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With the development of the coalbed methane industry and the requirements for the integration of unconventional resource exploration and development, the current exploration of coalbed methane is gradually advancing from the favorable area stage to the sweet spot stage. However, limited by the stage of exploration and development or the degree of data possession, most of the current area selection evaluation systems do not take into account the fracability of coal reservoir and the recoverability of coalbed methane. Evaluation results are not effective in guiding development well location selection or infill wells. Based on the data of more than 100 coalbed methane parameter wells and development wells, this paper analyzes the resource conditions, reservoir fracturing improvement and coalbed methane recoverability of the No.3 coalbed methane development in Shizhuangbei Block, Qinshui Basin. An evaluation model was built with the gray fixed weight clustering method, and the sweet spot of ​​the coalbed methane project was evaluated, in an effort to provide a basis for the deployment of new wells and infill wells in the block. It was used coal seam gas content and coal thickness to reflect coal bed methane resource conditions; it was comprehensively considered the in-situ stress environment and rock mechanical properties to construct the reservoir fracability parameter, and then evaluates reservoir fracturing improvement; it was used the proportion of original structure coal and fragmented coal , temporary-reservoir ratio and original reservoir permeability to reflect the recoverability of coalbed methane. Each parameter is graded by the word clustering method, and the comprehensive goodness of each point (each well) is calculated and sorted, and finally the sweet spot for coalbed methane development is divided. The evaluation results show that the central part with the comprehensive goodness value >65 is the sweet spot for further development in the study area.

In order to study the relations between in-situ stress and fracture pressure, 112 vertical coalbed methane wells were selected as the research object in Shizhuangnan Block. The in-situ stress and fracture pressure were obtained by hydraulic fracturing calculation method. The correlation models between the fracture pressure and the horizontal principal stress and effective stress were established respectively. The characteristics of the in-situ stress and fracture pressure of the No.3 coal reservoir were revealed. The relationships between in-situ stress and fracture pressure were analyzed. The results show that the No.3 coal reservoir in Shizhangnan Block was a medium-high stress area as a whole and the stress field types were converted in the vertical direction. The stress field in the buried depth within 400-640 m area was a reverse fault stress field, the stress field in the buried depth within 640-810 m area was a strike-slip fault stress field and the stress field in the buried depth deeper than 810 m area was a normal fault stress field. The side pressure coefficient was 0.38-1.99. With a burial depth lower than 600 m, most side pressure coefficient was greater than 1; within 600-800 m, the side pressure coefficient was 0.52-1.93, and within the depth deeper than 800 m was less than 1. The fracture pressure was 12.89-36.10 MPa and its gradient was 1.47-6.09 MPa/hm. The fracture pressure and the buried depth showed a reversed S-shaped change. It had a large discreteness with a burial depth lower than 810 m, which was negatively correlated with the buried depth as a whole. The fracture pressure was positively correlated with the buried depth deeper than 810 m. To some extent, the maximum horizontal principal stress, minimum horizontal principal stress and their stress gradient were positively correlated to the fracture pressure of coal reservoir, but the correlation was not too strong. Under the same buried depth, the fracture pressure varied with the horizontal stress, but the influence of the minimum horizontal principal stress on the fracture pressure was obviously greater than that of the maximum horizontal principal stress. The fracture pressure and the horizontal stress deviation showed a reversed N-shaped change, and it had positive correlation with effective stress. The fracture pressure decreased with the increase of the stress deviation when the horizontal stress deviation was 2-4 MPa. The fracture pressure increases with the increase of the stress deviation when the horizontal stress deviation was 4-8 MPa. The fracture pressure decreases with the increase of the stress deviation when the stress difference was higher than 8 MPa.

BackgroundObservational studies have indicated that obesity is a risk factor for anorectal abscess (ARB). However, it remains unclear whether a causal genetic relationship exists between obesity and ARB.MethodsUnivariate and multivariate Mendelian randomization (MR) were conducted using data from a large, published genome-wide association study (GWAS) of European ancestry to infer a causal relationship between obesity and ARB. Inverse variance weighted (IVW) analysis served as the primary analysis method, with results reported as odds ratios (OR).ResultsMR analysis revealed that body mass index (BMI) positively affects ARB (OR 1.974, 95% confidence interval (CI) 1.548–2.519, p = 4.34 × 10−8). The weighted median method (OR = 1.879, 95% CI 1.248–2.829, p = 0.002) and Bayesian model averaging (BMA) (OR = 1.88, 95% CI 1.477–2.392, p = 2.85 × 10−7) also demonstrated consistent results. Subsequently, the impact of several obesity-related characteristics on ARB was assessed. Body fat percentage (BF), whole body fat mass (FM), waist circumference (WC), and hip circumference (HC) were found to be causally associated with an increased risk of ARB. However, these associations vanished after adjusting for BMI effects.ConclusionThe study confirms a positive causal effect of obesity on ARB, highlighting that reasonable weight control is an important strategy to reduce the incidence of ARB.

