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Introduction: As one of the major components of the tumor microenvironment, tumor-associated macrophages (TAMs) possess profound inhibitory activity against T cells and facilitate tumor escape from immune checkpoint blockade therapy. Converting this pro-tumorigenic toward the anti-tumorigenic phenotype thus is an important strategy for enhancing adaptive immunity against cancer. However, a plethora of mechanisms have been described for pro-tumorigenic differentiation in cancer, metabolic switches to program the anti-tumorigenic property of TAMs are elusive. Materials and methods: From an unbiased analysis of single-cell transcriptome data from multiple tumor models, we discovered that anti-tumorigenic TAMs uniquely express elevated levels of a specific fatty acid receptor, G-protein-coupled receptor 84 (GPR84). Genetic ablation of GPR84 in mice leads to impaired pro-inflammatory polarization of macrophages, while enhancing their anti-inflammatory phenotype. By contrast, GPR84 activation by its agonist, 6-n-octylaminouracil (6-OAU), potentiates pro-inflammatory phenotype via the enhanced STAT1 pathway. Moreover, 6-OAU treatment significantly retards tumor growth and increases the anti-tumor efficacy of anti-PD-1 therapy. Conclusion: Overall, we report a previously unappreciated fatty acid receptor, GPR84, that serves as an important metabolic sensing switch for orchestrating anti-tumorigenic macrophage polarization. Pharmacological agonists of GPR84 hold promise to reshape and reverse the immunosuppressive TME, and thereby restore responsiveness of cancer to overcome resistance to immune checkpoint blockade. [ABSTRACT FROM AUTHOR]

Deep learning analysis of single-cell data in empowering clinical implementation Keywords: deep learning; single cell; translational study EN deep learning single cell translational study 1 4 4 08/03/22 20220701 NES 220701 Recent advances in single-cell sequencing technologies enable the characterization of cellular heterogeneity and biological processes in complex diseases. Similar DL technologies may be used to detect circulating tumor cells (CTCs, isolated tumor cells entering the circulatory system of a patient with cancer), which are considered an effective tool for diagnosing malignancy. It enables multiple analysis, including batch effect removal, cell cluster prediction, gene imputation, and differentially expressed gene identification.10 scVI can identify CSC populations and determine what types of cells CSCs can differentiate into. [Extracted from the article]

Identifying cis -regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis -regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs. [ABSTRACT FROM AUTHOR]

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities. [ABSTRACT FROM AUTHOR]

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward. Advances in single-cell sequencing technologies open a window to understanding the heterogeneous regulatory landscape (HRL) encoded in complex diseases, by inferring various biological networks. The development of single-cell multimodal omics (scMulti-omics) technologies combined with scMulti-omics integration tools provides multimodal measurements of HRL. Application of HRL to complex diseases presents opportunities and poses challenges. Among the remaining challenges, establishing a robust benchmarking pipeline is paramount. In support of the integration of diverse single-cell modalities, bulk and single-cell omics, the deep learning and artificial intelligence (AI) omics become the major trends. [ABSTRACT FROM AUTHOR]

Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses. Applying integrative methods to scMulti-omics data opens a new window into the understanding of heterogeneous mechanism landscapes and cell–cell interactions. Integration of cross-experiment data poses a special challenge. A comprehensive understanding of the underlying methods is necessary to determine which pipeline is appropriate for a given scMulti-omics data set. We designed and implemented two case studies to demonstrate the application of available scMulti-omics tools, where new insights and practical challenges are generated. Among the numerous remaining challenges in scMulti-omics, establishing a robust benchmarking pipeline is paramount. Trends observed in traditional multi-omics, including machine learning, artificial intelligence, and evolving technologies, are paralleled in scMulti-omics methods. [ABSTRACT FROM AUTHOR]

Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs. [ABSTRACT FROM AUTHOR]

Motivation The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. Results We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli , Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. Availability and implementation The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Results Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. Availability and implementation The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency. [ABSTRACT FROM AUTHOR]

