Self-supervised learning has shown superior performance on graph-related tasks in recent years. The most advanced methods are based on contrast learning, which severely limited by structured data augmentation techniques and complex training methods. Generative self-supervised learning, especially graph autoencoders (GAEs), can prevent the above dependence and has been demonstrated as an effective approach. In addition, most previous works only reconstruct the graph topological structure or node features. Few works consider both and combine them together to obtain their complementary information. To overcome these problems, we propose a generative self-supervised graph representation learning methodology named Multi-View Dual-decoder Graph Autoencoder (MDGA). Specifically, we first design a multi-sample graph learning strategy which benefits the generalization of the dual-decoder graph autoencoder. Moreover, the proposed model reconstructs the graph topological structure with a traditional GAE and extracts node attributes by masked feature reconstruction. Experimental results on five public benchmark datasets demonstrate that MDGA outperforms state-of-the-art methods in both node classification and link prediction tasks.

The nucleosome is the fundamental building block of chromatin. Changes taking place at the nucleosome level are the molecular basis of chromatin transactions with various enzymes and factors. These changes are directly and indirectly regulated by chromatin modifications such as DNA methylation and histone post-translational modifications including acetylation, methylation, and ubiquitylation. Nucleosomal changes are often stochastic, unsynchronized, and heterogeneous, making it very difficult to monitor with traditional ensemble averaging methods. Diverse single-molecule fluorescence approaches have been employed to investigate the structure and structural changes of the nucleosome in the context of its interactions with various enzymes such as RNA Polymerase II, histone chaperones, transcription factors, and chromatin remodelers. We utilize diverse single-molecule fluorescence methods to study the nucleosomal changes accompanying these processes, elucidate the kinetics of these processes, and eventually learn the implications of various chromatin modifications in directly regulating these processes. The methods include two- and three-color single-molecule fluorescence resonance energy transfer (FRET), single-molecule fluorescence correlation spectroscopy, and fluorescence (co-)localization. Here we report the details of the two- and three-color single-molecule FRET methods we currently use. This report will help researchers design their single-molecule FRET approaches to investigating chromatin regulation at the nucleosome level.

Recent advances in single molecule imaging have allowed the evolution of biochemical techniques that directly visualize protein-DNA interactions in real-time. These techniques rely on diffraction limited total internal reflection microscopy (TIRFM), and have significantly improved our understanding of RNA transcription, DNA replication, homologous recombination, and general DNA repair in the context of chromatin. Here we described a general single molecule TIRFM technique called DNA curtains to directly visualize how enzymes function on chromatinized DNA. The goal of this manuscript is to introduce the reader to methods to express and reconstitute nucleosomes on long stretches of DNA, and to directly visualize this process using DNA curtains with TIRFM.

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure pulse rate variability (PRV) using a camera without any sensor-skin contact, which is highly correlated to HRV, thus, enabling contactless assessment of emotional states. In this study, we employed ten machine learning techniques to identify emotions using camera-based PRV features. Our experimental results show that the best classification model achieved a coordination correlation coefficient of 0.34 for value recognition and 0.36 for arousal recognition. The rPPG-based measurement has demonstrated promising results in detecting HAHV (high-arousal high-valence) emotions with high accuracy. Furthermore, for emotions with less noticeable variations, such as sadness, the rPPG-based measure outperformed the baseline deep network for facial expression analysis.

CONTEXT: Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in rehabilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity. OBJECTIVE: Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition. METHOD: We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition. RESULTS: We compared the proposed method against existing methods on two Ninapro datasets consisting of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset.

Advances in Nuclear Magnetic Resonance (NMR) spectroscopy have allowed for the identification and characterization of movements in enzymes over the last 20 years that has also revealed the complexities of allosteric coupling. For example, many of the inherent movements of enzymes, and proteins in general, have been shown to be highly localized but nonetheless still coupled over long distances. Such partial couplings provide challenges to both identifying allosteric networks of dynamic communication and determining their roles in catalytic function. We have developed an approach to help identify and engineer enzyme function, called Relaxation And Single Site Multiple Mutations (RASSMM). This approach is a powerful extension of mutagenesis and NMR that is based on the observation that multiple mutations to a single site distal to the active site allosterically induces different effects to networks. Such an approach generates a panel of mutations that can also be subjected to functional studies in order to match catalytic effects with changes to coupled networks. In this review, the RASSMM approach is briefly outlined together with two applications that include cyclophilin-A and Biliverdin Reductase B.

The implementation of early cancer detection benefits the treatment outcomes with remarkably improved survival rate through the detection of rare circulating biomarkers in body fluids. Spectroscopic technologies play a crucial role in sensitive biomarker measurements by outputting extremely strong signals. In particular, the aggregation enhanced fluorescence and Raman technologies feature the detection of targets down to single-molecule level, thereby demonstrating the great promise of early cancer detection. In this review, we focus on the aggregation-induced emission (AIE) and aggregation-related surface-enhanced Raman scattering (SERS) spectroscopic strategies for detecting cancer biomarkers. We discuss the AIE and SERS based biomarker detection using target-driven aggregation as well as the aggregated nanoprobes. Furthermore, we deliberate on the progress of developing AIE and SERS integrated platforms. Ultimately, we put forth the potential challenges and perspectives on the way to use these two spectroscopic technologies in clinical settings. It is expected this review can inspire the design of AIE and SERS integrated platform for highly sensitive and accurate cancer detection.

CONTEXT: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates. OBJECTIVE: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design. METHOD: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task. RESULTS: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

DNA microarrays are very useful tools to study the realm of nucleic acids interactions at high throughput. The conventional approach to microarray synthesis employs phosphoramidite chemistry and yields unmodified DNA generally attached to a surface at the 3' terminus. Having a freely accessible 3'-OH instead of 5'-OH is desirable too, and being able to introduce nucleoside analogs in a combinatorial manner is highly relevant in the context of nucleic acid therapeutics and in aptamer research. Here, we describe an enzymatic approach to the synthesis of high-density DNA microarrays that can also contain chemical modifications. The method uses a standard DNA microarray, to which a DNA primer is covalently bound through photocrosslinking. The extension of the primer with a DNA polymerase yields double-stranded DNA but is also amenable to the incorporation of modified dNTPs. Further processing with T7 exonuclease, which catalyzes the degradation of DNA in a specific (5'→3') direction, results in template strand removal. Overall, the method produces surface-bound natural and non-natural DNA oligonucleotides, is applicable to commercial microarrays and paves the way for the preparation of combinatorial, chemically modified aptamer libraries.

