Spatial transcriptomics reveals gene interactions and signaling pathway dynamics in rat embryos with anorectal malformation.

Anorectal malformation (ARM) is a prevalent early pregnancy digestive tract anomaly. The intricate anatomy of the embryonic cloaca region makes it challenging for traditional high-throughput sequencing methods to capture location-specific information. Spatial transcriptomics was used to sequence libraries of frozen sections from embryonic rats at gestational days (GD) 14 to 16, covering both normal and ARM cases. Bioinformatics analyses and predictions were performed using methods such as WGCNA, GSEA, and PROGENy. Immunofluorescence staining was used to verify gene expression levels. Gene expression data was obtained with anatomical annotations of clusters, focusing on the cloaca region's location-specific traits. WGCNA revealed gene modules linked to normal and ARM cloacal anatomy development, with cooperation between modules on GD14 and GD15. Differential gene expression profiles and functional enrichment were presented. Notably, protein levels of Pcsk9, Hmgb2, and Sod1 were found to be downregulated in the GD15 ARM hindgut. The PROGENy algorithm predicted the activity and interplay of common signaling pathways in embryonic sections, highlighting their synergistic and complementary effects. A competing endogenous RNA (ceRNA) regulatory network was constructed from whole transcriptome data. Spatial transcriptomics provided location-specific cloaca region gene expression. Diverse bioinformatics analyses deepened our understanding of ARM's molecular interactions, guiding future research and providing insights into gene regulation in ARM development.

Robust Point Cloud Registration Network for Complex Conditions.

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.

Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information.

Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized that the main limitations are due to the inaccuracy of the target position and the uncertainty in the depth distribution of the foreground target. These two problems arise from the inaccurate depth estimation. To deal with the aforementioned problems, we propose two innovative solutions. The first is a novel method based on joint image segmentation and geometric constraints, used to predict the target depth and provide the depth prediction confidence measure. The predicted target depth is fused with the overall depth of the scene and results in the optimal target position. For the second, we utilize the target scale, normalized with the Gaussian function, as a priori information. The uncertainty of depth distribution, which can be visualized as long-tail noise, is reduced. With the refined depth information, we convert the optimized depth map into the point cloud representation, called a pseudo-LiDAR point cloud. Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset. The results demonstrate that our proposed framework outperforms various state-of-the-art methods by more than 12.37% and 5.34% on the easy and hard settings of the KITTI validation subset, respectively. On the KITTI test set, our framework also outperformed state-of-the-art methods by 5.1% and 1.76% on the easy and hard settings, respectively.

The gut bacterial flora associated with brown planthopper is affected by host rice varieties.

Gut microbiota plays vital roles in the development, evolution and environmental adaptation of the host insects. The brown planthopper (BPH) is one of the most destructive pests of rice, but little is known about its gut microbiota. In this study, we investigated the gut bacterial communities in two BPH populations feeding on susceptible and resistant rice varieties by high-throughput amplicon sequencing. Our results revealed that the gut bacterial communities in BPH were species diverse. A total of 29 phyla and 367 genera were captured, with Proteobacteria and Acinetobacter being the most prominent phylum and genus, respectively. Comparative analysis showed that significant differences in the profile of gut bacterial communities existed between the two BPH populations. The species richness detected in the population feeding on the resistant rice variety was significantly higher than that in the population rearing on the susceptible rice variety. Although the most dominant gut bacteria at all taxonomic levels showed no significant differences between the two BPH populations, the relative abundances of two subdominant phyla (Firmicutes and Bacteroidetes) and two subdominant classes (Bacteroidia and Clostridia) were significantly different. FAPROTAX analysis further indicated that host rice varieties might induce changes of the gut bacterial flora in BPH, as significant differences in five metabolism-related functional categories (fermentation, methylotrophy, xylanolysis, nitrate reduction and ureolysis) were detected between the two BPH populations. Our results are informative for studies which focused on the interactions between BPH and its symbiotic microbes and could also provide the basis of future BPH biological management.

Rack1-mediated ferroptosis affects hindgut development in rats with anorectal malformations: Spatial transcriptome insights.