The aim of the study was to investigate the application of tumor abnormal protein (TAP) combined with transvaginal ultrasound in the diagnosis of early-stage endometrial cancer. A total of 248 patients with suspected endometrial cancer who were admitted to the Gynecology Department of the Second People's Hospital of Liaocheng from September 2013 to September 2015 were selected and randomly divided into the control (n=124) and the observation group (n=124). The control group received conventional ultrasound examination, while the observation, underwent TAP combined with conventional ultrasound examination. Differences in the definite diagnostic results of the two diagnostic methods and curettage were compared, and the application of TAP combined with transvaginal ultrasound in the diagnosis of early-stage endometrial cancer was studied. Among 248 patients receiving hysteroscopy and diagnostic curettage examination, there were 75 patients with early-stage endometrial cancer, and 173 benign patients. The total diagnostic accordance rate of conventional ultrasound for endometrial lesions was 87.90% (n=218), and the accordance rate for early-stage endometrial carcinoma was 90.67% (n=68); the total diagnostic accordance rate of TAP combined with vaginal ultrasound for endometrial lesions was 94.35% (n=234), and for early-stage endometrial cancer was 94.67% (n=71); of TAP combined with conventional ultrasound for endometrial lesions and endometrial cancer were higher than those of simple conventional ultrasound (P<0.05). The area under the curve (AUC) of conventional ultrasound in the diagnosis of endometrial cancer was 0.754 [95% confidence interval (CI): 0.211-2.534]. The AUC of TAP combined with vaginal ultrasound in the diagnosis of endometrial cancer was 0.814 (95% CI: 0.517-0.932), and a comparison between the two groups was statistically significant (P=0.011). The accuracy rate of TAP combined with transvaginal ultrasound in the diagnosis of early-stage endometrial cancer is relatively high, and it is worthy promoting and applying in clinical practice. [ABSTRACT FROM AUTHOR]

The gut and intratumoral microbiota significantly affect cancer development and progression by interacting with the host's immune system, that is, the immuno–oncology–microbiome (IOM). Available microbial data in IOM studies are mined from existing host bulk sequencing and single-cell sequencing datasets to provide unprecedented opportunities for investigating IOM in various cancer types. The development of rigorous computational methods for characterizing and elucidating IOM is urgently needed to guide researchers to unravel new biological insights and develop precision cancer therapeutics. A reliable benchmarking system is needed to model IOM interactions, analyze IOM data, and evaluate computational predictions. In-depth functional analyses of IOM mechanisms are necessary to develop novel therapeutic strategies targeting microbiota to improve cancer treatment outcomes. The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno–oncology–microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research. [ABSTRACT FROM AUTHOR]

