Faculty Opinions recommendation of Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication.

2020 ◽  
Author(s):  
Lei Wang
Keyword(s):  
Stepwise Selection ◽  
Soybean Domestication

Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication

2020 ◽  
Vol 52 (4) ◽  
pp. 428-436 ◽  
Author(s):  
Sijia Lu ◽  
Lidong Dong ◽  
Chao Fang ◽  
Shulin Liu ◽  
Lingping Kong ◽  
...  
Keyword(s):  
Stepwise Selection ◽  
Soybean Domestication

Stepwise selection of natural variations at CTB2 and CTB4a improves cold adaptation during domestication of japonica rice

2021 ◽  
Author(s):  
Jilong Li ◽  
Yawen Zeng ◽  
Yinghua Pan ◽  
Lei Zhou ◽  
Zhanying Zhang ◽  
...  
Keyword(s):  
Cold Adaptation ◽  
Japonica Rice ◽  
Stepwise Selection ◽  
Natural Variations ◽  
Selection Of

Predicting Surgical Margins in Patients With Borderline Resectable and Locally Advanced Pancreatic Cancer Undergoing Resection

2021 ◽  
pp. 000313482110111
Author(s):  
Weizheng Ren ◽  
Dimitrios Xourafas ◽  
Stanley W. Ashley ◽  
Thomas E. Clancy
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Univariate Analysis ◽  
Locally Advanced ◽  
Advanced Pancreatic Cancer ◽  
Ductal Adenocarcinoma ◽  
Resection Margins ◽  
Borderline Resectable ◽  
Stepwise Selection ◽  
R1 Resection ◽  
Positive Resection Margins

Background Many patients with borderline resectable/locally advanced pancreatic ductal adenocarcinoma (borderline resectable [BR]/locally advanced [LA] pancreatic ductal adenocarcinoma [PDAC]) undergoing resection will have positive resection margins (R1), which is associated with poor prognosis. It might be useful to preoperatively predict the margin (R) status. Methods Data from patients with BR/LA PDAC who underwent a pancreatectomy between 2008 and 2018 at Brigham and Women’s Hospital were retrospectively reviewed. Logistic regression analysis was used to evaluate the association between R status and relevant preoperative factors. Significant predictors of R1 resection on univariate analysis ( P < .1) were entered into a stepwise selection using the Akaike information criterion to define the final model. Results A total of 142 patients with BR/LA PDAC were included in the analysis, 60(42.3%) had R1 resections. In stepwise selection, the following factors were identified as positive predictors of an R1 resection: evidence of lymphadenopathy at diagnosis (OR = 2.06, 95% CI: 0.99-4.36, P = .056), the need for pancreaticoduodenectomy (OR = 3.81, 96% CI: 1.15-15.70, P = .040), extent of portal vein/superior mesenteric vein involvement at restaging (<180°, OR = 3.57, 95% CI: 1.00-17.00, P = .069, ≥180°, OR = 7,32, 95% CI: 1.75-39.87, P = .010), stable CA 19-9 serum levels (less than 50% decrease from diagnosis to restaging, OR = 2.27, 95% CI: 0.84-6.36 P = .107), and no preoperative FOLFIRINOX (OR = 2.17, 95% CI: 0.86-5.64, P = .103). The prognostic nomogram based on this model yielded a probability of achieving an R1 resection ranging from <5% (0 factors) to >70% (all 5 factors). Conclusions Relevant preoperative clinicopathological characteristics accurately predict positive resection margins in patients with BR/LA PDAC before resection. With further development, this model might be used to preoperatively guide surgical decision-making in patients with BR/LA PDAC.

scholarly journals A simple new approach to variable selection in regression, with application to genetic fine-mapping

2018 ◽  
Author(s):  
Gao Wang ◽  
Abhishek Sarkar ◽  
Peter Carbonetto ◽  
Matthew Stephens
Keyword(s):  
Variable Selection ◽  
Fine Mapping ◽  
Posterior Distribution ◽  
Zero Element ◽  
Variational Approximation ◽  
New Approach ◽  
Stepwise Selection ◽  
Fitting Procedure ◽  
Highly Correlated ◽  
Credible Set

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model — the “Sum of Single Effects” (SuSiE) model — which comes from writing the sparse vector of regression coefficients as a sum of “single-effect” vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure — Iterative Bayesian Stepwise Selection (IBSS) — which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a Credible Set of variables for each selection. Our methods are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outper-form existing methods for this task, and illustrate their application to fine-mapping genetic variants influencing alternative splicing in human cell-lines. We also discuss the potential and challenges for applying these methods to generic variable selection problems.

scholarly journals 008 Associations between comorbidities and adverse events of antiepileptic drugs and quality of life: a survey of epilepsy patients in australia

2019 ◽  
Vol 90 (e7) ◽  
pp. A3.2-A3
Author(s):  
Jeremy M Welton ◽  
Christine Walker ◽  
Kate Riney ◽  
Alvin Ng ◽  
Lisa M Todd ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Adverse Events ◽  
Antiepileptic Drugs ◽  
List Type ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Stepwise Selection ◽  
Memory Problems ◽  
The Impact

