Novel Score Tests to Increase Power in Association Test by Integrating External Controls

Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high-quality genotype or sequence data to identify high-impact variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost-effective approach to increase the power of association testing. Various challenges in practice, however, hinder the use of external sources of controls, among which include differences in sequencing platforms, genotype calling procedures, population stratification, etc. Differences in these aspects could lead to a systematic batch effect between genetic data in different studies. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, etc. Hence, based on the insight of iECAT, we propose a novel score-based test, iECAT-Score, that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age-related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium (IAMDGC) and Michigan Genomics Initiative (MGI). The iECAT-Score test has improved power for testing association between a single variant and the disease status, and yet single-variant tests could be underpowered to identify causal rare variants. Hence, in the second project, we extend the single-variant iECAT-Score test to a region-based test, which assesses the combined genetic effect of rare variants within a gene or region. The iECAT-Score region-based test aggregates the single-variant test statistics using a weighted linear or quadratic sum, or a linear combination of both. Through simulation studies and the application of our method to the rare-variant association studies of AMD from the IAMDGC and UK Biobank data, we show that our proposed iECAT-Score region-based test efficiently identifies disease-associated genes while controlling for type I error rates. When sequenced data are used in association studies, quality of the genotype calls could influence the performance of the testing methods. The quality of genotype calls is subject to many factors such as read depth, genotype-calling error rates, quality control (QC) pipelines, etc., all of which could result in bias in the estimation of minor allele frequencies (MAFs), leading to more profound batch effect between internal and external control samples. As whole genome/exome sequencing become the design of choice, to address the associated problems using genotyped data, we propose in the third project to integrate the above-mentioned QC parameters utilizing sequencing data. Through the incorporation of these factors, we develop a framework of integrating external controls that is applicable to both genotyped and sequencing data, further honing the statistical methods needed to identify disease-causing variants within the human genome.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169933/1/yatongli_1.pd

Efficacy and safety of consolidation durvalumab after chemoradiation therapy for stage III non-small-cell lung cancer: a systematic review, meta-analysis, and meta-regression of real-world studies

Background: The current review aimed to pool real-world evidence on the efficacy and toxicity of consolidation durvalumab for stage III unresectable non-small cell lung cancer (NSCLC) after curative chemoradiotherapy.Methods: PubMed, CENTRAL, ScienceDirect, Embase, and Google Scholar were searched for observational studies reporting the use of durvalumab for NSCLC till 12th April 2022. Twenty-three studies with 4,400 patients were included.Results: The pooled 1-year overall survival (OS) and progression-free survival rates (PFS) were 85% (95% CI: 81%–89%) and 60% (95% CI: 56%–64%) respectively. Pooled incidence of all-grade pneumonitis, grade ≥3 pneumonitis and discontinuation of durvalumab due to pneumonitis were 27% (95% CI: 19%–36%), 8% (95% CI: 6%–10%) and 17% (95% CI: 12%–23%) respectively. The pooled proportion of patients experiencing endocrine, cutaneous, musculoskeletal, and gastrointestinal adverse events was 11% (95% CI: 7%–18%), 8% (95% CI: 3%–17%), 5% (95% CI: 3%–6%), and 6% (95% CI: 3%–12%), respectively.Conclusion: Meta-regression indicated that performance status significantly influenced PFS, while age, time to durvalumab, and programmed death-ligand 1 status significantly affected pneumonitis rates. Real-world evidence suggests that the short-term efficacy and safety of durvalumab are consistent with that of the PACIFIC trial. The congruence of results lends support to durvalumab use in improving outcomes of unresectable stage III NSCLC.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022324663, identifier CRD42022324663

Social behavioral sensing: an exploratory study to assess learning motivation and perceived relatedness of university students using mobile sensing

Learning motivation plays a crucial role in student’s daily study life since it greatly affects academic performance and engagement. Perceived relatedness, based on self-determined theory, is an important predictor of learning motivation. Today, assessment for both of them still relies on subjective evaluations and self-reports, which is time-consuming and onerous. Hence, we propose a novel approach blended with mobile sensing by simultaneously collecting psychological measurements and objective mobile sensing data from N=58 undergraduates to explore new methods of assessing learning motivation and perceived relatedness. We identify a variety of social behavioral patterns from mobile sensing data, and investigate associations between psychological measures and these patterns. Our study helps enlighten what the new forms of assessing learning motivation and perceived relatedness in education could be, and paves the way for personalizing intervention in future research.This research was supported by the National Natural Science Foundation of China (No. 62077027), and the Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 20YJC190034)

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods

A miniaturized ultrasonic micro-hole perforator for minimally invasive craniotomy

