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Rockburst is a complex dynamic disaster in coal mining and affected by many factors. To accurately predict the rockburst hazard among complex influencing factors, a prediction model of rockburst hazard based on the Gaussian process for binary classification (GPC) was proposed after the identification of the intrinsic relationship between multiple factors of coal mines and rockburst. Through computerized machine learning and integrated intelligent analysis, the non-linear mapping of rockburst hazard and its influencing factors was established. The multi-factor pattern recognition model was constructed using artificial intelligence. The prediction criteria of the rockburst hazard probability and the hazard probability value of the prediction area unit were determined by applying neural network and fuzzy inference methods. In addition, the rockburst hazardous zone was classified, and the corresponding technical scheme for the prevention was put forward. The validity and feasibility of the regional prediction of rockburst hazard based on GPC were verified in the engineering practice. This method is highly targeted and can improve the accuracy and precision of rockburst prediction, thus contributing to the safe and efficient production of coal mines.

Abstract Background Next-generation sequencing technology is developing rapidly and target capture sequencing has become an important technique. Several different platforms for library preparation and target capture with different bait types respectively are commercially available. Here we compare the performance of the four platforms with different bait types to find out their advantages and limitations. The purpose of this study is to help investigators and clinicians select the appropriate platform for their particular application and lay the foundation for the development of a better target capture platform for next-generation sequencing. Results We formulate capture efficiency as a novel parameter that can be used to better evaluations of specificity and coverage depth among the different capture platforms. Target coverage, capture efficiency, GC bias, AT Dropout, sensitivity in single nucleotide polymorphisms, small insertions and deletions detection, and the feature of each platform were compared for low input samples. In general, all platforms perform well and small differences among them are revealed. In our results, RNA baits have stronger binding power than DNA baits, and with ultra deep sequencing, double stranded RNA baits perform better than single stranded RNA baits in all aspects. DNA baits got better performance in the region with high GC content and RNA baits got lower AT dropout suggesting that the binding power is different between DNA and RNA baits to genome regions with different characteristics. Conclusions The platforms with double stranded RNA baits have the most balanced capture performance. Our results show the key differences in performance among the four updated platforms with four different bait types. The better performance of double stranded RNA bait with ultra deep sequencing suggests that it may improve the sensitivity of ultra low frequent mutation detection. In addition, we further propose that the mixed baits of double stranded RNA and single stranded DNA may improve target capture performance.

Abstract Background Variant calling and refinement from whole genome/exome sequencing data is a fundamental task for genomics studies. Due to the limited accuracy of NGS sequencing and variant callers, IGV-based manual review is required for further false positive variant filtering, which costs massive labor and time, and results in high inter- and intra-lab variability. Results To overcome the limitation of manual review, we developed a novel approach for Variant Filter by Automated Scoring based on Tagged-signature (VariFAST), and also provided a pipeline integrating GATK Best Practices with VariFAST, which can be easily used for high quality variants detection from raw data. Using the bam and vcf files, VariFAST calculates a v-score by sum of weighted metrics causing false positive variations, and marks tags in the manner of keeping high consistency with manual review, for each variant. We validated the performance of VariFAST for germline variant filtering using the benchmark sequencing data from GIAB, and also for somatic variant filtering using sequencing data of both malignant carcinoma and benign adenomas as well. VariFAST also includes a predictive model trained by XGBOOST algorithm for germline variants refinement, which reveals better MCC and AUC than the state-of-the-art VQSR, especially outcompete in INDEL variant filtering. Conclusion VariFAST can assist researchers efficiently and conveniently to filter the false positive variants, including both germline and somatic ones, in NGS data analysis. The VariFAST source code and the pipeline integrating with GATK Best Practices are available at https://github.com/bioxsjtu/VariFAST.

