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Engineering multimetallic nanocatalysts with the entropy‐mediated strategy to reduce reaction activation energy is regarded as an innovative and effective approach to facilitate efficient heterogeneous catalysis. Accordingly, conformational entropy‐driven high‐entropy alloys (HEAs) are emerging as a promising candidate to settle the catalytic efficiency limitations of nanozymes, attributed to their versatile active site compositions and synergistic effects. As proof of the high‐entropy nanozymes (HEzymes) concept, elaborate PdMoPtCoNi HEA nanowires (NWs) with abundant active sites and tuned electronic structures, exhibiting peroxidase‐mimicking activity comparable to that of natural horseradish peroxidase are reported. Density functional theory calculations demonstrate that the enhanced electron abundance of HEA NWs near the Fermi level (EF) is facilitated via the self‐complementation effect among the diverse transition metal sites, thereby boosting the electron transfer efficiency at the catalytic interface through the cocktail effect. Subsequently, the HEzymes are integrated with a portable electronic device that utilizes Internet of Things‐driven signal conversion and wireless transmission functions for point‐of‐care diagnosis to validate their applicability in digital biosensing of urinary biomarkers. The proposed HEzymes underscore significant potential in enhancing nanozymes catalysis through tunable electronic structures and synergistic effects, paving the way for reformative advancements in nano‐bio analysis. [ABSTRACT FROM AUTHOR]

Strong substrate affinity and high catalytic efficiency are persistently pursued to generate high‐performance nanozymes. Herein, with unique surface atomic configurations and distinct d‐orbital coupling features of different metal components, a class of highly efficient MnFeCoNiCu transition metal high‐entropy nanozymes (HEzymes) is prepared for the first time. Density functional theory calculations demonstrate that improved d‐orbital coupling between different metals increases the electron density near the Fermi energy level (EF) and shifts the position of the overall d‐band center with respect to EF, thereby boosting the efficiency of site‐to‐site electron transfer while also enhancing the adsorption of oxygen intermediates during catalysis. As such, the proposed HEzymes exhibit superior substrate affinities and catalytic efficiencies comparable to that of natural horseradish peroxidase (HRP). Finally, HEzymes with superb peroxidase (POD)‐like activity are used in biosensing and antibacterial applications. These results suggest that HEzymes have great potential as new‐generation nanozymes. [ABSTRACT FROM AUTHOR]

Pulmonary lobe segmentation is an important prerequisite for accurately quantifying pulmonary damage in many pulmonary diseases and planning treatment. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. In this study, a Learnable Interpolation and Extrapolation Network (LIE‐Net) is proposed to form complete and consecutive fissure surfaces by learning to extract information of the fissures from existing fissure points and absent points (unsegmented points belonging to fissures) to predict the z coordinate of the absent fissure points. The completed pulmonary fissures are further used for accurate pulmonary lobe segmentation. Specifically, LIE‐Net takes the coordinate information of existing fissure points (their (x, y, z) coordinates) and absent fissure points (their (x, y) coordinates) as two independent inputs, and predicts the z coordinates of absent points. The proposed LIE‐Net makes voxel‐wise predictions based on the spatial structure characteristics of the lung fissure, and is able to provide a consecutive fissure surface in space. According to the evaluation of radiologists, the lobe segmentation performance was remarkably enhanced in approximately 76% of patients in our additional dataset after the application of LIE‐Net, especially for those cases with large‐scale missing fissures. [ABSTRACT FROM AUTHOR]

Abstract Background Various aspects of genome organization have been explored based on data from distinct technologies, including histone modification ChIP-Seq, 3C, and its derivatives. Recently developed Hi-C techniques enable the genome wide mapping of DNA interactomes, thereby providing the opportunity to study genome organization in detail, but these methods also pose challenges in methodology development. Results We developed Genome Segmentation from Intra Chromosomal Associations, or GeSICA, to explore genome organization and applied the method to Hi-C data in human GM06990 and K562 cells. GeSICA calculates a simple logged ratio to efficiently segment the human genome into regions with two distinct states that correspond to rich and poor functional element states. Inside the rich regions, Markov Clustering was subsequently applied to segregate the regions into more detailed clusters. The binding sites of the insulator, cohesion, and transcription complexes are enriched in the boundaries between neighboring clusters, indicating that inferred clusters may have fine organizational features. Conclusions Our study presents a novel analysis method, known as GeSICA, which gives insight into genome organization based on Hi-C data. GeSICA is open source and freely available at: http://web.tongji.edu.cn/~zhanglab/GeSICA/

Motivation Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. Results We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. Availability and implementation Code can be downloaded from https://github.com/Zoesgithub/MtBNN. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding. [ABSTRACT FROM AUTHOR]

