Mammalian thioredoxin reductase 1 in antioxidant defense, regulation of adipocyte differentiation and as an anticancer drug target

Reactive oxygen species (ROS) are oxygen containing reactive molecules generated as by-products of cellular metabolism. At physiological concentrations, ROS acts as secondary messengers in cellular signaling transduction, but excessive amounts of ROS result in oxidative stress and cellular damage. Several antioxidant enzyme systems include the thioredoxin (Trx)- and glutathione (GSH)-dependent systems together with superoxide dismutases and catalases may act in concert to protect cells and organisms from the toxic effects of excessive ROS. Mammalian thioredoxin reductase 1 (TrxR1), which is a cytosolic selenoprotein with a selenocysteine (Sec, U) residue in a conserved C-terminal GCUG motif, catalyzes the reduction of thioredoxin using NADPH and is known to be involved in antioxidant defense, redox regulation and cell proliferation. This thesis has focused on studying multiple aspects of cellular events and signaling pathways that are modulated by TrxR1. Paper I. The Sec residue in the C-terminal motif of TrxR1 is highly nucleophilic and can be easily targeted by electrophiles. In this study, we found the mutant p53 activator and anticancer drug lead named APR-246 (PRIMA-1Met) targeted and inhibited both recombinant and cellular TrxR activity. The inhibited TrxR1 still maintained its NADPH oxidase activity, which thus could contribute to the oxidative stress and cell death that are triggered by APR-246. Our findings provide insights into the p53 independent cytotoxicity mechanisms of APR-246 on tumor cells. Paper II. In this study, we used thiophosphate (SPO3) and selenite to modulate the Sec incorporation into TrxR1 in mammalian cells. We found that SPO3 promoted expression of Sec-to-cysteine substituted forms of TrxR1 and, conversely, selenite increased Sec incorporation in TrxR1. SPO3 treatment also attenuated cisplatin induced toxicity on A549 and HCT116 cells, while selenite supplementation sensitized NIH 3T3 cells to cisplatin but decreased the dependence of these cells on GSH. Taken together, these results show that the selenium status of cells can modulate the cytotoxicity of drugs that target TrxR1 and the glutathione dependence of the cells. Paper III. Here we utilized Txnrd1 depleted (Txnrd1-/-) mouse embryonic fibroblasts (MEFs) and observed massive cell death upon cultured at low-density in high-glucose medium. The cell death was linked to excessive H2O2 production promoted by high-glucose metabolism. Reconstitution of the cells with Sec-containing TrxR1, but not with the Sec-to-Cys substituted variant, rescued the MEFs from this lethality. These results show that Sec-containing TrxR1 is essential to maintain self-sufficiency of MEFs under high-glucose conditions, due to an essential role in control of glucose-derived H2O2 production. This study is, to our knowledge, the first time identified an essential biological role of Seccontaining TrxR1 that cannot be sustained by the Cys-mutant of the enzyme. Paper IV. Txnrd1-/- MEFs revealed a strong increase of spontaneous lipogenesis and hormonally induced adipocyte differentiation. The highly promoted adipocyte differentiation capacity was due to unlimited mitotic clonal expansion capacity and dramatically upregulated PPARγ expression. These effects were likely to be connected to increased oxidative signaling in Txnrd1-/- MEFs, because NAC treatment abolished the adipocyte differentiation by blocking mitotic clonal expansion. An increased Akt signaling in Txnrd1-/- MEFs induced by decreased cellular PTEN activity and increased ROS, also contributes to the enhanced adipogenesis. These results suggest that the selenoprotein TrxR1 suppress adipocyte differentiation through inhibition insulin signaling events, mitotic clonal expansion and PPARγ expression. In summary, this study shows that TrxR1 plays an essential role in antioxidant defense, regulation of adipocyte differentiation and servers as an anticancer drug target

SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views

We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3). SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction. Moreover, several strategies are introduced to effectively leverage sparse views for high-quality reconstruction, including 1) a multi-level geometry reasoning framework to recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme for more reliable color prediction; 3) a consistency-aware fine-tuning scheme to control the inconsistent regions caused by occlusion and noise. Extensive experiments demonstrate that our approach not only outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility.Comment: Project page: https://www.xxlong.site/SparseNeuS

