Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Genomic epidemiology of CVA10 in Guangdong, China, 2013-2021.

Hand, Foot and Mouth Disease (HFMD) is a highly contagious viral illness primarily affecting children globally. A significant epidemiological transition has been noted in mainland China, characterized by a substantial increase in HFMD cases caused by non-Enterovirus A71 (EV-A71) and non-Coxsackievirus A16 (CVA16) enteroviruses (EVs). Our study conducts a retrospective examination of 36,461 EV-positive specimens collected from Guangdong, China, from 2013 to 2021. Epidemiological trends suggest that, following 2013, Coxsackievirus A6 (CVA6) and Coxsackievirus A10 (CVA10) have emerged as the primary etiological agents for HFMD. In stark contrast, the incidence of EV-A71 has sharply declined, nearing extinction after 2018. Notably, cases of CVA10 infection were considerably younger, with a median age of 1.8 years, compared to 2.3 years for those with EV-A71 infections, possibly indicating accumulated EV-A71-specific herd immunity among young children. Through extensive genomic sequencing and analysis, we identified the N136D mutation in the 2 A protein, contributing to a predominant subcluster within genogroup C of CVA10 circulating in Guangdong since 2017. Additionally, a high frequency of recombination events was observed in genogroup F of CVA10, suggesting that the prevalence of this lineage might be underrecognized. The dynamic landscape of EV genotypes, along with their potential to cause outbreaks, underscores the need to broaden surveillance efforts to include a more diverse spectrum of EV genotypes. Moreover, given the shifting dominance of EV genotypes, it may be prudent to re-evaluate and optimize existing vaccination strategies, which are currently focused primarily target EV-A71.

Retrospective charts for reporting, analysing, and evaluating disaster emergency response: a systematic review.

OBJECTIVE: Given the frequency of disasters worldwide, there is growing demand for efficient and effective emergency responses. One challenge is to design suitable retrospective charts to enable knowledge to be gained from disasters. This study provides comprehensive understanding of published retrospective chart review templates for designing and updating retrospective research. METHODS: We conducted a systematic review and text analysis of peer-reviewed articles and grey literature on retrospective chart review templates for reporting, analysing, and evaluating emergency responses. The search was performed on PubMed, Cochrane, and Web of Science and pre-identified government and non-government organizational and professional association websites to find papers published before July 1, 2022. Items and categories were grouped and organised using visual text analysis. The study is registered in PROSPERO (374,928). RESULTS: Four index groups, 12 guidelines, and 14 report formats (or data collection templates) from 21 peer-reviewed articles and 9 grey literature papers were eligible. Retrospective tools were generally designed based on group consensus. One guideline and one report format were designed for the entire health system, 23 studies focused on emergency systems, while the others focused on hospitals. Five papers focused specific incident types, including chemical, biological, radiological, nuclear, mass burning, and mass paediatric casualties. Ten papers stated the location where the tools were used. The text analysis included 123 categories and 1210 specific items; large heterogeneity was observed. CONCLUSION: Existing retrospective chart review templates for emergency response are heterogeneous, varying in type, hierarchy, and theoretical basis. The design of comprehensive, standard, and practicable retrospective charts requires an emergency response paradigm, baseline for outcomes, robust information acquisition, and among-region cooperation.

LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation.

The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (https://hz-t3.matpool.com:28765?token=aQjYR4hqMI), revealing real-time performance with no login requirements.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction.

Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI prediction. Consequently, there has been a notable surge in the development of network-based computational approaches for predicting DDIs. However, current approaches face limitations in capturing the spatial relationships between neighboring nodes and their higher-level features during the aggregation of neighbor representations. To address this issue, this study introduces a novel model, KGCNN, designed to comprehensively tackle DDI prediction tasks by considering spatial relationships between molecules within the biomedical knowledge graph (BKG). KGCNN is built upon a message-passing GNN framework, consisting of propagation and aggregation. In the context of the BKG, KGCNN governs the propagation of information based on semantic relationships, which determine the flow and exchange of information between different molecules. In contrast to traditional linear aggregators, KGCNN introduces a spatial-aware capsule aggregator, which effectively captures the spatial relationships among neighboring molecules and their higher-level features within the graph structure. The ultimate goal is to leverage these learned drug representations to predict potential DDIs. To evaluate the effectiveness of KGCNN, it undergoes testing on two datasets. Extensive experimental results demonstrate its superiority in DDI predictions and quantified performance.

Electrochemical Deconstructive and Ring-Expansion Functionalization of Unstrained Cycloalkanols.

An efficient and sustainable electrochemical method for the synthesis of cyclic ethers and acyclic aldehydes from alkanols has been reported. This strategy has been successfully applied to cycloalkanols bearing different ring sizes and different types of nucleophiles. In addition, mechanistic investigations show that the reactions undergo sequential processes, including anodic oxidation, ß-scission, and nucleophilic addition. This method provides a new synthetic approach to constructing cyclic ethers and terminal aldehydes from cycloalkanols and nucleophiles.

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives.

scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures.

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064 nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Molecular epidemiology and population immunity of SARS-CoV-2 in Guangdong (2022-2023) following a pivotal shift in the pandemic.

The SARS-CoV-2 Omicron variant sparked the largest wave of infections worldwide. Mainland China eased its strict COVID-19 measures in late 2022 and experienced two nationwide Omicron waves in 2023. Here, we investigated lineage distribution and virus evolution in Guangdong, China, 2022-2023 by comparing 5813 local viral genomes with the datasets from other regions of China and worldwide. Additionally, we conducted three large-scale serological surveys involving 1696 participants to measure their immune response to the BA.5 and XBB.1.9 before and after the corresponding waves. Our findings revealed the Omicron variants, mainly the BA.5.2.48 lineage, causing infections in over 90% of individuals across different age groups within a month. This rapid spread led to the establishment of widespread immunity, limiting the virus's ability to further adaptive mutation and dissemination. While similar immune responses to BA.5 were observed across all age groups after the initial wave, children aged 3 to 11 developed a stronger cross immune response to the XBB.1.9 strain, possibly explaining their lower infection rates in the following XBB.1 wave. Reinfection with Omicron XBB.1 variant triggered a more potent neutralizing immune response among older adults. These findings highlight the impact of age-specific immune responses on viral spread in potential future waves.

Giant zero-field cooling exchange-bias-like behavior in antiperovskite Mn3Co0.61Mn0.39N compound.

Giant zero-field cooling exchange-bias-like behavior with H EB = 3.49kOe was found in an antiperovskite Mn3Co0.61Mn0.39N compound. The magnetic structure of Mn3Co0.61Mn0.39N was resolved to be ferrimagentic ordering composed of canted Γ5g antiferromagnetic (AFM) and ferromagnetic (FM) along the [111] direction by the neutron diffraction technique. The exchange coupling model was proposed together with the first principles calculation for further understanding this exchange-bias-like behavior. It was found that the ferromagnetic exchange interaction between FM and the canted Γ5g AFM play an important role in the particular exchange-bias-like behavior. The exchange coupling constructed in the lattice is distinct from the interactions between collinear AFM and FM in conventional exchange bias system. In addition to the enhanced horizontal shift, hysteresis loops obtained after FC cooling also exhibited vertical shift. The macroscopic vertical shift of the magnetization is ascribed to the increase of the magnetic moment of canted Γ5g spins along the external magnetic field. This finding will promote the development of advanced magnetic devices.