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Path planning is one of the important contents of research on coal mine rescue robots. A path planning method for coal mine rescue robots based on improved A* algorithm is proposed to address the unstructured features of post disaster coal mine environments and the problems of non-shortest path length, multiple turns, and poor smoothness of path planned by traditional A* algorithm. The method constructs raster maps by binarizing map information in real environments, determines the relative position between the current point and the target point, and uses the improved A* algorithm for path planning. Then a path from the current point to the target point is obtained. Douglas-Pucker (D-P) algorithm is used to extract key nodes on the path, and cubic spline interpolation function is used to fit the key nodes, thereby completing the smooth processing of the path. The improved A* algorithm expands the traditional A* algorithm's 8 neighborhood search to a purposeful 13 neighborhood search. When conducting path search, the position relationship between the current point and the target point is first determined, thereby reducing path nodes and length, and improving path smoothness. The Matlab simulation results show that compared with the 8 neighborhood A* algorithm, 24 neighborhood A* algorithm, and 48 neighborhood A* algorithm, the improved A* algorithm has certain optimizations in path length, number of turns and smoothness. It is more suitable for path planning of coal mine rescue robots. Compared with the Fuzzy algorithm, the improved A* algorithm achieve shorter path planning time, shorter planned path length, and fewer turns.

Objective:The aim of our study was to determine the molecular mechanism of BMP4 (bone morphogenetic protein 4) in DR (diabetic retinopathy).Methods: Human retinal endothelial cell (HRECs) induced by high glucose to simulate one of the pathogenesis in the diabetic retinopathy (DR) model. RT-qPCR and western blot were used to detect the mRNA and protein levels of BMP4 in the STZ/HG group. Flow cytometry and TUNEL staining were performed to detect the apoptosis. Angiogenesis was evaluated by tube formation assay. Transwell assay and wound healing assay were used to detect cell migration ability. H&E staining was used to evaluate the pathological changes.Results: BMP4 was significantly upregulated in the STZ/HG group. Sh-BMP4 significantly inhibited the migration and angiogenesis of RVECs induced by HG. In addition, both in vivo and in vitro experiments confirmed that sh-BMP4 could significantly promote RVECs apoptosis in the HG/STZ group. Western blot results showed that sh-BMP4 could down-regulate the expressions of p-smad1, p-smad5 and VEGF.Conclusions: Inhibition of BMP4 could alleviate the damage of diabetic retinopathy by regulating the p-smad1/5/VEGF signaling axis, inhibiting angiogenesis and promoting apoptosis. [ABSTRACT FROM AUTHOR]

In livestock, genome‐wide association studies (GWAS) are usually conducted in a single population (single‐GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta‐analysis of GWAS (meta‐GWAS) and mega‐analysis of GWAS (mega‐GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single‐GWAS, meta‐GWAS, and mega‐GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p‐values (pC). In Yorkshire, with the largest sample size, mega‐GWAS, meta‐GWAS and single‐GWAS detected 149, 38 and 20 significant SNPs (pC < 1E‐5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega‐GWAS was the lowest, followed by meta‐GWAS and single‐GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta‐GWAS and mega‐GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega‐GWAS versus single‐GWAS. [ABSTRACT FROM AUTHOR]

Supramolecular soft crystals are periodic structures that are formed by the hierarchical assembly of complex constituents, and occur in a broad variety of 'soft-matter' systems.sup.1. Such soft crystals exhibit many of the basic features (such as three-dimensional lattices and space groups) and properties (such as band structure and wave propagation) of their 'hard-matter' atomic solid counterparts, owing to the generic symmetry-based principles that underlie both.sup.2,3. 'Mesoatomic' building blocks of soft-matter crystals consist of groups of molecules, whose sub-unit-cell configurations couple strongly to supra-unit-scale symmetry. As yet, high-fidelity experimental techniques for characterizing the detailed local structure of soft matter and, in particular, for quantifying the effects of multiscale reconfigurability are quite limited. Here, by applying slice-and-view microscopy to reconstruct the micrometre-scale domain morphology of a solution-cast block copolymer double gyroid over large specimen volumes, we unambiguously characterize its supra-unit and sub-unit cell morphology. Our multiscale analysis reveals a qualitative and underappreciated distinction between this double-gyroid soft crystal and hard crystals in terms of their structural relaxations in response to forces--namely a non-affine mode of sub-unit-cell symmetry breaking that is coherently maintained over large multicell dimensions. Subject to inevitable stresses during crystal growth, the relatively soft strut lengths and diameters of the double-gyroid network can easily accommodate deformation, while the angular geometry is stiff, maintaining local correlations even under strong symmetry-breaking distortions. These features contrast sharply with the rigid lengths and bendable angles of hard crystals. Slice-and-view scanning electron microscopy tomography is used to characterize a double-gyroid block copolymer, finding mesoatomic distortions that break the symmetry of these soft-matter crystals across multiple scales., Author(s): Xueyan Feng [sup.1] , Christopher J. Burke [sup.2] , Mujin Zhuo [sup.1] , Hua Guo [sup.1] , Kaiqi Yang [sup.1] , Abhiram Reddy [sup.2] , Ishan Prasad [sup.3] , [...]

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high‐throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios. Our results showed that, when using a training set of 16 individuals per breed and 32 features (SNPs), the accuracy of breed identification with feature selection (eXtreme Gradient Boosting, XGBoost) could exceed 95.00% (nine breeds), and was improved by 7.04% over the results with random selection. For stacking ensemble learning, feature selection methods (including random selection method) were used before different base learners. When these base learners' training set had 16 individuals per breed and 32 features, the accuracy of stacking ensemble learning improved by 9.24% over the best base learner (nine breeds), but did not significantly increase the advantage over the models with XGBoost feature selection. When using a training set of 16 individuals and 512 features per breed, breed identification with anomaly detection (local outlier factor, LOF) and random selection could achieve an accuracy of 89.06% (15 breeds). These results show that machine learning could be an effective tool for breed identification and this study will also provide useful information for the application of machine learning in animal genetics and breeding. [ABSTRACT FROM AUTHOR]