Model-agnostic counterfactual reasoning for identifying and mitigating answer bias in knowledge tracing.

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT. We have released our code at https://github.com/lucky7-code/CORE.

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma.

Multiple Myeloma (MM) remains incurable despite advances in treatment options. Although tumor subtypes and specific DNA abnormalities are linked to worse prognosis, the impact of immune dysfunction on disease emergence and/or treatment sensitivity remains unclear. We established a harmonized consortium to generate an Immune Atlas of MM aimed at informing disease etiology, risk stratification, and potential therapeutic strategies. We generated a transcriptome profile of 1,149,344 single cells from the bone marrow of 263 newly diagnosed patients enrolled in the CoMMpass study and characterized immune and hematopoietic cell populations. Associating cell abundances and gene expression with disease progression revealed the presence of a proinflammatory immune senescence-associated secretory phenotype in rapidly progressing patients. Furthermore, signaling analyses suggested active intercellular communication involving APRIL-BCMA, potentially promoting tumor growth and survival. Finally, we demonstrate that integrating immune cell levels with genetic information can significantly improve patient stratification.

Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci.

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.

[Retrospective review of airway obstruction in 19 infants with pharyngeal cysts].

Objective:To discuss the clinical characteristic and treatment of laryngeal cysts in infants. Methods:The clinical data of 19 patients diagnosed with laryngeal cysts in Department of Otolaryngology, Sichuan Provincial Maternity and Child Health Care Hospital from November 2017 to April 2023 were retrospectively analyzed. Results:All of the 19 patients were diagnosed as laryngeal cysts, with clinical manifestations included respiratory distress, inspiratory dyspnea, difficulty in feeding and low and weak crying, etc. All of them were cured after surgical treatment. Conclusion:Misdiagnosis and missed diagnosis of laryngeal cysts are prone to occur in infants and young children. After diagnosis, patients should undergo early surgical treatment to remove airway obstruction and improve ventilation.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review.

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Comprehensive Analyses Reveal Effects on Tumor Immune Infiltration and Immunotherapy Response of APOBEC Mutagenesis and Its Molecular Mechanisms in Esophageal Squamous Cell Carcinoma.

The apolipoprotein B mRNA editing enzyme catalytic polypeptide (APOBEC) mutagenesis is prevalent in esophageal squamous cell carcinoma (ESCC). However, the functional role of APOBEC mutagenesis has yet to be fully delineated. To address this, we collect matched multi-omics data of 169 ESCC patients and evaluate characteristics of immune infiltration using multiple bioinformatic approaches based on bulk and single-cell RNA sequencing (scRNA-seq) data and verified by functional assays. We find that APOBEC mutagenesis prolongs overall survival (OS) of ESCC patients. The reason for this outcome is probably due to high anti-tumor immune infiltration, immune checkpoints expression and immune related pathway enrichment, such as interferon (IFN) signaling, innate and adaptive immune system. The elevated AOBEC3A (A3A) activity paramountly contributes to the footprints of APOBEC mutagenesis and is first discovered to be transactivated by FOSL1. Mechanistically, upregulated A3A exacerbates cytosolic double-stranded DNA (dsDNA) accumulation, thus stimulating cGAS-STING pathway. Simultaneously, A3A is associated with immunotherapy response which is predicted by TIDE algorithm, validated in a clinical cohort and further confirmed in mouse models. These findings systematically elucidate the clinical relevance, immunological characteristics, prognostic value for immunotherapy and underlying mechanisms of APOBEC mutagenesis in ESCC, which demonstrate great potential in clinical utility to facilitate clinical decisions.

Epithelial cells activate fibroblasts to promote esophageal cancer development.

Esophageal squamous-cell carcinoma (ESCC) develops through multistage epithelial cancer formation, i.e., from normal epithelium, low- and high-grade intraepithelial neoplasia to invasive carcinoma. However, how the precancerous lesions progress to carcinoma remains elusive. Here, we report a comprehensive single-cell RNA sequencing and spatial transcriptomic study of 79 multistage esophageal lesions from 29 patients with ESCC. We reveal a gradual and significant loss of ANXA1 expression in epithelial cells due to its transcription factor KLF4 suppression along the lesion progression. We demonstrate that ANXA1 is a ligand to formyl peptide receptor type 2 (FPR2) on fibroblasts that maintain fibroblast homeostasis. Loss of ANXA1 leads to uncontrolled transformation of normal fibroblasts into cancer-associated fibroblasts (CAFs), which can be enhanced by secreted TGF-ß from malignant epithelial cells. Given the role of CAFs in cancer, our study underscores ANXA1/FPR2 signaling as an important crosstalk mechanism between epithelial cells and fibroblasts in promoting ESCC.

