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In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collected 3D points is low, and its distribution is unbalanced in the scene, which influences the accuracy of 3D object detectors in regards to object location and identification. Although corresponding high-resolution scene images from cameras can be used as supplemental information, poor fusion strategies can result in decreased accuracy compared with that of LiDAR-point-only detectors. Thus, to improve the detection performance for the classification, localization, and even boundary location of 3D objects, a two-stage detector with density-and-sparsity feature aggregation, called DASANet, is proposed in this paper. In the first stage, dense pseudo point clouds are generated with images from cameras and are used to obtain the initial proposals. In the second stage, two novel feature aggregation modules are designed to fuse LiDAR point information and pseudo point information, which refines the semantic and detailed representation of the feature maps. To supplement the semantic information of the highest-scale LiDAR features for object localization and classification, a triple differential information supplement (TDIS) module is presented to extract the LiDAR-pseudo differential features and enhance them in spatial, channel, and global dimensions. To increase the detailed information of the LiDAR features for object boundary location, a Siamese three-dimension coordinate attention (STCA) module is presented to extract stable LiDAR and pseudo point cloud features with a Siamese encoder and fuse these features using a three-dimension coordinate attention. Experiments using the KITTI Vision Benchmark Suite demonstrate the improved performance of our DASANet in regards to the localization and boundary location of objects. The ablation studies demonstrate the effectiveness of the TDIS and the STCA modules.

Background Recently, increasing evidence supports that some complex diseases are not attributed to a given pathogen, but dysbiosis in the host intestinal microbiota (IM). The full intestinal ecosystem alterations, rather than a single pathogen, are associated with white feces syndrome (WFS), a globally severe non-infectious shrimp disease, while no experimental evidence to explore the causality. Herein, we conducted comprehensive metagenomic and metabolomic analysis, and intestinal microbiota transplantation (IMT) to investigate the causal relationship between IM dysbiosis and WFS. Results Compared to the Control shrimp, we found dramatically decreased microbial richness and diversity in WFS shrimp. Ten genera, such as Vibrio , Candidatus Bacilloplasma, Photobacterium , and Aeromonas , were overrepresented in WFS, whereas 11 genera, including Shewanella , Chitinibacter , and Rhodobacter were enriched in control. The divergent changes in these populations might contribute the observation that a decline of pathways conferring lipoic acid metabolism and mineral absorption in WFS. Meanwhile, some sorts of metabolites, especially lipids and organic acids, were found to be related to the IM alteration in WFS. Integrated with multiomics and IMT, we demonstrated that significant alterations in the community composition, functional potentials, and metabolites of IM were closely linked to shrimp WFS. The distinguished metabolites which were attributed to the IM dysbiosis were validated by feed-supplementary challenge. Both homogenous selection and heterogeneous selection process were less pronounced in WFS microbial community assembly. Notably, IMT shrimp from WFS donors eventually developed WFS clinical signs, while the dysbiotic IM can be recharacterized in recipient shrimp. Conclusions Collectively, our findings offer solid evidence of the causality between IM dysbiosis and shrimp WFS, which exemplify the ‘microecological Koch’s postulates’ (an intestinal microbiota dysbiosis, a disease) in disease etiology, and inspire our cogitation on etiology from an ecological perspective. FQ9SoidJJGxv1orPr3YvZs Video abstract

