Western - style fast food, good appetite and long time learning were 6. 658, 3. 579, 2. 291, 0. 528 and 0. 830 respectively. [ Conclusion] High-fat and high- sugar diet and long time learning are high risk factors to early hypertension of children. Corrected eating habit and exercise behavior can prevent the occurrence of hypertension on children with simple obesity and reduce the risk of cardiovascular disease.%目的 研究肥胖儿童发生血压升高与运动和饮食行为的相关性,旨在探究影响肥胖儿童发生血压升高的不良生活因素.方法 采用Cox比例风险模型对124例肥胖儿童进行高血压的运动和饮食行为的分析.结果 多因素Cox回归分析结果显示,喜欢吃肉类、甜点、西式快餐、食欲旺盛和学习时间的相对危险度分别为6.658、3.579、2.291、0.528和0.830.结论 高脂、高糖饮食,学习时间过长是肥胖儿童早期发生高血压的高危因素.纠正不良饮食、运动行为,可防止肥胖儿童发生高血压,降低心血管疾病的危险.
Relative risk regression analysis of epidemiologic data.
Prentice, R L
1985-11-01
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation
In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attemp...
Iuel-Brockdorf, Ann-Sophie; Ouedraogo, Albertine; Ritz, Christian; Draebel, Tania Aase; Ashorn, Per; Filteau, Suzanne; Michaelsen, Kim F
2016-12-02
Feeding behaviors have an important impact on children's nutritional status and are essential to consider when implementing nutrition programs. The objective of this study was to explore and compare feeding behaviors related to supplementary feeding with corn-soy blends (CSB) and lipid-based nutrient supplements (LNS) based on best practice feeding behaviors. The study was conducted as part of a randomized controlled trial assessing the effectiveness of new formulations of CSB and LNS and comprised 1,546 children from 6 to 23 months. The study included a mixed methods approach using questionnaires, focus group discussions and home visits and interviews with a subsample of 20 caretakers of trial participants. We found that LNS, compared to CSB, were more likely to be mixed into other foods (OR [95% CI] 1.7 [1.3-2.2], p = feeding style (mean difference in percentage points [95% CI] 23% [6%:40%], p = .01). CSB were more likely to be fed using a forced feeding style (mean difference in percentage points [95% CI] 18% [3%:33%], p = .02) and were often observed to be served unprepared. The main differences in feeding behaviors between the two diet groups were linked to how and when supplements were served. Educational instructions should therefore be adapted according to the supplement provided; when providing CSB, efforts should be made to promote an encouraging feeding style, and emphasis should be made to ensure preparations are made according to recommendations.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Regressive language in severe head injury.
Thomsen, I V; Skinhoj, E
1976-09-01
In a follow-up study of 50 patients with severe head injuries three patients had echolalia. One patient with initially global aphasia had echolalia for some weeks when he started talking. Another patient with severe diffuse brain damage, dementia, and emotional regression had echolalia. The dysfunction was considered a detour performance. In the third patient echolalia and palilalia were details in a total pattern of regression lasting for months. The patient, who had extensive frontal atrophy secondary to a very severe head trauma, presented an extreme state of regression returning to a foetal-body pattern and behaving like a baby.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Variable and subset selection in PLS regression
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Cardiorespiratory fitness and laboratory stress: a meta-regression analysis.
Jackson, Erica M; Dishman, Rod K
2006-01-01
We performed a meta-regression analysis of 73 studies that examined whether cardiorespiratory fitness mitigates cardiovascular responses during and after acute laboratory stress in humans. The cumulative evidence indicates that fitness is related to slightly greater reactivity, but better recovery. However, effects varied according to several study features and were smallest in the better controlled studies. Fitness did not mitigate integrated stress responses such as heart rate and blood pressure, which were the focus of most of the studies we reviewed. Nonetheless, potentially important areas, particularly hemodynamic and vascular responses, have been understudied. Women, racial/ethnic groups, and cardiovascular patients were underrepresented. Randomized controlled trials, including naturalistic studies of real-life responses, are needed to clarify whether a change in fitness alters putative stress mechanisms linked with cardiovascular health.
Robust Logistic Regression to Static Geometric Representation of Ratios
Alireza Bahiraie
2009-01-01
Full Text Available Problem statement: Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-salacity were solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. This new method would be a general methodological guideline associated with financial data and bankruptcy risk. Approach: We proposed the use of a new measure of risk, the Share Risk (SR measure. We provided evidence of the extent to which changes in values of this index are associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and directions. Our simple methodology provided a geometric illustration of the new proposed risk measure and transformation behavior. This study also employed Robust logit method, which extends the logit model by considering outlier. Results: Results showed new SR method obtained better numerical results in compare to common ratios approach. With respect to accuracy results, Logistic and Robust Logistic Regression Analysis illustrated that this new transformation (SR produced more accurate prediction statistically and can be used as an alternative for common ratios. Additionally, robust logit model outperforms logit model in both approaches and was substantially superior to the logit method in predictions to assess sample forecast performances and regressions. Conclusion/Recommendations: This study presented a new perspective on the study of firm financial statement and bankruptcy. In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR was proposed. With respect to forecast results, robust loigt method was substantially superior to the logit method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the
Webster, H B; Morin, D; Jarrell, V; Shipley, C; Brown, L; Green, A; Wallace, R; Constable, P D
2013-10-01
This study assessed the effects of flunixin meglumine (FM) and a local anesthetic block (LA) on postcastration performance, plasma cortisol concentration, and behavior in dairy calves. Thirty 2- to 3-mo-old Holstein-Friesian bull calves were allocated to 5 treatments: castration with LA (2% lidocaine injected into the testes and subcutaneously), castration with FM (1.1mg/kg, i.v.), castration with LA+FM, castration without drugs (CC), and sham castration (SC). Castration was performed using a Newberry knife and Henderson castrating tool. Feed intake and body weight gain were recorded for 10d postcastration. Plasma cortisol concentration and behavior frequency and duration were monitored for 8h postcastration. Variables with repeated measures were analyzed using PROC MIXED (SAS Institute Inc., Cary, NC); one-way ANOVA was used for nonrepeated measures. No differences in feed intake or body weight gain were detected among groups. Calves in the CC, LA, and FM groups had transient (<60, <60, and <45 min, respectively) increases in plasma cortisol concentration after castration, with a second increase at 120 min in the LA group, whereas cortisol concentration remained at baseline in the LA+FM and SC groups. Mean cortisol concentrations were lower for calves in the LA+FM and SC groups than in the CC group. The area under the plasma cortisol concentration curve during the first 3h postcastration was greater in CC- and LA-treated calves than in SC controls. Castration without drugs was associated with higher frequencies of crouching and statue standing and less oral activity compared with SC controls. Administering LA alone before castration was associated with higher frequencies of head turning, statue standing, and postural changes, and less feeding behavior compared with SC controls. More leg lifting to groom was seen in LA+FM-treated calves than in SC controls. Calves administered FM alone before castration exhibited less crouching than CC calves, fewer postural shifts
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
random vector X. We focus on regression functions of the form f(x) = c0 + 〈c, h(x)〉, c0 ∈ IR, c ∈ IRm , for a given “basis” function h : IRn → IRm ...but not dealt with in this dissertation. We now define the Superquantile Regression Problem SqR, for any h : IRn → IRm and α ∈ (0, 1), where Z(c0, c... IRm +1 the set of optimal solutions of SqR and refer to (c̄0, c̄) ∈ C̄ as a regression vector. Superquantile Regression Problem: SqR : min c0∈IR,c∈ IRm
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding e
Patterns of Regression in Rett Syndrome
J Gordon Millichap
2002-10-01
Full Text Available Patterns and features of regression in a case series of 53 girls and women with Rett syndrome were studied at the Institute of Child Health and Great Ormond Street Children’s Hospital, London, UK.
Prediction of Dynamical Systems by Symbolic Regression
Quade, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-01-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting a...
A new bivariate negative binomial regression model
Faroughi, Pouya; Ismail, Noriszura
2014-12-01
This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.
Heteroscedastic regression analysis method for mixed data
FU Hui-min; YUE Xiao-rui
2011-01-01
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data, type- I censored data and type- Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients, as well as the confidence limits of the location parameter and scale parameter were given. Furthermore, the point estimations and confidence limits of percentiles were obtained. Thus, the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.
Effects of social comparison on aggression and regression in groups of young children.
Santrock, J W; Smith, P C; Bourbeau, P E
1976-09-01
The influence of negative, equal, and positive social comparison and of nonsocial comparison upon 4- and 5-year-old black children's subsequent aggressive and regressive behavior in 3-member groups was investigated. The group behavior of boys included more physical agression following negative social comparison than the other treatments, and their group behavior also consisted of more nonverbal teasing behavior following the negative comparison treatment than that of the equal and nonsocial comparison groups. When the behavior of the nontarget partners was controlled, children initiated more physical aggression, nonverbal teasing, and regression after experiencing negative social comparison with the partners than after following the other treatments. There was some evidence to support the reciprocal influence of children's aggressive behavior on each other, particularly for boys following imbalanced social comparison treatments.
余学; 戴秀英; 李秋丽; 王玲玲; 李林贵
2014-01-01
Objective To understand the behavioral problems and related factors of left-behind children in rural areas of Ningxia . Methods A total of 2000 students were sampled by stratified randomly and cluster from the whole class of the southern rural areas in Ningxia.The general self-made questionnaire,Egma Minnen av Bardndosnauppforstran (EMBU),Eysenck Personality Questionnaire(EPQ Children),Piers-Harri Children's Self-concept Scale(PHCSS)and Achenbach's Child behavior Checklist(CBCL parent version)were used to investigate the related factors .Results ①The overall detection rate of child behavior problems was 18.0%(343/1905).The detection rate of left-behind children and non-left-behind children was 20.9%and 16.0%respectively,and the difference was significant between the two groups(χ2 =7.66,P=0.006).The behavior problems of Han left behind and non-left-behind children were detected in 17.0%and 11.2%respectively,and there was significant difference between two groups (χ2 =6.64,P=0.010).The detection rate of Muslim left-behind children behavioral problems (25.1%) was significantly higher than that of Han left-behind children(17.0%)(χ2 =7.51,P=0.006).②Multivariate logistic regression analysis showed that parents working outside (OR=1.239),Extraversion personality (OR=0.807),Neurotic personality(OR=1.310),father overprotection(OR=1.727),mother refusing and denying(OR=1.561)and total score of self-awareness(OR=0.613)eventually entered the equation,which could be directly predicted the occurrence of psycholog-ical and behavioral problems .Emotional instability ,father overprotection ,mother refusing and denying ,and both parents working outside were risk factors ,whereas the extroversion ( high score features ) and good self-awareness were protective factors .Conclusion Left-behind children have higher incidence of behavioral problems in rural areas of Ningxia ,which has many influence factors .Measures should be taken from social ,family,character building and other
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Spontaneous regression of metastatic Merkel cell carcinoma.
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
Marginal longitudinal semiparametric regression via penalized splines.
Kadiri, M Al; Carroll, R J; Wand, M P
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Drouet, J-B; Fauvelle, F; Maunoir-Regimbal, S; Fidier, N; Maury, R; Peinnequin, A; Denis, J; Buguet, A; Canini, F
2015-01-29
In patients suffering from stress-related pathologies and depression, frontal cortex GABA and glutamate contents are reported to decrease and increase, respectively. This suggests that the GABA and/or glutamate content may participate in pathological phenotype expression. Whether differences in frontal cortex GABA and glutamate contents would be associated with specific behavioral and neurobiological patterns remains unclear, especially in the event of exposure to moderate stress. We hypothesized that an increase in prefrontal cortex GABA/glutamate ratio would be associated with a blunted prefrontal cortex activation, an enhanced hypothalamo-pituitary-adrenocortical (HPA) axis activation and changes in behavior. Rats being restrained for 1-h were then tested in an open-field test in order to assess their behavior while under stress, and were sacrificed immediately afterward. The GABA/glutamate ratio was assessed by (1)H high-resolution magic angle spinning magnetic resonance spectroscopy ((1)H-HRMAS-MRS). The neurobiological response was evaluated through prefrontal cortex mRNA expression and plasma corticosterone levels. The stressed rats were distributed into two subgroups according to their high (H-G/g) or low (L-G/g) GABA/glutamate ratio. Compared to the L-G/g rats, the H-G/g rats exhibited a decrease in c-fos, Arc, Npas4, Nr4a2 mRNA expression suggesting blunted prefrontal cortex activation. They also showed a more pronounced stress with an enhanced rise in corticosterone, alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), creatine kinase (CK) and lactate dehydrogenase (LDH) levels, as well as behavioral disturbances with decreased locomotion speed. These changes were independent from prefrontal cortex energetic status as mammalian target of rapamycin (mTOR) and adenosine monophosphate-activated protein kinase (AMPK) pathway activities were similar in both subpopulations. The differences in GABA/glutamate ratio in the frontal cortex observed
[Iris movement mediates pupillary membrane regression].
Morizane, Yuki
2007-11-01
In the course of mammalian lens development, a transient capillary meshwork called as the pupillary membrane (PM) forms. It is located in the pupil area to nourish the anterior surface of the lens, and then regresses to clear the optical path. Although the involvement of the apoptotic process has been reported in PM regression, the initiating factor remains unknown. We initially found that regression of the PM coincided with the development of iris motility, and that iris movement caused cessation and resumption of blood flow within the PM. Therefore, we investigated whether the development of the capacity of the iris to constrict and dilate can function as an essential signal that induces apoptosis in the PM. Continuous inhibition of iris movement with mydriatic agents suppressed apoptosis of the PM and resulted in the persistence of PM in rats. The distribution of apoptotic cells in the regressing PM was diffuse and showed no apparent localization. These results indicated that iris movement induced regression of the PM by changing the blood flow within it. This study suggests the importance of the physiological interactions between tissues-in this case, the iris and the PM-as a signal to advance vascular regression during organ development.
Post-processing through linear regression
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Correlation, Regression, and Cointegration of Nonstationary Economic Time Series
Johansen, Søren
), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population...... values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient...