As an important task of natural language processing, medication recommendation aims to recommend medication combinations according to the electronic health record, which can also be regarded as a multi-label classification task. But patients often have multiple diseases simultaneously, and the model must consider drug-drug interactions (DDI) of medication combinations when recommending medications, making medication recommendation more difficult. There is little existing work to explore the changes in patient conditions. However, these changes may point to future trends in patient conditions that are critical for reducing DDI rates in recommended drug combinations. In this paper, we proposed the Patient Information Mining Network (PIMNet), which models the current core medications of patient by mining the temporal and spatial changes of patient medication order and patient condition vector, and allocates some auxiliary medications as the currently recommended medication combination. The experimental results show that the proposed model greatly reduces the recommended DDI of medications while achieving results no lower than the state-of-the-art results.

Many membrane proteins form functional complexes that are either homo- or hetero-oligomeric. However, it is challenging to characterize membrane protein oligomerization in intact lipid bilayers, especially for polydisperse mixtures. Native mass spectrometry of membrane proteins and peptides inserted in lipid nanodiscs provides a unique method to study the oligomeric state distribution and lipid preferences of oligomeric assemblies. To interpret these complex spectra, we developed novel data analysis methods using macromolecular mass defect analysis. Here, we provide an overview of how mass defect analysis can be used to study oligomerization in nanodiscs, discuss potential limitations in interpretation, and explore strategies to resolve these ambiguities. Finally, we review recent work applying this technique to studying formation of antimicrobial peptide, amyloid protein, and viroporin complexes with lipid membranes.

Gut microbiota plays a crucial role in modulating pig development and health, and gut microbiota characteristics are associated with differences in feed efficiency. To answer open questions in feed efficiency analysis, biologists seek to retrieve information across multiple heterogeneous data sources. However, this is error-prone and time-consuming work since the queries can involve a sequence of multiple sub-queries over several databases. We present an implementation of an ontology-based Swine Gut Microbiota Federated Query Platform (SGMFQP) that provides a convenient, automated, and efficient query service about swine feeding and gut microbiota. The system is constructed based on a domain-specific Swine Gut Microbiota Ontology (SGMO), which facilitates the construction of queries independent of the actual organization of the data in the individual sources. This process is supported by a template-based query interface. A Datalog+-based federated query engine transforms the queries into sub-queries tailored for each individual data source, and an automated workflow orchestration mechanism executes the queries in each source database and consolidates the results. The efficiency of the system is demonstrated on several swine feeding scenarios.

Small molecules that bind to oligomeric protein species such as membrane proteins and fibrils are of clinical interest for development of therapeutics and diagnostics. Definition of the binding site at atomic resolution via NMR is often challenging due to low binding stoichiometry of the small molecule. For fibrils and aggregation intermediates grown in the presence of lipids, we report atomic-resolution contacts to the small molecule at sub nm distance via solid-state NMR using dynamic nuclear polarization (DNP) and orthogonally labelled samples of the protein and the small molecule. We apply this approach to α-synuclein (αS) aggregates in complex with the small molecule anle138b, which is a clinical drug candidate for disease modifying therapy. The small central pyrazole moiety of anle138b is detected in close proximity to the protein backbone and differences in the contacts between fibrils and early intermediates are observed. For intermediate species, the 100 K condition for DNP helps to preserve the aggregation state, while for both fibrils and oligomers, the DNP enhancement is essential to obtain sufficient sensitivity.

Global Run-On sequencing is a reliable and widely used approach for monitoring nascent transcription on a genomewide scale. The assay has been successfully used for studying global transcription in humans, plants, worms, flies, and fission yeast. Here we describe a GRO-seq protocol for studying transcription in budding yeast, Saccharomyces cerevisiae. Briefly, the technique involves permeabilization of actively growing yeast cells, allowing transcription to proceed in permeabilized cells in the presence of brominated UTP, affinity purification of bromo-UMP incorporated nascent transcripts followed by cDNA library construction, deep sequencing, and mapping against the reference genome. The approach maps the position of transcriptionally active RNA polymerase on a genomewide basis. In addition to identifying the complete set of transcriptionally active genes in a cell under a given set of conditions, the method can be used to determine elongation rate, termination defect and promoter directionality at the genomewide level. The approach is especially useful in identifying short-lived unstable transcripts that are rapidly degraded even before they leave the nucleus.

The transcription factor NF-ĸB is a central mediator of immune and inflammatory responses. To understand the regulation of NF-ĸB, it is important to probe the underlying thermodynamics, kinetics, and conformational dynamics of the NF-ĸB/IĸBα/DNA interaction network. The development of genetic incorporation of non-canonical amino acids (ncAA) has enabled the installation of biophysical probes into proteins with site specificity. Recent single-molecule FRET (smFRET) studies of NF-ĸB with site-specific labeling via ncAA incorporation revealed the conformational dynamics for kinetic control of DNA-binding mediated by IĸBα. Here we report the design and protocols for incorporating the ncAA p-azidophenylalanine (pAzF) into NF-ĸB and site-specific fluorophore labeling with copper-free click chemistry for smFRET. We also expanded the ncAA toolbox of NF-ĸB to include p-benzoylphenylalanine (pBpa) for UV crosslinking mass spectrometry (XL-MS) and incorporated both pAzF and pBpa into the full-length NF-ĸB RelA subunit which includes the intrinsically disordered transactivation domain.