Saltpans extraction is vital for coastal resource utilization and production management. However, it is challenging to extract saltpans, even by visual inspection, because of their spatial and spectral similarities with aquaculture ponds. Saltpans are composed of crystallization and evaporation ponds. From the whole images, existing saltpans extraction algorithms could only extract part of the saltpans, i.e., crystallization ponds. Meanwhile, evaporation ponds could not be efficiently extracted by only spectral analysis, causing the degeneration of saltpans extraction. In addition, manual intervention was required. Thus, it is essential to study the automatic saltpans extraction algorithm of the whole image. As to the abovementioned problems, this paper proposed a novel method with an amendatory saltpan index (ASI) and local spatial parallel similarity (ASI-LSPS) for extracting coastal saltpans. To highlight saltpans and aquaculture ponds in coastal water, the Hessian matrix has been exploited. Then, a new amendatory saltpans index (ASI) is proposed to extract crystallization ponds to reduce the negative influence of turbid water and dams. Finally, a new local parallel similarity criterion is proposed to extract evaporation ponds. The Landsat-8 OLI images of Tianjin and Dongying, China, have been used in experiments. Experiments have shown that ASI can reach at least 70% in intersection over union (IOU) and 78% in Kappa for extraction of crystallization in saltpans. Moreover, experiments also demonstrate that ASI-LSPS can reach at least 82% in IOU and 89% in Kappa on saltpans extraction, at least 13% and 17% better than comparing algorithms in IOU and Kappa, respectively. Furthermore, the ASI-LSPS algorithm has the advantage of automaticity in the whole imagery. Thus, this study can provide help in coastal saltpans management and scientific utilization of coastal resources.

Abstract Reliability factors in Markov random field (MRF) could be used to improve classification performance for synthetic aperture radar (SAR) and optical images; however, insufficient utilization of reliability factors based on characteristics of different sources leaves more room for classification improvement. To solve this problem, a Markov random field (MRF) with amendment reliability factors classification algorithm (MRF-ARF) is proposed. The ARF is constructed based on the coarse label field of urban region, and different controlling factors are utilized for different sensor data. Then, ARF is involved into the data energy of MRF, to classify the sand, vegetation, farmland, and urban regions, with the gray level co-occurrence matrix textures of Sentinel-1 imagery and the spectral values of the Landsat 8 imagery. In the experiments, Sentinel-1 and Landsat-8 images are used with overall accuracy and Kappa coefficient to evaluate the proposed algorithm with other algorithms. Results show that the overall accuracy of the proposed algorithm has the superiority of about 20% in overall precision and at least 0.2 in Kappa coefficient than the comparison algorithms. Thus, the problem of insufficient utilization of different sensors data could be solved.

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

Cerebral organoids recapitulate in vivo phenotypes and physiological functions of the brain and have great potential in studying brain development, modeling diseases, and conducting neural network research. It is essential to obtain whole-mount three-dimensional (3D) images of cerebral organoids at cellular levels to explore their characteristics and applications. Existing histological strategies sacrifice inherent spatial characteristics of organoids, and the strategy for volume imaging and 3D analysis of entire organoids is urgently needed. Here, we proposed a high-resolution imaging pipeline based on fluorescent labeling by viral transduction and 3D immunostaining with fluorescence micro-optical sectioning tomography (fMOST). We were able to image intact organoids using our pipeline, revealing cytoarchitecture information of organoids and the spatial localization of neurons and glial fibrillary acidic protein positive cells (GFAP+ cells). We performed single-cell reconstruction to analyze the morphology of neurons and GFAP+ cells. Localization and quantitative analysis of cortical layer markers revealed heterogeneity of organoids. This pipeline enabled acquisition of high-resolution spatial information of millimeter-scale organoids for analyzing their cell composition and morphology.