Nonparametric and semiparametric group testing regression models

This dissertation consists of three projects in the area of group testing. The method of group testing, through the use of pooling, has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest, such as infection status. The salient feature of group testing that provides for these gains in efficiency is that testing is performed on pooled specimens, rather than testing specimens one-by-one. In Chapter 1, we present a general introduction of group testing. Typically, the statistical literature surrounding group testing has investigated the implementation of pooled testing for the purposes of either case identification or estimation. In this dissertation, we mainly focuses on the estimation problem which involves the development of regression models that relate individual level covariates to testing responses observed from pooled specimens. Primarily, the existing research in the area of estimation in group testing has focused on parametric regression models, where the shape of the link function is assumed as known and only a finite number of regression parameters has to be estimated. Recently, for the purpose of obviating the specification of the link function and increasing the flexibility of modeling, nonparametric group testing regression models have been studied. %It considers the case where each individual has one continuous explanatory variable and the link function is a univariate probability curve. Existing methods of estimating this unknown function are based on local moment estimators. In Chapter 2, we propose a new nonparametric estimation procedure using a local likelihood approach. For easy illustration, in this part we consider the situation where each individual is assigned to exactly one pool and only this pooled specimen is tested. Further, we assume the assay used for screening is perfect. Both of these two assumptions will be relaxed in the rest chapters of this dissertation. We show that our proposed estimator enjoys an asymptotic normal distribution with the optimal nonparametric estimation rate. Finite sample performance of the method is exhibited via some simulated examples and a real data analysis. To pursue a more suitable technique of modeling group testing data, in Chapter 3, we develop a general semiparametric framework which allows for the inclusion of only not one continuous covariate, but also multiple explanatory variables, all variants of decoding information, and imperfect testing. The asymptotic properties of our estimators are presented and guidance on finite sample implementation is provided. We illustrate the performance of our methods through simulation and by applying them to chlamydia and gonorrhea data collected by the Nebraska Public Health Laboratory as a part of the Infertility Prevention Project. In Chapter 4, we focus on the evaluation of misclassification effect of testing pools which are constructed according to any types of group testing algorithms. The existing assumption regarding them are somehow restrictive. If they are invalid, the estimation procedure can lead to severely biased estimator. In this work, we relax previously made assumptions regarding testing error rates by acknowledging the underlying mechanistic structure of the diagnostic test being employed. For easy illustration of this methodology, we mainly concentrate in parametric regression methods and propose a general estimation framework that allows for the analysis of data arising from all group testing strategies. The finite sample performance of our proposed methodology are investigated through simulation and by applying our techniques to hepatitis B data from a study involving Irish prisoners. Through these studies, we show that our methods can result in more efficient parameter estimates, when compared to competing procedures that make use of individual level data, at a fraction of the cost of data collection. Before proceeding to the main body of this dissertation, I would like to clarify that the notations defined in this work are self-contained in each separated chapter

First-principles high-throughput screening of bulk piezo-photocatalytic materials for sunlight-driven hydrogen production

A high-throughput screening of piezo-photocatalytic materials based on first-principles calculations and a simple electrostatic model is presented that identifies new bulk compounds able to catalyse the water splitting reaction under sunlight.Peer ReviewedPostprint (author's final draft

COULD LONG-TERM EXERCISE IMPROVE THE OBSTACLE-CROSSING ABILITY OF ELDERLY WOMEN? EFFECTS OF TAI CHI AND AEROBIC DANCE EXERCISES

The purpose of this study was to evaluate the effects of long-term exercises (TaiChi (TC), aerobic dance) on the obstacle-crossing ability of elderly women, as well as to identify whether the exercise could considerably improve stability. Forty-five elderly women include TC, aerobic dance and no exercising groups participated in our study. They walked a short distance to cross the obstacle (30% of leg length). Results showed that long-term exercise had a positive effect on muscle strength and the practitioners used an obstacle-crossing strategy that increasing the force in medial–lateral and anterior-posterior directions of the trailing foot to cross obstacle. The TC strategy was better than aerobic dance in improving balance and increasing the height of the leg during obstacle-crossing

Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts

Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve as strong priors during test-time. However, this is an inherently subjective and hard-to-reproduce process. The variability in user expertise and inherently ambiguous boundaries in medical images can lead to inconsistent prompt selections, potentially affecting segmentation accuracy. This issue has not yet been extensively explored for medical imaging. In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts. For a given target region, the point is classified into three sub-regions: boundary, margin, and center. Our goal is to identify a straightforward and efficient approach for optimal prompt selection during test-time based on three considerations: (1) benefits of additional prompts, (2) effects of prompt placement, and (3) strategies for optimal prompt selection. We conduct extensive experiments on the public Medical Segmentation Decathlon dataset for challenging colon tumor segmentation task. We suggest an optimal strategy for prompt selection during test-time, supported by comprehensive results. The code is publicly available at https://github.com/MedICL-VU/variabilit

Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models

To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce reliable segmentation results. However, several existing barriers between domains need to be broken down, including addressing contrast discrepancies, managing anatomical variability, and adapting 2D pretrained models for 3D segmentation tasks. In this paper, we propose ProMISe,a prompt-driven 3D medical image segmentation model using only a single point prompt to leverage knowledge from a pretrained 2D image foundation model. In particular, we use the pretrained vision transformer from the Segment Anything Model (SAM) and integrate lightweight adapters to extract depth-related (3D) spatial context without updating the pretrained weights. For robust results, a hybrid network with complementary encoders is designed, and a boundary-aware loss is proposed to achieve precise boundaries. We evaluate our model on two public datasets for colon and pancreas tumor segmentations, respectively. Compared to the state-of-the-art segmentation methods with and without prompt engineering, our proposed method achieves superior performance. The code is publicly available at https://github.com/MedICL-VU/ProMISe.Comment: updated acknowledgments and fixed typo

EFFECT OF ILLUMINATION ON THE OBSTACLE-CROSSING BEHAVIORS OF ELDERLY WOMEN

The purpose of this study was to determine how illumination affect elderly women when stepping over obstacles. A motion capture system was used to collect the kinematics data of 15 elderly women. The results revealed that the obstacle-crossing behavior of elderly women were affected by the illumination. Compare to the high illumination condition, the elderly women decreased their toe distance and heel distance (

Pedicled iliac crest bone flap transfer for the treatment of upper femoral shaft fracture nonunion: An anatomic study and clinical applications

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146632/1/micr30278.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146632/2/micr30278_am.pd

BIOMECHANICS ANALYSIS OF "BRUSH KNEE AND TWIST STEPS" MOVEMENT IN TAI CHI

The purpose of this study was to analyze the biomechanical characteristics of a typical Tai Chi (TC) movement - "brush knee and twist steps". A 3-Dimensional fixed video filming method was used for data collection. Three elite professional athletes of TC performed this movement three times and the best one was selected for analysis. The kinematics data included the distance of hands and feet, the angle between the feet, the joint angles of wrist, elbow and knee, the 3-dimensional displacement, velocity and acceleration of CG. The analysis showed that TC exercise could enhance the lower extremity muscular strength movement coordination, and the neuromuscular control for posture and balance

Group testing models with unknown link function

Group testing through the use of pooling has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest such as infection status. A topic of key interest in this area involves the development of regression models that relate the individual level covariates to the binary pool testing responses. The research in this area has primarily focused on parametric regression models. In this poster, we will introduce a new estimation method which can handle multi-dimensional covariates while assuming the link is unknown. The asymptotic properties of our estimators are also presented. We investigate the performance of our method through simulation and by applying it to a hepatitis data set obtained from the National Health and Nutrition Examination Survey