Biodiversity and activity of the gut microbiota across the life history of the insect herbivore Spodoptera littoralis

Microbes that live inside insects play critical roles in host nutrition, physiology, and behavior. Although Lepidoptera (butterflies and moths) are one of the most diverse insect taxa, their microbial symbionts are little-studied, particularly during metamorphosis. Here, using ribosomal tag pyrosequencing of DNA and RNA, we investigated biodiversity and activity of gut microbiotas across the holometabolous life cycle of Spodoptera littoralis, a notorious agricultural pest worldwide. Proteobacteria and Firmicutes dominate but undergo a structural “metamorphosis” in tandem with its host. Enterococcus, Pantoea and Citrobacter were abundant and active in early-instar, while Clostridia increased in late-instar. Interestingly, only enterococci persisted through metamorphosis. Female adults harbored high proportions of Enterococcus, Klebsiella and Pantoea, whereas males largely shifted to Klebsiella. Comparative functional analysis with PICRUSt indicated that early-instar larval microbiome was more enriched for genes involved in cell motility and carbohydrate metabolism, whereas in late-instar amino acid, cofactor and vitamin metabolism increased. Genes involved in energy and nucleotide metabolism were abundant in pupae. Female adult microbiome was enriched for genes relevant to energy metabolism, while an increase in the replication and repair pathway was observed in male. Understanding the metabolic activity of these herbivore-associated microbial symbionts may assist the development of novel pest-management strategies

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased

One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration

In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration. However, in order to cope with general function approximators, most of them involve impractical algorithmic components to incentivize exploration, such as optimization within data-dependent level-sets or complicated sampling procedures. To address this challenge, we propose an easy-to-implement RL framework called \textit{Maximize to Explore} (\texttt{MEX}), which only needs to optimize \emph{unconstrainedly} a single objective that integrates the estimation and planning components while balancing exploration and exploitation automatically. Theoretically, we prove that \texttt{MEX} achieves a sublinear regret with general function approximations for Markov decision processes (MDP) and is further extendable to two-player zero-sum Markov games (MG). Meanwhile, we adapt deep RL baselines to design practical versions of \texttt{MEX}, in both model-free and model-based manners, which can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards. Compared with existing sample-efficient online RL algorithms with general function approximations, \texttt{MEX} achieves similar sample efficiency while enjoying a lower computational cost and is more compatible with modern deep RL methods

Integration of metabolomics and transcriptomics provides insights into enhanced osteogenesis in Ano5Cys360Tyr knock-in mouse model

IntroductionGnathodiaphyseal dysplasia (GDD; OMIM#166260) is a rare autosomal dominant disorder characterized by diaphyseal sclerosis of tubular bones and cemento-osseous lesions in mandibles. GDD is caused by point mutations in the ANO5 gene. However, the mechanisms underlying GDD have not been disclosed. We previously generated the first knock-in mouse model for GDD expressing a human mutation (p.Cys360Tyr) in ANO5 and homozygous Ano5 knock-in (Ano5KI/KI) mice exhibited representative traits of human GDD especially including enhanced osteogenesis.MethodsMetabolomics and transcriptomics analyses were conducted for wildtype (Ano5+/+) and Ano5KI/KI mature mouse calvarial osteoblasts (mCOBs) grown in osteogenic cultures for 14 days to identify differential intracellular metabolites and genes involved in GDD. Subsequently, related differential genes were validated by qRT-PCR. Cell proliferation was confirmed by CCK8 assay and calcium content in mineral nodules was detected using SEM-EDS.ResultsMetabolomics identified 42 differential metabolites that are primarily involved in amino acid and pyrimidine metabolism, and endocrine and other factor-regulated calcium reabsorption. Concomitantly, transcriptomic analysis revealed 407 differentially expressed genes in Ano5KI/KI osteoblasts compared with wildtype. Gene ontology and pathway analysis indicated that Ano5Cys360Tyr mutation considerably promoted cell cycle progression and perturbed calcium signaling pathway, which were confirmed by validated experiments. qRT-PCR and CCK-8 assays manifested that proliferation of Ano5KI/KI mCOBs was enhanced and the expression of cell cycle regulating genes (Mki67, Ccnb1, and Ccna2) was increased. In addition, SEM-EDS demonstrated that Ano5KI/KI mCOBs developed higher calcium contents in mineral nodules than Ano5+/+ mCOBs, while some calcium-related genes (Cacna1, Slc8a1, and Cyp27b1) were significantly up-regulated. Furthermore, osteocalcin which has been proved to be an osteoblast-derived metabolic hormone was upregulated in Ano5KI/KI osteoblast cultures.DiscussionOur data demonstrated that the Ano5Cys360Tyr mutation could affect the metabolism of osteoblasts, leading to unwonted calcium homeostasis and cellular proliferation that can contribute to the underlying pathogenesis of GDD disorders

Evaluating Open-QA Evaluation

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. Our evaluation of these methods utilizes human-annotated results to measure their performance. Specifically, the work investigates methods that show high correlation with human evaluations, deeming them more reliable. We also discuss the pitfalls of current methods and methods to improve LLM-based evaluators. We believe this new QA-Eval task and corresponding dataset EVOUNA will facilitate the development of more effective automatic evaluation tools and prove valuable for future research in this area. All resources are available at \url{https://github.com/wangcunxiang/QA-Eval} and it is under the Apache-2.0 License