Search

Objective: Asthma and gastroesophageal reflux disease (GERD) often occur simultaneously, with GERD being a comorbidity of asthma. This study aimed to explore the biological markers related to asthma and GERD by bioinformatics analysis. Methods: Initially, gene expression datasets for asthma and GERD were obtained from the Gene Expression Omnibus database, and subsequent differential expression analysis yielded 620 differentially expressed genes (DEGs) for asthma and 2367 DEGs for GERD. The intersection of these two gene sets yielded a total of 84 DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these genes may be involved in steroid hormone secretion and cellular stress response. Five hub genes (PTGDR2, CPA3, FCER1A, TPSAB1, and IL1RL1) were identified by a protein–protein interaction (PPI) network analysis and topological algorithm. Results: Enrichment analysis results indicated that hub genes may be involved in hormone secretion and disease development, particularly in regulating the renin–angiotensin system and systemic arterial blood pressure. PTGDR2, CPA3, TPSAB1, and IL1RL1 were upregulated in both asthma and GERD patient groups, while FCER1A was upregulated in asthma patients but downregulated in GERD patients. Through drug prediction, 22 drugs targeting hub genes PTGDR2, FCER1A, and TPSAB1 were identified. By constructing a transcription factor (TF)-target gene network, we found that eight TFs may regulate the expression of PTGDR2, FCER1A, and IL1RL1. Conclusion: Hence, Asthma and GERD were related to steroid hormone secretion and the renin–angiotensin system. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the “three-rate method” in determining the sensitive indicators for coal roadway outburst prediction and the threat of outburst to personnel during detection, in this paper, a laboratory method for determining the sensitive index and critical value of coal roadway outburst prediction is presented. According to the principle that the apparent density and moisture of simulated soft stratification are consistent with the original coal seam in the field, the forming pressure and pressing steps of simulated soft stratification were determined. Then filling the simulated soft layer with low pressure and high pressure gas, drilling and measuring various coal roadway outburst prediction indexes respectively, the relative sensitivity of each predictor is known and one of the highest predictors of sensitivity can be obtained. Through outburst simulation or measuring the initial release gas expansion energy, the critical value of the index can be determined. Through the application in the coal mine site, it is proved that the prediction index and critical value obtained by this method are effective, and can be directly used in the field coal roadway prediction.

Taking Huangyuchuan Coal Mine as the research background, aiming at the problem that the suspected collapse column found in the geological exploration of the mining area has not yet been clear about its development range and water content, based on the geological and geophysical characteristics of the suspected collapse column, this paper adopts controlled source audio magnetotelluric method(CSAMT) to finely investigate the suspected collapse columns and applies the 2D Occam inversion method to finely process the measured data and analyze the electrical response characteristics of the geological anomalies in combination with geological data. The electrical anomaly has obvious characteristics of low resistance electrical response, and finally delineated the geological anomaly and inferred that the anomaly is caused by a water-bearing collapse column. The collapse column is mainly developed in the 6# upper coal seam near the Ordovician limestone, and the diameter is about 30 m at the corresponding 6# coal seam position(depth 395 m), and the diameter increases to the deep. Subsequent underground drilling verified the accuracy of the CSAMT exploration results.

SUMMARY: Ear length (EL) is a key trait that greatly contributes to yield in maize. Although dozens of EL quantitative trait loci have been mapped, very few causal genes have been cloned, and the molecular mechanisms remain largely unknown. Our previous study showed that YIGE1 is involved in sugar and auxin pathways to regulate ear inflorescence meristem (IM) development and thus affects EL in maize. Here, we reveal that YIGE2, the paralog of YIGE1, regulates maize ear development and EL through auxin pathway. Knockout of YIGE2 causes a significant decrease of auxin level, IM length, floret number, EL, and grain yield. yige1 yige2 double mutants had even shorter IM and ears implying that these two genes redundantly regulate IM development and EL. The genes controlling auxin levels are differential expressed in yige1 yige2 double mutants, leading to lower auxin level. These results elucidated the critical role of YIGE2 and the redundancy between YIGE2 and YIGE1 in maize ear development, providing a new genetic resource for maize yield improvement. [ABSTRACT FROM AUTHOR]

Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. To improve the classification performance of a single machine learning (ML) model, this study selects logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), and proposes a novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability in a typical landslide area in Yanbian Prefecture, China. Firstly, a dataset consisting of 1531 historical landslides was collected from field investigations and historical records, and a spatial database containing 16 predisposing factors was established. The dataset was divided into a training set and a test set in a ratio of 7:3. The results showed that SWA effectively improved the Accuracy, AUC, and robustness of the model compared to a single ML model. The SWA achieved the best classification results (Accuracy = 91.39% and AUC = 0.967). To verify the generalization ability of SWA, we selected published landslide datasets from Yanshan country and Yongxin country in China for testing. SWA also performed well, with an AUC of 0.871 and 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is the factor that has the greatest impact on landslide occurrence. The landslide susceptibility maps obtained from this study will provide an effective reference program for land use planning and disaster prevention and mitigation projects in Yanbian Prefecture, China. [ABSTRACT FROM AUTHOR]

The paper introduced structure and measuring principle of drilling trajectory measurement system, and elaborated error sources and compensation method in details from three aspects including sensor itself, environmental conditions and sensor installation. The simulation of the physical scene of horizontal directional drilling, and analysis of attitude angle before and after the error compensation show that making appropriate error compensation for different error source can improve the accuracy of attitude angle measurement.