Abstract Background Potato, the third most important crop worldwide, plays a critical role in human food security. Brown rot, one of the most destructive potato diseases caused by Ralstonia solanacearum, results in huge economic losses every year. A quick, stable, low cost and high throughout method is required to meet the demands of identification of germplasm resistance to bacterial wilt in potato breeding programs. Results Here we present a novel R. solanacearum hydroponic infection assay on potato plants grown in vitro. Through testing wilt symptom appearance and bacterial colonization in aerial part of plants, we found that the optimum conditions for in vitro potato infection were using an OD600 0.01 bacterial solution suspended with tap water for infection, broken potato roots and an open container. Infection using R. solanacearum strains with differential degree of aggressivity demonstrated that this infection system is equally efficient as soil-drench inoculation for assessment of R. solanacearum virulence on potato. A small-scale assessment of 32 potato germplasms identified three varieties highly resistant to the pathogen, which indicates this infection system is a useful method for high-throughout screening of potato germplasm for resistance. Furthermore, we demonstrate the utility of a strain carrying luminescence to easily quantify bacterial colonization and the detection of latent infections in hydroponic conditions, which can be efficiently used in potato breeding programs. Conclusions We have established a quick and efficient in vitro potato infection system, which may facilitate breeding for new potato cultivars with high resistance to R. solanacearum.

As one of the most serious shock dynamic disasters in coal mining, rock burst only occurs under the certain geodynamic environment. Geodynamic is the necessary requirement for the occurrence of rock burst, and the disturbance of mining engineering is the sufficient requirement. In terms of the fault structure, the method of geodynamic zoning is used to classify fault structure forms of rock burst in mines, and a model of geological structure is established to reveal the connection between fault structure and mine engineering. Besides, the influence of fault structure on rock burst is analyzed, and the controlling mechanism of the fault structure on the tectonic evolution of the mine area and the occurrence of rock burst is revealed. This research provides a treatment plan for the prediction and prevention of rock burst and guides the safe production in the coal mining engineering.

Phospholipase D (PLD) is the most important phospholipid hydrolase in plants, which can hydrolyze phospholipids into phosphatidic acid (PA) and choline. When plants encounter low temperature, drought and high salt stress, phospholipase D and its products play an important role in regulating plant growth and development and coping with stress. In this study, 16 members of StPLD gene family were identified in potato genome, which were distributed in α, β, δ, and ζ subfamilies, and their expression patterns under salt, high temperature, drought, and ABA stress were detected by qRT-PCR method. Gene expression analysis showed that the expression of StPLD genes in potato was upregulated and downregulated to varying degrees under the four stresses, indicating that the PLD gene family is involved in the interaction of potato plant hormones and abiotic stress signals. Chromosome distribution showed that StPLD gene was unevenly distributed on 8 chromosomes, and only one pair of tandem repeat genes was found. All StPLD promoters contain hormone and stress-related cis-regulatory elements to respond to different stresses. Structural analysis showed that StPLD genes in the same subgroup had a similar exon–intron structure. Our study provides a valuable reference for further research of the function and structure of PLD gene.

Drought is a major global challenge in agriculture that decreases crop production. 1-Aminobutyric acid (GABA) interfaces with drought stress in plants; however, a mechanistic understanding of the interaction between GABA accumulation and drought response remains to be established. Here we showed the potassium/proton exchanger TaNHX2 functions as a positive regulator in drought resistance in wheat by mediating cross-talk between the stomatal aperture and GABA accumulation. TaNHX2 interacted with glutamate decarboxylase TaGAD1, a key enzyme that synthesizes GABA from glutamate. Furthermore, TaNHX2 targeted the C-terminal auto-inhibitory domain of TaGAD1, enhanced its activity, and promoted GABA accumulation under drought stress. Consistent with this, the tanhx2 and tagad1 mutants showed reduced drought tolerance, and transgenic wheat with enhanced TaNHX2 expression had a yield advantage under water deficit without growth penalty. These results shed light on the plant stomatal movement mechanism under drought stress and the TaNHX2-TaGAD1 module may be harnessed for amelioration of negative environmental effects in wheat as well as other crops. [ABSTRACT FROM AUTHOR]