TransPTM: a transformer-based model for non-histone acetylation site prediction.

Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been developed, there is a lack of benchmark dataset and bespoke predictors for non-histone acetylation site prediction. To address these problems, we have contributed to both dataset creation and predictor benchmark in this study. First, we construct a non-histone acetylation site benchmark dataset, namely NHAC, which includes 11 subsets according to the sequence length ranging from 11 to 61 amino acids. There are totally 886 positive samples and 4707 negative samples for each sequence length. Secondly, we propose TransPTM, a transformer-based neural network model for non-histone acetylation site predication. During the data representation phase, per-residue contextualized embeddings are extracted using ProtT5 (an existing pre-trained protein language model). This is followed by the implementation of a graph neural network framework, which consists of three TransformerConv layers for feature extraction and a multilayer perceptron module for classification. The benchmark results reflect that TransPTM has the competitive performance for non-histone acetylation site prediction over three state-of-the-art tools. It improves our comprehension on the PTM mechanism and provides a theoretical basis for developing drug targets for diseases. Moreover, the created PTM datasets fills the gap in non-histone acetylation site datasets and is beneficial to the related communities. The related source code and data utilized by TransPTM are accessible at https://www.github.com/TransPTM/TransPTM.

DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.

Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7 k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.

MOTIVATION: Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. RESULTS: In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. AVAILABILITY AND IMPLEMENTATION: The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs.

The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e. heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25 000 inferentially effective heterodimeric TFs in the human cells. It is not practically feasible to validate all heterodimeric motifs due to cost and labor. We introduce DeepMotifSyn, a deep learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrate that DeepMotifSyn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe DeepMotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis. Contact:kc.w@cityu.edu.hk.

CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation.

MOTIVATION: The identification of drug-target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug-target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors. RESULTS: Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights. AVAILABILITY: Source code are publicly available at https://github.com/Layne-Huang/CoaDTI.

Synthesis of WS42- Intercalated NiZnAl LDHs as Effective Adsorbents to Remove Copper Ions from Water.

A series of WS42- intercalated NiZnAl ternary-layered double-hydroxides (LDHs) with various Ni/Zn ratios were synthesized by an ion-exchange method and used as adsorbents to remove Cu2+ from water. The introduction of Zn produced ZnS on the surface of LDHs. The LDH with the Ni/Zn/Al molar ratio of 0.1/1.9/1 showed the best adsorption ability. Cu2+ ions are removed via three routes: forming [Cu-WS4]n- complexes via soft acid-soft base interaction between WS42- and Cu2+, isomorphic substitution of Zn2+ in sheets by Cu2+, and cation exchange of Cu2+, with ZnS on the surface of LDHs. With the increased Cu2+ concentration, the complexes dominated the adsorption because polynuclear [Cu-WS4]n- complexes with high Cu/W ratios (2-6) may be formed. Cu+ is present in such complexes, which is produced by the internal redox. Even at Cu2+ concentration up to 600 mg·L-1, neither amorphous CuWS4 nor decreased interlayer distance was observed. Contrarily, the interlayer distance was slightly enlarged due to forming bigger [Cu-WS4]n- complexes. The adsorption followed the pseudo-second-order kinetics and Langmuir isotherm model. The experimental maximum adsorption capacity reached 555.4 mg·g-1.

Controlled and Regioselective Ring-Opening Polymerization for Poly(disulfide)s by Anion-Binding Catalysis.

Poly(disulfide)s are an emerging class of sulfur-containing polymers with applications in medicine, energy, and functional materials. However, the constituent dynamic covalent S-S bond is highly reactive in the presence of the sulfide (RS-) anion, imposing a persistent challenge to control the polymerization. Here, we report an anion-binding approach to arrest the high reactivity of the RS- chain end to control the synthesis of linear poly(disulfide)s, realizing a rapid, living ring-opening polymerization of 1,2-dithiolanes with narrow dispersity and high regioselectivity (Mw/Mn ∼ 1.1, Ps ∼ 0.85). Mechanistic studies support the formation of a thiourea-base-sulfide ternary complex as the catalytically active species during the chain propagation. Theoretical analyses reveal a synergistic catalytic model where the catalyst preorganizes the protonated base and anionic chain end to establish spatial confinement over the bound monomer, effecting the observed regioselectivity. The catalytic system is amenable to monomers with various functional groups, and semicrystalline polymers are also obtained from lipoic acid derivatives by enhancing the regioselectivity.

Human host status inference from temporal microbiome changes via recurrent neural networks.

With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the host status is predicted from longitudinal microbiome data. However, single-point-based methods cannot capture the dynamic patterns between the temporal changes and host status. Therefore, it remains challenging to build good predictive models as well as scaling to different microbiome contexts. On the other hand, existing methods are mainly targeted for disease prediction and seldom investigate other host statuses. To fill the gap, we propose a comprehensive deep learning-based framework that utilizes longitudinal microbiome data as input to infer the human host status. Specifically, the framework is composed of specific data preparation strategies and a recurrent neural network tailored for longitudinal microbiome data. In experiments, we evaluated the proposed method on both semi-synthetic and real datasets based on different sequencing technologies and metagenomic contexts. The results indicate that our method achieves robust performance compared to other baseline and state-of-the-art classifiers and provides a significant reduction in prediction time.

