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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]

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]

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]

[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]

[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]

• 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]