Anorectal malformation (ARM), a common congenital anomaly of the digestive tract, is a result of insufficient elongation of the urorectal septum. The cytoplasmic protein Receptor of Activated C-Kinase 1 (Rack1) is involved in embryonic neural development; however, its role in embryonic digestive tract development and ARM formation is unexplored. Our study explored the hindgut development and cell death mechanisms in ARM-affected rats using spatial transcriptome analysis. We induced ARM in rats by administering ethylenethiourea via gavage on gestational day (GD) 10. On GDs 14-16, embryos from both normal and ARM groups underwent spatial transcriptome sequencing, which identified key genes and signalling pathways. Rack1 exhibited significant interactions among differentially expressed genes on GDs 15 and 16. Reduced Rack1 expression in the ARM-affected hindgut, verified by Rack1 silencing in intestinal epithelial cells, led to increased P38 phosphorylation and activation of the MAPK signalling pathway. The suppression of this pathway downregulated Nqo1 and Gpx4 expression, resulting in elevated intracellular levels of ferrous ions, reactive oxygen species (ROS) and lipid peroxides. Downregulation of Gpx4 expression in the ARM hindgut, coupled with Rack1 co-localisation and consistent mitochondrial morphology, indicated ferroptosis. In summary, Rack1, acting as a hub gene, modulates ferrous ions, lipid peroxides, and ROS via the P38-MAPK/Nqo1/Gpx4 axis. This modulation induces ferroptosis in intestinal epithelial cells, potentially influencing hindgut development during ARM onset.

Effect of antibiotics, antibiotic-resistant bacteria, and extracellular antibiotic resistance genes on the fate of ARGs in marine sediments.

Surface runoff is a prevalent source via which emerging pollutants (i.e., antibiotics, antibiotic-resistant bacteria (ARB), and antibiotic resistance genes (ARGs)) enter marine sediments. However, few studies have investigated the effect of emerging pollutants on the fate of ARGs in marine sediments. Therefore, three systems were established to measure the relative abundances of four common ARGs (i.e., blaTEM, tetA, tetC, and aphA) and the integron-integrase gene (intI1) after exposure to emerging pollutants in marine sediments from the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea in China. The results revealed that antibiotic exposure could decrease the relative abundance of most ARGs (including blaTEM, tetA, and tetC) in these marine sediment samples. The exceptions were the relative abundance of blaTEM in the Bohai Sea marine sediments under ampicillin exposure and tetC in the Yellow Sea marine sediments under tetracycline exposure, which increased significantly. Among marine sediments challenged with ARB, the relative abundance of aphA in all four marine sediments displayed a decreasing trend, whereas the abundances of blaTEM and tetA in the marine sediments from the Bohai Sea and the South China Sea showed an increasing trend. The relative abundance of tetA in the marine sediments from the Yellow Sea and the East China Sea dropped markedly when exposed to extracellular ARG (eARG). Significant changes in blaTEM abundance were observed in the four marine sediments under eARG exposure. Gene aphA abundance showed the same trend as the intI1 abundance. IntI1 showed a decreasing trend under the exposure of antibiotic, ARB, or eARG, apart from the East and the South China Sea marine sediments under ampicillin conditions and the South China Sea marine sediments under RP4 plasmid condition. These findings suggest that dosing with emerging pollutants does not increase ARG abundance in marine sediments.

Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model.

In recent years, China has been facing severe ozone (O3) pollution, which poses a remarkable threat to human health. Most estimation methods only provide ozone products at a relatively coarse resolution, such as 5 km, but high-resolution ozone data are essential for ozone pollution prevention and control. To this end, we proposed a new framework for estimating ozone concentrations at 300 m resolution in China based on Landsat 8 infrared (IR) bands and meteorological data using a deep forest (DF) model. DF combines the excellent performance of tree integration with the expressive power of hierarchical distributed representations of neural networks. The accuracy and mapping results of DF are considerably better than some widely used machine learning methods (generalized regression neural regression network and random forest). The sample-based cross-validation (CV), station-based CV, time-based CV, and extrapolation validation show that the estimations of DF are in high agreement with the station observations with determination coefficient values of 0.938, 0.926, 0.687, and 0.660, respectively. The proposed method was used to analyze the spatial and temporal ozone variations at fine scales in three typical Chinese cities (Beijing, Wuhan and Guangzhou), where the mean ozone concentrations during the polluted season are consistent with the land use and urban heat island distribution. The rationality of ozone estimates was verified, and the advantages of high-resolution mapping was demonstrated by comparing the monitoring data from municipal controlling stations in Beijing, 10 km ozone products, and satellite images. Our product can represent spatial details and locate local pollution sources, such as temples. The proposed method has important implications for the fine-scale monitoring of ozone pollution.

Host-Plant Induced Shifts in Microbial Community Structure in Small Brown Planthopper, Laodelphax striatellus (Homoptera: Delphacidae).