Accumulation of dissolved organic matter in the transition from fresh to aged seasonal snow in an industrial city in NE China.

Dissolved organic matter (DOM) plays a significant role in the reduction of snow albedo and the acceleration of snowmelt, but its accumulation in snow remains poorly understood. This study investigated the accumulation of DOM in seasonal snow including its accumulation rate, molecular characteristics, and biological and chemical processing. Sixteen snow samples of both fresh and aged snow were collected at one-day interval in Changchun, a typical industrial city in NE China. The snow DOM contents increased linearly with accumulation time at a rate of 30.3 µg L-1 d-1. The optical properties, including fluorescence intensity and optical absorption coefficient, of snowmelt increased exponentially with time owing to the rapid accumulation of terrestrial humic-like fluorophores through snow-soil exchange and deposition of soil-derived substances. Fourier transform-ion cyclotron resonance-mass spectrometry highlighted the properties of DOM at a molecular level, indicating that compounds derived from underlying soil and vascular plants make the largest contribution to DOM. Microbe-derived compounds contribute 35.5 % to the DOM pool. Degrees of saturation and oxidation increase slightly after accumulation, with the impacts of photo- and bio-chemistry on DOM molecules being non-negligible. This study provides a new perspective concerning the accumulation and fate of organic contaminants in snow ecosystems.

AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection.

Although deep learning-based techniques for salient object detection have considerably improved over recent years, estimated saliency maps still exhibit imprecise predictions owing to the internal complexity and indefinite boundaries of salient objects of varying sizes. Existing methods emphasize the design of an exemplary structure to integrate multi-level features by employing multi-scale features and attention modules to filter salient regions from cluttered scenarios. We propose a saliency detection network based on three novel contributions. First, we use a dense feature extraction unit (DFEU) by introducing large kernels of asymmetric and grouped-wise convolutions with channel reshuffling. The DFEU extracts semantically enriched features with large receptive fields and reduces the gridding problem and parameter sizes for subsequent operations. Second, we suggest a cross-feature integration unit (CFIU) that extracts semantically enriched features from their high resolutions using dense short connections and sub-samples the integrated information into different attentional branches based on the inputs received for each stage of the backbone. The embedded independent attentional branches can observe the importance of the sub-regions for a salient object. With the constraint-wise growth of the sub-attentional branches at various stages, the CFIU can efficiently avoid global and local feature dilution effects by extracting semantically enriched features via dense short-connections from high and low levels. Finally, a contour-aware saliency refinement unit (CSRU) was devised by blending the contour and contextual features in a progressive dense connected fashion to assist the model toward obtaining more accurate saliency maps with precise boundaries in complex and perplexing scenarios. Our proposed model was analyzed with ResNet-50 and VGG-16 and outperforms most contemporary techniques with fewer parameters.

Characterization of genomic alterations and neoantigens and analysis of immune infiltration identified therapeutic and prognostic biomarkers in adenocarcinoma at the gastroesophageal junction.