A Model-based Multi-Agent Personalized Short-Video Recommender System

Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement learning (RL) framework has attracted increasing attention from both academic and industry communities. In this paper, we propose a RL-based industrial short-video recommender ranking framework, which models and maximizes user watch-time in an environment of user multi-aspect preferences by a collaborative multi-agent formulization. Moreover, our proposed framework adopts a model-based learning approach to alleviate the sample selection bias which is a crucial but intractable problem in industrial recommender system. Extensive offline evaluations and live experiments confirm the effectiveness of our proposed method over alternatives. Our proposed approach has been deployed in our real large-scale short-video sharing platform, successfully serving over hundreds of millions users

Boris Johnson in hospital: a Chinese gaze at Western democracies in the COVID-19 pandemic

In this article, we examine Chinese assessments of Western democratic systems in the context of the COVID-19 pandemic. This research is based on an up-to-date case study of how Chinese Internet users discussed the UK Prime Minister – Boris Johnson’s infection with COVID-19 in late March and early April 2020. The research collected original data from the Chinese community question-answering (CQA) site – Zhihu. Using a mixed-method approach, consisting of content analysis (CA) and thematic analysis (TA), we show how Zhihu users evaluate the incident (1) as a way to express their sentiments towards Boris Johnson, (2) as a case to assess British politics and (3) as a vehicle for rationalizing their views on Western democratic systems in relation to China’s domestic politics. The research findings shed new light on a Chinese gaze at Western democratic systems in the COVID-19 pandemic crisis

Technical Debt Management in OSS Projects: An Empirical Study on GitHub

Technical debt (TD) refers to delayed tasks and immature artifacts that may bring short-term benefits but incur extra costs of change during maintenance and evolution in the long term. TD has been extensively studied in the past decade, and numerous open source software (OSS) projects were used to explore specific aspects of TD and validate various approaches for TD management (TDM). However, there still lacks a comprehensive understanding on the practice of TDM in OSS development, which penetrates the OSS community's perception of the TD concept and how TD is managed in OSS development. To this end, we conducted an empirical study on the whole GitHub to explore the adoption and execution of TDM based on issues in OSS projects. We collected 35,278 issues labeled as TD (TD issues) distributed over 3,598 repositories in total from the issue tracking system of GitHub between 2009 and 2020. The findings are that: (1) the OSS community is embracing the TD concept; (2) the analysis of TD instances shows that TD may affect both internal and external quality of software systems; (3) only one TD issue was identified in 31.1% of the repositories and all TD issues were identified by only one developer in 69.0% of the repositories; (4) TDM was ignored in 27.3% of the repositories after TD issues were identified; and (5) among the repositories with TD labels, 32.9% have abandoned TDM while only 8.2% adopt TDM as a consistent practice. These findings provide valuable insights for practitioners in TDM and promising research directions for further investigation.Comment: 15 pages, 8 images, 10 tables, Manuscript submitted to a Journal (2022

Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules

FedMT: Federated Learning with Mixed-type Labels

In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple data centers without exchanging data across them, which improves the sample efficiency. However, the conventional FL setting assumes the same labeling criterion in all data centers involved, thus limiting its practical utility. This limitation becomes particularly notable in domains like disease diagnosis, where different clinical centers may adhere to different standards, making traditional FL methods unsuitable. This paper addresses this important yet under-explored setting of FL, namely FL with mixed-type labels, where the allowance of different labeling criteria introduces inter-center label space differences. To address this challenge effectively and efficiently, we introduce a model-agnostic approach called FedMT, which estimates label space correspondences and projects classification scores to construct loss functions. The proposed FedMT is versatile and integrates seamlessly with various FL methods, such as FedAvg. Experimental results on benchmark and medical datasets highlight the substantial improvement in classification accuracy achieved by FedMT in the presence of mixed-type labels.Comment: 23 page