Mode Hypergraph Neural Network.

The hypergraph neural network (HGNN) is an emerging powerful tool for modeling and learning complex, high-order correlations among entities upon hypergraph structures. While existing HGNN-based approaches excel in modeling high-order correlations among data using hyperedges, they often have difficulties in distinguishing diverse semantics ( e.g.

Rapid aging of influenza epidemics in China from 2005/06 to 2016/17: A population-based study.

Background: China is an aging society, and the older population is at a higher risk of influenza infection and influenza-related mortality. However, there is limited knowledge regarding the aging of influenza epidemics, which is crucial for estimating the disease burden.

Methods: We collected weekly influenza surveillance data from 2005/06 to 2016/17, and quantified the aging of influenza-like illness (ILI) and influenza virus-positive cases in China via the mean age of the influenza cases and the proportion of individuals aged 65 and above among the influenza cases.

DYRK1A-TGF-β signaling axis determines sensitivity to OXPHOS inhibition in hepatocellular carcinoma.

Intervening in mitochondrial oxidative phosphorylation (OXPHOS) has emerged as a potential therapeutic strategy for certain types of cancers. Employing kinome-based CRISPR screen, we find that knockout of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) synergizes with OXPHOS inhibitor IACS-010759 in liver cancer cells. Targeting DYRK1A combined with OXPHOS inhibitors activates TGF-β signaling, which is crucial for OXPHOS-inhibition-triggered cell death.

Deciphering the role of the MALT1-RC3H1 axis in regulating GPX4 protein stability.

Ferroptosis, a unique form of iron-dependent cell death triggered by lipid peroxidation accumulation, holds great promise for cancer therapy. Despite the crucial role of GPX4 in regulating ferroptosis, our understanding of GPX4 protein regulation remains limited. Through FACS-based genome-wide CRISPR screening, we identified MALT1 as a regulator of GPX4 protein.

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy.

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME).

Multi-model analysis of gallbladder cancer reveals the role of OxLDL-absorbing neutrophils in promoting liver invasion.

Background: Gallbladder cancer (GBC) is the most common and lethal malignancy of the biliary tract that lacks effective therapy. In many GBC cases, infiltration into adjacent organs or distant metastasis happened long before the diagnosis, especially the direct liver invasion, which is the most common and unfavorable way of spreading.

Methods: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), proteomics, and multiplexed immunohistochemistry (mIHC) were performed on GBC across multiple tumor stages to characterize the tumor microenvironment (TME), focusing specifically on the preferential enrichment of neutrophils in GBC liver invasion (GBC-LI).

Neutrophil profiling illuminates anti-tumor antigen-presenting potency.

Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life, it remains challenging to explore how neutrophils adopt specific fates in cancer. Here, we generated and integrated single-cell neutrophil transcriptomes from 17 cancer types (225 samples from 143 patients).

Dual Inhibition of CDK4/6 and XPO1 Induces Senescence With Acquired Vulnerability to CRBN-Based PROTAC Drugs.

Background & Aims: Despite the increasing number of treatment options available for liver cancer, only a small proportion of patients achieve long-term clinical benefits. Here, we aim to develop new therapeutic approaches for liver cancer.

Methods: A compound screen was conducted to identify inhibitors that could synergistically induce senescence when combined with cyclin-dependent kinase (CDK) 4/6 inhibitor.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics.

Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval.

The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes the categories of objects in the retrieval stage are all seen in the training stage. Existing methods under this setting may tend to only lazily discriminate their categories, while not learning a generalized 3D object embedding. Under such circumstances, it is still a challenging and open problem in real-world applications due to the existence of various unseen categories.

Impact of respiratory aerosol size and number distribution on the relative importance of different routes in SARS-CoV-2 transmission.

Exploring the relative importance of different routes in SARS-CoV-2 transmission is crucial in infection prevention. However, even in the same environmental setting, the relative importance of different routes has varied in different studies. We hypothesize that respiratory aerosol size and number distribution might play a key role.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology.

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis.

An Inflammatory Checkpoint Generated by IL1RN Splicing Offers Therapeutic Opportunity for KRAS-Mutant Intrahepatic Cholangiocarcinoma.

Unlabelled: KRAS mutations are causally linked to protumor inflammation and are identified as driving factors in tumorigenesis. Here, using multiomics data gathered from a large set of patients, we showed that KRAS mutation was associated with a specific landscape of alternative mRNA splicing that connected to myeloid inflammation in intrahepatic cholangiocarcinoma (iCCA). Then, we identified a negative feedback mechanism in which the upregulation of interleukin 1 receptor antagonist (IL1RN)-201/203 due to alternative splicing confers vital anti-inflammatory effects in KRAS-mutant iCCA.