Early cancer detection from genome-wide cell-free DNA fragmentation via shuffled frog leaping algorithm and support vector machine.

MOTIVATION: Early cancer detection is significant for patient mortality rate reduction. Although machine learning has been widely employed in that context, there are still deficiencies. In this work, we studied different machine learning algorithms for early cancer detection and proposed an Adaptive Support Vector Machine (ASVM) method by synergizing Shuffled Frog Leaping Algorithm and Support Vector Machine (SVM) in this study. RESULTS: Since ASVM regulates SVM for parameter adaption based on data characteristics, the experimental results reflected the robust generalization capability of ASVM on different datasets under different settings; for instance, ASVM can enhance the sensitivity by over 10% for early cancer detection compared with SVM. Besides, our proposed ASVM outperformed Grid Search + SVM and Random Search + SVM by significant margins in terms of the area under the ROC curve (AUC) (0.938 versus 0.922 versus 0.921). AVAILABILITY AND IMPLEMENTATION: The proposed algorithm and dataset are available at https://github.com/ElaineLIU-920/ASVM-for-Early-Cancer-Detection. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome.

The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time-consuming for early-stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high-throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell-free DNA and microbiome data generated by amplicon sequencing or whole-genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.

Impaired dopamine D1 receptor-mediated vasorelaxation of mesenteric arteries in obese Zucker rats.

BACKGROUND: Obesity plays an important role in the pathogenesis of hypertension. Renal dopamine D1-like receptor-mediated diuresis and natriuresis are impaired in the obese Zucker rat, an obesity-related hypertensive rat model. The role of arterial D1 receptors in the hypertension of obese Zucker rats is not clear. METHODS: Plasma glucose and insulin concentrations and blood pressure were measured. The vasodilatory response of isolated mesenteric arteries was evaluated using a small vessel myograph. The expression and phosphorylation of D1 receptors were quantified by co-immunoprecipitation and immunoblotting To determine the effect of hyperinsulinemia and hyperglycemia on the function of the arterial D1 receptor, we studied obese Zucker rats (six to eight-weeks old) fed (6 weeks) vehicle or rosiglitazone, an insulin sensitizer (10 mg/kg per day) and lean Zucker rats (eight to ten-weeks old), fed high-fat diet to induce hyperinsulinemia or injected intraperitoneally with streptomycin (STZ) to induce hyperglycemia. RESULTS: In obese Zucker rats, the vasorelaxant effect of D1-like receptors was impaired that could be ascribed to decreased arterial D1 receptor expression and increased D1 receptor phosphorylation. In these obese rats, rosiglitazone normalized the arterial D1 receptor expression and phosphorylation and improved the D1-like receptor-mediated vasorelaxation. We also found that D1 receptor-dependent vasorelaxation was decreased in lean Zucker rats with hyperinsulinemia or hyperglycemia but the D1 receptor dysfunction was greater in the former than in the latter group. The ability of insulin and glucose to decrease D1 receptor expression and increase its phosphorylation were confirmed in studies of rat aortic smooth muscle cells. CONCLUSIONS: Both hyperinsulinemia and hyperglycemia caused D1 receptor dysfunction by decreasing arterial D1 receptor expression and increasing D1 receptor phosphorylation. Impaired D1 receptor-mediated vasorelaxation is involved in the pathogenesis of obesity-related hypertension.

Comparison of complications of the suprameatal approach and mastoidectomy with posterior tympanotomy approach in cochlear implantation: a meta-analysis.

BACKGROUND: Cochlear implantation (CI) is a popular procedure to preserve hearing in patients with severe-to-profound hearing loss. Evidence shows that the suprameatal approach (SMA) may help reducing the risk of the incidence of complications and shortening the surgery time, but there is still dispute. OBJECTIVES: The aim of this study was to compare the incidence of complications of SMA and the mastoidectomy with posterior tympanotomy approach (MPTA), and to find whether SMA yields better outcomes than MPTA. METHODS: We searched PubMed, the Cochrane Library, the Web of Science and Chinese Biomedical Literature databases, Chinese National Knowledge Infrastructure, the Chinese Science and Technology Journal Full-Text database, and Wangfang database. The latest data was accessed in March 2013. Review Manager 5.1 software was used for comprehensive quantification data analysis. RESULTS: Three studies were included in the meta-analysis, composed of 799 participants and reporting major and minor complications. The meta-analysis indicated no statistically significant difference in major and minor complications between the two approaches, except for facial nerve and chorda tympani injuries (OR = 0.13; 95% CI: 0.02, 0.67; p = 0.02; I(2) = 0%). CONCLUSIONS: Current evidence suggests that SMA may be clearly a good alternative to the classical surgery technique for CI in terms of reducing the incidence of facial nerve injury and chorda tympani sacrifice.

Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal.

The employment of surface electromyographic (sEMG) signals in the estimation of hand kinematics represents a promising non-invasive methodology for the advancement of human-machine interfaces. However, the limitations of existing subject-specific methods are obvious as they confine the application to individual models that are custom-tailored for specific subjects, thereby reducing the potential for broader applicability. In addition, current cross-subject methods are challenged in their ability to simultaneously cater to the needs of both new and existing users effectively. To overcome these challenges, we propose the Cross-Subject Lifelong Network (CSLN). CSLN incorporates a novel lifelong learning approach, maintaining the patterns of sEMG signals across a varied user population and across different temporal scales. Our method enhances the generalization of acquired patterns, making it applicable to various individuals and temporal contexts. Our experimental investigations, encompassing both joint and sequential training approaches, demonstrate that the CSLN model not only attains enhanced performance in cross-subject scenarios but also effectively addresses the issue of catastrophic forgetting, thereby augmenting training efficacy.

scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.

Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by transformer-based engine. Results illustrated scGREAT outperformed other contemporary methods on benchmarks. Besides, gene representations from scGREAT provide valuable gene regulation insights, and external validation on spatial transcriptomics illuminated the mechanism behind scGREAT annotation. Moreover, scGREAT identified several TF target regulations corroborated in studies.

MotifHub: Detection of trans-acting DNA motif group with probabilistic modeling algorithm.

BACKGROUND: Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting elements and regulate the transcription efficiency of target genes. The progressive development in high-throughput chromatin capture technology (e.g., Hi-C) enables the identification of chromatin-interacting sequence groups where trans-acting DNA motif groups can be discovered. The problem difficulty lies in the combinatorial nature of DNA sequence pattern matching and its underlying sequence pattern search space. METHOD: Here, we propose to develop MotifHub for trans-acting DNA motif group discovery on grouped sequences. Specifically, the main approach is to develop probabilistic modeling for accommodating the stochastic nature of DNA motif patterns. RESULTS: Based on the modeling, we develop global sampling techniques based on EM and Gibbs sampling to address the global optimization challenge for model fitting with latent variables. The results reflect that our proposed approaches demonstrate promising performance with linear time complexities. CONCLUSION: MotifHub is a novel algorithm considering the identification of both DNA co-binding motif groups and trans-acting TFs. Our study paves the way for identifying hub TFs of stem cell development (OCT4 and SOX2) and determining potential therapeutic targets of prostate cancer (FOXA1 and MYC). To ensure scientific reproducibility and long-term impact, its matrix-algebra-optimized source code is released at http://bioinfo.cs.cityu.edu.hk/MotifHub.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets.

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning.

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

[Extranodal NK/T cell lymphoma, nasal type involving the larynx and digestive tract: a case report and literature review].

Extranodal NK/T cell lymphoma, nasal type（ENKTL） is a highly aggressive malignant tumor derived from NK cells. This article reports a case of ENKTL invading the larynx and digestive tract. The clinical clinical manifestations include hoarseness and intranasal masses.

A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals.

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following µ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects' number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods.

The effects of nano-silver loaded zirconium phosphate on antibacterial properties, mechanical properties and biosafety of room temperature curing PMMA materials.

Polymethyl methacrylate (PMMA) frequently features in dental restorative materials due to its favorable properties. However, its surface exhibits a propensity for bacterial colonization, and the material can fracture under masticatory pressure. This study incorporated commercially available RHA-1F-II nano-silver loaded zirconium phosphate (Ag-ZrP) into room-temperature cured PMMA at varying mass fractions. Various methods were employed to characterize Ag-ZrP. Subsequently, an examination of the effects of Ag-ZrP on the antimicrobial properties, biosafety, and mechanical properties of PMMA materials was conducted. The results indicated that the antibacterial rate against Streptococcus mutans was enhanced at Ag-ZrP additions of 0%wt, 0.5%wt, 1.0%wt, 1.5%wt, 2.0%wt, 2.5%wt, and 3.0%wt, achieving respective rates of 53.53%, 67.08%, 83.23%, 93.38%, 95.85%, and 98.00%. Similarly, the antibacterial rate against Escherichia coli registered at 31.62%, 50.14%, 64.00%, 75.09%, 86.30%, 92.98%. When Ag-ZrP was introduced at amounts ranging from 1.0% to 1.5%, PMMA materials exhibited peak mechanical properties. However, mechanical strength diminished beyond additions of 2.5%wt to 3.0%wt, relative to the 0%wt group, while PMMA demonstrated no notable cytotoxicity below a 3.0%wt dosage. Thus, it is inferred that optimal antimicrobial and mechanical properties of PMMA materials are achieved with nano-Ag-ZrP (RHA-1F-II) additions of 1.5%wt to 2.0%wt, without eliciting cytotoxicity.