Defects engineering simultaneously enhances activity and recyclability of MOFs in selective hydrogenation of biomass

The development of synthetic methodologies towards enhanced performance in biomass conversion is desirable due to the growing energy demand. Here we design two types of Ru impregnated MIL-100-Cr defect engineered metal-organic frameworks (Ru@DEMOFs) by incorporating defective ligands (DLs), aiming at highly efficient catalysts for biomass hydrogenation. Our results show that Ru@DEMOFs simultaneously exhibit boosted recyclability, selectivity and activity with the turnover frequency being about 10 times higher than the reported values of polymer supported Ru towards D-glucose hydrogenation. This work provides in-depth insights into (i) the evolution of various defects in the cationic framework upon DLs incorporation and Ru impregnation, (ii) the special effect of each type of defects on the electron density of Ru nanoparticles and activation of reactants, and (iii) the respective role of defects, confined Ru particles and metal single active sites in the catalytic performance of Ru@DEMOFs for D-glucose selective hydrogenation as well as their synergistic catalytic mechanism

Comparative Genomic Analysis and Phenotypic Characterization of Bronchoscope-Associated Klebsiella aerogenes

Bronchoscopes have been linked to outbreaks of nosocomial infections. The phenotypic and genomic profiles of bronchoscope-associated Klebsiella aerogenes isolates are largely unknown. In this work, a total of 358 isolates and 13 isolates were recovered from samples after clinical procedures and samples after decontamination procedures, respectively, over the five months. Antimicrobial susceptibility testing found seven K. aerogenes isolates exhibiting a low-level resistance to antimicrobial agents. Among seven K. aerogenes isolates, we found five sequence types (STs) clustered into three main clades. Collectively, this study described for the first time the phenotypic and genomic characteristics of bronchoscope-associated K. aerogenes

Indigo: a natural molecular passivator for efficient perovskite solar cells

Organic–inorganic hybrid lead halide perovskite solar cells have made unprecedented progress in improving photovoltaic efficiency during the past decade, while still facing critical stability challenges. Herein, the natural organic dye Indigo is explored for the first time to be an efficient molecular passivator that assists in the preparation of high-quality hybrid perovskite film with reduced defects and enhanced stability. The Indigo molecule with both carbonyl and amino groups can provide bifunctional chemical passivation for defects. In-depth theoretical and experimental studies show that the Indigo molecules firmly binds to the perovskite surfaces, enhancing the crystallization of perovskite films with improved morphology. Consequently, the Indigo-passivated perovskite film exhibits increased grain size with better uniformity, reduced grain boundaries, lowered defect density, and retarded ion migration, boosting the device efficiency up to 23.22%, and ˜21% for large-area device (1 cm2). Furthermore, the Indigo passivation can enhance device stability in terms of both humidity and thermal stress. These results provide not only new insights into the multipassivation role of natural organic dyes but also a simple and low-cost strategy to prepare high-quality hybrid perovskite films for optoelectronic applications based on Indigo derivatives.Peer ReviewedPostprint (author's final draft

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.Comment: Pre-print under review, submitted to Proceedings of Machine Learning Researc

Evolutionary Analysis of Structural Protein Gene VP1 of Foot-and-Mouth Disease Virus Serotype Asia 1

Foot-and-mouth disease virus (FMDV) serotype Asia 1 was mostly endemic in Asia and then was responsible for economically important viral disease of cloven-hoofed animals, but the study on its selection and evolutionary process is comparatively rare. In this study, we characterized 377 isolates from Asia collected up until 2012, including four vaccine strains. Maximum likelihood analysis suggested that the strains circulating in Asia were classified into 8 different groups (groups I–VIII) or were unclassified (viruses collected before 2000). On the basis of divergence time analyses, we infer that the TMRCA of Asia 1 virus existed approximately 86.29 years ago. The result suggested that the virus had a high mutation rate (5.745 × 10−3 substitutions/site/year) in comparison to the other serotypes of FMDV VP1 gene. Furthermore, the structural protein VP1 was under lower selection pressure and the positive selection occurred at many sites, and four codons (positions 141, 146, 151, and 169) were located in known critical antigenic residues. The remaining sites were not located in known functional regions and were moderately conserved, and the reason for supporting all sites under positive selection remains to be elucidated because the power of these analyses was largely unknown

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

Elevated ADAR expression is significantly linked to shorter overall survival and immune infiltration in patients with lung adenocarcinoma