Altarifi, Ahmad A; Rice, Kenner C; Negus, S Stevens
2015-02-01
Pain is associated with stimulation of some behaviors and depression of others, and μ-opioid receptor agonists are among the most widely used analgesics. This study used parallel assays of pain-stimulated and pain-depressed behavior in male Sprague-Dawley rats to compare antinociception profiles for six μ-agonists that varied in efficacy at μ-opioid receptors (from highest to lowest: methadone, fentanyl, morphine, hydrocodone, buprenorphine, and nalbuphine). Intraperitoneal injection of diluted lactic acid served as an acute noxious stimulus to either stimulate stretching or depress operant responding maintained by electrical stimulation in an intracranial self-stimulation (ICSS). All μ-agonists blocked both stimulation of stretching and depression of ICSS produced by 1.8% lactic acid. The high-efficacy agonists methadone and fentanyl were more potent at blocking acid-induced depression of ICSS than acid-stimulated stretching, whereas lower-efficacy agonists displayed similar potency across assays. All μ-agonists except morphine also facilitated ICSS in the absence of the noxious stimulus at doses similar to those that blocked acid-induced depression of ICSS. The potency of the low-efficacy μ-agonist nalbuphine, but not the high-efficacy μ-agonist methadone, to block acid-induced depression of ICSS was significantly reduced by increasing the intensity of the noxious stimulus to 5.6% acid. These results demonstrate sensitivity of acid-induced depression of ICSS to a range of clinically effective μ-opioid analgesics and reveal distinctions between opioids based on efficacy at the μ-receptor. These results also support the use of parallel assays of pain-stimulated and -depressed behaviors to evaluate analgesic efficacy of candidate drugs.
Kruk-Slomka, Marta; Boguszewska-Czubara, Anna; Slomka, Tomasz; Budzynska, Barbara; Biala, Grazyna
2016-01-01
The endocannabinoid system, through cannabinoid (CB) receptors, is involved in memory-related responses, as well as in processes that may affect cognition, like oxidative stress processes. The purpose of the experiments was to investigate the impact of CB1 and CB2 receptor ligands on the long-term memory stages in male Swiss mice, using the passive avoidance (PA) test, as well as the influence of these compounds on the level of oxidative stress biomarkers in the mice brain. A single injection of a selective CB1 receptor antagonist, AM 251, improved long-term memory acquisition and consolidation in the PA test in mice, while a mixed CB1/CB2 receptor agonist WIN 55,212-2 impaired both stages of cognition. Additionally, JWH 133, a selective CB2 receptor agonist, and AM 630, a competitive CB2 receptor antagonist, significantly improved memory. Additionally, an acute administration of the highest used doses of JWH 133, WIN 55,212-2, and AM 630, but not AM 251, increased total antioxidant capacity (TAC) in the brain. In turn, the processes of lipids peroxidation, expressed as the concentration of malondialdehyde (MDA), were more advanced in case of AM 251. Thus, some changes in the PA performance may be connected with the level of oxidative stress in the brain.
Marta Kruk-Slomka
2016-01-01
Full Text Available The endocannabinoid system, through cannabinoid (CB receptors, is involved in memory-related responses, as well as in processes that may affect cognition, like oxidative stress processes. The purpose of the experiments was to investigate the impact of CB1 and CB2 receptor ligands on the long-term memory stages in male Swiss mice, using the passive avoidance (PA test, as well as the influence of these compounds on the level of oxidative stress biomarkers in the mice brain. A single injection of a selective CB1 receptor antagonist, AM 251, improved long-term memory acquisition and consolidation in the PA test in mice, while a mixed CB1/CB2 receptor agonist WIN 55,212-2 impaired both stages of cognition. Additionally, JWH 133, a selective CB2 receptor agonist, and AM 630, a competitive CB2 receptor antagonist, significantly improved memory. Additionally, an acute administration of the highest used doses of JWH 133, WIN 55,212-2, and AM 630, but not AM 251, increased total antioxidant capacity (TAC in the brain. In turn, the processes of lipids peroxidation, expressed as the concentration of malondialdehyde (MDA, were more advanced in case of AM 251. Thus, some changes in the PA performance may be connected with the level of oxidative stress in the brain.
Batch Mode Active Learning for Regression With Expected Model Change.
Cai, Wenbin; Zhang, Muhan; Zhang, Ya
2016-04-20
While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the difference between the current model parameters and the updated parameters after the inclusion of the newly selected examples. In light of the stochastic gradient descent learning rule, we approximate the change as the gradient of the loss function with respect to each single candidate instance. Under the EMCM framework, we propose novel AL algorithms for the linear and nonlinear regression models. In addition, by simulating the behavior of the sequential AL policy when applied for k iterations, we further extend the algorithms to batch mode AL to simultaneously choose a set of k most informative instances at each query time. Extensive experimental results on both UCI and StatLib benchmark data sets have demonstrated that the proposed algorithms are highly effective and efficient.
Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
Cook, R Dennis; Rothman, Adam J; 10.1214/11-AOS962
2012-01-01
We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.
On the interactions between top-down anticipation and bottom-up regression
Jun Tani
2007-11-01
Full Text Available This paper discusses the importance of anticipation and regression in modeling cognitive behavior. The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments. The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process.
Hyperglycemia impairs atherosclerosis regression in mice.
Gaudreault, Nathalie; Kumar, Nikit; Olivas, Victor R; Eberlé, Delphine; Stephens, Kyle; Raffai, Robert L
2013-12-01
Diabetic patients are known to be more susceptible to atherosclerosis and its associated cardiovascular complications. However, the effects of hyperglycemia on atherosclerosis regression remain unclear. We hypothesized that hyperglycemia impairs atherosclerosis regression by modulating the biological function of lesional macrophages. HypoE (Apoe(h/h)Mx1-Cre) mice express low levels of apolipoprotein E (apoE) and develop atherosclerosis when fed a high-fat diet. Atherosclerosis regression occurs in these mice upon plasma lipid lowering induced by a change in diet and the restoration of apoE expression. We examined the morphological characteristics of regressed lesions and assessed the biological function of lesional macrophages isolated with laser-capture microdissection in euglycemic and hyperglycemic HypoE mice. Hyperglycemia induced by streptozotocin treatment impaired lesion size reduction (36% versus 14%) and lipid loss (38% versus 26%) after the reversal of hyperlipidemia. However, decreases in lesional macrophage content and remodeling in both groups of mice were similar. Gene expression analysis revealed that hyperglycemia impaired cholesterol transport by modulating ATP-binding cassette A1, ATP-binding cassette G1, scavenger receptor class B family member (CD36), scavenger receptor class B1, and wound healing pathways in lesional macrophages during atherosclerosis regression. Hyperglycemia impairs both reduction in size and loss of lipids from atherosclerotic lesions upon plasma lipid lowering without significantly affecting the remodeling of the vascular wall.
Regression Test Selection for C# Programs
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
Federico A. Sturzeneger
1992-03-01
Full Text Available Currency Substitution and the Regressivity of Inflationary Taxation The purpose of this paper is to show that in the presence of financial adaptation or currency substitution. the inflation tax is extremely regressive. This regressivity arises from the existence of a fixed cost of switching to inflation-proof transactions technologies. This fixed cost makes it optimal only for those agents with sufficiently high incomes to switch out of domestic currency. The effects are illustrated and quantified for a particular case.
LINEAR REGRESSION WITH R AND HADOOP
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.
Uncertainty quantification in DIC with Kriging regression
Wang, Dezhi; DiazDelaO, F. A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, Eann A.; Mottershead, John E.
2016-03-01
A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined.
KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
HANY DEVITA
2015-02-01
Full Text Available Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR as an extension of Generalized Ridge Regression (GRR for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
Unsupervised K-Nearest Neighbor Regression
Kramer, Oliver
2011-01-01
In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or for face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UKNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UKNN formulation allows an efficient strategy of iteratively embedding latent points to fixed neighborhood topologies. The approaches will be tested experimentally.
Rapidly Regressive Unilateral Fetal Pleural Effusion
Tuncay Yuce
2015-03-01
Full Text Available Intrauterine pleural effusion of fetal lungs rarely regresses without intervention. In our case we treated a women at 32th weeks of gestation. Her pregnancy was complicated with fetal pleural effusion and polyhydramniosis. A therapeutic thoracocentesis was planned and she received two courses of betamethasone prior to procedure. On the day of planned procedure, a substantial regression of pleural effusion was observed and procedure was postponed. During her antenatal follow-up a complete regression of pleural effusion was observed. After delivery pleural effusion did not relapse. These findings hint there may be a role of antenatal steroids in treatment of fetal pleural effusion, which is known to be resistant to treatment modalities both during antenatal and postnatal period. [Cukurova Med J 2015; 40(Suppl 1: 25-28
SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE
RODRIGO PINTO MOREIRA
2008-01-01
Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos pa...
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
On Solving Lq-Penalized Regressions
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
A tutorial on Bayesian Normal linear regression
Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens
2015-12-01
Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
David C. Randall
2011-08-01
Full Text Available We recorded via telemetry the arterial blood pressure (BP and heart rate (HR response to classical conditioning following the spontaneous onset of autoimmune diabetes in BBDP/Wor rats versus age-matched, diabetes resistant control (BBDR/Wor rats. Our purpose was to evaluate the autonomic regulatory responses to an acute stress in a diabetic state of up to 12 months duration. The stress was a 15 sec. pulsed tone (CS+ followed by a 0.5 sec. tail shock. The initial, transient increase in BP (i.e., the ‘first component’, or C1, known to be derived from an orienting response and produced by a sympathetic increase in peripheral resistance, was similar in diabetic and control rats through ~9 months of diabetes; it was smaller in diabetic rats 10 months after diabetes onset. Weakening of the C1 BP increase in rats that were diabetic for > 10 months is consistent with the effects of sympathetic neuropathy. A longer-latency, smaller, but sustained ‘second component’ (C2 conditional increase in BP, that is acquired as a rat learns the association between CS+ and the shock, and which results from an increase in cardiac output, was smaller in the diabetic vs. control rats starting from the first month of diabetes. A concomitant HR slowing was also smaller in diabetic rats. The difference in the C2 BP increase, as observed already during the first month of diabetes, is probably secondary to the effects of hyperglycemia upon myocardial metabolism and contractile function, but it may also result from effects on cognition. The small HR slowing concomitant with the C2 pressor event is probably secondary to differences in baroreflex activation or function, though parasympathetic dysfunction may contribute later in the duration of diabetes. The nearly immediate deficit after disease onset in the C2 response indicates that diabetes alters BP and HR responses to external challenges prior to the development of structural changes in the vasculature or autonomic
无
2007-01-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study,measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity office brown spot using hyperspectral reflectance data at the leaf level.
Wig J
1978-01-01
Full Text Available 550 cases of acute abdomen have been analysed in detail includ-ing their clinical presentation and operative findings. Males are more frequently affected than females in a ratio of 3: 1. More than 45% of patients presented after 48 hours of onset of symptoms. Intestinal obstruction was the commonest cause of acute abdomen (47.6%. External hernia was responsible for 26% of cases of intestinal obstruction. Perforated peptic ulcer was the commonest cause of peritonitis in the present series (31.7% while incidence of biliary peritonitis was only 2.4%.. The clinical accuracy rate was 87%. The mortality in operated cases was high (10% while the over-all mortality rate was 7.5%.
Recurrence of hepatocellular carcinoma with rapid growth after spontaneous regression
Tomoki Nakajima; Michihisa Moriguchi; Tadashi Watanabe; Masao Noda; Nobuaki Fuji; Masahito Minami; Yoshito Itoh; Takeshi Okanoue
2004-01-01
We report an 80-year-old man who presented with spontaneous regression of hepatocellular carcinoma (HCC). He complained of sudden right flank pain and low-grade fever.The level of protein induced by vitamin K antagonist (PIVKA)-Ⅱ was 1 137 mAU/mL. A computed tomography scan in November 2000 demonstrated a low-density mass located in liver S4 with marginal enhancement and a cystic mass of 68 mmx55 mm in liver S6, with slightly high density content and without marginal enhancement. Angiography revealed that the tumor in S4 with a size of 25 mm×20 mm was a typical hypervascular HCC, and transarterial chemoembolization was performed. However, the tumor in S6 was hypovascular and atypical of HCC, and thus no therapy was given. In December 2000, the cystic mass regressed spontaneously to 57 mmx44 mm, and aspiration cytology revealed bloody fluid, and the mass was diagnosed cytologically as class Ⅰ.The tumor in S4 was treated successfully with a 5 mm margin of safety around it. The PⅣKA-Ⅱ level normalized in February2001. In July 2001, the tumor regressed further but presented with an enhanced area at the posterior margin. In November2001, the enhanced area extended, and a biopsy revealed well-differentiated HCC, although the previous tumor in S4 disappeared. Angiography demonstrated two tumor stains, one was in S6, which was previously hypovascular,and the other was in S8. Subsequently, the PⅣKA-Ⅱ level started to rise with the doubling time of 2-3 wk, and the tumor grew rapidly despite repeated transarterial embolization with gel foam. In February 2003, the patient died of bleeding into the peritoneal cavity from the tumor that occupied almost the entire right lobe. Considering the acute onset of the symptoms, we speculate that local ischemia possibly due to rapid tumor growth, resulted in intratumoral bleeding and/or hemorrhagic necrosis, and finally spontaneous regression of the initial tumor in S6.
Logistic regression when binary predictor variables are highly correlated.
Barker, L; Brown, C
Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.
Modeling data for pancreatitis in presence of a duodenal diverticula using logistic regression
Dineva, S.; Prodanova, K.; Mlachkova, D.
2013-12-01
The presence of a periampullary duodenal diverticulum (PDD) is often observed during upper digestive tract barium meal studies and endoscopic retrograde cholangiopancreatography (ERCP). A few papers reported that the diverticulum had something to do with the incidence of pancreatitis. The aim of this study is to investigate if the presence of duodenal diverticula predisposes to the development of a pancreatic disease. A total 3966 patients who had undergone ERCP were studied retrospectively. They were divided into 2 groups-with and without PDD. Patients with a duodenal diverticula had a higher rate of acute pancreatitis. The duodenal diverticula is a risk factor for acute idiopathic pancreatitis. A multiple logistic regression to obtain adjusted estimate of odds and to identify if a PDD is a predictor of acute or chronic pancreatitis was performed. The software package STATISTICA 10.0 was used for analyzing the real data.