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

Histone post-translational modifications (PTMs) on lysine residues, including methylation, ubiquitylation, and sumoylation, have been studied using semisynthetic histones reconstituted into nucleosomes. These studies have revealed the in vitro effects of histone PTMs on chromatin structure, gene transcription, and biochemical crosstalk. However, the dynamic and transient nature of most enzyme-chromatin interactions poses a challenge toward identifying specific enzyme-substrate interactions. To address this, we report methodology for the synthesis of two ubiquitylated activity-based probe histones, H2BK120ub(G76C) and H2BK120ub(G76Dha), that may be used to trap enzyme active-site cysteines as disulfides or in the form of thioether linkages, respectively. The general synthetic method we report for converting ubiquitylated nucleosomes into activity-based probes may also be applied to other histone sites of ubiquitylation in order to facilitate the identification of enzyme-chromatin interactions.

Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.

MicroRNA(miRNA) is a class of short non-coding RNAs with a length of about 22 nucleotides, which participates in various biological processes of cells. A number of studies have shown that miRNAs are closely related to the occurrence of cancer and various human diseases. Therefore, studying miRNA-disease associations is helpful to understand the pathogenesis of diseases as well as the prevention, diagnosis, treatment and prognosis of diseases. Traditional biological experimental methods for studying miRNA-disease associations have disadvantages such as expensive equipment, time-consuming and labor-intensive. With the rapid development of bioinformatics, more and more researchers are committed to developing effective computational methods to predict miRNA-disease associations in roder to reduce the time and money cost of experiments. In this study, we proposed a neural network-based deep matrix factorization method named NNDMF to predict miRNA-disease associations. To address the problem that traditional matrix factorization methods can only extract linear features, NNDMF used neural network to perform deep matrix factorization to extract nonlinear features, which makes up for the shortcomings of traditional matrix factorization methods. We compared NNDMF with four previous classical prediction models (IMCMDA, GRMDA, SACMDA and ICFMDA) in global LOOCV and local LOOCV, respectively. The AUCs achieved by NNDMF in two cross-validation methods were 0.9340 and 0.8763, respectively. Furthermore, we conducted case studies on three important human diseases (lymphoma, colorectal cancer and lung cancer) to validate the effectiveness of NNDMF. In conclusion, NNDMF could effectively predict the potential miRNA-disease associations.

Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and complex differential patterns in gene expression. Statistical or traditional machine learning methods are inefficient, and the accuracy needs to be improved. The methods based on deep learning can not directly process non-Euclidean spatial data, such as cell diagrams. In this study, we have developed graph autoencoders and graph attention network for scRNA-seq analysis based on a directed graph neural network named scDGAE. Directed graph neural networks cannot only retain the connection properties of the directed graph but also expand the receptive field of the convolution operation. Cosine similarity, median L1 distance, and root-mean-squared error are used to measure the gene imputation performance of different methods with scDGAE. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score are used to measure the cell clustering performance of different methods with scDGAE. Experiment results show that the scDGAE model achieves promising performance in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels. Furthermore, it is a robust framework that can be applied to general scRNA-Seq analyses.

Nanocomposites and low-viscous materials lack translation in additive manufacturing technologies due to deficiency in rheological requirements and heterogeneity of their preparation. This work proposes the chemical crosslinking between composing phases as a universal approach for mitigating such issues. The model system is composed of amine-functionalized bioactive glass nanoparticles (BGNP) and light-responsive methacrylated bovine serum albumin (BSAMA) which further allows post-print photocrosslinking. The interfacial interaction was conducted by 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide crosslinking agent and N-Hydroxysuccinimide between BGNP-grafted amines and BSAMA's carboxylic groups. Different chemical crosslinking amounts and percentages of BGNP in the nanocomposites were tested. The improved interface interactions increased the elastic and viscous modulus of all formulations. More pronounced increases were found with the highest crosslinking agent amounts (4 % w/v) and BGNP concentrations (10 % w/w). This formulation also displayed the highest Young's modulus of the double-crosslinked construct. All composite formulations could effectively immobilize the BGNP and turn an extremely low viscous material into an appropriate inks for 3d printing technologies, attesting for the systems' tunability. Thus, we describe a versatile methodology which can successfully render tunable and light-responsive nanocomposite inks with homogeneously distributed bioactive fillers. This system can further reproducibly recapitulate phases of other natures, broadening applicability.

Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.

While natural oligonucleotides (ONs) are increasingly used as therapeutic and diagnostic tools, they still face certain challenges such as low resistance to enzymatic degradation, potential immunogenicity, and delivery issues, which can limit their applications. Peptide Nucleic Acids (PNAs) are promising alternatives due to their high affinity for DNA and RNA, the high resistance to enzymatic degradation, and the easy introduction of a wide range of potential modifications. Chemical modifications that enable the covalent targeting of specific DNA and RNA strands offer additional advantages, including enhanced potency. The current study focuses on the utilization of furan-PNAs as pro-reactive probe systems and their applications to DNA and RNA targeting. Specifically, in this methodological paper, we provide practical insights into the design, synthesis, and application of furan-containing PNA probes for achieving efficient PNA-DNA and PNA-RNA interstrand crosslinking (ICL), as well as ON-templated PNA-PNA ligation systems. Furthermore, we discuss the applications of these probes in targeting DNA secondary structures, such as G-quadruplexes and i-motifs, target pull-down assays, and on-surface detection.

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.

Transcriptomic profiling is a mainstay of translational cancer research and is often used to identify cancer subtypes, stratify responders vs. non-responders patients, predict survival, and identify potential targets for therapeutic intervention. Analysis of gene expression data gathered by RNA sequencing (RNA-seq) and microarray is generally the first step in identifying and characterizing cancer-associated molecular determinants. The methodological advancements and reduced costs associated with transcriptomic profiling have increased the number of publicly available gene expression profiles for cancer subtypes. Data integration from multiple datasets is routinely done to increase the number of samples, improve statistical power, and provide better insight into the heterogeneity of the biological determinant. However, utilizing raw data from multiple platforms, species, and sources introduces systematic variations due to noise, batch effects, and biases. As such, the integrated data is mathematically adjusted through normalization, which allows direct comparison of expression measures among studies while minimizing technical and systemic variations. This study applied meta-analysis to multiple independent Affymetrix microarray and Illumina RNA-seq datasets available through the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA). We have previously identified a tripartite motif containing 37 (TRIM37), a breast cancer oncogene, that drives tumorigenesis and metastasis in triple-negative breast cancer. In this article, we adapted and assessed the validity of Stouffer's z-score normalization method to interrogate TRIM37 expression across different cancer types using multiple large-scale datasets.