Objectives: Adenocarcinoma at the gastroesophageal junction (ACGEJ) refers to a malignant tumor that occurs at the esophagogastric junction. Despite some progress in targeted therapies for HER2, FGFR2, EGFR, MET, Claudin 18.2 and immune checkpoints in ACGEJ tumors, the 5-year survival rate of patients remains poor. Thus, it is urgent to explore genomic alterations and neoantigen characteristics of tumors and identify CD8+ T-cell infiltration-associated genes to find potential therapeutic targets and develop a risk model to predict ACGEJ patients' overall survival (OS). Methods: Whole-exome sequencing (WES) was performed on 55 paired samples from Chinese ACGEJ patients. Somatic mutations and copy number variations were detected by Strelka2 and FACETS, respectively. SigProfiler and SciClone were employed to decipher the mutation signature and clonal structure of each sample, respectively. Neoantigens were predicted using the MuPeXI pipeline. RNA sequencing (RNA-seq) data of ACGEJ samples from our previous studies and The Cancer Genome Atlas (TCGA) were used to identify genes significantly associated with CD8+ T-cell infiltration by weighted gene coexpression network analysis (WGCNA). To construct a risk model, we conducted LASSO and univariate and multivariate Cox regression analyses. Results: Recurrent MAP2K7, RNF43 and RHOA mutations were found in ACGEJ tumors. The COSMIC signature SBS17 was associated with ACGEJ progression. CCNE1 and VEGFA were identified as putative CNV driver genes. PI3KCA and TP53 mutations conferred selective advantages to cancer cells. The Chinese ACGEJ patient neoantigen landscape was revealed for the first time, and 58 potential neoantigens common to TSNAdb and IEDB were identified. Compared with Siewert type II samples, Siewert type III samples had significant enrichment of the SBS17 signature, a lower TNFRSF14 copy number, a higher proportion of samples with complex clonal architecture and a higher neoantigen load. We identified 10 important CD8+ T-cell infiltration-related Hub genes (CCL5, CD2, CST7, GVINP1, GZMK, IL2RB, IKZF3, PLA2G2D, P2RY10 and ZAP70) as potential therapeutic targets from the RNA-seq data. Seven CD8+ T-cell infiltration-related genes (ADAM28, ASPH, CAMK2N1, F2R, STAP1, TP53INP2, ZC3H3) were selected to construct a prognostic model. Patients classified as high risk based on this model had significantly worse OS than low-risk patients, which was replicated in the TCGA-ACGEJ cohort. Conclusions: This study provides new neoantigen-based immunotherapeutic targets for ACGEJ treatment and effective disease prognosis biomarkers.

Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction.

Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this article, we reveal the students' behavior trajectories by mining campus smartcard records, and capture the characteristics inherent in trajectories for academic performance prediction. Particularly, we carefully design a tri-branch convolutional neural network (CNN) architecture, which is equipped with rowwise, columnwise, and depthwise convolutions and attention operations, to effectively capture the persistence, regularity, and temporal distribution of student behavior in an end-to-end manner, respectively. However, different from existing works mainly targeting at improving the prediction performance for the whole students, we propose to cast academic performance prediction as a top-k ranking problem, and introduce a top-k focused loss to ensure the accuracy of identifying academically at-risk students. Extensive experiments were carried out on a large-scale real-world dataset, and we show that our approach substantially outperforms recently proposed methods for academic performance prediction. For the sake of reproducibility, our codes have been released at https://github.com/ZongJ1111/Academic-Performance-Prediction.

Metabolomic Analysis Reveals Changes of Bioactive Compounds in Mung Beans (Vigna radiata) during γ-Aminobutyric Acid Enrichment Treatment.

Soaking together with Heat and Relative Humidity (HRH) treatment has been applied successfully to enrich γ-aminobutyric acid (GABA) in mung beans. However, whether and how the above GABA enrichment processing influences the other bioactive molecules is elusive. In the present study, mung beans were soaked and then treated by HRH for 5 or 7 h. By using metabolomics techniques, the changes of 496 metabolites were determined. The relative content of flavonoids and phenolic acids increased during soaking but slightly decreased during HRH. Intriguingly, soaking and HRH had the opposite effects on the glycosylation of polyphenols. The relative content of glycosylated or un-glycosylated polyphenols increased during soaking or HRH, respectively. The relative content of α-ketoglutaric acid increased more than 20 times after 5 h HRH treatment. Bioactive molecules could be enriched during GABA enrichment processing. Depending on the desired bioactive compounds, soaking and different duration of HRH treatment could be selected.

Computational Identification of Preneoplastic Cells Displaying High Stemness and Risk of Cancer Progression.