Ivanka Jerić
2011-11-01
Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Modeling confounding by half-sibling regression
Schölkopf, Bernhard; Hogg, David W; Wang, Dun
2016-01-01
We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both...
Panel data specifications in nonparametric kernel regression
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Macroeconomic Forecasting Using Penalized Regression Methods
Smeekes, Stephan; Wijler, Etiënne
2016-01-01
We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized regressio
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Genetic Programming Transforms in Linear Regression Situations
Castillo, Flor; Kordon, Arthur; Villa, Carlos
The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.
A Skew-Normal Mixture Regression Model
Liu, Min; Lin, Tsung-I
2014-01-01
A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…
Finite Algorithms for Robust Linear Regression
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Nonparametric regression with martingale increment errors
Delattre, Sylvain
2010-01-01
We consider the problem of adaptive estimation of the regression function in a framework where we replace ergodicity assumptions (such as independence or mixing) by another structural assumption on the model. Namely, we propose adaptive upper bounds for kernel estimators with data-driven bandwidth (Lepski's selection rule) in a regression model where the noise is an increment of martingale. It includes, as very particular cases, the usual i.i.d. regression and auto-regressive models. The cornerstone tool for this study is a new result for self-normalized martingales, called ``stability'', which is of independent interest. In a first part, we only use the martingale increment structure of the noise. We give an adaptive upper bound using a random rate, that involves the occupation time near the estimation point. Thanks to this approach, the theoretical study of the statistical procedure is disconnected from usual ergodicity properties like mixing. Then, in a second part, we make a link with the usual minimax th...
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Selecting a Regression Saturated by Indicators
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain...
Regression Segmentation for M³ Spinal Images.
Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo
2015-08-01
Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.
Prediction of dynamical systems by symbolic regression
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Acute Pancreatitis Concomitant Acute Coronary Syndrome
Okay Abacı
2013-03-01
Full Text Available Acute pancreatitis is an inflammatory syndrome with unpredictable progression to systemic inflammation and multi-organ dysfunction. As in our case rarely, acute pancreatitis can be presented with the coexistance of acute coronary syndrome. To prevent a misdiagnosis of acute situation presented with chest or abdominal pain, physicians must be aware for coexisting pathophysiologies and take into account the differential diagnosis of all life-threatening causes such as cardiac ischemia or acute abdominal situations.
Iman Zarei
2013-02-01
Full Text Available Background: The development of toxicity tests regarding toxic responses of different fish species could be more effectively used in predictive toxicology and risk assessment. In this study lethal concentrations (LC50-96 h values of copper sulphate; an important toxic industrial pollutant, on Capoeta fusca were determined. Behavioral changes at different concentrations of CuSO4 were determined for the C.fusca. Methods: The sample fishes were collected from Qanat in Birjand and were transported to the laboratory in polythene bags. The exposure time of fish to CuSO4 was 96 hours. Mortalities were recorded at 24, 48, 72, and 96 hours of exposure, and the dead fish were removed regularly from the test aquariums. Physicochemical parameters, such as dissolved oxygen, pH and Total hardness of aquaria were monitored daily. Results: The LC50 values for CuSO4 at 24, 48, 72, and 96 h of exposure, were 43.62, 12.6, 7.66, and 6.85 mg/L, respectively. The median LC50 value of CuSO4 for C.fusca was found to be 6.928 mg/L by EPA method and estimated to be 6.787 mg/L with SPSS statistical software. Conclusion: The mortality decreased with time, and most of the deaths occurred during the first 24 h. In addition, behavioural changes increased with increased concentration. This metal is an important constituent in industrial effluents discharged into freshwaters. The results obtained in this study clearly revealed the fact that it is necessary to control the use of a heavy metal such as copper.
Progression and regression of the atherosclerotic plaque.
de Feyter, P J; Vos, J; Deckers, J W
1995-08-01
In animals in which atherosclerosis was induced experimentally (by a high cholesterol diet) regression of the atherosclerotic lesion was demonstrated after serum cholesterol was reduced by cholesterol- lowering drugs or a low-fat diet. Whether regression of advanced coronary arterly lesions also takes place in humans after a similar intervention remains conjectural. However, several randomized studies, primarily employing lipid-lowering intervention or comprehensive changes in lifestyle, have demonstrated, using serial angiograms, that it is possible to achieve less progression, arrest or even (small) regression of atherosclerotic lesions. The lipid-lowering trials (NHBLI, CLAS, POSCH, FATS, SCOR and STARS) studied 1240 symptomatic patients, mostly men, with moderately elevated cholesterol levels and moderately severe angiographic-proven coronary artery disease. A variety of lipid-lowering drugs, in addition to a diet, were used over an intervention period ranging from 2 to 3 years. In all but one study (NHBLI), the progression of coronary atherosclerosis was less in the treated group, but regression was induced in only a few patients. The overall relative risk of progression of coronary atherosclerosis was 0 x 62 and 2 x 13, respectively. The induced angiographic differences were small and did not produce any significant haemodynamic benefit. The most important result was tht the disease process could be stabilized in the majority of patients. Three comprehensive lifestyle change trials (the Lifestyle Heart study, STARS and the Heidelberg Study) studied 183 patients, who were subjected to stress management, and/or intensive exercise, in addition to a low fat diet, over a period ranging from 1 to 3 years. All three trials demonstrated less progression, and more regression with overall relative risks of 0 x 40 and 2 x 35 respectively, in the intervention groups. Angiographic trials demonstrated that retardation or arrest of coronary atherosclerosis was possible
Polat, Esra; Gunay, Suleyman
2013-10-01
One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.
Interpreting parameters in the logistic regression model with random effects
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Constrained regression models for optimization and forecasting
P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.
Multiple Kernel Spectral Regression for Dimensionality Reduction
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
Model selection in kernel ridge regression
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Online support vector regression for reinforcement learning
Yu Zhenhua; Cai Yuanli
2007-01-01
The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online support vector regression (OSVR) is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of support vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable convergence speed and can solve continuous problems that are infeasible using lookup table.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
Wavelet Scattering Regression of Quantum Chemical Energies
Hirn, Matthew; Poilvert, Nicolas
2016-01-01
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is represented by an electronic density function. A multiscale invariant dictionary is calculated with wavelet scattering invariants. It cascades a first wavelet transform which separates scales, with a second wavelet transform which computes interactions across scales. Sparse scattering regressions give state of the art results over two databases of organic planar molecules. On these databases, the regression error is of the order of the error produced by DFT codes, but at a fraction of the computational cost.
Nonexistence in Reciprocal and Logarithmic Regression
Josef Bukac
2003-01-01
Fitting logarithmic b ln(clx), a+bln(c+x) or reciprocal b/(c+x), a+b/(c+x) regression models to data by the least squares method asks for the determination of the closure of the set of each type of these functions defined on a finite domain. It follows that a minimal solution may not exist. But it does exist when the closure is considered.
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
Curvatures for Parameter Subsets in Nonlinear Regression
1986-01-01
The relative curvature measures of nonlinearity proposed by Bates and Watts (1980) are extended to an arbitrary subset of the parameters in a normal, nonlinear regression model. In particular, the subset curvatures proposed indicate the validity of linearization-based approximate confidence intervals for single parameters. The derivation produces the original Bates-Watts measures directly from the likelihood function. When the intrinsic curvature is negligible, the Bates-Watts parameter-effec...
Realization of Ridge Regression in MATLAB
Dimitrov, S.; Kovacheva, S.; Prodanova, K.
2008-10-01
The least square estimator (LSE) of the coefficients in the classical linear regression models is unbiased. In the case of multicollinearity of the vectors of design matrix, LSE has very big variance, i.e., the estimator is unstable. A more stable estimator (but biased) can be constructed using ridge-estimator (RE). In this paper the basic methods of obtaining of Ridge-estimators and numerical procedures of its realization in MATLAB are considered. An application to Pharmacokinetics problem is considered.
General regression and representation model for classification.
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
A Dirty Model for Multiple Sparse Regression
Jalali, Ali; Sanghavi, Sujay
2011-01-01
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recovered. A natural question in this setting is whether one can use the sharing to further decrease the overall number of samples required. A line of recent research has studied the use of \\ell_1/\\ell_q norm block-regularizations with q>1 for such problems; however these could actually perform worse in sample complexity -- vis a vis solving each problem separately ignoring sharing -- depending on the level of sharing. We present a new method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. A very simple idea: we decompose the parameters into two components and regularize these differently. We show both theore...
Bayesian Inference of a Multivariate Regression Model
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Outlier Detection Using Nonconvex Penalized Regression
She, Yiyuan
2010-01-01
This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the $n$ data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual $L_1$ penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The $L_1$ penalty corresponds to soft thresholding. We introduce a thresholding (denoted by $\\Theta$) based iterative procedure for outlier detection ($\\Theta$-IPOD). A version based on hard thresholding correctly identifies outliers on some hard test problems. We find that $\\Theta$-IPOD is much faster than iteratively reweighted least squares for large data because each iteration costs at most $O(np)$ (and sometimes much less) avoiding an $O(np^2)$ least squares estimate. We describe the connection between $\\Theta$-IPOD and $M$-estimators. Our proposed method has one tuning parameter with which to both identify outliers and estimate regression...
Acute arterial occlusion - kidney
Acute renal arterial thrombosis; Renal artery embolism; Acute renal artery occlusion; Embolism - renal artery ... kidney can often result in permanent kidney failure. Acute arterial occlusion of the renal artery can occur after injury or trauma to ...
Cerebellar ataxia; Ataxia - acute cerebellar; Cerebellitis; Post-varicella acute cerebellar ataxia; PVACA ... Acute cerebellar ataxia in children, especially younger than age 3, may occur several weeks after an illness caused by a virus. ...
Quantile regression modeling for Malaysian automobile insurance premium data
Fuzi, Mohd Fadzli Mohd; Ismail, Noriszura; Jemain, Abd Aziz
2015-09-01
Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from quantile regression show that some rating classes increase as quantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (τ = O.5) is always smaller than Gamma regression in all risk factors.
谢萍; 许勤; 陈娟
2015-01-01
目的：调查急性胰腺炎出院患者健康行为能力的现状,分析其影响因素。方法采用一般资料评估表、健康行为能力自评量表( SRAHP)、健康意识量表、抑郁自评量表( SDS)、社会支持评定量表( SSRS)对入住某三甲医院胆胰中心的193例首发急性胰腺炎出院患者进行调查。结果急性胰腺炎出院患者健康行为能力平均得分为(64.09±15.25)分。4个维度中,运动维度得分最高,其次是心理调适、营养和健康责任。70例(36.27%)患者健康行为能力处于良好水平,118例(61.14%)处于中等水平,5例(2.59%)处于较差水平。急性胰腺炎出院患者健康意识平均得分为(7.19±3.12)分,抑郁平均得分为(49.86±8.04)分。单因素分析显示,职业、文化程度、个人月收入、医疗费用承担情况、生活方式状况、社会支持、抑郁状况与健康意识是急性胰腺炎出院患者健康行为能力的影响因素(P<0.05)。多元逐步回归分析显示,患者的健康意识、生活方式状况、社会支持状况、医疗费用承担情况与健康行为能力呈正相关(β值分别为0.99,1.00,0.56,1.01；P<0.05)；抑郁状况与健康行为能力呈负相关(β=-0.68,P<0.05)。结论急性急性胰腺炎出院患者的健康行为能力亟待提高,护理人员在制定干预措施时,要注重帮助患者建立和强化健康意识,改变其不良生活方式,提高社会支持水平,保持良好情绪,以更好地提高其健康行为能力。%Objective To conduct the research on the health behavior ability of discharged acute pancreatitis ( AP) patients, and analyze the relevant influencing factors. Methods Totals of 193 first-episode discharged AP in a third-grade class-A hospital were investigated with the General Information Evaluation Scale, Self-rated Abilities for Health Practices Scale ( SRAHP) , Health Awareness Scale, Self-rating Depression Scale ( SDS) and Social Support Rate Scale ( SSRS) . Results Among
Kepler AutoRegressive Planet Search: Motivation & Methodology
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal
Moretti, Paolo; Peters, Sarika U.; del Gaudio, Daniela; Sahoo, Trilochan; Hyland, Keith; Bottiglieri, Teodoro; Hopkin, Robert J.; Peach, Elizabeth; Min, Sang Hee; Goldman, David; Roa, Benjamin; Bacino, Carlos A.; Scaglia, Fernando
2008-01-01
We studied seven children with CNS folate deficiency (CFD). All cases exhibited psychomotor retardation, regression, cognitive delay, and dyskinesia; six had seizures; four demonstrated neurological abnormalities in the neonatal period. Two subjects had profound neurological abnormalities that precluded formal behavioral testing. Five subjects…
STEENKAMP, JBEM; WEDEL, M
1993-01-01
This article describes a new technique for benefit segmentation, fuzzy clusterwise regression analysis (FCR). It combines clustering with prediction and is based on multiattribute models of consumer behavior. FCR is especially useful when the number of observations per subject is small, when the rel
Computed tomography perfusion imaging denoising using Gaussian process regression
Zhu, Fan; Carpenter, Trevor; Rodriguez Gonzalez, David; Atkinson, Malcolm; Wardlaw, Joanna
2012-06-01
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.
Tind, Sofie; Qvist, Niels
2017-01-01
BACKGROUND: The classification of acute appendicitis (AA) into various grades is not consistent, partly because it is not clear whether the perioperative or the histological findings should be the foundation of the classification. When comparing results from the literature on the frequency...... patients were included. In 116 (89 %) of these cases, appendicitis was confirmed histological. There was low concordance between the perioperative and histological diagnoses, varying from 16 to 76 % depending on grade of AA. Only 44 % of the patients receiving antibiotics postoperatively had a positive...... peritoneal fluid cultivation. CONCLUSION: There was a low concordance in clinical and histopathological diagnoses of the different grades of appendicitis. Perioperative cultivation of the peritoneal fluid as a standard should be further examined. The potential could be a reduced postoperative antibiotic use...