Objective: Micro-hole perforation on skull is urgently desired for minimally invasive insertion of micro-tools in brain for diagnostic or treatment purpose. However, a micro drill bit would easily fracture, making it difficult to safely generate a micro-hole on the hard skull. Methods: In this study, we present a method for ultrasonic vibration assisted micro-hole perforation on skull in a manner similar to subcutaneous injection on soft tissue. For this purpose, a high amplitude miniaturized ultrasonic tool with a 500 μm tip diameter micro-hole perforator was developed with simulation and experimental characterization. In-depth investigation of micro-hole generation mechanism was performed with systematic experiments on animal skull with a bespoke test rig; effects of vibration amplitude and feed rate on hole forming characteristics were systematically studied. It was observed that by exploiting skull bone's unique structural and material properties, the ultrasonic micro-perforator could locally damage bone tissue with micro-porosities, induce sufficient plastic deformation to bone tissue around the micro-hole and refrain elastic recovery after tool withdraw, generating a micro-hole on skull without material. Results : Under optimized conditions, high quality micro-holes could be formed on the hard skull with a force (< 1N) even smaller than that for subcutaneous injection on soft skin. Conclusion: This study would provide a safe and effective method and a miniaturized device for micro-hole perforation on skull for minimally invasive neural interventions

Improving the expression of recombinant pullulanase by increasing mRNA stability in Escherichia coli

Background: Pullulanase production in both wild-type strains and recombinantly engineered strains remains low. The Shine-Dalgarno (SD) sequence and stem-loop structure in the 5\u2032 or 3\u2032 untranslated region (UTR) are well-known determinants of mRNA stability. This study investigated the effect of mRNA stability on pullulanase heterologous expression. Results: We constructed four DNA fragments, pulA, SD-pulA, pulA-3t, and SD-pulA-3t,whichwere cloned into the expression vector pHT43 to generate four pullulanase expression plasmids. The DNA fragment pulA was the coding sequence (CDS) of pulA in Klebsiella variicola Z-13. SD-pulA was constructed by the addition of the 5\u2032 SD sequence at the 5\u2032 UTR of pulA. pulA-3t was constructed by the addition of a 3\u2032 stem-loop structure at the 3\u2032 UTR of pulA. SD-pulA-3t was constructed by the addition of the 5\u2032 SD sequence at the 5\u2032 UTR and a 3\u2032 stem-loop structure at the 3\u2032 UTR of pulA. The four vectors were transformed into Escherichia coli BL21(DE3). The pulA mRNA transcription of the transformant harboring pHT43-SD-pulA-3t was 338.6%, 34.9%, and 79.9% higher than that of the other three transformants, whereas the fermentation enzyme activities in culture broth and intracellularly were 107.0 and 584.1 times, 1.2 and 2.0 times, and 62.0 and 531.5 times the amount of the other three transformants (pulA, SD-pulA, and pulA-3 t), respectively. Conclusion: The addition of the 5\u2032 SD sequence at the 5\u2032 UTR and a 3\u2032 stem-loop structure at the 3\u2032 UTR of the pulA gene is an effective approach to increase pulA gene expression and fermentation enzyme activity

Metabolomic and transcriptomice analyses of flavonoid biosynthesis in apricot fruits

IntroductionFlavonoids, as secondary metabolites in plants, play important roles in many biological processes and responses to environmental factors.MethodsApricot fruits are rich in flavonoid compounds, and in this study, we performed a combined metabolomic and transcriptomic analysis of orange flesh (JN) and white flesh (ZS) apricot fruits.Results and discussionA total of 222 differentially accumulated flavonoids (DAFs) and 15855 differentially expressed genes (DEGs) involved in flavonoid biosynthesis were identified. The biosynthesis of flavonoids in apricot fruit may be regulated by 17 enzyme-encoding genes, namely PAL (2), 4CL (9), C4H (1), HCT (15), C3’H (4), CHS (2), CHI (3), F3H (1), F3’H (CYP75B1) (2), F3’5’H (4), DFR (4), LAR (1), FLS (3), ANS (9), ANR (2), UGT79B1 (6) and CYP81E (2). A structural gene-transcription factor (TF) correlation analysis yielded 3 TFs (2 bHLH, 1 MYB) highly correlated with 2 structural genes. In addition, we obtained 26 candidate genes involved in the biosynthesis of 8 differentially accumulated flavonoids metabolites in ZS by weighted gene coexpression network analysis. The candidate genes and transcription factors identified in this study will provide a highly valuable molecular basis for the in-depth study of flavonoid biosynthesis in apricot fruits