Evidence points toward the differentiation state of cells as a marker of cancer risk and progression. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Recent maps of somatic mutagenesis in normal tissues from young healthy individuals have revealed cancer driver mutations, indicating that these do not correlate well with differentiation state and that other molecular events also contribute to cancer development. We hypothesized that the differentiation state of single cells can be measured by estimating the regulatory activity of the transcription factors (TF) that control differentiation within that cell lineage. To this end, we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA sequencing data. CancerStemID is validated in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. Spatial transcriptomics and whole-genome bisulfite sequencing demonstrated that differentiation activity of tissue-specific TFs was decreased in cancer cells compared with the basal cell-of-origin layer and established that differentiation state correlated with differential DNA methylation at the promoters of these TFs, independently of underlying NOTCH1 and TP53 mutations. The findings were replicated in a mouse model of ESCC development, and the broad applicability of CancerStemID to other cancer-types was demonstrated. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy to identify stem-like preneoplastic cells that undergo positive selection. SIGNIFICANCE: This study develops a computational strategy to dissect the heterogeneity of differentiation states within a preneoplastic cell population, allowing identification of stem-like cells that may drive cancer progression.

Effect of an Environment Friendly Heat and Relative Humidity Approach on γ-Aminobutyric Acid Accumulation in Different Highland Barley Cultivars.

In this study, heat and relative humidity (HRH) treatment was applied in highland barley for γ-aminobutyric acid (GABA) accumulation. Tibetan highland barley cultivars (25) were selected for comparison and analysis. HRH treatment could accumulate GABA in several hours with low moisture content and high temperature, and the grains were treated for 2.5 h at 65 °C in this study. The GABA content of processed grains under HRH optimal condition ranged from 26.91 to 76.28 mg·100 g−1, which was significantly higher than the initial content (12.78−43.00 mg·100 g−1). The highest GABA accumulation capacity was observed in two-row yellow cultivars (YT1), increasing from 36.52 to 76.28 mg·100 g−1. Correlation analysis showed that the accumulation of GABA after HRH treatment was positively and significantly (p < 0.05) correlated with the contents of protein (0.52), total free amino acids (0.68), threonine (0.53), serine (0.51), glutamate (0.69), glycine (0.49), alanine (0.46), cysteine (0.57), tyrosine (0.50), lysine (0.53), proline (0.40), and glutamate decarboxylase (GAD) activity (0.62), which were closely related to GABA-shunt pathway. The polyamines contents, diamine oxidase (DAO) and polyamine oxidase (PAO) activities, as the substrates and critical enzymes of polyamine degradation pathway, showed no significant correlation with GABA accumulation. The results suggested that the main pathway of GABA accumulation in highland barley under HRH treatment was GABA-shunt pathway.

Preparation of formate-free PMA@MOF-808 catalysts for deep oxidative desulfurization of model fuels.

Due to the increasingly serious environmental problems caused by the combustion of sulfides in fuel, deep desulfurization of fuel became particularly urgent. Herein, the catalyst (PMA@MOF-808) of the Zr-based metal-organic framework (MOF-808) encapsulating phosphomolybdic acid (PMA) was prepared via a one-pot hydrothermal method. Besides, the formate ions of PMA@MOF-808 were removed by posttreatment with methanol, resulting in formate-free PMA@MOF-808-H catalysts with unsaturated open metal sites. The as-synthesized catalysts were systematically characterized by XRD, FT-IR, SEM, BET, TGA, 1H NMR and XPS. The catalysts were also applied in catalytic oxidation desulfurization of fuel. The results indicated that the introduction of PMA and the removal of formate ions can improve the desulfurization performance of catalysts. Formate-free 0.2-PMA@MOF-808-H catalyst can reach 100% desulfurization rate for DBT. Besides, the kinetic properties were studied, and the apparent activation energy was 29.34 kJ/mol.

Identification of Pyruvate Carboxylase as the Cellular Target of Natural Bibenzyls with Potent Anticancer Activity against Hepatocellular Carcinoma via Metabolic Reprogramming.

Cancer cell proliferation in some organs often depends on conversion of pyruvate to oxaloacetate via pyruvate carboxylase (PC) for replenishing the tricarboxylic acid cycle to support biomass production. In this study, PC was identified as the cellular target of erianin using the photoaffinity labeling-click chemistry-based probe strategy. Erianin potently inhibited the enzymatic activity of PC, which mediated the anticancer effect of erianin in human hepatocellular carcinoma (HCC). Erianin modulated cancer-related gene expression and induced changes in metabolic intermediates. Moreover, erianin promotes mitochondrial oxidative stress and inhibits glycolysis, leading to insufficient energy required for cell proliferation. Analysis of 14 natural analogs of erianin showed that some compounds exhibited potent inhibitory effects on PC. These results suggest that PC is a cellular target of erianin and reveal the unrecognized function of PC in HCC tumorigenesis; erianin along with its analogs warrants further development as a novel therapeutic strategy for the treatment of HCC.

