Can the opening of public data improve the quality of urban employment?-- evidence from China.

In the era of digital economy, the opening of public data holds significant importance as a pivotal means to maximize the utilization of data elements and enhance the quality of urban employment. Leveraging urban-level data spanning from 2011 to 2021 in China, this study employs a Multi-period DID method to systematically investigate the mechanisms underlying the impact of public data openness on urban employment quality.The empirical findings reveal that the openness of public data markedly elevates the quality of urban employment.

Characterization of the complete chloroplast genome sequence of Blume 1826 (Plantaginaceae).

Blume 1826 is a perennial amphibious herb with ornamental and water purification value that is widespread in temperate and tropical Asia. In the present study, we sequenced, assembled, and annotated the complete chloroplast (cp) genome of . It is 152,395 bp in length, with a typical quadripartite structure, comprising a pair of inverted repeat regions (IRs; 25,545 bp), a large single-copy region (LSC; 83,163 bp), and a small single-copy (SSC; 18,142 bp) region.

Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data.

Previous observational studies reported associations between non-steroidal anti-inflammatory drugs (NSAIDs) and major depressive disorder (MDD), however, these associations are often inconsistent and underlying biological mechanisms are still poorly understood. We conducted a two-sample Mendelian randomisation (MR) study to examine relationships between genetic variants and NSAID target gene expression or DNA methylation (DNAm) using publicly available expression, methylation quantitative trait loci (eQTL or mQTL) data and genetic variant-disease associations from genome-wide association studies (GWAS of MDD). We also assessed drug exposure using gene expression and DNAm levels of NSAID targets as proxies.

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data.

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score.

A Mendelian randomization-based exploration of red blood cell distribution width and mean corpuscular volume with risk of hemorrhagic strokes.

Red blood cell distribution width (RCDW) and mean corpuscular volume (MCV) are associated with different risk factors for hemorrhagic stroke. However, whether RCDW and MCV are causally related to hemorrhagic stroke remains poorly understood. Therefore, we explored the causality between RCDW/MCV and nontraumatic hemorrhagic strokes using Mendelian randomization (MR) methods.

Assessment of Bidirectional Relationships between Leisure Sedentary Behaviors and Neuropsychiatric Disorders: A Two-Sample Mendelian Randomization Study.

(1) Background: Increasing evidence shows that sedentary behaviors are associated with neuropsychiatric disorders (NPDs) and thus may be a modifiable factor to target for the prevention of NPDs. However, the direction and causality for the relationship remain unknown; sedentary behaviors could increase or decrease the risk of NPDs, and/or NPDs may increase or decrease engagement in sedentary behaviors. (2) Methods: This Mendelian randomization (MR) study with two samples included independent genetic variants related to sedentary behaviors ( = 408,815), Alzheimer's disease (AD; = 63,926), schizophrenia (SCZ; = 105,318), and major depressive disorder (MDD; = 500,199), which were extracted from several of the largest non-overlapping genome-wide association studies (GWASs), as instrumental variables.

Surgical Repair of a Quadricuspid Aortic Valve With Severe Regurgitation Utilizing "Tricuspidization" and Annular Banding: A Case and Technique Details Report.

The quadricuspid aortic valve (QAV) is a rare congenital disease with a prevalence of 0. 013-0.043% of cardiac cases.

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking.

The Lung Cancer Associated MicroRNAs and Single Nucleotides Polymorphisms: a Mendelian Randomization Analysis.

Lung cancer is a major public health burden and among the highest incidence and mortality rates of the cancers. MicroRNAs (miRNAs) play an important role in the development of lung cancer. The aim of this study was to investigate whether there was a potential causal relation between miRNAs and non-small-cell lung cancer (NSCLC).

A micro GC detector array based on chemiresistors employing various surface functionalized monolayer-protected gold nanoparticles.

Aspects of the design, fabrication, and characterization of a chemiresistor type of microdetector for use in conjunction with gas chromatograph are described. The detector was manufactured on silicon chips using microelectromechanical systems (MEMS) technology. Detection was based on measuring changes in resistance across a film comprised of monolayer-protected gold nanoclusters (MPCs).