Acute Rhabdomyolysis Following Synthetic Cannabinoid Ingestion
Adedinsewo, Demilade A.; Oluwaseun Odewole; Taylor Todd
2016-01-01
Context: Novel psychoactive substances, including synthetic cannabinoids, are becoming increasingly popular, with more patients being seen in the emergency room following acute ingestion. These substances have been associated with a wide range of adverse effects. However, identification of complications, clinical toxicity, and management remain challenging. Case Report: We present the case of a young African-American male who developed severe agitation and bizarre behavior following acute K2 ...
MPTP所致急性帕金森病小鼠焦虑行为的评价%Evaluation of the anxiety behavior in acute PD mice induced by MPTP
叶素贞; 张书平; 施剑; 梁艳; 黄汉津
2016-01-01
Objective To investigate the anxious behavior in acute parkinson's mice that were induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) injection.Methods Twenty mice were randomly divided into the control group (n =10) and model group(n =10);The model group was induced by injecting MPTP dosage,and the control group was induced by the same dose of saline.The anxious behaviors in mice were tested by the elevated plus-maze test and the light/dark box.Results The model group mice spent a longer time than the control group in the dark box (P ＜ 0.05).The open arm entry (OE),open arm time (OT) and OE％ of model group was significantly less than that in control group in the elevated plus-maze test (P ＜ 0.01),the OT％ was significantly less than control group (P ＜0.05).Conclusions Anxiety symptoms appeared in the model group of early parkinson disease (PD)mice.%目的 研究1-甲基-4-苯基-1,2,3,6-四氢吡啶(MPTP)所致急性帕金森病(PD)小鼠模型的焦虑行为.方法 选取20只C57BL/6J小鼠,随机分为对照组(n=10)和模型组(n=10);模型组用MPTP制作急性PD小鼠模型,对照组用等量生理盐水注射.用高架十字迷宫和明暗箱检测小鼠的焦虑症行为.结果 模型组小鼠在暗箱的停留时间明显高于对照组(P＜0.05);高架十字迷宫中模型组进入开放臂次数(OE)、开放臂停留时间(OT)及进入开放臂次数比例(OE％)与对照组比较差异有统计学意义(P＜0.01),开放臂停留时间比例(OT％)显著低于对照组(P＜0.05).结论 急性PD小鼠模型早期即会出现焦虑症状.
Mapping geogenic radon potential by regression kriging.
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-02-15
Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.
Hierarchical linear regression models for conditional quantiles
TIAN Maozai; CHEN Gemai
2006-01-01
The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.
[Refractive regression after intraocular lens implantation].
Ma, Z Z; Momose, A
1991-05-01
Study of refractive changes after IOL implantation in 147 eyes revealed that astigmatism tended to increase, and the natural regressive course followed a negative exponential function, with the steep phase within 3 weeks for spherical, and 5 weeks for cylindrical errors. One (1) week after surgery, the axis of astigmatism was predominantly with the rule, and 2 months after operation, patients with preoperative WRA changed into various astigmatic axial directions, while 76.4% of the patients with preoperative ARA reverted to ARA. Those eyes in which the astigmatic axis was not horizontal 1 week after operation ended with stronger astigmatism in 2 months.
Bayesian regression of piecewise homogeneous Poisson processes
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Mapping geogenic radon potential by regression kriging
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Privacy Preserving Linear Regression on Distributed Databases
Fida K. Dankar
2015-04-01
Full Text Available Studies that combine data from multiple sources can tremendously improve the outcome of the statistical analysis. However, combining data from these various sources for analysis poses privacy risks. A number of protocols have been proposed in the literature to address the privacy concerns; however they do not fully deliver on either privacy or complexity. In this paper, we present a (theoretical privacy preserving linear regression model for the analysis of data owned by several sources. The protocol uses a semi-trusted third party and delivers on privacy and complexity.
Paraneoplastic pemphigus regression after thymoma resection
Stergiou Eleni
2008-08-01
Full Text Available Abstract Background Among human neoplasms thymomas are associated with highest frequency with paraneoplastic autoimmune diseases. Case presentation A case of a 42-year-old woman with paraneoplastic pemphigus as the first manifestation of thymoma is reported. Transsternal complete thymoma resection achieved pemphigus regression. The clinical correlations between pemphigus and thymoma are presented. Conclusion Our case report provides further evidence for the important role of autoantibodies in the pathogenesis of paraneoplastic skin diseases in thymoma patients. It also documents the improvement of the associated pemphigus after radical treatment of the thymoma.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
Stability Analysis for Regularized Least Squares Regression
Rudin, Cynthia
2005-01-01
We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for regression, and they involve the minimization of regularized risk functionals, such as L(f) := 1/N sum_i (f(x_i)-y_i)^2+ lambda ||f||_H^2. We shall call the algorithm `stable' if, when y_i is a noisy version of f*(x_i) for some function f* in H, the output of the algorithm converges to f* as the regularization term and noise simultaneously vanish. We consider two flavors of...
Bayesian model selection in Gaussian regression
Abramovich, Felix
2009-01-01
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.
Learning regulatory programs by threshold SVD regression.
Ma, Xin; Xiao, Luo; Wong, Wing Hung
2014-11-04
We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.
Neutrosophic Correlation and Simple Linear Regression
A. A. Salama
2014-09-01
Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.
Inferring gene regression networks with model trees
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Remission and regression of diabetic nephropathy
Hovind, Peter; Tarnow, Lise; Parving, Hans-Henrik
2004-01-01
diabetic patients with overt nephropathy, remission (decrease in albuminuria to diabetic nephropathy (rate of decline in GFR treatment. Furthermore, remission of nephrotic-range albuminuria......Diabetic kidney disease is considered to be an irreversible and inexorable progressive disease. Therefore, prevention of development of ESRD is extremely important. Animal studies have demonstrated that regression of existing renal morphologic lesions is feasible. In a sizable fraction of type 1...... in diabetic patients, an aggressive multifactorial approach, aiming at lowering blood pressure and albuminuria, and improving glycemic control, must be applied....
Cyclodextrin promotes atherosclerosis regression via macrophage reprogramming
2016-01-01
Atherosclerosis is an inflammatory disease linked to elevated blood cholesterol concentrations. Despite ongoing advances in the prevention and treatment of atherosclerosis, cardiovascular disease remains the leading cause of death worldwide. Continuous retention of apolipoprotein B...... that increases cholesterol solubility in preventing and reversing atherosclerosis. We showed that CD treatment of murine atherosclerosis reduced atherosclerotic plaque size and CC load and promoted plaque regression even with a continued cholesterol-rich diet. Mechanistically, CD increased oxysterol production...... of CD as well as for augmented reverse cholesterol transport. Because CD treatment in humans is safe and CD beneficially affects key mechanisms of atherogenesis, it may therefore be used clinically to prevent or treat human atherosclerosis....
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Acute pollution of recipients in urban areas
Rauch, W.; Harremoës, P.
1997-01-01
Oxygen and ammonia concentration are key parameters of acute water pollution in urban rivers. These two abiotic parameters are statistically assessed for a historical rain series by means of a simplified deterministic model of the integrated drainage system. Continuous simulation of the system...... performance indicates that acute water pollution is caused by intermittent discharges from both sewer system and wastewater treatment plant. Neglecting one of them in the evaluation of the environmental impact gives a wrong impression of total system behavior. Detention basins and alternative operational...... modes in the treatment plant under wet weather loading have a limited positive effect for minimizing acute water pollution. (C) 1997 IAWQ. Published by Elsevier Science Ltd....
Gabriele eBuruck
2014-05-01
Full Text Available Psychosocial stress affects resources for adequate coping with environmental demands. A crucial question in this context is the extent to which acute psychosocial stressors impact empathy and emotion regulation. In the present study, 120 participants were randomly assigned to a control group vs. a group confronted with the Trier Social Stress Test, an established paradigm for the induction of acute psychosocial stress. Empathy for pain as a specific subgroup of empathy was assessed via pain intensity ratings during a pain-picture task. Self-reported emotion regulation skills were measured as predictors using an established questionnaire. Stressed individuals scored significantly lower on the appraisal of pain pictures. A regression model was chosen to find variables that further predict the pain ratings. These findings implicate that acute psychosocial stress might impair empathic processes to observed pain in another person and the ability to accept one’s emotion additionally predicts the empathic reaction. Furthermore, the ability to tolerate negative emotions modulated the relation between stress and pain judgments, and thus influenced core cognitive-affective functions relevant for coping with environmental challenges. In conclusion, our study emphasizes the necessity of reducing negative emotions in terms of empathic distress when confronted with pain of another person under psychosocial stress, in order to be able to retain pro-social behavior.
Buruck, Gabriele; Wendsche, Johannes; Melzer, Marlen; Strobel, Alexander; Dörfel, Denise
2014-01-01
Psychosocial stress affects resources for adequate coping with environmental demands. A crucial question in this context is the extent to which acute psychosocial stressors impact empathy and emotion regulation. In the present study, 120 participants were randomly assigned to a control group vs. a group confronted with the Trier Social Stress Test (TSST), an established paradigm for the induction of acute psychosocial stress. Empathy for pain as a specific subgroup of empathy was assessed via pain intensity ratings during a pain-picture task. Self-reported emotion regulation skills were measured as predictors using an established questionnaire. Stressed individuals scored significantly lower on the appraisal of pain pictures. A regression model was chosen to find variables that further predict the pain ratings. These findings implicate that acute psychosocial stress might impair empathic processes to observed pain in another person and the ability to accept one's emotion additionally predicts the empathic reaction. Furthermore, the ability to tolerate negative emotions modulated the relation between stress and pain judgments, and thus influenced core cognitive-affective functions relevant for coping with environmental challenges. In conclusion, our study emphasizes the necessity of reducing negative emotions in terms of empathic distress when confronted with pain of another person under psychosocial stress, in order to be able to retain pro-social behavior.
Acute Myopericarditis Mimicking Acute Myocardial Infarction
Seval İzdeş
2011-08-01
Full Text Available Acute coronary syndromes among young adults are relatively low when compared with older population in the intensive care unit. Electrocardiographic abnormalities mimicking acute coronary syndromes may be caused by non-coronary syndromes and the differential diagnosis requires a detailed evaluation. We are reporting a case of myopericarditis presenting with acute ST elevation and elevated cardiac enzymes simulating acute coronary syndrome. In this case report, the literature is reviewed to discuss the approach to distinguish an acute coronary syndrome from myopericarditis. (Journal of the Turkish Society Intensive Care 2011; 9:68-70
Variable Selection in Logistic Regression Mo del
ZHANG Shangli; ZHANG Lili; QIU Kuanmin; LU Ying; CAI Baigen
2015-01-01
Variable selection is one of the most impor-tant problems in pattern recognition. In linear regression model, there are many methods can solve this problem, such as Least absolute shrinkage and selection operator (LASSO) and many improved LASSO methods, but there are few variable selection methods in generalized linear models. We study the variable selection problem in logis-tic regression model. We propose a new variable selection method–the logistic elastic net, prove that it has grouping eff ect which means that the strongly correlated predictors tend to be in or out of the model together. The logistic elastic net is particularly useful when the number of pre-dictors (p) is much bigger than the number of observations (n). By contrast, the LASSO is not a very satisfactory vari-able selection method in the case when p is more larger than n. The advantage and eff ectiveness of this method are demonstrated by real leukemia data and a simulation study.
Supporting Regularized Logistic Regression Privately and Efficiently.
Wenfa Li
Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Regression Models for Count Data in R
Christian Kleiber
2008-06-01
Full Text Available The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inﬂated regression models in the functions hurdle( and zeroinfl( from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inﬂated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences—better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be ﬁtted, inspected and tested in practice.
Revisit of Sheppard corrections in linear regression
无
2010-01-01
Dempster and Rubin(D&R) in their JRSSB paper considered the statistical error caused by data rounding in a linear regression model and compared the Sheppard correction,BRB correction and the ordinary LSE by simulations.Some asymptotic results when the rounding scale tends to 0 were also presented.In a previous research,we found that the ordinary sample variance of rounded data from normal populations is always inconsistent while the sample mean of rounded data is consistent if and only if the true mean is a multiple of the half rounding scale.In the light of these results,in this paper we further investigate the rounding errors in linear regressions.We notice that these results form the basic reasons that the Sheppard corrections perform better than other methods in D&R examples and their conclusion in general cases is incorrect.Examples in which the Sheppard correction works worse than the BRB correction are also given.Furthermore,we propose a new approach to estimate the parameters,called "two-stage estimator",and establish the consistency and asymptotic normality of the new estimators.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Regression Models For Saffron Yields in Iran
S. H, Sanaeinejad; S. N, Hosseini
Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.
Jackknife bias reduction for polychotomous logistic regression.
Bull, S B; Greenwood, C M; Hauck, W W
1997-03-15
Despite theoretical and empirical evidence that the usual MLEs can be misleading in finite samples and some evidence that bias reduced estimates are less biased and more efficient, they have not seen a wide application in practice. One can obtain bias reduced estimates by jackknife methods, with or without full iteration, or by use of higher order terms in a Taylor series expansion of the log-likelihood to approximate asymptotic bias. We provide details of these methods for polychotomous logistic regression with a nominal categorical response. We conducted a Monte Carlo comparison of the jackknife and Taylor series estimates in moderate sample sizes in a general logistic regression setting, to investigate dichotomous and trichotomous responses and a mixture of correlated and uncorrelated binary and normal covariates. We found an approximate two-step jackknife and the Taylor series methods useful when the ratio of the number of observations to the number of parameters is greater than 15, but we cannot recommend the two-step and the fully iterated jackknife estimates when this ratio is less than 20, especially when there are large effects, binary covariates, or multicollinearity in the covariates.