To date, few studies have investigated whether the RNA-editing enzymes adenosine deaminases acting on RNA (ADARs) influence RNA functioning in lung adenocarcinoma (LUAD). To investigate the role of ADAR in lung cancer, we leveraged the advantages of The Cancer Genome Atlas (TCGA) database, from which we obtained transcriptome data and clinical information from 539 patients with LUAD. First, we compared ARAR expression levels in LUAD tissues with those in normal lung tissues using paired and unpaired analyses. Next, we evaluated the influence of ADARs on multiple prognostic indicators, including overall survival at 1, 3 and 5 years, as well as disease-specific survival and progression-free interval, in patients with LUAD. We also used Kaplan-Meier survival curves to estimate overall survival and Cox regression analysis to assess covariates associated with prognosis. A nomogram was constructed to validate the impact of the ADARs and clinicopathological factors on patient survival probabilities. The volcano plot and heat map revealed the differentially expressed genes associated with ADARs in LUAD. Finally, we examined ADAR expression versus immune cell infiltration in LUAD using Spearman's analysis. Using the Gene Expression Profiling Interactive Analysis (GEPIA2) database, we identified the top 100 genes most significantly correlated with ADAR expression, constructed a protein-protein interaction network and performed a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analysis on these genes. Our results demonstrate that ADARs are overexpressed in LUAD and correlated with poor patient prognosis. ADARs markedly increase the infiltration of T central memory, T helper 2 and T helper cells, while reducing the infiltration of immature dendritic, dendritic and mast cells. Most immune response markers, including T cells, tumor-associated macrophages, T cell exhaustion, mast cells, macrophages, monocytes and dendritic cells, are closely correlated with ADAR expression in LUAD

Preschool Federations as a Strategy for the Sustainable Development of Early Childhood Education in China

Teaming a strong preschool with less-developed, rural, or newly established preschools is an effective strategy to promote quality. Since 2015, Shanghai has sought to improve preschool education quality through collaboration. Guided by the “3A2S” theoretical framework, this study evaluated the development and effectiveness of preschool federations in the city using a mixed-methods approach. First, document analysis was conducted to depict features of preschool federations in Shanghai, which identified three main features. Next, a survey study including 702 stakeholders was conducted to assess the evaluations of preschool administrators, teachers, and parents of the effectiveness of preschool federations. Finally, an interview study including 15 stakeholders was conducted to triangulate the findings of the survey study. Results revealed that the stakeholders highly approbated the preschool federations, but parents’ evaluation was significantly lower than that of other stakeholders. Finally, the sustainability and affordability of the preschool federation policy are discussed herein. Implications for policy development and preschool management are also presented

Seminal plasma biomarkers for predicting successful sperm retrieval in patients with nonobstructive azoospermia: a narrative review of human studies

Abstract Background Non-obstructive azoospermia (NOA) is considered to be the most severe form of male infertility. Before the emergence of surgical testicular sperm extraction and assisted reproductive technology, NOA patients could hardly become biological fathers of their children. However, failure of the surgery could cause physical and psychological harm to patients such as testicular damage, pain, hopeless of fertility and additional cost. Therefore, predicting the successful sperm retrieval (SSR) is so important for NOA patients to make their choice whether to do the surgery or not. Because seminal plasma is secreted by the testes and accessory gonads, it can reflect the spermatogenic environment, making it a preferential choice for SSR valuation. The purpose of this paper is to summarize the available evidence and provide the reader with a broad overview of biomarkers in seminal plasma for SSR prediction. Results A total of 15,390 studies were searched from PUBMED, EMBASE, CENTRAL and Web of Science, but only 6615 studies were evaluated after duplications were removed. The abstracts of 6513 articles were excluded because they were irrelevant to the topic. The full texts of 102 articles were obtained, with 21 of them being included in this review. The included studies range in quality from medium to high. In the included articles, surgical sperm extraction methods included conventional testicular sperm extraction (TESE) and microdissection testicular sperm extraction (micro-TESE). Currently, the biomarkers in seminal plasma used to predict SSR are primarily RNAs, metabolites, AMH, inhibin B, leptin, survivin, clusterin, LGALS3BP, ESX1, TEX101, TNP1, DAZ, PRM1 and PRM2. Conclusion The evidence does not conclusively indicate that AMH and INHB in seminal plasma are valuable to predict the SSR. It is worth noting that RNAs, metabolites and other biomarkers in seminal plasma have shown great potential in predicting SSR. However, existing evidence is insufficient to provide clinicians with adequate decision support, and more prospective, large sample size, and multicenter trials are urgently needed