The mechanochemical S/FeS modified high sulfur petroleum coke (HSPC) were prepared in this work. The mercury removal performance of the adsorbents was evaluated in a simulated flue gas fixed bed experimental device. The Brunauer-Emmett-Teller (BET), Fourier transform infrared spectrometer (FTIR), X-ray diffractometer (XRD), X-ray photoelectron spectrometer (XPS), Hg temperature-programmed desorption (Hg-TPD) and thermogravimetric (TG) were used to obtain the physical and chemical properties of some typical samples. It shows that the mercury removal efficiency (MRE) of mechanochemical S/FeS modified HSPC (MC-S/FeS-PC) increases with increasing the S or FeS addition amount under the same mechanical conditions (600/300 rpm, 60 min), where S is superior to FeS. The mass ratio of carbon to sulfur (RCS) of 4.5 for the MC-S-PC is the optimal sulfur addition amount with the MRE of 98.6%. Mechanochemical S/FeS modification is conducive to the development pore structure of raw HSPC with certain effects. S is the main sulfur form on the surface of MC-S-PC, while thiophene, FeS, and Fe 2 (SO 4) 3 are the main sulfur forms of MC-FeS-PC. The mercury compound on used adsorbents is mainly HgS (black). Moreover, another immature mechanism hypothesis about the mercury removal by MC-S-PC is proposed. [Display omitted] • Mechanochemical S/FeS modified high sulfur petroleum coke (MC-S/FeS-PC) were prepared. • Mercury removal efficiency (MRE) of MC-S/FeS-PC increases with increasing S or FeS addition amount. • RCS of 4.5 for the MC-S-PC is selected as the optimal sulfur addition amount with MRE of 98.6%. • Modification mechanism and mercury removal mechanism of MC-S/FeS-PC were deeply explored. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effects of activation parameters on demercuration of PPC and RPC/PPC-M were studied. • Final pyrolysis temperature and medium and small balls benefit for pore structure development. • Pyrolysis and mechanochemical activation cause changes in surface S total and sulfur forms. • Sulfone acts avital role in mercury removal of petroleum coke-based adsorbents. • Needing additional additives to further improve demercuration ability of PC-based adsorbents. With the signing of the Minamata Convention , the task of reducing mercury emission from coal-fired power plants is imminent. High-sulfur petroleum coke (PC), a by-product of the petrochemical industry, has the potential to prepare high-performance mercury removal adsorbents. The effects of activation parameters of pyrolysis, mechanochemistry, pyrolysis + mechanochemistry on the mercury removal ability of activated PC were systematically studied in this work. The TG, BET, FTIR, XPS and Hg-TPD were used to characterize physical and chemical properties, and adsorbed mercury compounds of some selected samples. The results show that pyrolysis, mechanochemistry, pyrolysis + mechanochemistry can enhance the mercury capture performance of Raw PC (RPC). PPC10-60–900, RPC-M-15 M, and PPC-M-15B have the best mercury removal rate of 29.15%, 25.68%, and 27.86%, respectively. Influence of pyrolysis parameters, mechanochemical activation parameters (R bm , R ms) on mercury removal of activated RPC or pyrolyzed petroleum coke (PPC) are not regular, while final pyrolysis temperature plays a vital role on mercury capture for the PPC. At the fixed R bm , mechanochemical activation with R ms = 1.08 g/cm2 (M) and 0.62 g/cm2(S) benefits for the pore structure improvement of RPC and PPC. The pyrolysis will make the C–H, C-O, C-O-C, and C = O disappear from PC, while mechanochemical activation almost has no effect on it. The conversion of sulfur forms occurs in both the pyrolysis and mechanochemical activation process. Sulfone acts an important role in the mercury removal of PC-based adsorbents. The mercury captured on the PPC, mechanochemically activated RPC (RPC-M) and PPC (PPC-M) mainly exists in the more stable form of HgS. Additional modifiers are needed to further improve the mercury removal performance of the PC-based adsorbent to facilitate its large-scale industrial application. [ABSTRACT FROM AUTHOR]

• Mercury removal from coal-fired flue gas by PC-based adsorbent is reviewed. • Physicochemical and demercuration properties of modified PC are discussed. • Mechanochemically modified PC is a promising mercury removal adsorbent. • Optimal modification parameters for each PC-based adsorbent need further study. Mercury pollution from coal combustion has attracted widespread attention. Petroleum coke (PC), a by-product from petrochemical industry, is a potential substance to replace activated carbon mercury removal adsorbent. Most of the published articles on PC-based mercury removal adsorbent are reviewed in this work. It shows raw PC has little mercury removal ability (5%). Pyrolysis can increase mercury removal ability of PC with limited improvement effect (below 60%). KOH activation benefits for pore structure development but results in sulfur removal. SO 2 activation improve surface pore structure and sulfur content of the PC, which can realize Hg0 capture rate higher than 90%. The mercury species on the used SO 2 impregnated PC are mainly HgS black and HgS red. The mechanochemical liquid bromine modified PC can achieve very high mercury removal efficiency of above 98%. However, the effects of pyrolysis, SO 2 activation, and mechanochemical modification parameters on the surface physical–chemical properties especially sulfur forms of PC, as well as the modification mechanism and mercury removal mechanism need to be further studied. Mechanochemically modified PC is a promising mercury removal adsorbent. This work hopes to provide guidance for the development of high-performance PC-based mercury removal adsorbents and its industrial application. [ABSTRACT FROM AUTHOR]