Nucleosomes are the repeating units of chromatin. The presence of nucleosomes poses a major impediment to all DNA-dependent processes. As a result, access to DNA in chromatin is dynamically regulated by many factors, including ATP-dependent chromatin remodeling complexes. Digestion of chromatin by micrococcal nuclease (MNase) followed by chromatin immunoprecipitation (ChIP) and sequencing can be leveraged to determine nucleosome occupancy, positioning, and the ability of chromatin interacting factors to alter chromatin accessibility. Here we describe the procedure for performing MNase and MNase ChIP-seq in detail.

Proteins are expressed from genes via sequential biological processes of transcription, mRNA processing, export and translation, and play their roles in maintaining cellular functions via interactions with proteins, DNAs or RNAs. Thus, it is important to study the protein interactions during biological processes in living cells towards understanding their mechanisms-of-action in real time. Methodologies have been developed over the years to study protein interactions in vivo. One state-of-the-art approach is formaldehyde crosslinking-based immuno- or chemi-precipitation to analyze selective as well as genome/proteome-wide interactions in living cells. It is a popular and widely used methodology for cellular analysis of the protein-protein and protein-nucleic acid interactions. Here, we describe this approach to analyze protein-protein/nucleic acid interactions in vivo.

Disulfide bonds drive protein correct folding, prevent protein aggregation, and stabilize three-dimensional structures of proteins and their assemblies. Dysregulation of this activity leads to several disorders, including cancer, neurodegeneration, and thrombosis. A family of 20+ enzymes, called thiol-isomerases (TIs), oversee this process in the endoplasmic reticulum of human cells to ensure efficacy and accuracy. While the biophysical and biochemical properties of cysteine residues are well-defined, our structural knowledge of how TIs select, interact and process their substrates remains poorly understood. How TIs structurally and functionally respond to changes in redox environment and other post-translational modifications remain unclear, too. We recently developed a workflow for site-specific incorporation of non-canonical amino acids into protein disulfide isomerase (PDI), the prototypical member of TIs. Combined with click chemistry, this strategy enabled us to perform single-molecule biophysical studies of PDI under various solution conditions. This paper details protocols and discusses challenges in performing these experiments. We expect this approach, combined with other emerging technologies in single-molecule biophysics and structural biology, to facilitate the exploration of the mechanisms by which TIs carry out their fascinating but poorly understood roles in humans, especially in the context of thrombosis.

Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.

MOTIVATION: In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on deep learning. It uses multiple convolution layers to extract features from the simplified molecular input system (SMILES) strings of the compounds and amino acid sequences of proteins and then performs affinity prediction analysis. However, the semantic information contained in low-level features can gradually be lost due to the increasing network depth, which affects the prediction performance. RESULT: We propose a novel method called the Pyramid Network Convolution Drug-Target Binding Affinity (PCNN-DTA) method for drug-target binding affinity prediction. The PCNN-DTA method, which is based on a feature pyramid network (FPN), fuses the features extracted from each layer of a multilayer convolution network to retain more low-level feature information, thus improving the prediction accuracy. PCNN-DTA is compared with other typical algorithms on three benchmark datasets, namely, the KIBA, Davis, and Binding DB datasets. Experimental results show that the PCNN-DTA method is superior to existing regression prediction methods using convolutional neural networks, which further demonstrates its effectiveness.

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

Increasing awareness of the health and environment impacts of the antibiotics misuse or overuse, such as tetracycline (TC) in treatment or prevention of infections and diseases, has driven the development of robust methods for their detection in biological, environmental and food systems. In this work, we report the development of a new europium(III) complex functionalized silica nanoprobe (SiNPs-Eu3+) for highly sensitive and selective detection of TC residue in aqueous solution and food samples (milk and meat). The nanoprobe is developed by immobilization of Eu3+ ion onto the surface of silica nanoparticles (SiNPs) as the emitter and TC recognition unit. The β-diketone configuration of TC can further coordinate with Eu3+ steadily on the surface of nanoprobe, facilitating the absorption of light excitation for Eu3+ emitter activation and luminescence "off-on" response. The dose-dependent luminescence enhancement of SiNPs-Eu3+ nanoprobe exhibits good linearities, allowing the quantitative detection of TC. The SiNPs-Eu3+ nanoprobe shows high sensitivity and selectivity for TC detection in buffer solution. Time resolved luminescence analysis enables the elimination of autofluorescence and light scattering for highly sensitive detection of TC in milk and pork mince with high accuracy and precision. The successful development of SiNPs-Eu3+ nanoprobe is anticipated to provide a rapid, economic, and robust approach for TC detection in real world samples.

Single-cell RNA-sequencing (scRNA-seq) data suffer from a lot of zeros. Such dropout events impede the downstream data analyses. We propose BayesImpute to infer and impute dropouts from the scRNA-seq data. Using the expression rate and coefficient of variation of the genes within the cell subpopulation, BayesImpute first determines likely dropouts, and then constructs the posterior distribution for each gene and uses the posterior mean to impute dropout values. Some simulated and real experiments show that BayesImpute can effectively identify dropout events and reduce the introduction of false positive signals. Additionally, BayesImpute successfully recovers the true expression levels of missing values, restores the gene-to-gene and cell-to-cell correlation coefficient, and maintains the biological information in bulk RNA-seq data. Furthermore, BayesImpute boosts the clustering and visualization of cell subpopulations and improves the identification of differentially expressed genes. We further demonstrate that, in comparison to other statistical-based imputation methods, BayesImpute is scalable and fast with minimal memory usage.