Xin Su Ning-A Review of Basic and Clinical Pharmacology Integrated With Traditional Chinese Medicine Antiarrhythmic Theory.

Xin Su Ning (XSN) is a patented multicomponent medicine, which was certified in 2005 by the China State Food and Drug Administration to be produced pharmaceutically and to be used clinically. The XSN capsule was developed from an effective formula composed by Prof. Shuwen Ding of Shandong University of Traditional Chinese Medicine. Through more than 30 years of clinical observation, Prof. Ding concluded that XSN has a significant effect on arrhythmia with phlegm-heat heart-disturbed syndrome according to the traditional Chinese medicine (TCM) diagnosis. XSN, derived from a classical TCM formula Huanglian Wen Dan Decoction, is formulated with 11 Chinese herbal medicines to treat cardiac ventricular arrhythmia. Clinical evidence suggests that it is particularly efficacious for the arrhythmias induced by cardiac ischemia and viral myocarditis without obvious adverse reactions being reported. Cellular electrophysiological studies in ventricular myocytes revealed that XSN prolongs the duration and suppresses the amplitude of the action potential (AP), which is supported by the blockage of sodium and potassium channels indicating the characteristics of class I and III antiarrhythmic drugs. A recently reported double-blind, placebo-controlled, multicenter clinical trial of XSN enrolled 861 patients (ChiCTR-TRC-14004180) and showed that XSN significantly inhibited premature ventricular contraction (PVC). The cellular electrophysiological discoveries provided the mechanistic evidence for the clinical efficacy on inhibition of PVC by XSN as demonstrated in the clinical trial. These studies, for the first time, provided exclusive evidence that multicomponent TCM antiarrhythmic medicine can be evaluated using conventional research methods that have been used for antiarrhythmic drug discoveries for decades. We aimed to give a comprehensive review on XSN including its origin with the support of TCM theory, its pre-licensing clinical use and development, and its pharmacological and clinical study discoveries. The review will be summarized with the discoveries reported in a novel network pharmacological study that introduced a weight coefficient, which made it possible to evaluate the pharmacological properties of the TCM formula with regard to its formation based on TCM theory. Limitations regarding XSN's basic and clinical research and possible future studies are listed. We hope that the advances in how XSN was studied may offer useful guidance on how other TCM could be studied with respect to the integrity of the TCM formulas.

Traditional Chinese medicine in COVID-19.

COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread across the globe, posing an enormous threat to public health and safety. Traditional Chinese medicine (TCM), in combination with Western medicine (WM), has made important and lasting contributions in the battle against COVID-19. In this review, updated clinical effects and potential mechanisms of TCM, presented in newly recognized three distinct phases of the disease, are summarized and discussed. By integrating the available clinical and preclinical evidence, the efficacies and underlying mechanisms of TCM on COVID-19, including the highly recommended three Chinese patent medicines and three Chinese medicine formulas, are described in a panorama. We hope that this comprehensive review not only provides a reference for health care professionals and the public to recognize the significant contributions of TCM for COVID-19, but also serves as an evidence-based in-depth summary and analysis to facilitate understanding the true scientific value of TCM.

A body map of somatic mutagenesis in morphologically normal human tissues.

Somatic mutations that accumulate in normal tissues are associated with ageing and disease1,2. Here we performed a comprehensive genomic analysis of 1,737 morphologically normal tissue biopsies of 9 organs from 5 donors. We found that somatic mutation accumulations and clonal expansions were widespread, although to variable extents, in morphologically normal human tissues. Somatic copy number alterations were rarely detected, except for in tissues from the oesophagus and cardia. Endogenous mutational processes with the SBS1 and SBS5 mutational signatures are ubiquitous among normal tissues, although they exhibit different relative activities. Exogenous mutational processes operate in multiple tissues from the same donor. We reconstructed the spatial somatic clonal architecture with sub-millimetre resolution. In the oesophagus and cardia, macroscopic somatic clones that expanded to hundreds of micrometres were frequently seen, whereas in tissues such as the colon, rectum and duodenum, somatic clones were microscopic in size and evolved independently, possibly restricted by local tissue microstructures. Our study depicts a body map of somatic mutations and clonal expansions from the same individual.