The new second generation sequencing technology revolutionizes many biology-related research fields and poses various computational biology challenges. One of them is transcriptome assembly based on RNA-Seq data, which aims at reconstructing all full-length mRNA transcripts simultaneously from millions of short reads. In this article, we consider three objectives in transcriptome assembly: the maximization of prediction accuracy, minimization of interpretation, and maximization of completeness. The first objective, the maximization of prediction accuracy, requires that the estimated expression levels based on assembled transcripts should be as close as possible to the observed ones for every expressed region of the genome. The minimization of interpretation follows the parsimony principle to seek as few transcripts in the prediction as possible. The third objective, the maximization of completeness, requires that the maximum number of mapped reads (or 'expressed segments' in gene models) be explained by (i.e., contained in) the predicted transcripts in the solution. Based on the above three objectives, we present IsoLasso, a new RNA-Seq based transcriptome assembly tool. IsoLasso is based on the well-known LASSO algorithm, a multivariate regression method designated to seek a balance between the maximization of prediction accuracy and the minimization of interpretation. By including some additional constraints in the quadratic program involved in LASSO, IsoLasso is able to make the set of assembled transcripts as complete as possible. Experiments on simulated and real RNA-Seq datasets show that IsoLasso achieves, simultaneously, higher sensitivity and precision than the state-of-art transcript assembly tools. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of the antioxidant capacity of foods is of great significance for estimating food's nutritional value and preventing oxidative changes in food. Herein, we demonstrated an easy and selective colorimetric method for the total antioxidant capacity (TAC) assay based on 3,3',5,5'-tetramethyl-benzidine (TMB), hydrogen peroxide (H2O2) and synthetic Lycium barbarum polysaccharide-iron (III) chelate (LBPIC) with high peroxidase (POD)-like activity. The results of steady-state kinetics study showed that the Km values of LBPIC toward H2O2 and TMB were 5.54 mM and 0.16 mM, respectively. The detection parameters were optimized, and the linear interval and limit of detection (LOD) were determined to be 2–100 μM and 1.51 μM, respectively. Additionally, a subsequent study of the determination of TAC in six commercial fruit and vegetable beverages using the established method was successfully carried out. The results implied an expanded application of polysaccharide-iron (III) chelates with enzymatic activity in food antioxidant analysis and other biosensing fields. [ABSTRACT FROM AUTHOR]

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network, and then, breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log-fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our tests on three widely used protein-protein interaction data sets and comparisons with several latest methods for protein complex identification demonstrate the strong performance of our method in predicting novel protein complexes in terms of its specificity and efficiency. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes. [ABSTRACT FROM PUBLISHER]

Background: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP)., Methods: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves., Results: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs . 0.71) and achieved human specialist-level performance., Conclusions: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-5328). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

The artificial intelligence based on medical aid diagnosis has been in full swing in these years. How to better and more safely utilize this new technology to improve the diagnostic efficiency and quality of doctors poses new challenges for our hospital management. This paper aims to explore relevant management problems and corresponding solutions from seven aspects:data security, system integration, technical parameters, risks, workflows and diagnosis results by introducing a new intelligent image screening system. After these management problems have been better solved, we found that the intelligent image screening system can improve the diagnostic efficiency and quality of doctors.

The organization of nucleosomes influences transcriptional activity by controlling accessibility of DNA binding proteins to the genome. Genome-wide nucleosome binding profiles have identified a canonical nucleosome organization at gene promoters, where arrays of well-positioned nucleosomes emanate from nucleosome-depleted regions. The mechanisms of formation and the function of canonical promoter nucleosome organization remain unclear. Here we analyze the genome-wide location of nucleosomes during zebrafish embryogenesis and show that well-positioned nucleosome arrays appear on thousands of promoters during the activation of the zygotic genome. The formation of canonical promoter nucleosome organization is independent of DNA sequence preference, transcriptional elongation, and robust RNA polymerase II (Pol II) binding. Instead, canonical promoter nucleosome organization correlates with the presence of histone H3 lysine 4 trimethylation (H3K4me3) and affects future transcriptional activation. These findings reveal that genome activation is central to the organization of nucleosome arrays during early embryogenesis.

Chromatin dynamics across cellular differentiation states is an emerging perspective from which the mechanism of global gene expression regulation may be better understood. While the roles of some histone marks have been partially interpreted in terms of their association with gene transcription, the dynamics of histone marks from a loci-specific perspective during cellular differentiation is not well studied. We established a method to systematically assess the histone modification variations of genes across various cellular differentiation states. We calculated the histone modification variation scores of H3K4me3, H3K27me3 and H3K36me3 for over 1300 curated transcription factors (TFs) during human blood cell differentiation. Hematopoietic-specific TFs (identified by literature mining) were significantly overrepresented by TFs with higher histone modification variation scores. Hierarchical clustering of all TFs based on the histone modification variation scores defined a group of TFs where known or potential hematopoietic-specific TFs were remarkably enriched. Our results suggest that local chromatin state dynamics of transcription factors across cellular differentiation states could imply cell lineage-specific functions. More importantly, our method can be applied to broader systems, holding the promise to discover de novo, lineage-specific TFs by interrogating their histone modification dynamics across cell lineages.

Model-based Analysis of ChIP-Seq (MACS) is a command-line tool designed by X. Shirley Liu and colleagues to analyze data generated by ChIP-Seq experiments in eukaryotes, especially mammals. MACS can be used to identify transcription factor binding sites and histone modification-enriched regions if the ChIP-Seq data, with or without control samples, are given. This unit describes two basic protocols that provide detailed information on how to use MACS to identify either the binding sites of a transcription factor or the enriched regions of a histone modification with broad peaks. Furthermore, the basic ideas for the MACS algorithm and its appropriate usage are discussed.

Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages of RNA-Seq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging. In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information. We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch. Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the state-of-the-art statistical method and a 60 times faster speed. Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS, and PAS information, especially for isoforms whose expression levels are significantly high. The software is publicly available for free at http://www.cs.ucr.edu/∼jianxing/IsoInfer.html.

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. Although it has been demonstrated in the literature that methods using protein-protein interaction data alone can successfully predict a large number of protein complexes, the incorporation of gene expression profiles could help refine the putative complexes and hence improve the accuracy of the computational methods. By combining protein-protein interaction data and microarray gene expression profiles, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our extensive tests on three widely used protein-protein interaction datasets and comparisons with the latest methods for protein complex identification demonstrate the superior performance of our method in terms of accuracy, efficiency, and capability in predicting novel protein complexes. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.