Amblyopia is an abnormal visual processing-induced developmental disorder of the central nervous system that affects static and dynamic vision, as well as binocular visual function. Currently, changes in static vision in one eye are the gold standard for amblyopia diagnosis. However, there have been few comprehensive analyses of changes in dynamic vision, especially eye movement, among children with amblyopia. Here, we proposed an optimization scheme involving a video eye tracker combined with an "artificial eye" for comprehensive examination of eye movement in children with amblyopia; we sought to improve the diagnostic criteria for amblyopia and provide theoretical support for practical treatment. The resulting eye movement data were used to construct a deep learning approach for diagnostic and predictive applications. Through efforts to manage the uncooperativeness of children with strabismus who could not complete the eye movement assessment, this study quantitatively and objectively assessed the clinical implications of eye movement characteristics in children with amblyopia. Our results indicated that an amblyopic eye is always in a state of adjustment, and thus is not "lazy." Additionally, we found that the eye movement parameters of amblyopic eyes and eyes with normal vision are significantly different. Finally, we identified eye movement parameters that can be used to supplement and optimize the diagnostic criteria for amblyopia, providing a diagnostic basis for evaluation of binocular visual function.

In recent years, healthcare data from various sources such as clinical institutions, patients, and pharmaceutical industries have become increasingly abundant. However, due to the complex healthcare system and data privacy concerns, aggregating and utilizing these data in a centralized manner can be challenging. Federated learning (FL) has emerged as a promising solution for distributed training in edge computing scenarios, utilizing on-device user data while reducing server costs. In traditional FL, a central server trains a global model sampled client data randomly, and the server combines the collected model from different clients into one global model. However, for not independent and identically distributed (non-i.i.d.) datasets, randomly selecting users to train server is not an optimal choice and can lead to poor model training performance. To address this limitation, we propose the Federated Multi-Center Clustering algorithm (FedMCC) to enhance the robustness and accuracy for all clients. FedMCC leverages the Model-Agnostic Meta-Learning (MAML) algorithm, focusing on training a robust base model during the initial training phase and better capturing features from different users. Subsequently, clustering methods are used to ensure that features among users within each cluster are similar, approximating an i.i.d. training process in each round, resulting in more effective training of the global model. We validate the effectiveness and generalizability of FedMCC through extensive experiments on public healthcare datasets. The results demonstrate that FedMCC achieves improved performance and accuracy for all clients while maintaining data privacy and security, showcasing its potential for various healthcare applications.

Image anomaly detection (AD) is widely researched on many occasions in computer vision tasks. High-dimensional data, such as image data, with noise and complex background is still challenging to detect anomalies under the situation that imbalanced or incomplete data are available. Some deep learning methods can be trained in an unsupervised way and map the original input into low-dimensional manifolds to predict larger differences in anomalies according to normal ones by dimension reduction. However, training a single low-dimension latent space is limited to present the low-dimensional features due to the fact that the noise and irreverent features are mapped into this space, resulting in that the manifolds are not discriminative for detecting anomalies. To address this problem, a new autoencoder framework is proposed in this study with two trainable mutually orthogonal complementary subspaces in the latent space, by latent subspace projection (LSP) mechanism, which is named as LSP-CAE. Specifically, latent subspace projection is used to train the latent image subspace (LIS) and the latent kernel subspace (LKS) in the latent space of the autoencoder-like model respectively, which can enhance learning power of different features from the input instance. The features of normal data are projected into the latent image subspace, while the latent kernel subspace is trained to extract the irrelevant information according to normal features by end-to-end training. To verify the generality and effectiveness of the proposed method, we replace the convolutional network with the fully-connected network contucted in the real-world medical datasets. The anomaly score based on projection norms in two subspace is used to evaluate the anomalies in the testing. Consequently, our proposed method can achieve the best performance according to four public datasets in comparison of the state-of-the-art methods.

Deep convolutional neural networks (DCNNs) have shown remarkable performance in medical image segmentation tasks. However, medical images frequently exhibit distribution discrepancies due to variations in scanner vendors, operators, and image quality, which pose significant challenges to the robustness of trained models when applied to unseen clinical data. To address this issue, domain generalization methods have been developed to enhance the generalization ability of DCNNs. Feature space-based data augmentation methods have been proven effective in improving domain generalization, but they often rely on prior knowledge or assumptions, which can limit the diversity of source domain data. In this study, we propose a novel random feature augmentation (RFA) method to diversify source domain data at the feature level without prior knowledge. Specifically, our RFA method perturbs domain-specific information while preserving domain-invariant information, thereby adequately diversifying the source domain data. Furthermore, we propose a dual-branches invariant synergistic learning strategy to capture domain-invariant information from the augmented features of RFA, enabling DCNNs to learn a more generalized representation. We evaluate our proposed method on two challenging medical image segmentation tasks, optic cup/disc segmentation on fundus images and prostate segmentation on MRI images. Extensive experimental results demonstrate the superior performance of our method over state-of-the-art domain generalization methods.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