Motivation Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell–cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. Results The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L 1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell–cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. Availability and implementation scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Overlapping structures of protein–protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein–protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well. [ABSTRACT FROM AUTHOR]

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughly investigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequence data, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models. A comprehensive understanding of heterogeneous tumor subpopulations will benefit drug sensitivity prediction and combination drug treatment design. Deep learning models are powerful and extensively used in drug sensitivity prediction and in inferring drug–target interactions. Single-cell sequencing techniques offer precise and accurate profiling of tumor subpopulations and reveal subtle differences in their response to drug treatments. Applying deep transfer learning to predict drug sensitivity allows us to not only take advantage of prior knowledge obtained from massive bulk sequencing data but also utilize the heterogeneous landscapes generated by single-cell sequencing techniques. The integration of single-cell multi-omic data for drug sensitivity prediction using transfer learning methods poses a special challenge. [ABSTRACT FROM AUTHOR]

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed. [ABSTRACT FROM AUTHOR]

Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Cervical cancer is a common gynecological malignancy. Hiwi exhibits a high level of expression in cervical cancer cells. However, the effects of Hiwi expression in cervical cancer cells remain unresolved. In the present study, the effects of Hiwi downregulation on the growth and epithelial‑mesenchymal transition of cervical cancer cells were investigated. The results of the present study revealed that the suppression of Hiwi was able to inhibit the proliferation of cervical cancer cells and arrest cell cycle at G1 phase. The downregulation of Hiwi was also revealed to inhibit the epithelial‑mesenchymal transition process of cervical cancer cells by regulating the expression of E‑cadherin, N‑cadherin, vimentin, and snail. The present study demonstrated that the suppression of Hiwi was able to inhibit the growth and epithelial‑mesenchymal transition of cervical cancer cells. Therefore, the results suggest that Hiwi may function as an oncogene in cervical cancer cells and may become a potential target for cervical cancer therapy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effects of O 2 , CO 2 , SO 2 and NO on the Hg0 removal characteristics of S 2 Cl 2 -MC were revealed. • Hg0 removal characteristics were discussed by Hg0 removal efficiency, Hg2+ escape ratio, and Hg0 adsorption amount. • O 2 + CO 2 , 1200 ppm SO 2 , NO promote Hg0 removal while O 2 and low-concentration SO 2 have little effect. • Adsorbent surface structure plays an important role in the Hg0 adsorption. The effects of flue gas components (O 2 , CO 2 , SO 2 and NO) on the Hg0 removal of mechanochemical S 2 Cl 2 -modified BC (S 2 Cl 2 -MC) were explored based on the Hg0 removal efficiency/amount, Hg0 adsorption amount and Hg2+ escape amount/ratio. The BET, FTIR, XPS, and Hg-TPD were used to obtain relevant physicochemical properties. It shows O 2 and CO 2 promote the Hg2+ escape but inhibit Hg0 adsorption. O 2 slightly affects Hg0 removal, while CO 2 promotes it. 400 ∼ 800 ppm SO 2 slightly affect Hg0 removal, while 1200 ppm SO 2 facilitates Hg2+ escape and Hg0 adsorption. SO 2 affects Hg2+ escape ratio little. As NO concentration increases, the Hg2+ escape is promoted while Hg0 adsorption is inhibited. O 2 and CO 2 do not change the mercury forms. SO 2 and NO promote the production of HgSO 4 and Hg(NO 3) 2 , respectively. The Hg0 adsorption might mainly depend on the physical properties of adsorbents. [ABSTRACT FROM AUTHOR]

Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptide secondary structures and the exploration of downstream tasks. The framework includes a novel interpretable deep hypergraph multi‐head attention network that uses residue‐based reasoning for structure prediction. The algorithm can incorporate sequential semantic information from large‐scale biological corpus and structural semantic information from multi‐scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. The interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. The importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis is further demonstrated, highlighting the versatility of our models. To facilitate the use of the model, an online server is established which is accessible via http://inner.wei‐group.net/PHAT/. The work is expected to assist in the design of functional peptides and contribute to the advancement of structural biology research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Preparation conditions of mechanochemical S 2 Cl 2 -modified biomass coke based on mercury removal were optimized. • BC-10B-8S 2 Cl 2 -MC has the best Hg0 removal rate of 97.12%. • Thiophen/element sulfur and sulfoxide benefit for mercury removal. • Mechanochemical method has more application prospects than impregnation method. Mercury is a global pollutant. In this work, wood sawdust-based biomass coke and S 2 Cl 2 were selected as the raw material and modifier, respectively. Two modification methods were adopted to prepare the mechanochemical S 2 Cl 2 -modified biomass coke (BC-S 2 Cl 2 -MC) and S 2 Cl 2 impregnated-modified biomass coke (BC-S 2 Cl 2 -IM). The flue gas fixed mercury-removal experiment rig was used to evaluated the mercury removal performance of the adsorbents. The Brunauer-Emmett-Teller (BET), scanning electron microscope (SEM), Fourier transform infrared spectrometer (FTIR), and X-ray photoelectron spectroscopy (XPS) were adopted to characterize the surface physical and chemical properties of some typical samples. Hg temperature-programmed desorption (Hg-TPD) was used to analyze the main mercury forms in the used adsorbents. The results show both mechanochemistry and impregnation methods can significantly improve Hg0 removal performance with the addition of S 2 Cl 2. Increasing S 2 Cl 2 addition amount increases the Hg0 removal efficiency of BC-S 2 Cl 2 -MC overall. Considering the economy and mercury removal performance, 800 μL is the optimum addition amount for the 3 g raw biomass coke with the Hg0 removal rate of 91.94%. The change of ball milling parameters within a certain range has little effect on the mercury removal performance of BC-S 2 Cl 2 -MC. The best ball milling parameters in this work are a ball-to-material ratio of 10:1 and big balls (diameter = 20 mm), which corresponds to the Hg0 removal rate of 97.12%. Chemical adsorption plays a great part in the Hg0 removal of BC-S 2 Cl 2 -MC and BC-8S 2 Cl 2 -IM. The surface functional groups such as O–H, C = O, C = S and thiophen/element sulfur and sulfoxide are the main Hg0 adsorption active sites. HgS (black) is the main mercury form in the used BC-S 2 Cl 2 -MC and used BC-S 2 Cl 2 -IM. The mechanochemical method has more prospects for industrial application than the impregnation method. [ABSTRACT FROM AUTHOR]