Shape regression for vertebra fracture quantification
Lund, Michael Tillge; de Bruijne, Marleen; Tanko, Laszlo B.; Nielsen, Mads
2005-04-01
Accurate and reliable identification and quantification of vertebral fractures constitute a challenge both in clinical trials and in diagnosis of osteoporosis. Various efforts have been made to develop reliable, objective, and reproducible methods for assessing vertebral fractures, but at present there is no consensus concerning a universally accepted diagnostic definition of vertebral fractures. In this project we want to investigate whether or not it is possible to accurately reconstruct the shape of a normal vertebra, using a neighbouring vertebra as prior information. The reconstructed shape can then be used to develop a novel vertebra fracture measure, by comparing the segmented vertebra shape with its reconstructed normal shape. The vertebrae in lateral x-rays of the lumbar spine were manually annotated by a medical expert. With this dataset we built a shape model, with equidistant point distribution between the four corner points. Based on the shape model, a multiple linear regression model of a normal vertebra shape was developed for each dataset using leave-one-out cross-validation. The reconstructed shape was calculated for each dataset using these regression models. The average prediction error for the annotated shape was on average 3%.
A reconsideration of the concept of regression.
Dowling, A Scott
2004-01-01
Regression has been a useful psychoanalytic concept, linking present mental functioning with past experiences and levels of functioning. The concept originated as an extension of the evolutionary zeitgeist of the day as enunciated by H. Spencer and H. Jackson and applied by Freud to psychological phenomena. The value system implicit in the contrast of evolution/progression vs dissolution/regression has given rise to unfortunate and powerful assumptions of social, cultural, developmental and individual value as embodied in notions of "higher," "lower;" "primitive," "mature," "archaic," and "advanced." The unhelpful results of these assumptions are evident, for example, in attitudes concerning cultural, sexual, and social "correctness, " same-sex object choice, and goals of treatment. An alternative, a continuously constructed, continuously emerging mental life, in analogy to the ever changing, continuous physical body, is suggested. This view retains the fundamentals of psychoanalysis, for example, unconscious mental life, drive, defense, and psychic structure, but stresses a functional, ever changing, present oriented understanding of mental life as contrasted with a static, onion-layered view.
Supporting Regularized Logistic Regression Privately and Efficiently
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Stochastic search, optimization and regression with energy applications
Hannah, Lauren A.
models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.
Logistic regression applied to natural hazards: rare event logistic regression with replications
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Wheeler, David; Tiefelsdorf, Michael
2005-06-01
Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Hui, Charles Ps
2013-02-01
Acute otitis externa, also known as 'swimmer's ear', is a common disease of children, adolescents and adults. While chronic suppurative otitis media or acute otitis media with tympanostomy tubes or a perforation can cause acute otitis externa, both the infecting organisms and management protocol are different. This practice point focuses solely on managing acute otitis externa, without acute otitis media, tympanostomy tubes or a perforation being present.
2013-01-01
Acute otitis externa, also known as ‘swimmer’s ear’, is a common disease of children, adolescents and adults. While chronic suppurative otitis media or acute otitis media with tympanostomy tubes or a perforation can cause acute otitis externa, both the infecting organisms and management protocol are different. This practice point focuses solely on managing acute otitis externa, without acute otitis media, tympanostomy tubes or a perforation being present.
Subgroup finding via Bayesian additive regression trees.
Sivaganesan, Siva; Müller, Peter; Huang, Bin
2017-03-09
We provide a Bayesian decision theoretic approach to finding subgroups that have elevated treatment effects. Our approach separates the modeling of the response variable from the task of subgroup finding and allows a flexible modeling of the response variable irrespective of potential subgroups of interest. We use Bayesian additive regression trees to model the response variable and use a utility function defined in terms of a candidate subgroup and the predicted response for that subgroup. Subgroups are identified by maximizing the expected utility where the expectation is taken with respect to the posterior predictive distribution of the response, and the maximization is carried out over an a priori specified set of candidate subgroups. Our approach allows subgroups based on both quantitative and categorical covariates. We illustrate the approach using simulated data set study and a real data set. Copyright © 2017 John Wiley & Sons, Ltd.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Estimates on compressed neural networks regression.
Zhang, Yongquan; Li, Youmei; Sun, Jianyong; Ji, Jiabing
2015-03-01
When the neural element number n of neural networks is larger than the sample size m, the overfitting problem arises since there are more parameters than actual data (more variable than constraints). In order to overcome the overfitting problem, we propose to reduce the number of neural elements by using compressed projection A which does not need to satisfy the condition of Restricted Isometric Property (RIP). By applying probability inequalities and approximation properties of the feedforward neural networks (FNNs), we prove that solving the FNNs regression learning algorithm in the compressed domain instead of the original domain reduces the sample error at the price of an increased (but controlled) approximation error, where the covering number theory is used to estimate the excess error, and an upper bound of the excess error is given.
Remaining Phosphorus Estimate Through Multiple Regression Analysis
M. E. ALVES; A. LAVORENTI
2006-01-01
The remaining phosphorus (Prem), P concentration that remains in solution after shaking soil with 0.01 mol L-1 CaCl2 containing 60 μg mL-1 P, is a very useful index for studies related to the chemistry of variable charge soils. Although the Prem determination is a simple procedure, the possibility of estimating accurate values of this index from easily and/or routinely determined soil properties can be very useful for practical purposes. The present research evaluated the Premestimation through multiple regression analysis in which routinely determined soil chemical data, soil clay content and soil pH measured in 1 mol L-1 NaF (pHNaF) figured as Prem predictor variables. The Prem can be estimated with acceptable accuracy using the above-mentioned approach, and PHNaF not only substitutes for clay content as a predictor variable but also confers more accuracy to the Prem estimates.
Early development and regression in Rett syndrome.
Lee, J Y L; Leonard, H; Piek, J P; Downs, J
2013-12-01
This study utilized developmental profiling to examine symptoms in 14 girls with genetically confirmed Rett syndrome and whose families were participating in the Australian Rett syndrome or InterRett database. Regression was mostly characterized by loss of hand and/or communication skills (13/14) except one girl demonstrated slowing of skill development. Social withdrawal and inconsolable crying often developed simultaneously (9/14), with social withdrawal for shorter duration than inconsolable crying. Previously acquired gross motor skills declined in just over half of the sample (8/14), mostly observed as a loss of balance. Early abnormalities such as vomiting and strabismus were also seen. Our findings provide additional insight into the early clinical profile of Rett syndrome.
Model Selection in Kernel Ridge Regression
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Least square regularized regression in sum space.
Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu
2013-04-01
This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Logistic regression against a divergent Bayesian network
Noel Antonio Sánchez Trujillo
2015-01-01
Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
Adaptive Rank Penalized Estimators in Multivariate Regression
Bunea, Florentina; Wegkamp, Marten
2010-01-01
We introduce a new criterion, the Rank Selection Criterion (RSC), for selecting the optimal reduced rank estimator of the coefficient matrix in multivariate response regression models. The corresponding RSC estimator minimizes the Frobenius norm of the fit plus a regularization term proportional to the number of parameters in the reduced rank model. The rank of the RSC estimator provides a consistent estimator of the rank of the coefficient matrix. The consistency results are valid not only in the classic asymptotic regime, when the number of responses $n$ and predictors $p$ stays bounded, and the number of observations $m$ grows, but also when either, or both, $n$ and $p$ grow, possibly much faster than $m$. Our finite sample prediction and estimation performance bounds show that the RSC estimator achieves the optimal balance between the approximation error and the penalty term. Furthermore, our procedure has very low computational complexity, linear in the number of candidate models, making it particularly ...
Macrophages, Dendritic Cells, and Regression of Atherosclerosis
Jonathan E. Feig
2012-07-01
Full Text Available Atherosclerosis is the number one cause of death in the Western world. It results from the interaction between modified lipoproteins and monocyte-derived cells such as macrophages, dendritic cells, T cells, and other cellular elements of the arterial wall. This inflammatory process can ultimately lead to the development of complex lesions, or plaques, that protrude into the arterial lumen. Ultimately, plaque rupture and thrombosis can occur leading to the clinical complications of myocardial infarction or stroke. Although each of the cell types plays roles in the pathogenesis of atherosclerosis, in this review, the focus will be primarily on the monocyte derived cells- macrophages and dendritic cells. The roles of these cell types in atherogenesis will be highlighted. Finally, the mechanisms of atherosclerosis regression as it relates to these cells will be discussed.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Collaborative regression-based anatomical landmark detection
Gao, Yaozong; Shen, Dinggang
2015-12-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods.
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2016-11-11
We present a framework to design inverse rig-functions - functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
Logistic Regression Applied to Seismic Discrimination
BG Amindan; DN Hagedorn
1998-10-08
The usefulness of logistic discrimination was examined in an effort to learn how it performs in a regional seismic setting. Logistic discrimination provides an easily understood method, works with user-defined models and few assumptions about the population distributions, and handles both continuous and discrete data. Seismic event measurements from a data set compiled by Los Alamos National Laboratory (LANL) of Chinese events recorded at station WMQ were used in this demonstration study. PNNL applied logistic regression techniques to the data. All possible combinations of the Lg and Pg measurements were tried, and a best-fit logistic model was created. The best combination of Lg and Pg frequencies for predicting the source of a seismic event (earthquake or explosion) used Lg{sub 3.0-6.0} and Pg{sub 3.0-6.0} as the predictor variables. A cross-validation test was run, which showed that this model was able to correctly predict 99.7% earthquakes and 98.0% explosions for this given data set. Two other models were identified that used Pg and Lg measurements from the 1.5 to 3.0 Hz frequency range. Although these other models did a good job of correctly predicting the earthquakes, they were not as effective at predicting the explosions. Two possible biases were discovered which affect the predicted probabilities for each outcome. The first bias was due to this being a case-controlled study. The sampling fractions caused a bias in the probabilities that were calculated using the models. The second bias is caused by a change in the proportions for each event. If at a later date the proportions (a priori probabilities) of explosions versus earthquakes change, this would cause a bias in the predicted probability for an event. When using logistic regression, the user needs to be aware of the possible biases and what affect they will have on the predicted probabilities.
Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting
Cheng-Wen Lee
2016-10-01
Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.
Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi
2013-01-01
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, Plinear regression modeling, R(2) = 0.598, P<0.0001). Logarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
Makoto Suzuki
Full Text Available Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2 = 0.676, P<0.0001; linear regression modeling, R(2 = 0.598, P<0.0001. Logarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
Vargas, M.; Crossa, J.; Eeuwijk, van F.A.; Ramirez, M.E.; Sayre, K.
1999-01-01
Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEl). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only th
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).
Deep Wavelet Scattering for Quantum Energy Regression
Hirn, Matthew
Physical functionals are usually computed as solutions of variational problems or from solutions of partial differential equations, which may require huge computations for complex systems. Quantum chemistry calculations of ground state molecular energies is such an example. Indeed, if x is a quantum molecular state, then the ground state energy E0 (x) is the minimum eigenvalue solution of the time independent Schrödinger Equation, which is computationally intensive for large systems. Machine learning algorithms do not simulate the physical system but estimate solutions by interpolating values provided by a training set of known examples {(xi ,E0 (xi) } i physical invariants. Linear regressions of E0 over a dictionary Φ ={ϕk } k compute an approximation E 0 as: E 0 (x) =∑kwkϕk (x) , where the weights {wk } k are selected to minimize the error between E0 and E 0 on the training set. The key to such a regression approach then lies in the design of the dictionary Φ. It must be intricate enough to capture the essential variability of E0 (x) over the molecular states x of interest, while simple enough so that evaluation of Φ (x) is significantly less intensive than a direct quantum mechanical computation (or approximation) of E0 (x) . In this talk we present a novel dictionary Φ for the regression of quantum mechanical energies based on the scattering transform of an intermediate, approximate electron density representation ρx of the state x. The scattering transform has the architecture of a deep convolutional network, composed of an alternating sequence of linear filters and nonlinear maps. Whereas in many deep learning tasks the linear filters are learned from the training data, here the physical properties of E0 (invariance to isometric transformations of the state x, stable to deformations of x) are leveraged to design a collection of linear filters ρx *ψλ for an appropriate wavelet ψ. These linear filters are composed with the nonlinear modulus
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
Testing the gonadal regression-cytoprotection hypothesis.
Crawford, B A; Spaliviero, J A; Simpson, J M; Handelsman, D J
1998-11-15
Germinal damage is an almost universal accompaniment of cancer treatment as the result of bystander damage to the testis from cytotoxic drugs and/or irradiation. Cancer treatment for the most common cancers of the reproductive age group in men has improved such that most are now treated with curative intent, and many others are treated with likelihood of prolonged survival, so that the preservation of fertility is an important component of posttreatment quality of life. This has led to the consideration of developing adjuvant treatments that may reduce the gonadal toxicity of cancer therapy. One dominant hypothesis has been based on the supposition that the immature testis was resistant to cytotoxin damage. Hence, if hormonal treatment were able to cause spermatogenic regression to an immature state via an effective withdrawal of gonadotrophin secretion, the testis might be maintained temporarily in a protected state during cytotoxin exposure. However, clinical studies have been disappointing but have also been unable to test the hypothesis definitively thus far, due to the inability to completely suppress gonadotrophin secretion. Similarly, experimental models have also given conflicting results and, at best, a modest cytoprotection. To definitively test this hypothesis experimentally, we used the fact that the functionally hpg mouse has complete gonadotrophin deficiency but can undergo the induction of full spermatogenesis by testosterone. Thus, if complete gonadotrophin deficiency were an advantage during cytotoxin exposure, then the hpg mouse should exhibit some degree of germinal protection against cytotoxin-induced damage. We therefore administered three different cytotoxins (200 mg/kg procarbazine, 9 mg/kg doxorubicin, 8 Gy of X irradiation) to produce a range of severity in testicular damage and mechanism of action to either phenotypically normal or hpg mice. Testis weight and homogenization-resistant spermatid numbers were measured to evaluate the
Greening, Leilani; Stoppelbein, Laura; Luebbe, Aaron
2010-04-01
Given that parenting practices have been linked to suicidal behavior in adolescence, examining the moderating effect of parenting styles on suicidal behavior early in development could offer potential insight into possible buffers as well as directions for suicide prevention and intervention later in adolescence. Hence, the moderating effects of parenting styles, including authoritarian, permissive, and features of authoritative parenting, on depressed and aggressive children's suicidal behavior, including ideation and attempts, were evaluated with young children (N = 172; 72% male, 28% female) ranging from 6 to 12 years of age. African American (69%) and Caucasian (31%) children admitted for acute psychiatric inpatient care completed standardized measures of suicidal behavior, depressive symptoms, and proactive and reaction aggression. Their parents also completed standardized measures of parental distress and parenting style. Hierarchical regression analyses revealed that, while statistically controlling for age and gender, children who endorsed more depressive symptoms or reactive aggression reported more current and past suicidal behavior than children who endorsed fewer depressive or aggressive symptoms. The significant positive relationship observed between depressive symptoms and childhood suicidal behavior, however, was attenuated by parental use of authoritarian parenting practices for African-American and older children but not for younger and Caucasian children. The ethnic/racial difference observed for the buffering effect of authoritarian parenting practices offers potential theoretical and clinical implications for conceptualizing the moderating effects of parenting styles on African-American and Caucasian children's suicidal behavior.