IntroductionThis study explored the impact of specific types of comorbidities and adverse events (AEs) from antiepileptic drugs (AEDs) on quality of life (QoL) among adult patients with epilepsy (PwE) in Australia.MethodsCross-sectional surveys were completed by PwE, or caregiver proxies, recruited via the online pharmacy application MedAdvisor and Australian PwE Facebook groups from May–August 2018 Data were collected on demographics, epilepsy severity and management, AEs, comorbidities, and QoL (using QOLIE-10-P total score).1 Multiple linear regression models were constructed to explore associations between AEs or comorbidities and QOLIE-10-P, with possible confounders determined using stepwise selection.Results978 responses were included (mean age 44.5 years, 64% female, 52% employed). 97% reported recent AED use, 47% on AED monotherapy, 35% exposed to ≤2 lifetime AEDs, and 55% seizure-free for >1 year. After stepwise selection, control variables included in both models were: time since diagnosis, employment status, seizure frequency, number of currently prescribed AEDs, and number of general practitioner visits per year. In the model for comorbidities, ‘psychiatric disorders’ was associated with the largest QOLIE-10-P decrease (-23.30, p<0.001). In the model for AEs, which additionally controlled for depression and anxiety disorder, ‘memory problems’ was associated with the largest decrease in QOLIE-10-P (-14.27, p<0.001).ConclusionsIn this survey of Australian PwE, of which many had relatively well-controlled epilepsy, psychiatric and memory problems were common and associated with the greatest detrimental impact on QoL. Further research is needed to understand causality, relationships between possibly interrelated or overlapping symptoms, and management strategies. UCB Pharma-sponsored.ReferenceCramer JA, Perrine K, Devinsky O, Meador K. A brief questionnaire to screen for quality of life in epilepsy: The QOLIE-10. Epilepsia 1996;37:577–582.

scholarly journals STEPWISE SELECTION OF VARIABLES IN DEA USING CONTRIBUTION LOADS

2018 ◽  
Vol 38 (1) ◽  
pp. 31-52 ◽  
Author(s):  
Fernando Fernandez-Palacin ◽  
Maria Auxiliadora Lopez-Sanchez ◽  
Manuel Munõz-Márquez
Keyword(s):  
Stepwise Selection ◽  
Selection Of Variables ◽  
Selection Of

scholarly journals PREDICTOR BIOMARKERS OF NONELECTIVE HOSPITAL READMISSION AND MORTALITY IN MALNOURISHED HOSPITALIZED OLDER ADULTS

2020 ◽  
pp. 1-6
Author(s):  
K.M. Pencina ◽  
S. Bhasin ◽  
M. Luo ◽  
G.E. Baggs ◽  
S.L. Pereira ◽  
...  
Keyword(s):  
Hospital Readmission ◽  
Proportional Hazards ◽  
Nutritional Supplement ◽  
Cox Proportional Hazards ◽  
Hospitalized Patients ◽  
Direct Care ◽  
Stepwise Selection ◽  
Care Plans ◽  
C Statistic ◽  
Adjusted Model

Background: 90-day mortality and rehospitalizations are important hospital quality metrics. Biomarkers that predict these outcomes among malnourished hospitalized patients could identify those at risk and help direct care plans. Objectives: To identify biomarkers that predict 90-day (primary) and 30-day (secondary) mortality or nonelective rehospitalization. Design and Participants: An analysis of the ability of biomarkers to predict 90- and 30-day mortality and rehospitalization among malnourished hospitalized patients. Setting: 52 blood biomarkers were measured in 193 participants in NOURISH, a randomized trial that determined the effects of a nutritional supplement on 90-day readmission and death in patients >65 years. Composite outcomes were defined as readmission or death over 90-days or 30-days. Univariate Cox Proportional Hazards models were used to select best predictors of outcomes. Markers with the strongest association were included in multivariate stepwise regression. Final model of hospital readmission or death was derived using stepwise selection. Measurements: Nutritional, inflammatory, hormonal and muscle biomarkers. Results: Mean age was 76 years, 51% were men. In univariate models, 10 biomarkers were significantly associated with 90-day outcomes and 4 biomarkers with 30-day outcomes. In multivariate stepwise selection, glutamate, hydroxyproline, tau-methylhistidine levels, and sex were associated with death and readmission within 90-days. In stepwise selection, age-adjusted model that included sex and these 3 amino-acids demonstrated moderate discriminating ability over 90-days (C-statistic 0.68 (95%CI 0.61, 0.75); age-adjusted model that included sex, hydroxyproline and Charlson Comorbidity Index was predictive of 30-day outcomes (C-statistic 0.76 (95%CI 0.68, 0.85). Conclusions: Baseline glutamate, hydroxyproline, and tau-methylhistidine levels, along with sex and age, predict risk of 90-day mortality and nonelective readmission in malnourished hospitalized older patients. This biomarker set should be further validated in prospective studies and could be useful in prognostication of malnourished hospitalized patients and guiding in-hospital care.

Sign in / Sign up

Export Citation Format

Share Document