As an important member of reactive oxygen species, hydrogen peroxide (H2O2) plays a key role in oxidative stress and cell signaling. Abnormal levels of H2O2 in lysosomes can induce damage or even loss of lysosomal function, leading to certain diseases. Therefore, real-time monitoring of H2O2 in lysosomes is very important. In this work, we designed and synthesized a novel lysosome-targeted fluorescent probe for H2O2-specific detection based on a benzothiazole derivative. A morpholine group was used as a lysosome-targeted unit and a boric acid ester was chosen as the reaction site. In the absence of H2O2, the probe exhibited very weak fluorescence. In the presence of H2O2, the probe showed an increased fluorescence emission. The fluorescence intensity of the probe for H2O2 displayed a good linear relationship in the concentration range of H2O2 from 8.0 × 10-7 to 2.0 × 10-4 mol·L-1. The detection limit was estimated to be 4.6 × 10-7 mol·L-1 for H2O2. The probe possessed high selectivity, good sensitivity and short response time for the detection of H2O2. Moreover, the probe had almost no cytotoxicity and had been successfully applied to confocal imaging of H2O2 in lysosomes of A549 cells. These results illustrated that the developed fluorescent probe in this study could provide a good tool for the determination of H2O2 in lysosomes.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Ratiometric luminescence probes have attracted widespread attention because of their self-calibration capability. However, some defects, such as small emission shift, severe spectral overlap and poor water solubility, limit their application in the field of biological imaging. In this study, a unique luminescence probe, Ru-COU, has been developed by combining tris(bipyridine)ruthenium(II) complex with coumarin derivative through a formaldehyde-responsive linker. The probe exhibited a large emission shift (Δλ > 100 nm) and good water solubility, achieving ratiometric emission responses at 505 nm and 610 nm toward formaldehyde under acidic conditions. Besides, ratiometric luminescence imaging of formaldehyde in living cells and Alzheimer disease mouse's brain slices demonstrates the potential value of Ru-COU for the diagnosis and treatment of formaldehyde related diseases.

Post-translational modifications (PTMs) are reversible chemical modifications that can modulate protein structure and function. Methylation and acetylation are two such PTMs with integral and well-characterized biological roles, including modulation of chromatin structure; and unknown or poorly understood roles, exemplified by the influence of these PTMs on transcription factor structure and function. The need for biological insights into the function of these PTMs motivates the development of a nondestructive and label-free method that enables pursuit of molecular mechanisms. Here, we present a protocol for implementing nuclear magnetic resonance (NMR) methods that allow for unambiguous detection of methylation and acetylation events and demonstrate their utility by observing these marks on histone H3 tail as a model system. We leverage strategic isotopic enrichment of cofactor and peptide for visualization by [1H, 13C]-HSQC and 13C direct-detect NMR measurements. Finally, we present 13C-labeling schemes that facilitate one-dimensional NMR experiments, which combine reduced measurement time relative to two-dimensional spectroscopy with robust filtering of background signals that would otherwise create spectral crowding or limit detection of low-abundance analytes.

Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.

Long non-coding RNAs are a class of essential non-coding RNAs with a length of more than 200 nts. Recent studies have indicated that lncRNAs have various complex regulatory functions, which play great impacts on many fundamental biological processes. However, measuring the functional similarity between lncRNAs by traditional wet-experiments is time-consuming and labor intensive, computational-based approaches have been an effective choice to tackle this problem. Meanwhile, most sequences-based computation methods measure the functional similarity of lncRNAs with their fixed length vector representations, which could not capture the features on larger k-mers. Therefore, it is urgent to improve the predict performance of the potential regulatory functions of lncRNAs. In this study, we propose a novel approach called MFSLNC to comprehensively measure functional similarity of lncRNAs based on variable k-mer profiles of nucleotide sequences. MFSLNC employs the dictionary tree storage, which could comprehensively represent lncRNAs with long k-mers. The functional similarity between lncRNAs is evaluated by the Jaccard similarity. MFSLNC verified the similarity between two lncRNAs with the same mechanism, detecting homologous sequence pairs between human and mouse. Besides, MFSLNC is also applied to lncRNA-disease associations, combined with the association prediction model WKNKN. Moreover, we also proved that our method can more effectively calculate the similarity of lncRNAs by comparing with the classical methods based on the lncRNA-mRNA association data. The detected AUC value of prediction is 0.867, which achieves good performance in the comparison of similar models.

BACKGROUND AND OBJECTIVE: The gold standard for diagnosing epiretinal membranes is to observe the surface of the internal limiting membrane on optical coherence tomography images. The stages of the epiretinal membrane are used to decide the condition of the health of the membrane. The stages are not detected because some of them are similar. To accurately classify the stages, a deep-learning technology can be used to improve the classification accuracy. METHODS: A combinatorial fusion with multiple convolutional neural networks (CNN) algorithms are proposed to enhance the accuracy of a single image classification model. The proposed method was trained using a dataset of 1947 optical coherence tomography images diagnosed with the epiretinal membrane at the Taichung Veterans General Hospital in Taiwan. The images consisted of 4 stages; stages 1, 2, 3, and 4. RESULTS: The overall accuracy of the classification was 84%. The combination of five and six CNN models achieves the highest testing accuracy (85%) among other combinations, respectively. Any combination with a different number of CNN models outperforms any single CNN algorithm working alone. Meanwhile, the accuracy of the proposed method is better than ophthalmologists with years of clinical experience. CONCLUSIONS: We have developed an efficient epiretinal membrane classification method by using combinatorial fusion with CNN models on optical coherence tomography images. The proposed method can be used for screening purposes to facilitate ophthalmologists making the correct diagnoses in general medical practice.

Herein, a Eugenol-derived fluorescence 'turn-on' probe FLHE was synthesized by condensing 2-((3-(trifluoromethyl)phenyl)amino)benzohydrazide with 5-allyl-2-hydroxy-3-methoxybenzaldehyde. FLHE demonstrated very low fluorescence in the studied organic solvents of varying polarities. However, upon titration with Zn2+ in HEPES buffer (pH = 7.4, 50% ACN, v/v), FLHE showed 40-fold higher fluorescence signals indicating the formation of the FLHE-Zn2+ complex. The fluorescence turn-on phenomenon upon FLHE-Zn2+ complex formation results from a chelation-enhanced fluorescence (CHEF) effect. The FLHE-Zn2+ complexation demonstrated a stokes shift of 156 nm (λex = 350 nm, λem = 506 nm) and an about 33-fold increase in the quantum yield (FLHE, Φ = 0.007; FLHE-Zn2+ complex, Φ = 0.23). The binding constant (Ka) determined by the Benesi-Hildebrand plot for interaction between FLHE and Zn2+ was 5.33 × 103 M-1. FLHE demonstrated a LOD of 31.8 nM for detecting Zn2+ in the environmental samples without interference from other cations and anions. FLHE-based paper strip (FLHE-PS) assay was developed to quantify the Zn2+ ions in water and the water content of organic solvent. FLHE-PS allows the detection of Zn2+ in aqueous solutions with a LOD of 63.2 nM and quantifying water in acetonitrile with a LOD of 0.14%. These results indicate that the FLHE has high applicability for detecting Zn2+ in living cells and environmental samples and detecting the presence of water in the organic solvents.