• The pyrolysis temperature and atmosphere have great influence on Hg0 removal of BC. • N 2 + 7% O 2 , higher pyrolysis temperature and heating rate develop the pore structure. • BC-N 2 + 7% O 2 -800-10 has the best Hg0 removal rate of 34.89% under all the pyrolysis conditions. • Hg0 removal of BC mainly by chemical adsorption with main content of HgO. Effects of pyrolysis conditions, including pyrolysis temperatures, pyrolysis atmosphere, and heating rate, on Hg0 removal of wood chip-based biomass coke (BC) under the basic simulated flue gas were systematically studied. The BET, SEM, FTIR, XPS and Hg-TPD were used to analyze the physicochemical properties and specific mercury compounds. The results show that the final pyrolysis temperature and pyrolysis atmosphere have greater influence on the mercury removal performance while heating rate affects little. The BC-N 2 + 7%O 2 -800-10 has the best Hg0 removal efficiency under all the pyrolysis conditions, mainly due to the good pore structure and abundant surface oxygen-containing functional group. N 2 + 7% O 2 , higher pyrolysis temperature and heating rate benefit for BC's pore structure development. Oxygen-containing functional groups of O H, C O and C O O are the main functional groups on the BC surface, which promotes Hg0 adsorption. The physically adsorbed Hg0 and more chemically adsorbed HgO are the main mercury compounds in the used BC. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mechanochemical sulfur modified biomass coke was firstly prepared. • Hg0 removal follows BC-4-S 2 Cl 2 -MC > BC-CaHS-MC > BC-ES-MC > BC-NaHS-MC > BC-Na 2 S-MC > R-BC. • BC-S 2 Cl 2 -MC with 800 μL S 2 Cl 2 has the best Hg0 removal rate of 96.17%. • Sulfoxide benefits for mercury removal among all the sulfur forms. • Mercury forms in the used MC-organic sulfur-BC are HgS with good stability. Coal-fired power plants are the main anthropogenic source of mercury emissions in the atmosphere. In this work, the raw material of biomass (wood chips), modifiers of sulfur-containing substances (Na 2 S, NaHS, S, CaHS, S 2 Cl 2), and mechanochemical method were adopted to prepare the mechanochemical sulfur-modified biomass coke (BC-S-MC). The effects of sulfur addition types and sulfur addition amounts on mercury removal performance of BC-S-MC were investigated by the mercury removal experimental device. The modification mechanism and mercury removal mechanism was explored with the help of BET, XPS, FTIR, and Hg-TPD. The results show that the mercury removal efficiency of BC-S-MC under the same sulfur addition amount meets BC-4-S 2 Cl 2 -MC > BC-CaHS-MC > BC-ES-MC > BC-NaHS-MC > BC-Na 2 S-MC > R-BC. As S 2 Cl 2 addition amount increases, the mercury removal rate of BC-S 2 Cl 2 -MC firstly increases and then decreases with best value of 96.17%. Mechanochemical sulfur modification severely destroys the pore structure of R-BC, but affects the surface chemistry properties like sulfur forms, C = O, C = S, O–H, etc. Sulfoxide plays an important role in improving the mercury removal ability. The mercury forms in the used mechanochemical organic sulfur modified biomass coke are mainly HgS (black) and HgS (red), which have good stability. [ABSTRACT FROM AUTHOR]

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. Methods: Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. Results: Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8+ T cells, CD4+ T cells, and CD56+ natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. Conclusions: Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19. [ABSTRACT FROM AUTHOR]

Motivation Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. Results We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. Availability and implementation MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) is a severe disease of wheat (Triticum aestivum L.). Qfhb1 is the most important quantitative trait locus (QTL) for FHB resistance. We previously identified wheat gene WFhb1-1 (aka WFhb1-c1) as a candidate for FHB resistance gene. Here we report that WFhb1-1 has been cloned. The gene (GenBank # KU304333.1) consists of a single exon, encoding a putative membrane protein of 127 amino acids. WFhb1-1 protein produced in Pichia pastoris inhibits growth of both F. graminearum and P. pastoris in culture. Western Blotting with anti- WFhb1-1 antibody revealed a significant decrease (p < 0.01) in WFhb1-1 accumulation, 12 hours post Fusarium inoculation in non-Qfhb1-carrier wheat but not in Qfhb1-carrier wheat. Overexpressing WFhb1-1 in non-Qfhb1-carrier wheat led to a significant decrease (p < 0.01) in Fusarium-damaged rachis rate, Fusarium-diseased kernel rate and DON content in harvested kernels, while silencing WFhb1-1 in Qfhb1-carrier wheat resulted in a significant increase (p < 0.01) in FHB severity. Therefore, WFhb1-1 is an important FHB resistance gene with a potential antifungal function and probably a key functional component of Qfhb1 in wheat. A model regarding how WFhb1-1 functions in FHB resistance/susceptibility is hypothesized and discussed. [ABSTRACT FROM AUTHOR]

Motivation Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design. Results We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g -gap dipeptide composition (⁠ g -gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases. Availability and implementation The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

• A novel method (GcForest-PPI) to predict protein–protein interactions. • The PseAAC, AD, MMI, CTD, AAC-PSSM and DPC-PSSM are fused to extract feature information. • The elastic net is employed to eliminate redundant and irrelevant features. • We firstly use deep forest as classifier to predict PPIs via layer-by-layer processing of raw features. • GcForest-PPI model has good generalization ability on cross-species datasets and PPIs network. Prediction of protein–protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. Firstly, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), amino acid composition position-specific scoring matrix (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, we ensemble XGBoost, random forest, and extremely randomized trees to construct deep forest model via cascade architecture for PPIs prediction (GcForest-PPI). Benchmark experiments reveal that the proposed approach outperforms other state-of-the-art predictors on Saccharomyces cerevisiae and Helicobacter pylori. We also apply GcForest-PPI on independent test sets, CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. [ABSTRACT FROM AUTHOR]