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation, ......, Slepian models, Stochastic finite elements.......The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...
Regression Benchmarking: An Approach to Quality Assurance in Performance
2005-01-01
The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...
Pentoxifylline Treatment in Acute Pancreatitis (AP)
2016-09-14
Acute Pancreatitis (AP); Gallstone Pancreatitis; Alcoholic Pancreatitis; Post-ERCP/Post-procedural Pancreatitis; Trauma Acute Pancreatitis; Hypertriglyceridemia Acute Pancreatitis; Idiopathic (Unknown) Acute Pancreatitis; Medication Induced Acute Pancreatitis; Cancer Acute Pancreatitis; Miscellaneous (i.e. Acute on Chronic Pancreatitis)
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Meta-Regression: A Framework for Robust Reactive Optimization
McClary, Dan; Syrotiuk, Violet R.; Kulahci, Murat
2007-01-01
Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in ...... of a nonlinear system....
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval arithm
Dyslipidemia and Outcome in Patients with Acute Ischemic Stroke
XU Tian; ZHANG Jin Tao; YANG Mei; ZHANG Huan; LIU Wen Qing; KONG Yan; XU Tan; ZHANG Yong Hong
2014-01-01
ObjectiveTo study the relationship between dyslipidemia and outcome in patients with acute ischemic stroke. MethodsData about 1 568 patients with acute ischemic stroke werecollected from 4 hospitals in Shandong Province from January 2006 to December 2008. National Institute of Health Stroke Scale (NIHSS) >10 at discharge or death was defined as the outcome. Effect of dyslipidemia on outcome in patients with acute ischemic stroke was analyzed by multivariate logistic regression analysis and propensity score-adjusted analysis, respectively. ResultsThe serum levels of TC, LDL-C, and HDL-C were significantly associated with the outcome in patients with acute ischemic stroke. Multivariate logistic regression analysis and propensity score-adjusted analysis showed that the ORs and 95% CIs were 3.013 (1.259, 7.214)/2.655 (1.298, 5.43), 3.157(1.306, 7.631)/3.405(1.621, 7.154), and 0.482 (0.245, 0.946)/0.51 (0.282, 0.921), respectively, for patients with acute ischemic stroke. Hosmer-Lemeshow goodness-of-fit test showed no significant difference in observed and predicted risk in patients with acute ischemic stroke (chi-square=8.235, P=0.411). ConclusionSerum levels of TC, LDL-C, and HDL-C are positively related with the outcome in patients with acute ischemic stroke.
Free Software Development. 1. Fitting Statistical Regressions
Lorentz JÄNTSCHI
2002-12-01
Full Text Available The present paper is focused on modeling of statistical data processing with applications in field of material science and engineering. A new method of data processing is presented and applied on a set of 10 Ni–Mn–Ga ferromagnetic ordered shape memory alloys that are known to exhibit phonon softening and soft mode condensation into a premartensitic phase prior to the martensitic transformation itself. The method allows to identify the correlations between data sets and to exploit them later in statistical study of alloys. An algorithm for computing data was implemented in preprocessed hypertext language (PHP, a hypertext markup language interface for them was also realized and put onto comp.east.utcluj.ro educational web server, and it is accessible via http protocol at the address http://vl.academicdirect.ro/applied_statistics/linear_regression/multiple/v1.5/. The program running for the set of alloys allow to identify groups of alloys properties and give qualitative measure of correlations between properties. Surfaces of property dependencies are also fitted.
Semiparametric Bayesian Regression with Applications in Astronomy
Broadbent, Mary Elizabeth
In this thesis we describe a class of Bayesian semiparametric models, known as Levy Adaptive Regression Kernels (LARK); a novel method for posterior computation for those models; and the applications of these models in astronomy, in particular to the analysis of the photon fluence time series of gamma-ray bursts. Gamma-ray bursts are bursts of photons which arrive in a varying number of overlapping pulses with a distinctive "fast-rise, exponential decay" shape in the time domain. LARK models allow us to do inference both on the number of pulses, but also on the parameters which describe the pulses, such as incident time, or decay rate. In Chapter 2, we describe a novel method to aid posterior computation in infinitely-divisible models, of which LARK models are a special case, when the posterior is evaluated through Markov chain Monte Carlo. This is applied in Chapter 3, where time series representing the photon fluence in a single energy channel is analyzed using LARK methods. Due to the effect of the discriminators on BATSE and other instruments, it is important to model the gamma-ray bursts in the incident space. Chapter 4 describes the first to model bursts in the incident photon space, instead of after they have been distorted by the discriminators; since to model photons as they enter the detector is to model both the energy and the arrival time of the incident photon, this model is also the first to jointly model the time and energy domains.
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
A note on constrained M-estimation and its recursive analog in multivariate linear regression models
RAO; Calyampudi; R
2009-01-01
In this paper,the constrained M-estimation of the regression coeffcients and scatter parameters in a general multivariate linear regression model is considered.Since the constrained M-estimation is not easy to compute,an up-dating recursion procedure is proposed to simplify the com-putation of the estimators when a new observation is obtained.We show that,under mild conditions,the recursion estimates are strongly consistent.In addition,the asymptotic normality of the recursive constrained M-estimators of regression coeffcients is established.A Monte Carlo simulation study of the recursion estimates is also provided.Besides,robustness and asymptotic behavior of constrained M-estimators are briefly discussed.
Rank-preserving regression: a more robust rank regression model against outliers.
Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M
2016-08-30
Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd.
Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan
2015-06-01
Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.
Ahn, Kuk-Hyun; Palmer, Richard
2016-09-01
Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes 'spatial proximity' based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. 'Spatial proximity' based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.
Histoplasmosis - acute (primary) pulmonary
... this page: //medlineplus.gov/ency/article/000098.htm Histoplasmosis - acute (primary) pulmonary To use the sharing features on this page, please enable JavaScript. Acute pulmonary histoplasmosis is a respiratory infection that is caused by ...
Acute respiratory distress syndrome
... page: //medlineplus.gov/ency/article/000103.htm Acute respiratory distress syndrome To use the sharing features on this page, please enable JavaScript. Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that ...
Kidney failure; Renal failure; Renal failure - acute; ARF; Kidney injury - acute ... There are many possible causes of kidney damage. They include: ... cholesterol (cholesterol emboli) Decreased blood flow due to very ...
Full Text Available Acute bee paralysis virus [gbvrl]: 14 CDS's (15780 codons) fields: [triplet] [frequ...osomal protein / MAP kinase List of codon usage for each CDS (format) Homepage Acute bee paralysis virus ...
... Side Effects Additional Content Medical News Acute Mesenteric Ischemia By Parswa Ansari, MD, Department of Surgery, Lenox ... Abscesses Abdominal Wall Hernias Inguinal Hernia Acute Mesenteric Ischemia Appendicitis Ileus Intestinal Obstruction Ischemic Colitis Perforation of ...
Acute acalculous cholecystitis complicating chemotherapy for acute myeloblastic leukemia
Olfa Kassar; Feten Kallel; Manel Ghorbel; Hatem. Bellaaj; Zeineb Mnif; Moez Elloumi
2015-01-01
Acute acalculous cholecystitis is a rare complication in the treatment of acute myeloblastic leukemia. Diagnosis of acute acalculous cholecystitis remains difficult during neutropenic period. We present two acute myeloblastic leukemia patients that developed acute acalculous cholecystitis during chemotherapy-induced neutropenia. They suffered from fever, vomiting and acute pain in the epigastrium. Ultrasound demonstrated an acalculous gallbladder. Surgical management was required in one patie...
Deep Human Parsing with Active Template Regression.
Liang, Xiaodan; Liu, Si; Shen, Xiaohui; Yang, Jianchao; Liu, Luoqi; Dong, Jian; Lin, Liang; Yan, Shuicheng
2015-12-01
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches 64.38 percent by our ATR framework, significantly higher than 44.76 percent based on the state-of-the-art algorithm [28].
Weaker Regularity Conditions and Sparse Recovery in High-Dimensional Regression
Shiqing Wang
2014-01-01
Full Text Available Regularity conditions play a pivotal role for sparse recovery in high-dimensional regression. In this paper, we present a weaker regularity condition and further discuss the relationships with other regularity conditions, such as restricted eigenvalue condition. We study the behavior of our new condition for design matrices with independent random columns uniformly drawn on the unit sphere. Moreover, the present paper shows that, under a sparsity scenario, the Lasso estimator and Dantzig selector exhibit similar behavior. Based on both methods, we derive, in parallel, more precise bounds for the estimation loss and the prediction risk in the linear regression model when the number of variables can be much larger than the sample size.
Profile-driven regression for modeling and runtime optimization of mobile networks
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2010-01-01
of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike......Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels....
Assessment of acute cholangitis by MR imaging
Eun, Hyo Won, E-mail: namsanae@gmail.com [Health Promotion Center, Asan Medical Center, University of Ulsan, 388-1 Poongnap-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Kim, Jung Hoon, E-mail: jhkim2008@gmail.com [Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehang-no, Chongno-gu, Seoul 110-744 (Korea, Republic of); Hong, Seong Sook, E-mail: hongses@hosp.sch.ac.kr [Department of Radiology, Soonchunhyang University Hospital, 657 Hannam-Dong, Youngsan-Ku, Seoul 140-743 (Korea, Republic of); Kim, Young Jae, E-mail: rtwodtwo@hosp.sch.ac.kr [Department of Radiology, Soonchunhyang University Hospital, 657 Hannam-Dong, Youngsan-Ku, Seoul 140-743 (Korea, Republic of)
2012-10-15
Purpose: The purpose of this study is to assess the common MRI findings of acute cholangitis compared with those of non-acute cholangitis. Materials and methods: During a 31-month period, we performed MRCP and contrast-enhanced MRI on 173 patients with biliary abnormalities including duct dilatation or stricture. The causes of the biliary abnormalities included biliary stone disease (n = 85), cholangiocarcinoma (n = 47), periampullary cancer (n = 20), GB cancer (n = 4), and others (n = 17). Among 173 patients, 66 consecutive patients were confirmed with acute cholangitis diagnosed according to the Tokyo guideline, and 107 patients were confirmed as having non-acute cholangitis. Two radiologists retrospectively and independently accessed the MR findings, including the cause of biliary abnormality, increased periductal signal intensity on T2-weighted images, the transient periductal signal difference, and the presence of abscess, thrombosis, and ragged duct. They also measured the dilated duct and the thickened wall. The Student t-test and the Pearson chi-square were used. The κ statistics were used to determine interobserver agreement. Logistic regression was used to identify the MR findings that predicted acute cholangitis. Results: MRI correctly accessed the cause of biliary abnormality in 163 patients (94%). The statistically common findings for acute cholangitis were as follows: increased periductal signal intensity on T2-weighted imaging (n = 26, 39%, p < 0.05); transient periductal signal difference (n = 31, 47%, p < 0.05); abscess (n = 18, 27%, p < 0.05); thrombosis (n = 12, 18%, p < 0.05); and ragged duct (n = 11, 17%, p < 0.05). Interobserver agreement was good to excellent for each finding (κ = 0.74–0.97). The wall thickness showed a statistically significant difference between the acute cholangitis and the non-acute cholangitis group (2.65 mm:2.32 mm, p < 0.05), however, there was no significant difference in duct dilatation in the two groups. The
Acute solvent exposures may contribute to automobile accidents because they increase reaction time and decrease attention, in addition to impairing other behaviors. These effects resemble those of ethanol consumption, both with respect to behavioral effects and neurological mecha...
Using Raw VAR Regression Coefficients to Build Networks can be Misleading.
Bulteel, Kirsten; Tuerlinckx, Francis; Brose, Annette; Ceulemans, Eva
2016-01-01
Many questions in the behavioral sciences focus on the causal interplay of a number of variables across time. To reveal the dynamic relations between the variables, their (auto- or cross-) regressive effects across time may be inspected by fitting a lag-one vector autoregressive, or VAR(1), model and visualizing the resulting regression coefficients as the edges of a weighted directed network. Usually, the raw VAR(1) regression coefficients are drawn, but we argue that this may yield misleading network figures and characteristics because of two problems. First, the raw regression coefficients are sensitive to scale and variance differences among the variables and therefore may lack comparability, which is needed if one wants to calculate, for example, centrality measures. Second, they only represent the unique direct effects of the variables, which may give a distorted picture when variables correlate strongly. To deal with these problems, we propose to use other VAR(1)-based measures as edges. Specifically, to solve the comparability issue, the standardized VAR(1) regression coefficients can be displayed. Furthermore, relative importance metrics can be computed to include direct as well as shared and indirect effects into the network.
Anthonsen, Kristian; Høstmark, Karianne; Hansen, Søren;
2013-01-01
Conservative treatment of acute otitis media may lead to more complications. This study evaluates changes in incidence, the clinical and microbiological findings, the complications and the outcome of acute mastoiditis in children in a country employing conservative guidelines in treating acute...... otitis media....