Timing and Magnitude of the Second Wave of the COVID-19 Omicron Variant - 189 Countries and Territories, November 2021 to February 2023.

What Is Already Known About This Topic?: The first nationwide wave of coronavirus disease 2019 (COVID-19), driven by the Omicron variant, has largely subsided. However, subsequent epidemic waves are inevitable due to waning immunity and the ongoing evolution of the severe acute respiratory syndrome coronavirus 2.

What Is Added By This Report?: Insights gleaned from other nations offer guidance regarding the timing and scale of potential subsequent waves of COVID-19 in China.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets.

Genome-Scale CRISPR screen identifies LAPTM5 driving lenvatinib resistance in hepatocellular carcinoma.

cld-CASP3: cleaved caspase 3; cld-PARP: cleaved PARP; DTP: drug tolerant persister; GO: Gene Ontology; GTEx: The Genotype-Tissue Expression; HCC: hepatocellular carcinoma; HCQ: hydroxychloroquine; IC50: half maximal inhibitory concentration value; KEGG: Kyoto Encyclopedia of Genes and Genomes; LAPTM5: lysosomal protein transmembrane 5; NT: non-targeting; PDC: patient-derived primary cell lines; PDO: patient-derived primary organoid; TCGA: The Cancer Genome Atlas.

Multimodule characterization of immune subgroups in intrahepatic cholangiocarcinoma reveals distinct therapeutic vulnerabilities.

Background: Immune microenvironment is well recognized as a critical regulator across cancer types, despite its complex roles in different disease conditions. Intrahepatic cholangiocarcinoma (iCCA) is characterized by a tumor-reactive milieu, emphasizing a deep insight into its immunogenomic profile to provide prognostic and therapeutic implications.

Methods: We performed genomic, transcriptomic, and proteomic characterization of 255 paired iCCA and adjacent liver tissues.

Increasing contributions of airborne route in SARS-CoV-2 omicron variant transmission compared with the ancestral strain.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has become the dominant lineage worldwide. Experimental studies have shown that SARS-CoV-2 Omicron variant is more stable on various environmental surfaces than the ancestral strains of SARS-CoV-2. However, the influences on the role of the contact route in SARS-CoV-2 transmission are still unknown.

HGNN: General Hypergraph Neural Networks.

Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in practice. A few hypergraph-based methods have recently been proposed to address the problem of multi-modal/multi-type data correlation by directly concatenating the hypergraphs constructed from each single individual modality/type, which is difficult to learn an adaptive weight for each modality/type.

Graph Learning on Millions of Data in Seconds: Label Propagation Acceleration on Graph Using Data Distribution.

Graph-based semi-supervised learning methods have been used in a wide range of real-world applications, e.g., from social relationship mining to multimedia classification and retrieval.

Single-cell transcriptomic analysis suggests two molecularly subtypes of intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by performing single-cell RNA sequencing on 144,878 cells from 14 pairs of iCCA tumors and non-tumor liver tissues, we find that S100P and SPP1 are two markers for iCCA perihilar large duct type (iCCA) and peripheral small duct type (iCCA). S100P + SPP1- iCCA has significantly reduced levels of infiltrating CD4 T cells, CD56 NK cells, and increased CCL18 macrophages and PD1CD8 T cells compared to S100P-SPP1 + iCCA.

An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy.

Hydrogen sulfide (HS) is an active physiological molecule, and its intracellular level has great significance to life functions. In this study, an effective and sensitive method was developed for HS sensing with dark field microscopy (DFM). The proposed method employed AuNPs as the signal source, DFM as the readout system, and an intelligence algorithm as the image processing and output systems, respectively.

Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.

The continuous emergence of drug-target interaction data provides an opportunity to construct a biological network for systematically discovering unknown interactions. However, this is challenging due to complex and heterogeneous correlations between drug and target. Here, we describe a heterogeneous hypergraph-based framework for drug-target interaction (HHDTI) predictions by modeling biological networks through a hypergraph, where each vertex represents a drug or a target and a hyperedge indicates existing similar interactions or associations between the connected vertices.

Single nanoparticle identification coupled with auto-identify algorithm for rapid and accurate detection of L-histidine.

In this work, an auto-identify sensor was constructed for rapid and high-precision detection of L-histidine. The proposed strategy is based on the auto-identify algorithm and the aggregation of alkynyl and azide functionalized gold nanoparticles induced by the Cu catalyzed azides and alkynes cycloaddition (CuAAC) reaction. Specially, the color of scattering light spots for the aggregated gold nanoparticle (AuNPs) caused by CuAAC reaction was quite different from that of the monomers.