Lysine malonylation is one of the important post-translational modification (PTM) of proteins. Malonylated proteins can affect various cell functions of eukaryotes and prokaryotes, which play an important role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life activities. However, accurate identification of the malonylation sites is the key to further understand the molecular mechanism of malonylation. Currently, experimental identification is still a challenging task, which usually requires a large amount of laboratory work and considerable cost. Regarding this situation, there is an urgent need to establish useful calculation methods and develop effective predictors. We propose a new deep learning network model called DeepMal. Firstly, features are extracted by enhanced amino acid composition (EAAC), enhanced grouped amino acid composition (EGAAC), dipeptide deviation from the expected mean (DDE), K nearest neighbors (KNN) and BLOSUM62 matrix. Secondly, the linear convolutional neural network is used to extract the specific features of malonylation sites, select the relevant features and reduce the feature dimension through maximum pooling. Finally, malonylation sites and non-malonylation sites are classified by a multilayer neural network. And an independent dataset is used to assess the predictive ability of the model DeepMal. On the independent datasets Escherichia coli (E. coli), Homo sapiens (H. sapiens), Mus musculus (M. musculus), the AUC values are 0.974, 0.956 and 0.944, and the accuracies are 96.5%, 95.5% and 94.5%, respectively. Compared with other prediction models, the prediction accuracy is increased by 9.5%–18.5%, and this indicates the effectiveness of the prediction model DeepMal. Using deep learning network can enhance the robustness of DeepMal model for predicting malonylation sites. The source codes and all datasets are publicly available at https://github.com/QUST-AIBBDRC/DeepMal/. • A novel method (DeepMal) to predict the lysine malonylation sites of proteins. • Protein sequence information is extracted from sequence features, physicochemical properties, and evolutionary features. • Convolutional neural networks can learn to extract significant features associated with malonylation sites. • Deep neural networks can improve the accurate prediction of protein lysine malonylation sites of proteins. • The proposed method increases the prediction performance on independent test datasets compared with other methods. [ABSTRACT FROM AUTHOR]

As a reversible and widely existing post-translational modification of proteins, acetylation plays a crucial role in transcriptional regulation, apoptosis, and cytokine signaling. To better understand the molecular mechanism of acetylation, identification of acetylation sites is vital. The traditional experimental methods are time-consuming and cost-prohibitive, and the majority of acetylation sites remain unknown. It is necessary to develpoe an effective and accurate computational approach to predict the acetylation sites, especially utilizing the advanced deep learning technique. In this study, we propose a prokaryote acetylation sites prediction method based on the deep neural networks, named DNNAce. We extract the protein features from sequence information, physicochemical information, and evolution information of amino acid residues and obtain the initial feature set All. Moreover, we use Group Lasso to remove the irrelevant features that are not effective for classification in the sites prediction area. Compared to other machine learning methods, we use deep neural networks to predict acetylation sites. The 10-fold cross-validation on independent test datasets indicates that DNNAce has the highest values of accuracy for predicting acetylation sites. That DNNAce accurately identifies acetylation sites assists us in understanding its molecular mechanism, and provides related theoretical foundation for the development of drug targets for various diseases. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/DNNAce/. • A novel method (DNNAce) to predict the prokaryote lysine acetylation sites in nine datasets. • The protein sequences are encoded by combining sequence information, physicochemical information and evolution information. • Group Lasso method remove the redundant and irrelevant feature to select the optimal feature subset for the first time. • Compared with other classifiers, DNNAce can extract abstract features from protein sequence through the hidden layers. • The proposed method increases the prediction performance on independent test datasets compared with other tools. [ABSTRACT FROM AUTHOR]