Inverse probability weighting for covariate adjustment in randomized studies

Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented

Estimation of treatment effect in a subpopulation: An empirical Bayes approach

It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented

The Application Study in Solar Energy Technology for Highway Service Area: A Case Study of West Lushan Highway Low-Carbon Service Area in China

A lot of research works have been made concerning highway service area or solar technology and acquired great achievements. However, unfortunately, few works have been made combining the two topics together of highway service areas and solar energy saving to make a systemic research on solar technology application for highway service area. In this paper, taking West Lushan highway low-carbon service area in Jiangxi Province of China as the case study, the advantages, technical principles, and application methods of solar energy technology for highway service area including solar photoelectric technology and solar water heating technology were discussed based on the analysis of characteristics of highway low-carbon service area; the system types, operation mode, and installing tilt angle of the two kinds of solar systems suitable for highway service areas were confirmed. It was proved that the reduction of the cost by electricity savings of solar system was huge. Taking the investment of the solar systems into account, the payback period of solar photoelectric systems and solar water heating systems was calculated. The economic effect of the solar systems in West Lushan highway service area during the effective operation periods was also calculated and proved very considerable

The influence of SS-quasinormality of some subgroups on the structure of finite groups

AbstractThe following concept is introduced: a subgroup H of the group G is said to be SS-quasinormal (Supplement-Sylow-quasinormal) in G if H possesses a supplement B such that H permutes with every Sylow subgroup of B. Groups with certain SS-quasinormal subgroups of prime power order are studied. For example, fix a prime divisor p of |G| and a Sylow p-subgroup P of G, let d be the smallest generator number of P and Md(P) denote a family of maximal subgroups P1,…,Pd of P satisfying ⋂i=1d(Pi)=Φ(P), the Frattini subgroup of P. Assume that the group G is p-solvable and every member of some fixed Md(P) is SS-quasinormal in G, then G is p-supersolvable

Doubly Robust Estimation of Causal Effect: Upping the Odds of Getting the Right Answers

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.Comment: This manuscript is the accepted version for CVPR 201

Proteomics study of changes in soybean lines resistant and sensitive to Phytophthora sojae

<p>Abstract</p> <p>Background</p> <p><it>Phytophthora sojae </it>causes soybean root and stem rot, resulting in an annual loss of 1-2 billion US dollars in soybean production worldwide. A proteomic technique was used to determine the effects on soybean hypocotyls of infection with <it>P. sojae</it>.</p> <p>Results</p> <p>In the present study, 46 differentially expressed proteins were identified in soybean hypocotyls infected with <it>P. sojae</it>, using two-dimensional electrophoresis and matrix-assisted laser desorption/ionization tandem time of flight (MALDI-TOF/TOF). The expression levels of 26 proteins were significantly affected at various time points in the tolerant soybean line, Yudou25, (12 up-regulated and 14 down-regulated). In contrast, in the sensitive soybean line, NG6255, only 20 proteins were significantly affected (11 up-regulated and 9 down-regulated). Among these proteins, 26% were related to energy regulation, 15% to protein destination and storage, 11% to defense against disease, 11% to metabolism, 9% to protein synthesis, 4% to secondary metabolism, and 24% were of unknown function.</p> <p>Conclusion</p> <p>Our study provides important information on the use of proteomic methods for studying protein regulation during plant-oomycete interactions.</p

A MAP Kinase Dependent Feedback Mechanism Controls Rho1 GTPase and Actin Distribution in Yeast

In the yeast Saccharomyces cerevisiae the guanosine triphosphatase (GTPase) Rho1 controls actin polarization and cell wall expansion. When cells are exposed to various environmental stresses that perturb the cell wall, Rho1 activates Pkc1, a mammalian Protein Kinase C homologue, and Mpk1, a mitogen activated protein kinase (MAPK), resulting in actin depolarization and cell wall remodeling. In this study, we demonstrate a novel feedback loop in this Rho1-mediated Pkc1-MAPK pathway that involves regulation of Rom2, the guanine nucleotide exchange factor of Rho1, by Mpk1, the end kinase of the pathway. This previously unrecognized Mpk1-depedent feedback is a critical step in regulating Rho1 function. Activation of this feedback mechanism is responsible for redistribution of Rom2 and cell wall synthesis activity from the bud to cell periphery under stress conditions. It is also required for terminating Rho1 activity toward the Pkc1-MAPK pathway and for repolarizing actin cytoskeleton and restoring growth after the stressed cells become adapted

Binary Silanization and Silver Nanoparticle Encapsulation to Create Superhydrophobic Cotton Fabrics with Antimicrobial Capability

Cotton fabrics are functionalized with a binary solution of fluorine-free organosilanes and “encapsulated” with silver nanoparticles to achieve both superhydrophobic and antimicrobial properties. Derived from cellulose, cotton is one of the most abundant biologically generated materials and has been used in a wide variety of consumer goods. Nonetheless, cotton fabrics are not waterproof and prone to microbial contamination. Herein we report the rapid functionalization of cotton fabrics with a binary hexane solution of methyltrichlorosilane (MTS) and octadecyltrichlorosilane (OTS) at low concentration (0.17% v/v) followed by coating with colloidal silver nanoparticles (AgNP). The combined effects of binary silanization and AgNP encapsulation produced a surface that has remarkable water contact angle of 153&thinsp;±&thinsp;2° and antimicrobial properties (against gram-negative&nbsp;Escherichia coli). The superior performance of the modified cotton fabrics produced with fluorine-free organosilanes and silver nanoparticles augments the potential of improving the functionality of abundant biopolymers to be waterproof and contamination-resistant