Using Logistic Regression to Identify Risk Factors Causing Rollover Collisions
Essam Dabbour
2012-12-01
Full Text Available Rollover collisions are among the most serious collisions that usually result in severe injuries or fatalities. In 2009, there were 8,732 fatal rollover collisions in the United States of America that resulted in the death of 9,833 persons. Those numbers represent approximately 28% and 29% of the total numbers of fatal collisions and fatalities, respectively. The main objective of this paper is to examine the impact of different risk factors that may contribute to this type of serious collisions to help develop countermeasures that limit them. To avoid the bias that may be caused by interactions among different drivers, this analysis focuses on rollover related to single-vehicle collisions so that the behavior of the driver of the collided vehicle can be analyzed more effectively. Logistic regression technique is utilized to analyze single-vehicle rollover collisions that occurred on state and interstate highways in the states of Ohio and Washington in 2009. The results obtained from this analysis have the potential to help decision makers identify different strategies to limit the severity of this type of collisions.
Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann;
2012-01-01
Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... this investigation, we propose the optimal solution for regression estimation in case of noisy and inconsistent optical measurements, which is the case in many practical measurement systems. The principal component regression (PLS), partial least squares (PCR) and least angle regression (LAR) methods are compared...
喻秋珺; 赵晶; 刘芳娥; 李静; 朱伟军; 李小康
2006-01-01
组大鼠行为和情绪的变化.结果:纳人大鼠49只,最终进入结果分析49只,无脱失值.①大鼠情绪状态观察结果:实验结束后观察显示,睡眠剥夺+被动吸烟24 h组较睡眠剥夺24 h组,睡眠剥夺+被动吸烟54 h组较睡眠剥夺54 h组,睡眠剥夺54 h组较睡眠剥夺24 h组情绪低靡,蜷缩喜静,对外界刺激不敏感,反应冷漠,对其他鼠的攻击行为较少.②旷场实验测试结果:睡眠剥夺24 h组距旷场中心的平均距离明显小于空白对照组及睡眠剥夺+被动吸烟24 h组[(53.93±1.83,58.21±4.45,58.11±1.62)cm,(P＜0.01～0.05)],睡眠剥夺54 h组距旷场中心平均距离明显大于睡眠剥夺24 h组[(61.53±3.02,58.11±1.62)cm,(P＜0.01)],睡眠剥夺24 h组的运动总距离明显大于睡眠剥夺+被动吸烟24h组及空白对照组[(3 310.45±1 445.97,1 818.20±733.25,2 338.15±694.70)cm,(P＜0.01～0.05)],睡眠剥夺54 h组运动总距离明显大于睡眠剥夺+被动吸烟54 h组,小于睡眠剥夺24h组[(2410.70±548.64,1 473.50±945.89,3 310.45±1445.97)cm,(P＜0.05)].结论:大鼠的行为和情绪反应随睡眠剥夺时间的延长呈现先兴奋后抑制的趋势,而被动吸烟在整个大鼠睡眠剥夺过程中对大鼠的行为和情绪状态起抑制作用.%BACKGROUND:Sleep deprivation, resulting in a series of physiological and psychological reactions, is a common phenomenon in our modem society. Recently, many physical and medical therapies are on their way to eliminate the negative influence the sleep deprivation exerted on our human beings, and a large number of cigarette smokers believe that cigarette smoke can obviously improve their abnormal behavioral performance and negative emotional fluctuation caused by sleep deprivation in short terms.However, there are few researches but many disputes when it comes to effect of passive smoking on the behavior and emotion of rats on the occasion of acute sleep deprivation.OBJECTIVE: To investigate the effect of passive smoking (PS) on the
Umezu, Toyoshi, E-mail: umechan2@nies.go.jp; Shibata, Yasuyuki, E-mail: yshibata@nies.go.jp
2014-09-01
The present study aimed to clarify whether dose–response profiles of acute behavioral effects of 1,2-dichloroethane (DCE), 1,1,1-trichloroethane (TCE), trichloroethylene (TRIC), and tetrachloroethylene (PERC) differ. A test battery involving 6 behavioral endpoints was applied to evaluate the effects of DCE, TCE, TRIC, and PERC in male ICR strain mice under the same experimental conditions. The behavioral effect dose–response profiles of these compounds differed. Regression analysis was used to evaluate the relationship between the dose–response profiles and structural and physical properties of the compounds. Dose–response profile differences correlated significantly with differences in specific structural and physical properties. These results suggest that differences in specific structural and physical properties of DCE, TCE, TRIC, and PERC are responsible for differences in behavioral effects that lead to a variety of dose–response profiles. - Highlights: • We examine effects of 4 chlorinated hydrocarbons on 6 behavioral endpoints in mice. • The behavioral effect dose–response profiles for the 4 compounds are different. • We utilize regression analysis to clarify probable causes of the different profiles. • The compound's physicochemical properties probably produce the different profiles.
Persell, Stephen D.; Friedberg, Mark W.; Meeker, Daniella; Linder, Jeffrey A; Craig R. Fox; Goldstein, Noah J.; Shah, Parth D; Knight, Tara K; Doctor, Jason N
2013-01-01
Abstract Background Inappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption th...
Imaging of Acute Pancreatitis.
Thoeni, Ruedi F
2015-11-01
Acute pancreatitis is an acute inflammation of the pancreas. Several classification systems have been used in the past but were considered unsatisfactory. A revised Atlanta classification of acute pancreatitis was published that assessed the clinical course and severity of disease; divided acute pancreatitis into interstitial edematous pancreatitis and necrotizing pancreatitis; discerned an early phase (first week) from a late phase (after the first week); and focused on systemic inflammatory response syndrome and organ failure. This article focuses on the revised classification of acute pancreatitis, with emphasis on imaging features, particularly on newly-termed fluid collections and implications for the radiologist.
魏广和; 李清贤; 张金国; 张慧玲; 乔增勇; 王悦强
2008-01-01
目的 了解急性心肌梗死患者就医行为及其对就医延误的影响.方法 采用自行设计的问卷调查表,对53例急诊行冠脉介入治疗(PCI)的急性心肌梗死(AMI)患者的就医行为、就诊时间及其对预后的影响进行了调查和分析.结果 (1)AMI患者院前延迟时间中位数为3.20h,≤2.00h者占30.19%,2.00h者占69.81%;就诊延迟时间中位数为4.00h,≤6.00h者占88.68%,6.00h者占11.32%.(2)发病后,症状归因于心脏病者占45.28%,归因于非心脏病者占43.40%,不知道者占11.32%,其院前延迟时间中位数分别为2.10h、3.20h和3.90h(P<0.01),就诊延迟时间中位数分别为3.50h、4.80h和5.90h(P<0.01);立即就诊者占32.08%,等待或自行治疗者占60.38%,向朋友家人家庭医生咨询者占7.54%,其院前延迟时间中位数分别为1.10h、3.50h和3.50h(P<0.01),就诊延迟时间中位数分别为3.00h、4.90h和6.00h(P<0.01);采用救护车转运者占54.72%,采用出租车、自家车等其他方式转运者占45.28%,其院前延迟时间中位数分别为2.20h和3.80h(P<0.01),就诊延迟时间中位数分别为3.70h和5.15 h(P<0.01);首诊于三级医院者占79.25%,首诊于二级医院者占11.32%,首诊于社区医院或诊所者占9.43%,其院前延迟时间中位数分别为2.55h、4.60h和4.00h(P<0.01),就诊延迟时间中位数分别为3.75 h、5.95 h和5.50h(P<0.01);立即选择PCI治疗者占66.04%,向朋友家人家庭医生咨询者占30.19%,等待或观察者占3.77%,其就诊延迟时间中位数分别为3.70h、11.00h和5.15h(P<0.01).(3)院前延迟时间≤2.00h者1年内心血管事件发生率明显低于2.00h者(0,27.03%,P<0.05).就诊延迟时间≤6.00h者1年内心血管事件发生率也明显低于6.00h者(14.89%,50.0%,P<0.05).结论 AMI患者目前仍存在不良就医行为,就医延误依然存在,且对预后产生不利影响,改善患者就医行为不容忽视.%Objective To know about the hospitalizing behaviors of acute myocardial infarction
Tristán, Bekinschtein; Gleichgerrcht, Ezequiel; Manes, Facundo
2015-01-01
Acute loss of consciousness poses a fascinating scenario for theoretical and clinical research. This chapter introduces a simple yet powerful framework to investigate altered states of consciousness. We then explore the different disorders of consciousness that result from acute brain injury, and techniques used in the acute phase to predict clinical outcome in different patient populations in light of models of acute loss of consciousness. We further delve into post-traumatic amnesia as a model for predicting cognitive sequels following acute loss of consciousness. We approach the study of acute loss of consciousness from a theoretical and clinical perspective to conclude that clinicians in acute care centers must incorporate new measurements and techniques besides the classic coma scales in order to assess their patients with loss of consciousness.
A fast nonlinear regression method for estimating permeability in CT perfusion imaging.
Bennink, Edwin; Riordan, Alan J; Horsch, Alexander D; Dankbaar, Jan Willem; Velthuis, Birgitta K; de Jong, Hugo W
2013-11-01
Blood-brain barrier damage, which can be quantified by measuring vascular permeability, is a potential predictor for hemorrhagic transformation in acute ischemic stroke. Permeability is commonly estimated by applying Patlak analysis to computed tomography (CT) perfusion data, but this method lacks precision. Applying more elaborate kinetic models by means of nonlinear regression (NLR) may improve precision, but is more time consuming and therefore less appropriate in an acute stroke setting. We propose a simplified NLR method that may be faster and still precise enough for clinical use. The aim of this study is to evaluate the reliability of in total 12 variations of Patlak analysis and NLR methods, including the simplified NLR method. Confidence intervals for the permeability estimates were evaluated using simulated CT attenuation-time curves with realistic noise, and clinical data from 20 patients. Although fixating the blood volume improved Patlak analysis, the NLR methods yielded significantly more reliable estimates, but took up to 12 × longer to calculate. The simplified NLR method was ∼4 × faster than other NLR methods, while maintaining the same confidence intervals (CIs). In conclusion, the simplified NLR method is a new, reliable way to estimate permeability in stroke, fast enough for clinical application in an acute stroke setting.
Pre-hospital delay in acute myocardial infarction: judgement of symptoms and resistance to pain
Fernanda Carneiro Mussi
2014-02-01
Full Text Available Objective To estimate the time of decision (TD to look for medical care and the time of arrival (TA at the health service for men (M and women (W suffering from acute myocardial infarction and to analyze the influence of the interpretation of pain and pain resistance behaviors during these times. Methods This is an exploratory research, performed at the university hospital in Salvador/Bahia. 43 W and 54 M were interviewed. To study the dependence among sociodemographic and gender variables, the Fisher Exact Test was used. To analyze times, a geometric mean (GM was used. In order to verify the association between the GM of TD and TA and the judgment of pain, and between the GM of TD and TA and the behavior of resistance to pain, as well as to test the time of interaction between the gender variable and other variables of interest, the robust regression model was used. The statistical significance adopted was 5%. Results The GM of the TD for M was 1.13 h; for W, 0.74 h. The GM of the TA was 1.74 h for M and 1.47 h for W. Those who did not recognize the symptoms of AMI and presented behavior of resistance to pain had higher TD and TA, being the associations significant. Gender did not change the associations of interest. Conclusion The findings demonstrate the importance of health education aiming at the benefits of early treatment.
2013-01-22
Childhood Acute Myeloblastic Leukemia With Maturation (M2); Childhood Acute Promyelocytic Leukemia (M3); Recurrent Childhood Acute Lymphoblastic Leukemia; Recurrent Childhood Acute Myeloid Leukemia; Secondary Acute Myeloid Leukemia
Regression-kriging for characterizing soils with remotesensing data
Yufeng GE; J.Alex THOMASSON; Ruixiu SUI; James WOOTEN
2011-01-01
In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables.However,spatial correlation existing in soil samples usually violates a basic assumption of regression:sample independence.In this study,a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance,Mississippi,USA.The regressionkriging model was developed and tested by using 273 soil samples collected from the field.The result showed that by properly incorporating the spatial correlation information of regression residuals,the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model.Most strikingly,a 50％ increase in prediction accuracy was shown in soil sodium concentration.Potential usages of regressionkriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.
Asymptotic theory of nonparametric regression estimates with censored data
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
Local Linear Regression for Data with AR Errors
Runze Li; Yan Li
2009-01-01
In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques.We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one.From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.
ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.
Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won
2016-07-01
In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.
Zhang, Guosheng; Huang, Kuan-Chieh; Xu, Zheng; Tzeng, Jung-Ying; Conneely, Karen N; Guan, Weihua; Kang, Jian; Li, Yun
2016-05-01
DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS).
Logistic Regression Model on Antenna Control Unit Autotracking Mode
2015-10-20
412TW-PA-15240 Logistic Regression Model on Antenna Control Unit Autotracking Mode DANIEL T. LAIRD AIR FORCE TEST CENTER EDWARDS AFB, CA...OCT 15 4. TITLE AND SUBTITLE Logistic Regression Model on Antenna Control Unit Autotracking Mode 5a. CONTRACT NUMBER 5b. GRANT...alternative-hypothesis. This paper will present an Antenna Auto- tracking model using Logistic Regression modeling. This paper presents an example of
Combining logistic regression and neural networks to create predictive models.
Spackman, K. A.
1992-01-01
Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...
Penalized Weighted Least Squares for Outlier Detection and Robust Regression
Gao, Xiaoli; Fang, Yixin
2016-01-01
To conduct regression analysis for data contaminated with outliers, many approaches have been proposed for simultaneous outlier detection and robust regression, so is the approach proposed in this manuscript. This new approach is called "penalized weighted least squares" (PWLS). By assigning each observation an individual weight and incorporating a lasso-type penalty on the log-transformation of the weight vector, the PWLS is able to perform outlier detection and robust regression simultaneou...
The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?
Jiangshan Lai
Full Text Available Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.
