Geochemistry of High Arsenic Groundwaters in the Yinchuan Basin, P.R. China

AbstractHigh As groundwaters have been usually found in inland basins of north-west China. However, few data are available on groundwater As in the Yinchuan basin. Ninety-eight groundwater samples were taken from the alluvial fans, basin center, and fluvial plain of the Yellow River in the basin. Results showed that low As groundwater occurred in the alluvial fans and the basin center, while high As groundwater was present near the Yellow River. The redox potential was the key factor controlling the groundwater As concentrations. Arsenic was mobilized in reducing conditions via reductive dissolution of Fe oxides, which was supported by the positive correlation between As and Fe. In comparison to the Hetao basin, the Yinchuan groundwater had lower As concentration, lower pH, and higher redox potential

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

IntroductionMedical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.MethodsIn order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.Results and discussionWe conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset (i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art performance on complicated quotation extraction.Comment: Accepted by COLING 202

An Arginine Finger Regulates the Sequential Action of Asymmetrical Hexameric ATPase in the Double-Stranded DNA Translocation Motor

Biological motors are ubiquitous in living systems. Currently, how the motor components coordinate the unidirectional motion is elusive in most cases. Here, we report that the sequential action of the ATPase ring in the DNA packaging motor of bacteriophage ϕ29 is regulated by an arginine finger that extends from one ATPase subunit to the adjacent unit to promote noncovalent dimer formation. Mutation of the arginine finger resulted in the interruption of ATPase oligomerization, ATP binding/hydrolysis, and DNA translocation. Dimer formation reappeared when arginine mutants were mixed with other ATPase subunits that can offer the arginine to promote their interaction. Ultracentrifugation and virion assembly assays indicated that the ATPase was presenting as monomers and dimer mixtures. The isolated dimer alone was inactive in DNA translocation, but the addition of monomer could restore the activity, suggesting that the hexameric ATPase ring contained both dimer and monomers. Moreover, ATP binding or hydrolysis resulted in conformation and entropy changes of the ATPase with high or low DNA affinity. Taking these observations together, we concluded that the arginine finger regulates sequential action of the motor ATPase subunit by promoting the formation of the dimer inside the hexamer. The finding of asymmetrical hexameric organization is supported by structural evidence of many other ATPase systems showing the presence of one noncovalent dimer and four monomer subunits. All of these provide clues for why the asymmetrical hexameric ATPase gp16 of ϕ29 was previously reported as a pentameric configuration by cryo-electron microscopy (cryo-EM) since the contact by the arginine finger renders two adjacent ATPase subunits closer than other subunits. Thus, the asymmetrical hexamer would appear as a pentamer by cryo-EM, a technology that acquires the average of many images

Unveiling the microbiota-metabolite-myocardium axis: a novel perspective on cardiovascular health

IntroductionCardiovascular diseases, including myocardial infarction, remain a leading cause of death globally. Emerging evidence suggests the gut microbiota plays a crucial role in cardiovascular health. This study aims to explore the impact of gut microbiota on myocardial infarction using a mouse model.MethodsThe research utilizes a multi-omics approach, including 16S rDNA sequencing and LC-MS-based metabolomics to analyze fecal and serum samples from mice modeled to mimic myocardial infarction. This methodology allows for a comprehensive analysis of microbial populations and their metabolic output.ResultsThe findings reveal a significant reduction in gut microbiota α-diversity in mice with induced myocardial infarction compared to healthy controls. Notably, there is an increase in populations of Fusobacteria and Clostridia. Metabolomic analysis indicates disruptions in amino acid and energy metabolism, suggesting a metabolic dysregulation linked to myocardial health.DiscussionThe study proposes a novel microbiota-metabolite-myocardium axis, where specific microbial metabolites may directly affect heart health. This connection points to the gut microbiota as a potential player in the pathogenesis of myocardial infarction and may open new therapeutic avenues targeting the gut microbiome to combat cardiovascular diseases