Time: 12:00 PM Location: 251 BRB II/III
Thomas B. Jansson, M.D., Ph.D.
Professor, Head of the Division for Basic Reproductive Sciences
and Vice Chair of Research at the Department of Obstetrics and Gynecology
University of Colorado Denver
The role of mTOR signaling in placental nutrient sensing and maternal-fetal resource allocation
Time: 12:00 PM Location: 132 Hill Pavilion
Satoshi H. Namekawa, Ph.D.
Division of Reproductive Sciences and Developmental Biology
Cincinnati Children's Hospital Medical Center
Department of Pediatrics
University of Cincinnati College of Medicine
Germline epigenome and sex chromosome inactivation
*co-sponsored with the Center for Animal Transgenesis and Germ Cell Research
University of Pennsylvania
Cancer Metabolism – Basic Biology and Translational Opportunities
Transduction and Modulation of Touch Sensitivity in C. elegans.
"Some problems with splines"
Martin Clyde, PhD Professor Department of Mathematics and Statistics Texas Tech University
Abstract: Smoothing splines are a powerful tool for representing data as long as the data has enough nice properties. In this talk I will discuss three problems and what we are doing to solve them.
1)1) L1 splines rather than L2 splines useful when the data is not normal. Work with Masaki Nagahara.
2)2) The underlying state space is not Euclidian. Work with Wenzhen Fan and Jingyong Su.
3)3) Very large data sets such as those encountered in tracking eye and head movement. Work with Bijoy Ghosh and Jennifer Emerson.
Problem 1 arises when the data has many outliers such as climate data. L1 splines were much more efficient than L2 splines. Problem 2 arose in a certain tracking problem that was best stated as a problem on the manifold of lines in R3. It also arose in considering a certain climate data and was reduced to the manifold of intervals. Problem 3 arose in the study of head and eye movement. Data sets of 300000 to 600000 points were used.
"Inference for dynamic treatment regimes"
Eric Laber, PhD Assistant Professor Department of Statistics North Carolina State University
Abstract:Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention.Each decision rule maps up-to-date patient information to a recommended treatment.We review a critical inferential challenge that results from nonregularity, which often arise in inference for parameters in the optimal dynamic treatment regime.Nonregularity is associated with asymptotic distributions of estimators that are sensitive to local perturbations.We demonstrate that previous attempts to mitigate nonregularlity through shrinkage can be arbitrarily worse than no shrinkage at all.
We propose a locally consistent adaptive confidence interval for the parameters of the optimal dynamic treatment regime.We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example.
"Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic"
Ming Yang, PhD Assistant Professor Division of Biostatistics and Bioinformatics Penn State University
Abstract: Prediction models for disease risk and prognosis play an important role in biomedical research, where evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for a models for survival outcomes. Motivated by a prostate cancer study, we address several issues associated with evaluating prediction models for survival outcomes based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide the complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR) and propose a sensitivity analysis in the case of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to the setting of high-dimensional data. We apply the proposed approaches to assess predictive accuracy of prediction models for cancer recurrence after prostatectomy, and show that the estimated predictive accuracy for all models of interest is sensitive to the NCAR assumption but the model with best predictive accuracy is not. We further evaluate the performance of the proposed methods in both low-dimensional data and high-dimensional data settings through simulations.
Title and abstract: TBD
Limin Peng, PhD Associate Professor Division of Biostatistics and Bioinformatics Emory University
"Endothelial Dysfunction Biomarkers and Preterm Delivery: A Case-Control Study to Examine Association of Preterm Delivery and Pathogenic Precursor of CVD"
Xinhua Chen, MD, MS
Department of Obstetrics and Gynecology, Rowan University-SOM
"Including Brain Disorders in Global Health Epidemiology: Current Challenges"
Farrah Mateen, MD, PhD
Adjunct Professor, Department of Neurology, Harvard Medical School
"The Winch Model Can Explain both Coordinated and Uncoordinated Stepping of Cytoplasmic Dynein"
12pm in 702 Clinical Research Building
Ram Dixit, Ph.D.
Washington University in St. Louis
"Life inside a box: how the microtubule cytoskeleton contributes to plant cell shape”
2pm CRB Austrian Auditorium
R. John Solaro, Ph.D.
University of Illinois at Chicago
"Integration of Sarcomere Signaling in Cardiac Function"
2pm Austrian Auditorium
PENNSYLVANIA MUSCLE INSTITUTE SEMINAR- James Ervasti, PhD "Integration of Sarcomere Signaling in Cardiac Function" 2pm Austrian Auditorium
John Cooper, MD, PhD - Professor of Cell Biology and Physiology, Professor and Interim Head of Biochemistry and Molecular Biophysics, Washington University in St. Louis, "Capping Protein, CARMILs and Actin Assembly at Membranes"
2pm, Austrian Auditorium.
PMI Muscle Club
Benjamin L. Prosser, Ph.D.
"Beat-to-beat mechano-signaling in the heart”
1pm in 702CRB
1pm 702 CRB
"Statistical Analysis of High Throughput Sequencing Data with Mouse Collaborative Crosses"
Fei Zou, PhD Professor Department of Statistics Univeristy of North Carolina
Abstract: For decades, the mouse has been the most popular surrogate model for humans. In this talk, we will first introduce a new and superior mouse model, the Collaborative Cross (CC). The CC is a large panel of inbred mouse strains currently being developed at UNC through a community effort for modeling the heterogeneous human population.We will then discuss some statistical challenges and solutions associated with high throughput next generation sequencing data for assessing allelic imbalance (AI) in gene expression and DNA methylation, with F1 reciprocal mice derived from the CC founder or CC RI strains.The non-random X-chromosome in-activation will be demonstrated by a real mouse data. Models accounting for such chromosome-wide in-activation, allowing proper analysis of chromosome X expression will be discussed.
Towards Better Adolescent HIV Treatment Adherence
Elizabeth Lowenthal, MD, MSCE,
Assistant Professor of Pediatrics Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine