Analysis of Toxic Elements Pollution Sources and Crop Health Risks in Soil of Typical Thallium Mining Area.

The investigation focused on Tl, Hg, As, and Sb as the targeted contaminants in the soil surrounding a thallium mining region in southwestern China. Potential sources of toxic elements were identified using correlation analysis and principal component analysis. By interpreting the results of correlation and principal component analysis, the potential sources of Tl, Hg, As, and Sb were identified to include the mining and smelting industry.

Broad Evidence Triangulation Should Be Established for a Valid and Robust Causal Relation Between Air Pollution and Health Outcome.

This letter aims to provide valuable insights into broader evidence triangulation (i.e., a well-designed primary association analysis followed by elaborate approaches to control residual confounding effects from various design and modeling perspectives) for clarifying the association between air pollutants and health outcomes.

Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems.

In coal mining operations, the stable operation of hydraulic supports is crucial for ensuring mine safety. However, the nonlinear, non-stationary characteristics and noise interference in hydraulic support pressure data pose significant challenges for anomaly detection and fault diagnosis. This study proposes an anomaly detection and failure identification method based on Gated Recurrent Unit Autoencoder (GRU-AE), aimed at achieving anomaly detection in hydraulic support pressure data and equipment failure early warning.

Study on dynamic compression characteristics of coal containing gas under different strain rates.

To investigate the dynamic compression properties and crushing features of gas-containing coal under complex geological environments, a dynamic and static combined loading test system was independently developed for conducting triaxial dynamic compression tests. The dynamic stress-strain curves under different strain rates were analyzed to study the effects of strain rate and gas pressure on the dynamic mechanical characteristics. Crushed coal samples were sieved and analyzed using a standard sieve and fractal theory.

Adaptability evaluation model and experiment of full section SBM in deep strata based on AHP-fuzzy theory.

To delve into the adaptability of the full section SBM boring process during its inaugural application, this paper innovatively put forward an adaptability evaluation model for the SBM shaft boring within composite deep strata. This model is with the degree of adaptability T as the quantitative criterion. Initially, the evaluation index system of SBM boring adaptability is established.

DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.

Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in bioinformatics have leveraged deep learning and natural language processing (NLP) to enhance promoter prediction accuracy.

Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia.

Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective data mining. This study aimed to assess the effectiveness of a low-code approach for assisting clinicians with data mining for mortality and length of stay (LOS) prediction in patients with CAP.

Simulation study on catalytic oxidation of low concentration mine gas in an oxidation device.

Anthropogenic emissions of non-CO greenhouse gases, such as low-concentration coal mine methane (cCH < 30 vol%), have a significant impact on global warming. The main component of coal mine methane is methane (CH), which is both a greenhouse gas and a high-quality clean energy gas. To study the combustion and heat transfer reactions of low-concentration coal mine methane in a catalytic oxidation device, a numerical simulation approach was employed to establish a model of the catalytic oxidation device that includes periodic boundary conditions, methane combustion mechanisms, and turbulent-laminar flow characteristics.

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning models often rely on autoregressive generation, which suffers from error accumulation and slow inference speeds.

Research on the quantitative relationship between stress shadow effect of multiple thick and hard key layers and surface subsidence.

For a long time, the management of surface structures such as villages and rivers affected by underground coal mining has been a popular and difficult issue in coal mining. With the further tightening of environmental protection requirements, it has become challenging for some underground coal mines that lack the conditions for filling and grouting to ensure the recovery of coal resources while controlling surface subsidence. Furthermore, many such common issues have emerged in the Yushen and Binchang mining areas of Shanxi Province, as well as in several other coalfields, severely constraining the development of coal energy and ecological environmental protection.

Study on the simulation of bridge deformation in a mining subsidence area.

Bridges in mining areas deform primarily because of surface subsidence caused by underground mining. Analysis of these deformations should consider the synergistic effects between the foundation soil and the bridge superstructure. The geology of mining areas, which is inherently complex, significantly effects the selection of soil mechanics parameters, potentially leading to errors in model calculations.

Deep learning-based discovery of compounds for blood pressure lowering effects.

The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug side effects. In this study, we established a deep learning-based predictive model, utilizing a data set comprised of compounds known to either elevate or lower blood pressure.

Critical depth prediction based on in-situ stress and gas content model of deep coalbed methane in Liupanshui Coalfield in China.

In-situ stress plays a pivotal role in influencing the desorption, adsorption, and transportation of coalbed methane. The reservoir gas content represents a pivotal physical parameter, encapsulating both the coalbed methane enrichment capacity and the underlying enrichment law of the reservoir. This investigation collates, computes, and consolidates data concerning pore pressure, breakdown pressure, closure pressure, triaxial principal stress, gas content, lateral pressure coefficient, and other pertinent variables from coal reservoirs within several coal-bearing synclines in the Liupanshui coalfield, China.

Non-uniform failure mechanism and stability control of mining roadway under deviatoric stress field.

Based on the elastic-plastic theory, the analytical formula of the second invariant J of deviatoric stress at any point around the circular roadway under the non-uniform stress field is derived. The distribution law of J of surrounding rock under the three-dimensional non-isobaric stress field is studied by theoretical analysis and numerical simulation. Combined with the butterfly failure theory of surrounding rock of roadway, the close relationship between the distribution pattern of J and the distribution pattern of plastic zone is found, and the failure mechanism of surrounding rock is revealed.

Chromosome-level genome assembly of a destructive leaf-mining moth Eriocrania semipurpurella alpina.

Eriocraniidae (Lepidoptera) are widespread leaf miners and have unique adaptability to hypoxia and low temperatures, causing covert but devastating harm to Fagales (Betulaceae and Fagaceae) plants in the Holarctic. However, the lack of a high-quality genome of this most ancient family within the angiosperm-feeding group largely limits the studies on the phylogeny and environmental adaptation of the primitive Lepidoptera. In this study, utilizing Illumina sequencing, PacBio HiFi sequencing, and Hi-C technology, we constructed a chromosome-level genome assembly of E.

Study of the spatial strength distribution patterns and microstructure characteristics of ultra-fine tailings cemented paste backfill.

In this study, the spatial distribution of the mechanical strength of ultra-fine tailings cemented paste backfill (UCPB) in underground stopes was examined, and the micro-mechanism responsible for differences in spatial strength performance via changes in particle deposition was elucidated. To better understand this phenomenon, we constructed a similar backfilling stope model using the ultra-fine tailings of a gold mine. We manufactured specimens at different spatial locations and conducted a novel series of tests, including uniaxial compressive strength, shear strength, and conventional triaxial tests, to obtain the strength parameters in different spatial distributions.

Data mining and analysis of adverse events of Vedolizumab based on the FAERS database.

This study aims to mine and analyze adverse events (AEs) of Vedolizumab based on the FAERS database to better understand its safety and potential risks in the real world. Data from the second quarter of 2014 to the third quarter of 2023 were collected, employing various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). The study gathered 14,753,012 reports of AEs, of which 46,726 were related to Vedolizumab.

Universal, untargeted detection of bacteria in tissues using metabolomics workflows.

Fast and reliable identification of bacteria directly in clinical samples is a critical factor in clinical microbiological diagnostics. Current approaches require time-consuming bacterial isolation and enrichment procedures, delaying stratified treatment. Here, we describe a biomarker-based strategy that utilises bacterial small molecular metabolites and lipids for direct detection of bacteria in complex samples using mass spectrometry (MS).

The fully bio-based bilayered flame retardant treatment for paper via natural bio-materials.

In this paper, we report a novel method for enhancing the flame retardancy of wood-based paper by utilizing natural biomaterials. The research constructed a bilayered structure coating on paper fiber surfaces, incorporating mixed starch (MS), adenosine triphosphate (ATP), and phytic acid (PA) as natural bio-based flame retardants. The structural configuration of the coating comprises MS/ATP and MS/PA, which were sequentially assembled as bottom and top parts, respectively, through pneumatic spraying.

Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research.

Cancer research has been significantly advanced by the integration of transcriptomic data through high-throughput sequencing technologies like RNA sequencing (RNA-seq). This paper reviews the transformative impact of transcriptomics on understanding cancer biology, focusing on the use of extensive datasets such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). While transcriptomic data provides crucial insights into gene expression patterns and disease mechanisms, the analysis is fraught with technical and biological biases.

Proteomics reveals the key transcription-related factors mediating obstructive nephropathy in pediatric patients and mice.

Background: Obstructive nephropathy is one of the leading causes of kidney injury in infants and children. Increasing evidence has shown that transcription-related factors (TRFs), including transcription factors and cofactors, are associated with kidney diseases. However, a global landscape of dysregulated TRFs in pediatric patients with obstructive nephropathy is lacking.

Internal electric field triggered charge redistribution in CuO/FeO composite to regulate the peroxymonosulfate activation for enhancing the degradation of organic pollutants.

Herein, we adopt a feasible method to synthesize the CuO/FeO composite with heterostructure. Owing to the significant differences in work functions, an internal electric field is built at the interface of heterojunction after the combination of CuO with FeO, which can reduce interface resistance and accelerate charge transfer. Interestingly, under the induction of electrostatic interaction provided by internal electric field, the CuO/FeO composite will form electron-rich and electron-deficient active zones.

Untargeted metabolomics combined with lipidomics revealed the effects of myocardial infarction and exercise rehabilitation on blood circulation metabolism of patients based on liquid chromatography-mass spectrometry.

Myocardial infarction (MI) is a major cause of death worldwide. Exercise rehabilitation (ER) is a powerful tool to improve life quality and prognosis of MI patients. Herein, we developed an untargeted metabolomics combined with lipidomics method to qualitatively and quantitatively detect metabolites in plasma.

Genome mining of nonenzymatic ortho-quinone methide-based pseudonatural products from ascidian-derived fungus Diaporthe sp.SYSU-MS4722.

Ortho-quinone methides (o-QMs), generated by oxidative dehydration of clavatol, are highly reactive intermediates in biosynthesis that give rise to a variety of clavatol-containing pseudonatural products (PNPs) in fungi through intra- and intermolecular nonenzymatic cyclization/addition reaction, and some compounds have significant biological activities. Here we report our genome mining efforts on a cryptic clavatol biosynthetic gene cluster (BGC) from an ascidian-derived fungus Diaporthe sp. SYSU-MS4722.