Sediment environments harbor a repertoire of microorganisms that contribute to animal health and the microecosystem in aquaculture ecosystems, but their community diversity and the potential factors that control it remain unclear. Here, we applied 16S rRNA gene amplicon sequencing to investigate bacterial diversity and assembly mechanisms in the sediments of shrimp cultural ponds at the mesoscale. Our results showed that sediment bacterial communities contained 10,333 operational taxonomic units (OTUs) but had only 34 core OTUs and that the relative abundances of these core OTUs were significantly correlated with the physicochemical properties of the sediments. Proteobacteria, Bacteroidetes, Chloroflexi, Cyanobacteria, Acidobacteria, Firmicutes, Actinobacteria, Ignavibacteriae, Spirochaetae and Planctomycetes were the ten most abundant bacterial phyla. Notably, some opportunistic pathogens (e.g. Vibrio and Photobacterium) and potential functional microbes (e.g. Nitrospira, Nitrosomonas, Desulfobulbus and Desulfuromusa) were widely distributed in shrimp cultural pond sediments. More importantly, we found that there was a significant negative but weak distance-decay relationship among bacterial communities in shrimp culture pond sediments at the mesoscale, and that the spatial turnover of these bacterial communities appeared to be largely driven by stochastic processes. Additionally, environmental factors, such as pH and total nitrogen, also played important roles in influencing the sediment bacterial structure. Our findings enhance our understanding of microbial ecology in aquatic ecosystems and facilitate sediment microbiota management in aquaculture.Key points• Core bacterial taxa in cultural pond sediments contributed to the shrimp health and element cycling.• There was a significant negative distance-decay relationship among bacterial communities in shrimp culture pond sediments at the mesoscale, and its spatial turnover appeared to be largely driven by stochastic processes.• Environmental factors (e.g. pH and total nitrogen) played important roles in influencing bacterial structure in shrimp cultural pond sediments.

A simple fabrication of end-bonded contacts InAsSb NW (nanowire) array detector to weak light is demonstrated in this study. The detector is fabricated using InAsSb NW array grown by molecular beam epitaxy on GaAs substrate. The metal-induced gap states are induced by the end-bonded contact which suppresses the dark current at various temperatures. The existence of the interface dipole due to the interfacial gap states enhances the light excitation around the local field and thus upgrades the photoresponsivity and photodetectivity to the weak light. The light intensity of the infrared light source in this report is 14 nW/cm2 which is about 3 to 4 orders of magnitude less than the laser source. The responsivity of the detector has reached 28.57 A/W at room temperature with the light (945 nm) radiation, while the detectivity is 4.81 × 1011 cm·Hz1/2 W−1. Anomalous temperature-dependent performance emerges at the variable temperature experiments, and we discussed the detailed mechanism behind the nonlinear relationship between the photoresponse of the device and temperatures. Besides, the optoelectronic characteristics of the detector clarified that the light-trapping effect and photogating effect of the NWs can enhance the photoresponse to the weak light across ultraviolet to near-infrared. These results highlight the feasibility of the InAsSb NW array detector to the infrared weak light without a cooling system.

Ship instance segmentation in synthetic aperture radar (SAR) images is a hard and challenging task, which not only locates ships but also obtains their shapes with pixel-level masks. However, in ocean SAR images, because of the consistent reflective intensities of ships, the appearances of different ships are similar, thus making it far too difficult to distinguish ships when they are in densely packed groups. Especially when ships have incline directions and large aspect ratios, the horizontal bounding boxes (HB-Boxes) used by all the instance-segmentation networks that we know so far inevitably contain redundant backgrounds, docks, and even other ships, which mislead the following segmentation. To solve this problem, a novel ship instance-segmentation network, called SRNet, is proposed with rotated bounding boxes (RB-Boxes), which are taken as the foundation of segmentation. Along the directions of ships, the RB-Boxes can surround the ships tightly, but a minor deviation will corrupt the integrity of the ships’ masks. To improve the performance of the RB-Boxes, a dual feature alignment module (DAM) was designed to obtain the representative features with the direction and shape information of ships. On account of the difference between the classification task and regression task, two different sampling location calculation strategies were used in two convolutional kernels of the DAM, making these locations distributed dynamically on the ships’ bodies and along the ships’ boundaries. Moreover, to improve the effectiveness of training, a new adaptive Intersection-over-Union threshold (AIoU) was proposed based on the aspect-ratio information of ships to raise positive samples. To obtain the masks in the RB-Boxes, a new Mask-segmentation Head (MaskHead) with the twice sampling processes was explored. In experiments to evaluate the RB-Boxes, the accuracy of the RB-Boxes output from the Detection Head (DetHead) of SRNet outperformed eight rotated object-detection networks. In experiments to evaluate the final segmentation masks, compared with several classic and state-of-the-art instance-segmentation networks, our proposed SRNet achieved more accurate ship instance masks in SAR images. The ablation studies demonstrated the effectiveness of the DAM in the SRNet and the AIoU for our network training.

