Recent Progress in Block Copolymer Self-Assembly for the Fabrication of Structural Color Pigments.

The self-assembly of block copolymers (BCPs) into photonic materials has garnered increasing interest due to the versatility and ease of fabrication offered by the synthesized building blocks. BCPs are highly tunable, with their self-assembled structures' size being adjustable by modifying the block lengths, molecular weight(M), and polymer composition. This review provides a concise summary of the use of BCPs as photonic pigments, which generate color through structural manipulation rather than relying on chemical pigmentation.

Efficient urea electrosynthesis from CO and nitrate on amorphous TiS.

Electrosynthesis of urea via co-electrolysis of CO and NO (EUCN) offers a promising avenue for simultaneously addressing environmental concerns and producing valuable urea. In this study, we report that amorphous TiS (a-TiS) with rich S-vacancies (S) serves as an effective and robust EUCN catalyst. In a flow electrolyzer, a-TiS achieves the maximum urea-Faradaic efficiency of 34.

TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation.

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years.

Frank-Kasper phases in charge transfer complexes enable tunable photoelectronic properties.

Understanding how particles pack in space and the mechanisms underlying symmetry selection across soft matter is challenging. The Frank-Kasper (F-K) phase of complex spherical packing is amongst the most fascinating phases; however, it has not been observed in discotic liquid crystals until now. Herein, we report the first observation of F-K phases of charge transfer complexes (CTCs) obtained from triphenylene derivatives as donors and 2,4,7-trinitro-9-fluorenone as the acceptor.

Urea Electrosynthesis from Nitrate and CO on Diatomic Alloys.

Urea electrosynthesis from co-electrolysis of NO and CO (UENC) offers a promising technology for achieving sustainable and efficient urea production. Herein, a diatomic alloy catalyst (CuPdRh-DAA), with mutually isolated Pd and Rh atoms alloyed on Cu substrate, is theoretically designed and experimentally confirmed to be a highly active and selective UENC catalyst. Combining theoretical computations and operando spectroscopic characterizations reveals the synergistic effect of Pd-Cu and Rh-Cu active sites to promote the UENC via a tandem catalysis mechanism, where Pd-Cu site triggers the early C-N coupling and promotes *CONO-to-*CONH steps, while Rh-Cu site facilitates the subsequent protonation step of *CONH to *COOHNH toward the urea formation.

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.

P-Block Antimony-Copper Single-Atom Alloys for Selective Nitrite Electroreduction to Ammonia.

Electrocatalytic reduction of NO to NH (NORR) offers an effective method for alleviating NO pollution and generating valuable NH. Herein, a p-block single-atom alloy, namely, isolated Sb alloyed in a Cu substrate (SbCu), is explored as a durable and high-current-density NORR catalyst. As revealed by the theoretical calculations and operando spectroscopic measurements, we demonstrate that Sb incorporation can not only hamper the competing hydrogen evolution reaction but also optimize the d-band center of SbCu and intermediate adsorption energies to boost the protonation energetics of NO-to-NH conversion.

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.

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples.

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis.

Electrocatalytic reduction of nitrite to ammonia on undercoordinated Cu.

Electrocatalytic NO-to-NH reduction (NORR) has emerged as an intriguing route for simultaneous mitigation of harmful nitrites and production of valuable NH. Herein, we design for the first time undercoordinated Cu nanowires (u-Cu) as an efficient and selective NORR electrocatalyst, delivering the maximum NO-to-NH faradaic efficiency of 94.7% and an ammonia production rate of 494.

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.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions.

A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level.

Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level.

Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements.

The particle phase state plays a vital role in the gas-particle partitioning, multiphase reactions, ice nucleation activity, and particle growth in the atmosphere. However, the characterization of the atmospheric phase state remains challenging. Herein, based on measured aerosol chemical composition and ambient relative humidity (RH), a machine learning (ML) model with high accuracy ( = 0.

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results.

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task.

Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning.

Unsupervised clustering is an essential step in identifying cell types from single-cell RNA sequencing (scRNA-seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single-cell molecular heterogeneity.

Iron Diboride (FeB) for the Electroreduction of NO to NH.

We report iron diboride (FeB) as a high-performance metal diboride catalyst for electrochemical NO-to-NH reduction (NORR), which shows a maximum NH yield rate of 289.3 μmol h cm and a NH-Faradaic efficiency of 93.8% at -0.

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis.

GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data.

Graph and image are two common representations of Hi-C -contact maps. Existing computational tools have only adopted Hi-C data modeled as unitary data structures but neglected the potential advantages of synergizing the information of different views. Here we propose GILoop, a dual-branch neural network that learns from both representations to identify genome-wide CTCF-mediated loops.

scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.

Motivation: Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects.

Results: In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values.

Mini-review: Recent advances in post-translational modification site prediction based on deep learning.

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies.

The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events.

Bladder urothelial carcinoma (BLCA) is a complex disease with high morbidity and mortality. Changes in alternative splicing (AS) and splicing factor (SF) can affect gene expression, thus playing an essential role in tumorigenesis. This study downloaded 412 patients' clinical information and 433 samples of transcriptome profiling data from TCGA.

Subclass-Specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma.

Exploring the prognostic classification and biomarkers in Head and Neck Squamous Carcinoma (HNSC) is of great clinical significance. We hybridized three prominent strategies to comprehensively characterize the molecular features of HNSC. We constructed a 15-gene signature to predict patients' death risk with an average AUC of 0.