Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

Background: Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results: We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions: We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination

Unveiling the origin of catalytic sites of Pt nanoparticles decorated on oxygen-deficient vanadium-doped cobalt hydroxide nanosheet for hybrid sodium-air batteries

Highly active bifunctional electrocatalysts are crucial for improving the performance of rechargeable metal-air batteries. However, most reported bifunctional electrocatalysts feature poor electrocatalytic activity and stability toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Here, we have reported the first-ever study of an effective one-step reduction-assisted exfoliation method to exfoliate bulk vanadium-doped cobalt hydroxide (V-doped Co(OH)2, denoted as V-Co(OH)2) into ultrathin nanosheets with abundant oxygen vacancies (V-Co(OH)2-Ov) and simultaneously anchor them with highly dispersed ultrafine Pt nanoparticles (NPs) with a nominal size of 0.8-2.4 nm (denoted as Pt/V-Co(OH)2-Ov). The Pt/V-Co(OH)2-Ov catalyst exhibits improved catalytic performance in ORR/OER. X-ray absorption spectroscopy analysis and theoretical calculations reveal the strong interfacial electronic interactions between Pt NPs and V-Co(OH)2-Ov, which synergistically improves oxygen intermediates' adsorption/desorption, enhancing the ORR and OER performance. Using Pt/V-Co(OH)2-Ov as a catalyst in the air cathode, a hybrid sodium-air battery displays a record value of an ultralow charging-discharging voltage gap of 0.07 V at a current density of 0.01 mA cm-2 with remarkable stability of up to 1000 cycles. This reduction-assisted exfoliation approach provides a new strategy to generate oxygen vacancies in metal hydroxides, which act as anchoring sites for deposition of sub-nanometal NPs via a strong interfacial effect

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci.

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined

Methods to Dissect the Biology of Complex Phenotypes Using Genomic, Transcriptomic, and Phenomic Data

One of the big aims in human genetics is to understand the biological mechanism underlying the genetic associations. In the past decades, the rapid development of biotechnology has made tremendous progress to approach this aim. For instance, with advanced and specialized devices and data automation systems, more complex phenotypes can be measured at higher accuracy and in more individuals. And with the inventions in high-throughput sequencing, we can profile various types of biological molecules in organs, tissues, and cells. As a geneticist, we face a massive amount of biological data at different levels and of great diversity, creating unprecedented opportunities for making discoveries. However, making the best use of data and translating them into scientific insights remain challenging. In the current data-dominated era, statistical modeling has become a vital tool to fill the gap between biological data and scientific discoveries. My dissertation spans multiple topics in statistical genetics involving the handling of genomic, transcriptomic, and phenomic data. In Chapter 2, I pro- pose a unified statistical framework, along with computationally efficient implementation, leveraging signals from both total counts and allele-specific counts to study the genetic effect of variants on cis-regulation. In Chapter 3, I show the utility of predicted transcriptome-based polygenic risk scores in terms of the prediction performance in the matched ancestry and cross ancestry. In Chapter 4, I propose a method to impute the parental origin of the haplotypes by exploiting the parental phenome and analyze the potential benefit of using these imputed haplotypes in a GWAS with parental phenotypes and offspring genotypes. In Chapter 5, I design and implement a data analysis pipeline studying the relation between magnetic resonance imaging-derived brain features and complex phenotypes by leveraging genetic evidence rather than purely observational data. Besides methodological advancements, I also involve in collaborative efforts on analyzing and integrating the state-of-the-art datasets to decipher the genetic basis of transcriptome in multi-tissue setting and how it relates to complex phenotype genetics, which is shown in Appendix A

Reduction of selenite and tellurite by a highly metal-tolerant marine bacterium

Selenium (Se) and tellurium (Te) contaminations in soils and water bodies have been widely reported in recent years. Se(IV) and Te(IV) were regarded as their most dangerous forms. Microbial treatments of Se(IV)- and Te(IV)-containing wastes are promising approaches because of their environmentally friendly and sustainable advantages. However, the salt-tolerant microbial resources that can be used for selenium/tellurium pollution control are still limited since industrial wastewaters usually contain a large number of salts. In this study, a marine Shewanella sp. FDA-1 (FDA-1) was reported for efficient Se(IV) and Te(IV) reduction under saline conditions. Process and product analyses were performed to investigate the bioreduction processes of Se(IV) and Te(IV). The results showed that FDA-1 can effectively reduce Se(IV) and Te(IV) to Se-0 and Te-0 Se(IV)/Te(IV) to Se-0/Te-0 in 72 h, which were further confirmed by XRD and XPS analyses. In addition, enzymatic and RT-qPCR assays showed that flavin-related proteins, reductases, dehydrogenases, etc., could be involved in the bioreduction of Se(IV)/Te(IV). Overall, our results demonstrate the ability of FDA-1 to reduce high concentrations of Se(IV)/or Te(IV) to Se-0/or Te-0 under saline conditions and thus provide efficient microbial candidate for controlling Se and Te pollution

Stepwise Fabrication of Co-Embedded Porous Multichannel Carbon Nanofibers for High-Efficiency Oxygen Reduction