Microbiome associated with insects play vital roles in host ecology and physiology. The small brown planthopper (SBPH), Laodelphax striatellus, is a polyphagous insect pest that caused enormous damage to a wide range of cereal crops. Previous studies have assessed the effects of environmental factors, such as antibiotics, insecticide, and geographical habitat on the bacterial composition of SBPH. However, the influence of host plants on the microbial community in SBPH still unclear. Here, we characterized and compared the microbial community in three SBPH populations feeding on rice, barley, and wheat, respectively, using high-throughput amplicon sequencing. Our observations revealed that the microbiome harbored by SBPH was abundant and diverse. Ten phyla comprising 141 genera of bacteria were annotated, and four fungal phyla consisting of 47 genera were assigned. The bacteria belonging to the phylum Proteobacteria were the most prevalent and the fungi with the highest abundance were from the order Hypocreales. Comparative analysis showed that host plants could significantly induce structural changes of SBPH microbiome. Significant differences in abundance were observed in two main bacterial orders (Rickettsiales and Rhodospirillales) and three fungal classes (Sordariomycetes, an unclassified class in Ascomycota and Eurotiomycetes) among three host-adapted SBPH populations. Our results could broaden our understanding of interactions among SBPH, its microbial associates and host plants, and also represented the basis of future SBPH biological management.

Comprehensive insights into host-pathogen interaction between brown planthopper and a fungal entomopathogen by dual RNA sequencing.

BACKGROUND: The brown planthopper (BPH) is one of the most destructive pests of rice, causing tremendous yield and economic losses every year. The fungal entomopathogen Metarhizium anisopliae was previously proved to have great potential for BPH biocontrol. Genome-wide insight into the insect-fungus interaction is crucial for genetic improvement of M. anisopliae to enhance its virulence to BPH but still has been poorly explored. RESULTS: Using dual RNA-seq approach, we present here a global view of host and fungal gene expressions in BPH adults during the fungal infection. The results revealed that BPH could initiate strong defense responses against the fungal attack by upregulating the expressions of a large number of genes, including genes involved in cuticle formation, immune response, cell detoxification and biomacromolecule metabolism. Correspondingly, the fungal entomopathogen could induce a series of genes to infect and modulate BPH, including genes involved in fungal penetration, invasive growth, stress resistance and virulence. Three host defense-related genes (NlPCE4, NlPOD1 and NlCYP4DE1) were chosen for further function analysis. RNAi-mediated knockdown of NlPCE4 caused a significant decrease in BPH survival, but no obvious effects on the survival rates were detected by the suppression of NlPOD1 and NlCYP4DE1. Combination of dsRNA injection and fungal infection could significantly enhance the BPH-killing speed, as synergistic mortalities were observed in co-treatments of RNAi and M. anisopliae infection. CONCLUSION: Our study provides a comprehensive insight into molecular mechanisms of host-pathogen interaction between BPH and M. anisopliae and contributes to future development of new efficient biocontrol strategies for BPH biocontrol.

Effect of Welding Speed and Post Quenching on the Microstructure and Mechanical Properties of Laser-Welded B1500HS Joints.

In this research, B1500HS high-strength steel with different thicknesses were laser welded, and the effects of welding speed and post quenching were investigated by analyzing the microstructure, microhardness distribution, and high-temperature tensile properties of weld joints. The results show that an obvious difference can be found in the metallographic structure and grain morphology of the weld joint at different locations, which also lead to the significant uneven distribution of hardness. After quenching, the grain size of the original heat-affected zone was uniform, the columnar grains in the fusion zone were transformed into fine equiaxed grains, and no obvious hardness difference can be found in the weld joint. For the weld joint without quenching, the increase of welding speed can reduce the dimensions of grains of fusion zone and coarse grain zone, and slightly increase the hardness of these regions. In contrast, welding speed change has little influence on the microstructure and hardness of the weld joint after quenching. The high-temperature flow stress-strain curves of fusion zone welded under different welding speeds were calculated based on the mixture rule. The analysis results indicated that the fusion zone has higher strength but lower elongation than the base metal. In addition, the change of welding speed has a small impact on the high-temperature tensile properties of the fusion zone.

Constitutive Equations for Describing the Hot Compressed Behavior of TC4-DT Titanium Alloy.

Isothermal hot compression tests of TC4-DT titanium alloy were performed under temperatures of 1203-1293 K and strain rates of 0.001-10 s-1. The purpose of this study is to develop a new high-precision modified constitutive model that can describe the deformation behavior of TC4-DT titanium alloy. Both the modified strain-compensated Arrhenius-type equation and the modified Hensel-Spittel equation were established by revising the strain rate. The parameters in the above two modified constitutive equation were solved by combining regression analysis with iterative methods, which was used instead on the traditional linear regression methods. In addition, both the original strain-compensated Arrhenius-type equation and Hensel-Spittel equation were established to compare with the new modified constitutive equations. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the correlation coefficient (R). These results show the modified Arrhenius-type equation and the modified Hensel-Spittel equation is more accurate and efficient with a similar prediction accuracy. The AARE-value of the two modified constitutive equation is relatively low under various strain rates and their fluctuation is small as the strain rate changes.