A simple fluorescent probe (KS4) containing multiple reaction sites (phenolic -OH, imine and C = C bonds) is successfully synthesized and characterized using 1H NMR, 13C NMR, mass and single crystal XRD techniques. KS4 exhibits high selectivity towards CN- over a wide range of common anions in H2O:DMSO (1:1 v/v) leading to an amazing turn-on fluorescence at 505 nm via deprotonation of the phenolic -OH group. The limit of detection (1.3 µM) for CN- was much below the standard (1.9 µM) set by the World Health Organization (WHO). Stoichiometry of the interaction between KS4 and CN- was ascertained as 1:1 by the Job's plot method and the binding constant was determined to be 1.5x104 M-1. Density Functional Theory (DFT) and Time-Dependent Density Functional Theory (TD-DFT) based theoretical insight has been appealed to understand the optical properties of KS4 before and after the addition of CN- ion. The probe shows respectable real-time applicability for qualitative detection of CN- in almond and cassava powder as well as quantification in real water samples with excellent recoveries (98.8 - 99.8%). In addition, KS4 is found to safe towards living HeLa cells and successfully applied to the detection of endogenous cyanide ions in HeLa cells.

The cytokine interleukin-4 (IL-4) plays an important role in our immune system. IL-4 leads the way in the differentiation of naïve T-helper 0 cells (Th0) to T-helper 2 cells (Th2). The Th2 responses are characterized by the release of IL-4. CD4+ T cells produce the cytokine IL-4 in response to exogenous parasites. IL-4 has a critical role in the growth of CD8+ cells, inflammation, and responses of T-cells. We propose an ensemble model for the prediction of IL-4 inducing peptides. Four feature encodings were extracted to build an efficient predictor: pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, quasi-sequence-order, and Shannon entropy. We developed an ensemble learning model fusion of random forest, extreme gradient boost, light gradient boosting machine, and extra tree classifier in the first layer, and a Gaussian process classifier as a meta classifier in the second layer. The outcome of the benchmarking testing dataset, with a Matthews correlation coefficient of 0.793, showed that the meta-model (Meta-IL4) outperformed individual classifiers. The highest accuracy achieved by the Meta-IL4 model is 90.70%. These findings suggest that peptides that induce IL-4 can be predicted with reasonable accuracy. These models could aid in the development of peptides that trigger the appropriate Th2 response.

The Parkinson's disease associated protein α-synuclein (αS) has been found to contain numerous post-translational modifications (PTMs), in both physiological and pathological states. One PTM site of particular interest is serine 87, which is subject to both O-linked β-N-acetylglucosamine (gS) modification and phosphorylation (pS), with αS-pS87 enriched in Parkinson's disease. An often-overlooked aspect of these PTMs is their effect on the membrane-binding properties of αS, which are important to its role in regulating neurotransmitter release. Here, we show how one can study these effects by synthesizing αS constructs containing authentic PTMs and labels for single molecule fluorescence correlation spectroscopy measurements. We synthesize αS-gS87 and αS-pS87 by combining native chemical ligation with genetic code expansion approaches. We introduce the fluorophore by a click reaction with a non-canonical amino acid. Beyond the specific problem of PTM effects on αS, our studies highlight the value of this combination of methods for multiply modifying proteins.

Prostate specific antigen (PSA) is a widely-used biomarker for the diagnosis, screening, and prognosis of prostate cancer (PCa). It is critical to develop a rapid and convenient method to accurately detect PSA levels, especially when the PSA levels are in the clinical gray area of 4-10 ng/mL. We developed a novel upconversion nanoparticle (UCNP)-based fluorescence lateral flow test strip for qualitatively and quantitatively detecting PSA. The carboxyl group-modified UCNPs (UCNP-COOH) were labeled with anti-PSA antibodies via 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide (EDC) and N-hydroxysuccinimide (NHS) as labeling probes to recognize PSA. The fluorescence intensity of the UCNP-probe was then measured with a laser fluorescence scanner. A total of 1397 serum and 20 fingertip blood samples were collected to validate the UCNP strip. A reliable correlation between the area ratio (TC), reflecting the fluorescence intensity of the test/control line, and the PSA concentration was observed (r = 0.9986). The dose-dependent luminescence enhancement showed good linearity in the PSA concentration range from 0.1 to 100.0 ng/mL with a detection limit of 0.1 ng/mL. Our UCNP POCT strip demonstrated excellent accuracy, anti-interference and stability in the gray zone (4-10 ng/mL) of PSA clinical application and outperformed other PSA test strips. The UCNP strip showed good consistency with the Roche chemiluminescence assay in 1397 serum samples. It also showed good performance for PSA detection using fingertip blood samples. This novel UCNP-based test strip could be a sensitive and reliable POCT assay to detect PSA, facilitating the diagnosis and surveillance of PCa.

Oligonucleotides (ODNs) find applications as diagnostic and therapeutic tools due to their unique ability to interact, thanks to Watson-Crick base pairing, with a specific DNA or RNA target strand. Although most of the tools available today rely on mere hydrogen bond formation, chemical modifications to enable covalent interstrand-crosslinking (ICL) have been reported, and are gaining a place under the spotlight as they potentially offer a series of advantages over the state of the art, including a higher potency and selectivity. This methodological paper focuses on the use of a pro-reactive furan moiety and its subsequent oxidation for applications in ODN targeting. The design of effective capture and targeting probes to ensure high ICL yields is discussed and the mechanisms underlying the (photo)chemical oxidation of furan are explained. Furthermore, examples of furan-containing DNAs designed for different applications, including DNA-DNA or DNA-RNA ICL and DNA-peptide/protein targeting, are provided. The paper highlights the advantages of using different oxidative chemical triggers, such as N-bromosuccinimide or singlet oxygen, to offer additional selectivity control over the ICL reaction.

Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical parameters that ensures proper entrapment of drugs in the developed nanosystems. Several factors affect the entrapment efficiency of drugs and thus multiple assessments are required to ensure drug retention, and to reduce cost and time. Supervised machine learning can allow for the construction of algorithms that can mine data available from earlier studies to predict performance of specific types of nanoparticles. Comparative studies that utilize multiple regression algorithms to predict entrapment efficiency in nanomaterials are scarce. Herein, we report on a detailed methodology for prediction of entrapment efficiency in nanomaterials (e.g., niosomes) using different regression algorithms (i.e., CatBoost, linear regression, support vector regression and artificial neural network) to select the model that demonstrates the best performance for estimation of entrapment efficiency. The study concluded that CatBoost algorithm demonstrated the best performance with maximum R2 score (0.98) and mean square error (< 10-4). Among the various parameters that possess a role in entrapment efficiency of drugs into niosomes, the results obtained from CatBoost model revealed that the drug:lipid ratio is the major contributing factor affecting entrapment efficiency, followed by the lipid:surfactant molar ratio. Hence, supervised machine learning may be applied for future selection of the components of niosomes that achieve high entrapment efficiency of drugs while minimizing experimental procedures and cost.

Over 40% of eukaryotic proteomes and 15% of bacterial proteomes are predicted to be intrinsically disordered based on their amino acid sequence. Intrinsically disordered proteins (IDPs) exist as heterogeneous ensembles of interconverting conformations and pose a challenge to the structure-function paradigm by apparently functioning without possessing stable structural elements. IDPs play a prominent role in biological processes involving extensive intermolecular interaction networks and their inherently dynamic nature facilitates their promiscuous interaction with multiple structurally diverse partner molecules. NMR spectroscopy has made pivotal contributions to our understanding of IDPs because of its unique ability to characterize heterogeneity at atomic resolution. NMR methods such as Chemical Exchange Saturation Transfer (CEST) and relaxation dispersion have enabled the detection of 'invisible' excited states in biomolecules which are transiently and sparsely populated, yet central for function. Here, we develop a 1Hα CEST pulse sequence which overcomes the resonance overlap problem in the 1Hα-13Cα plane of IDPs by taking advantage of the superior resolution in the 1H-15N correlation spectrum. In this sequence, magnetization is transferred after 1H CEST using a triple resonance coherence transfer pathway from 1Hα (i) to 1HN(i + 1) during which the 15N(t1) and 1HN(t2) are frequency labelled. This approach is integrated with spin state-selective CEST for eliminating spurious dips in CEST profiles resulting from dipolar cross-relaxation. We apply this sequence to determine the excited state 1Hα chemical shifts of the intrinsically disordered DNA binding domain (CytRN) of the bacterial cytidine repressor (CytR), which transiently acquires a functional globally folded conformation. The structure of the excited state, calculated using 1Hα chemical shifts in conjunction with other excited state NMR restraints, is a three-helix bundle incorporating a helix-turn-helix motif that is vital for binding DNA.

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

Deep learning has brought a significant progress in medical image analysis. However, their lack of interpretability might bring high risk for wrong diagnosis with limited clinical knowledge embedding. In other words, we believe it's crucial for humans to interpret how deep learning work for medical analysis, thus appropriately adding knowledge constraints to correct the bias of wrong results. With such purpose, we propose Representation Group-Disentangling Network (RGD-Net) to explain the process of feature extraction and decision making inside deep learning framework, where we completely disentangle feature space of input X-ray images into independent feature groups, and each group would contribute to diagnose of a specific disease. Specifically, we first state problem definition for interpretable prediction with auto-encoder structure. Then, group-disentangled representations are extracted from input X-ray images with the proposed Group-Disentangle Module, which constructs semantic latent space by enforcing semantic consistency of attributes. Afterwards, adversarial constricts on mapping from features to diseases are proposed to prevent model collapse during training. Finally, a novel design of local tuning medical application is proposed based on RGB-Net, which is capable to aid clinicians for reasonable diagnosis. By conducting quantity of experiments on public datasets, RGD-Net have been superior to comparative studies by leveraging potential factors contributing to different diseases. We believe our work could bring interpretability in digging inherent patterns of deep learning on medical image analysis.

Schiff base probes (1 and 2) made from o-phenylenediamine and o-aminophenol were appeared as highly selective fluorimetric chemosensor of Cu2+ and Al3+ ions respectively. Strong fluorescence emission of probe 1 at 415 nm (excitation at 350 nm) was instantly turned off on addition of Cu2+. Very weak fluorescence of probe 2 at 506 nm (excitation at 400 nm) was immediately turned on specifically by Al3+. Job's plot and ESI-MS results suggested 1:1 molar stoichiometric ratio of metal ion and probe in their respective complexes. Probe 1 and 2 had demonstrated very low detection limit (9.9 and 2.5 nM respectively). Binding of Cu2+ with probe 1 was found chemically reversible on addition of EDTA, while complexation between Al3+ and probe 2 was not reversible. On the basis of density functional theory (DFT) and spectroscopic results, probable mode of sensing of the metal ions by the probes were proposed. Quenching of the fluorescence of probe 1 by Cu2+ was attributed to the extensive transfer of charge from the probe molecule to paramagnetic copper ion. Whereas, in the Al3+-complex of probe 2, photo-induced electron transfer (PET) process from the imine nitrogen to salicylaldehyde moiety was restricted and thereby the weak emission intensity of probe 2 was enhanced significantly. Effective pH range of sensing the metal ions by probe 1 and 2 were 4 to 8 and 6 to 10 respectively. Probe 1 was also applied in the design of a logic gate for Cu2+ detection. Moreover, probe 1 and 2 was also used in water sample analysis for quantitative estimation of Cu2+ and Al3+ respectively.