Association between acute pancreatitis and peptic ulcer disease
Kang-Moon Lee; Chang-Nyol Paik; Woo Chul Chung; Jin Mo Yang
2011-01-01
AIM:To evaluate the relationship between peptic ulcer disease (PUD) and acute pancreatitis.METHODS:A cohort of 78 patients with acute pancreatitis were included in this study.The presence of PUD and the Helicobacter pylori (H.pylori ) status were assessed by an endoscopic method.The severity of acute pancreatitis was assessed using Ranson's score, the Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score,computed tomography severity index and the clinical data during hospitalization,all of which were compared between the patients with and without PUD.The risk factors for PUD were also evaluated. RESULTS:Among 78 patients,41 patients (52.6%) with acute pancreatitis suffered from PUD,but only 13 (31.7%) patients with PUD were infected by H.pylori .On univariate analysis,male gender,an etiology of alcohol-induced pancreatitis,a history of smoking or alcohol consumption, elevated triglyceride and C-reactive protein levels, and high APACHE Ⅱ score were significantly associated with PUD.However,on multivariate logistic regression analysis,the APACHE Ⅱ score (odds ratio:7.69; 95% confidence interval:1.78-33.33; P < 0.01) was found to be the only independent risk factor for PUD.CONCLUSION:Patients with acute pancreatitis are liable to suffer from PUD.PUD is associated with severe acute pancreatitis according to the APACHE Ⅱ score, and treatment for PUD should be considered for patients with severe acute pancreatitis.
Regression on manifolds: Estimation of the exterior derivative
Aswani, Anil; Tomlin, Claire; 10.1214/10-AOS823
2011-01-01
Collinearity and near-collinearity of predictors cause difficulties when doing regression. In these cases, variable selection becomes untenable because of mathematical issues concerning the existence and numerical stability of the regression coefficients, and interpretation of the coefficients is ambiguous because gradients are not defined. Using a differential geometric interpretation, in which the regression coefficients are interpreted as estimates of the exterior derivative of a function, we develop a new method to do regression in the presence of collinearities. Our regularization scheme can improve estimation error, and it can be easily modified to include lasso-type regularization. These estimators also have simple extensions to the "large $p$, small $n$" context.
Neither fixed nor random: weighted least squares meta-regression.
Stanley, T D; Doucouliagos, Hristos
2016-06-20
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd.
Acute pancreatitis in acute viral hepatitis
无
2007-01-01
AIM: To elucidate the frequency and characteristics of pancreatic involvement in the course of acute (nonfulminant) viral hepatitis.METHODS: We prospectively assessed the pancreatic involvement in patients with acute viral hepatitis who presented with severe abdomimanl pain.RESULTS: We studied 124 patients with acute viral hepatitis, of whom 24 presented with severe abdominal pain. Seven patients (5.65%) were diagnosed to have acute pancreatitis. All were young males. Five patients had pancreatitis in the first week and two in the fourth week after the onset of jaundice. The pancreatitis was mild and all had uneventful recovery from both pancreatitis and hepatitis on conservative treatment.The etiology of pancreatitis was hepatitis E virus in 4,hepatitis A virus in 2, and hepatitis B virus in 1 patient.One patient had biliary sludge along with HEV infection.The abdominal pain of remaining seventeen patients was attributed to stretching of Glisson's capsule.CONCLUSION: Acute pancreatitis occurs in 5.65% of patients with acute viral hepatitis, it is mild and recovers with conservative management.
Acute otitis media and acute bacterial sinusitis.
Wald, Ellen R
2011-05-01
Acute otitis media and acute bacterial sinusitis are 2 of the most common indications for antimicrobial agents in children. Together, they are responsible for billions of dollars of health care expenditures. The pathogenesis of the 2 conditions is identical. In the majority of children with each condition, a preceding viral upper respiratory tract infection predisposes to the development of the acute bacterial complication. It has been shown that viral upper respiratory tract infection predisposes to the development of acute otitis media in 37% of cases. Currently, precise microbiologic diagnosis of acute otitis media and acute bacterial sinusitis requires performance of tympanocentesis in the former and sinus aspiration in the latter. The identification of a virus from the nasopharynx in either case does not obviate the need for antimicrobial therapy. Furthermore, nasal and nasopharyngeal swabs are not useful in predicting the results of culture of the middle ear or paranasal sinus. However, it is possible that a combination of information regarding nasopharyngeal colonization with bacteria and infection with specific viruses may inform treatment decisions in the future.
Acute chylous peritonitis due to acute pancreatitis.
Georgiou, Georgios K; Harissis, Haralampos; Mitsis, Michalis; Batsis, Haralampos; Fatouros, Michalis
2012-04-28
We report a case of acute chylous ascites formation presenting as peritonitis (acute chylous peritonitis) in a patient suffering from acute pancreatitis due to hypertriglyceridemia and alcohol abuse. The development of chylous ascites is usually a chronic process mostly involving malignancy, trauma or surgery, and symptoms arise as a result of progressive abdominal distention. However, when accumulation of "chyle" occurs rapidly, the patient may present with signs of peritonitis. Preoperative diagnosis is difficult since the clinical picture usually suggests hollow organ perforation, appendicitis or visceral ischemia. Less than 100 cases of acute chylous peritonitis have been reported. Pancreatitis is a rare cause of chyloperitoneum and in almost all of the cases chylous ascites is discovered some days (or even weeks) after the onset of symptoms of pancreatitis. This is the second case in the literature where the patient presented with acute chylous peritonitis due to acute pancreatitis, and the presence of chyle within the abdominal cavity was discovered simultaneously with the establishment of the diagnosis of pancreatitis. The patient underwent an exploratory laparotomy for suspected perforated duodenal ulcer, since, due to hypertriglyceridemia, serum amylase values appeared within the normal range. Moreover, abdominal computed tomography imaging was not diagnostic for pancreatitis. Following abdominal lavage and drainage, the patient was successfully treated with total parenteral nutrition and octreotide.
Acute chylous peritonitis due to acute pancreatitis
Georgios K Georgiou; Haralampos Harissis; Michalis Mitsis; Haralampos Batsis; Michalis Fatouros
2012-01-01
We report a case of acute chylous ascites formation presenting as peritonitis (acute chylous peritonitis) in a patient suffering from acute pancreatitis due to hypertriglyceridemia and alcohol abuse.The development of chylous ascites is usually a chronic process mostly involving malignancy,trauma or surgery,and symptoms arise as a result of progressive abdominal distention.However,when accumulation of "chyle" occurs rapidly,the patient may present with signs of peritonitis.Preoperative diagnosis is difficult since the clinical picture usually suggests hollow organ perforation,appendicitis or visceral ischemia.Less than 100 cases of acute chylous peritonitis have been reported.Pancreatitis is a rare cause of chyloperitoneum and in almost all of the cases chylous ascites is discovered some days (or even weeks) after the onset of symptoms of pancreatitis.This is the second case in the literature where the patient presented with acute chylous peritonitis due to acute pancreatitis,and the presence of chyle within the abdominal cavity was discovered simultaneously with the establishment of the diagnosis of pancreatitis.The patient underwent an exploratory laparotomy for suspected perforated duodenal ulcer,since,due to hypertriglyceridemia,serum amylase values appeared within the normal range.Moreover,abdominal computed tomography imaging was not diagnostic for pancreatitis.Following abdominal lavage and drainage,the patient was successfully treated with total parenteral nutrition and octreotide.
吕书红; 田本淳; 杨廷忠; 陈定湾; 池延花
2010-01-01
Objective To find out the perceived stress in general public during prevalence of severe acute respiratory syndrome (SARS) and its impact on health behavior. Methods A retrospective survey was conducted in Guangzhou, Hangzhou, and Taiyuan according to the epidemic situations of SARS, and 2532 subjects were randomly selected from constructive industry, school, and commercial business and residents in urban and rural areas. The perceive stress was measured by Chinese perceived stress scale (CPSS), and health related behavior during SARS was tested by uniform and self-made questionnaire. EpiData 2. 0 was used for data management and CPSS value was calculated according to answer to 14 questions contained in the scale. Health risk stress among different population group and health related behavior among low, medium and high stress state were analyzed by SPSS 11.5. Results 2424 subjects were involved in the survey. The CPSS value was measured from 0 -49(22.7±6. 8) ,M =24. 0. 39.3% (953/2379) subjects were under the health risk stress. The health related behaviors such as washing hands, opening the window for air, keeping away from others when cough and sneeze,doing exercises etc were reduced with the stress increased. Logistic regression indicated that compared with the persons with the thoughts of nothing serious of SARS,without any dread of SARS,and knowing nothing about prevention of SARS,the perceived stress was significantly related with perceiving of the thread to certain extent (β = 0. 41, Wald χ~2 = 4. 84, P = 0. 03), worrying little about the epidemic (β = 0. 50, Wald χ~2 = 6. 69, P = 0. 01), worrying about it to certain extent (β= 1.39, Wald χ~2 = 48. 59 ,P = 0. 00)and scared so much (β = 1.77, Wald χ~2= 53.59, P = 0. 00), and knowing little about the prevention(β = 0. 74, Wald χ~2 = 4. 48, P = 0. 03), knowing something about prevention (β = - 0. 98, Wald χ~2 = 8. 29, P = 0. 00) and knowing the prevention very well (β = - 1.18, Wald χ~2 = 10. 66
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Horst Entorf
2015-07-01
Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.
Pharm GKB: Leukemia, Nonlymphocytic, Acute [PharmGKB
Full Text Available Overview Alternate Names: Synonym ANLL; Acute Nonlymphoblastic Leukemia; Acute Nonl...ymphoblastic Leukemias; Acute Nonlymphocytic Leukemia; Acute Nonlymphocytic Leukemias; Leukemia, Acute Nonly...mphoblastic; Leukemia, Acute Nonlymphocytic; Leukemia, Nonlymphoblastic, Acute; Leukemias, Acute Nonlymphoblastic; Leukemias, Acute... Nonlymphocytic; Nonlymphoblastic Leukemia, Acute; Nonlymphoblastic Leukemias, Acut...e; Nonlymphocytic Leukemia, Acute; Nonlymphocytic Leukemias, Acute PharmGKB Accessi
Pharm GKB: Leukemia, Myeloid, Acute [PharmGKB
Full Text Available Amino Acid Translations are all sourced from dbSNP 144 Overview Alternate Names: Synonym AML - Acute... myeloblastic leukaemia; Acute Myeloblastic Leukemia; Acute Myeloblastic Leukemias; Acute... Myelocytic Leukemia; Acute Myelocytic Leukemias; Acute Myelogenous Leukemia; Acute Myelogenous Leukemias; Acute... granulocytic leukaemia; Acute myeloblastic leukemia; Acute myeloid leukaemia; Acute myeloid leukaemia - category; Acute... myeloid leukaemia, disease; Acute myeloid leukemia; Acute myelo
Survival after acute hemodialysis in Pennsylvania, 2005-2007: a retrospective cohort study.
Sarah J Ramer
Full Text Available BACKGROUND: Little is known about acute hemodialysis in the US. Here we describe predictors of receipt of acute hemodialysis in one state and estimate the marginal impact of acute hemodialysis on survival after accounting for confounding due to illness severity. MATERIALS AND METHODS: This is a retrospective cohort study of acute-care hospitalizations in Pennsylvania from October 2005 to December 2007 using data from the Pennsylvania Health Care Cost Containment Council. Exposure variable is acute hemodialysis; dependent variable is survival following acute hemodialysis. We used multivariable logistic regression to determine propensity to receive acute hemodialysis and then, for a Cox proportional hazards model, matched acute hemodialysis and non-acute hemodialysis patients 1∶5 on this propensity. RESULTS: In 2,131,248 admissions of adults without end-stage renal disease, there were 6,657 instances of acute hemodialysis. In analyses adjusted for predicted probability of death upon admission plus other covariates and stratified on age, being male, black, and insured were independent predictors of receipt of acute hemodialysis. One-year post-admission mortality was 43% for those receiving acute hemodialysis, compared to 13% among those not receiving acute hemodialysis. After matching on propensity to receive acute hemodialysis and adjusting for predicted probability of death upon admission, patients who received acute hemodialysis had a higher risk of death than patients who did not over at least 1 year of follow-up (hazard ratio 1·82, 95% confidence interval 1·68-1·97. CONCLUSIONS: In a populous US state, receipt of acute hemodialysis varied by age, sex, race, and insurance status even after adjustment for illness severity. In a comparison of patients with similar propensity to receive acute hemodialysis, those who did receive it were less likely to survive than those who did not. These findings raise questions about reasons for lack of
Margaritelis, Nikos V; Theodorou, Anastasios A; Paschalis, Vassilis; Veskoukis, Aristidis S; Dipla, Konstantina; Zafeiridis, Andreas; Panayiotou, George; Vrabas, Ioannis S; Kyparos, Antonios; Nikolaidis, Michalis G
2016-01-01
An important methodological threat when selecting individuals based on initial values for a given trait is the "regression to the mean" artifact. This artifact appears when a group with an extreme mean value during a first measurement tends to obtain a less extreme value (i.e. tends toward the mean) on a subsequent measurement. The main aim was to experimentally confirm the presence of this artifact in the responses of the reference oxidative stress biomarker (F2-isoprostanes) after exercise. Urine samples were collected before and immediately following acute exercise in order to determine the level of exercise-induced oxidative stress. Afterwards, participants were arranged into three groups based on their levels of exercise-induced oxidative stress (low, moderate and high oxidative stress groups; n = 12 per group). In order to verify the existence of the regression to the mean artifact, the three groups were subjected to a second exercise trial one week after the first trial. This study confirmed the regression to the mean artifact in a redox biology context and showed that this artifact can be minimized by performing a duplicate pretreatment measurement after completing a nonrandom sorting based on the first assessment. This study also indicated that different individuals experience high oxidative stress or reductive stress (or no stress) to the same exercise stimulus even after adjusting for regression to the mean. This finding substantiates the methodological choice to divide individuals based on their degree of exercise-induced oxidative stress in future experiments to investigate the role of reactive species in exercise adaptations.