Abstract A novel nonprecious metal material consisting of Co-embedded porous interconnected multichannel carbon nanofibers (Co/IMCCNFs) was rationally designed for oxygen reduction reaction (ORR) electrocatalysis. In the synthesis, ZnCo2O4 was employed to form interconnected mesoporous channels and provide highly active Co3O4/Co core–shell nanoparticle-based sites for the ORR. The IMC structure with a large synergistic effect of the N and Co active sites provided fast ORR electrocatalysis kinetics. The Co/IMCCNFs exhibited a high half-wave potential of 0.82 V (vs. reversible hydrogen electrode) and excellent stability with a current retention up to 88% after 12,000 cycles in a current–time test, which is only 55% for 30 wt% Pt/C

Deep reinforcement learning for time allocation and directional transmission in joint radar-communication

Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to non-learning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.Submitted/Accepted versionThis study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and inkind contribution from the industry partner(s)

Intelligent resource allocation in joint radar-communication with graph neural networks

Autonomous vehicles produce high data rates of sensory information from sensing systems. To achieve the advantages of sensor fusion among different vehicles in a cooperative driving scenario, high data-rate communication becomes essential. Current strategies for joint radar-communication (JRC) often rely on specialized hardware, prior knowledge of the system model, and entail diminished capability in either radar or communication functions. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help an intelligent vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a generalized form of the Markov Decision Process (MDP). We identify deep reinforcement learning algorithms (DRL) and algorithmic extensions suitable for solving our JRC problem. For multi-agent scenarios, we introduce a Graph Neural Network (GNN) framework via a control channel. This framework enables modular and fair comparisons of various algorithmic extensions. Our experiments show that DRL results in superior performance compared to non-learning algorithms. Learning of inter-agent coordination in the GNN framework, based only on the Markov task reward, further improves performance.AI SingaporeInfo-communications Media Development Authority (IMDA)Ministry of Education (MOE)National Research Foundation (NRF)This work was supported in part by the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, in part by cash and in-kind contribution from the industry partner(s), in part by programme DesCartes, in part by the National Research Foundation, Prime Minister’s Office, Singapore under the Campus for Research Excellence and Technological Enterprise (CREATE) programme and under its Emerging Areas Research Projects (EARP) Funding Initiative, NRF and Infocomm Media Development Authority under its Future Communications Research & Development Programme (FCP) (FCP-NTU-RG-2021-014), and Alibaba-NTU Singapore Joint Research Institute (JRI), in part by the National Research Foundation, Singapore through AI Singapore Programme (AISG) under Grant AISG2-RP-2020-019, and in part by the Singapore Ministry of Education (MOE) Tier 1 under Grant RG16/20

Preventative effects of resveratrol and estradiol on streptozotocin-induced diabetes in ovariectomized mice and the related mechanisms.

Resveratrol, a non-flavonoid polyphenolic compound, is structurally and functionally similar to estrogen and has drawn great attention for its potentially beneficial effects on diabetes. However, it is not known whether it shares the same protective effect against diabetes as estrogen and the underlying mechanisms. The aim of the present study was to investigate the protective effects of phytoestrogen resveratrol and exogenous 17β-estradiol against streptozotocin (STZ)-induced type 1 diabetes. Female mice were ovariectomized (OVX) and chronically injected with different concentrations of resveratrol (0.1, 1 or 10 mg/kg) and 17β-estradiol (0.01, 0.1 or 1 mg/kg) subcutaneously for 4 weeks, and the levels of blood glucose, plasma insulin, plasma antioxidant capacity, the changes of pancreatic islet cells and the expressions of glucose transporter 4 (GLUT4), insulin receptor substrate 1 (IRS-1) and phosphorylation of extracellular signal-regulated kinase (p-ERK) were detected. Resveratrol and 17β-estradiol significantly inhibited the increase of the blood glucose level and the rise of plasma malondialdehyde in STZ-induced diabetic mice, improved the levels of plasma antioxidant capacity and plasma insulin, protected the pancreatic islet cells, and increased the expressions of GLUT4 and IRS-1, but decreased p-ERK expression in skeletal muscle and myocardial tissue. The results suggest that resveratrol or 17β-estradiol shows obvious protection against STZ-induced diabetes in OVX mice, the mechanisms probably involve their ameliorating antioxidant activities and islet function, promoting muscle glucose uptake and inhibiting the expression of p-ERK

Expression and prognostic analyses of ITGA11, ITGB4 and ITGB8 in human non-small cell lung cancer

Background Integrins play a crucial role in the regulation process of cell proliferation, migration, differentiation, tumor invasion and metastasis. ITGA11, ITGB4 and ITGB8 are three encoding genes of integrins family. Accumulative evidences have proved that abnormal expression of ITGA11, ITGB4 and ITGB8 are a common phenomenon in different malignances. However, their expression patterns and prognostic roles for patients with non-small cell lung cancer (NSCLC) have not been completely illustrated. Methods We investigated the expression patterns and prognostic values of ITGA11, ITGB4 and ITGB8 in patients with NSCLC through using a series of databases and various datasets, including ONCOMINE, GEPIA, HPA, TCGA and GEO datasets. Results We found that the expression levels of ITGA11 and ITGB4 were significantly upregulated in both LUAD and LUSC, while ITGB8 was obviously upregulated in LUSC. Additionally, higher expression level of ITGB4 revealed a worse OS in LUAD. Conclusion Our findings suggested that ITGA11 and ITGB4 might have the potential ability to act as diagnostic biomarkers for both LUAD and LUSC, while ITGB8 might serve as diagnostic biomarker for LUSC. Furthermore, ITGB4 could serve as a potential prognostic biomarker for LUAD