Rice leaf diseases are critical factors affecting rice yield and quality, and their effective detection is crucial for ensuring stable production. However, existing detection models exhibit limitations in capturing irregular and fine-grained lesion features and are susceptible to interference from complex backgrounds. To address these challenges, this study proposes an Efficient Attention-Guided Network (EAG-YOLOv11n) for rice leaf disease detection. Specifically, an EMA-C3K2 module is proposed to enhance the network’s feature extraction capability. It integrates Efficient Multi-Scale Attention (EMA) into the shallow C3K2 layers, enabling the network to extract richer low-level feature representations. In addition, a Global Local Complementary Attention module (GLC-PSA) is proposed, which integrates a Local Importance Attention (LIA) branch to enhance the local feature representation of the original C2PSA. This design strengthens the perception of lesion regions while effectively suppressing background interference. Furthermore, an Adaptive Threshold Focal Loss (ATFL) is employed to guide the optimization of model parameters during training, alleviating sample imbalance and adaptively emphasizing the learning of challenging samples. Experimental results demonstrate that EAG-YOLOv11n achieves a mean Average Precision (mAP) of 87.3%, representing a 2.7% improvement over the baseline. Furthermore, compared with existing mainstream detection methods, including RT-DETR-L, YOLOv7tiny, YOLOv8n, YOLOv9tiny, YOLOv10n, and YOLOv12n, EAG-YOLOv11n improves mAP by 4.2%, 4.3%, 1.9%, 1.1%, 8.7%, and 2.9%, respectively. Overall, these results highlight its superior effectiveness in rice leaf disease detection, providing reliable technical support for stable yield and sustainable agricultural development.

Aquaculture offers a promising source of economic and healthy protein for human consumption, which can improve wellbeing. Viral diseases are the most serious type of diseases affecting aquatic animals and a major obstacle to the development of the aquaculture industry. In the background of antibiotic-free farming, the development and application of antibiotic alternatives has become one of the most important issues in aquaculture. In recent years, many medicinal plants and their active pharmaceutical ingredients have been found to be effective in the treatment and prevention of viral diseases in aquatic animals. Compared with chemical drugs and antibiotics, medicinal plants have fewer side-effects, produce little drug resistance, and exhibit low toxicity to the water environment. Most medicinal plants can effectively improve the growth performance of aquatic animals; thus, they are becoming increasingly valued and widely used in aquaculture. The present review summarizes the promising antiviral activities of medicinal plants and their active pharmaceutical ingredients against aquatic viruses. Furthermore, it also explains their possible mechanisms of action and possible implications in the prevention or treatment of viral diseases in aquaculture. This article could lay the foundation for the future development of harmless drugs for the prevention and control of viral disease outbreaks in aquaculture.

In this work, in order to investigate the short-range interactions between molecules, the spin-magnetic unit nitronyl nitroxide (NN) was introduced to synthesize self-assembly single radical molecules with hydrogen bond donors and acceptors. The structures and magnetic properties were extensively investigated and characterized by UV-Vis absorption spectroscopy, electron paramagnetic resonance (EPR), and superconducting quantum interference devices (SQUIDs). Interestingly, it was observed that the single molecules can form two different dimers (ring-closed dimer and “L”-type dimer) in different solvents, due to hydrogen bonding, when using EPR to track the molecular spin interactions. Both dimers exhibit ferromagnetic properties (for ring-closed dimer, J/kB = 0.18 K and ΔES−T = 0.0071 kcal/mol; for “L”-type dimer, the values were J/kB = 9.26 K and ΔES−T = 0.037 kcal/mol). In addition, the morphologies of the fibers formed by the two dimers were characterized by transmission electron microscopy (TEM) and atomic force microscopy (AFM).

To reveal overwintering dormancy (diapause) mechanisms of Culex pipiens pallens (L.), global protein expression differences at three separate time points represent nondiapause, diapause preparation and overwintering diapause phases of Cx . pipiens pallens were compared using iTRAQ. Cx . pipiens pallens females accumulate more lipid droplets during diapause preparation and overwintering diapause maintenance than during the nondiapause phase. A total of 1030 proteins were identified, among which 1020 were quantified and compared. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), Domain and Clusters of Orthologous Groups (COG) analyses revealed key groups of proteins, pathways and domains differentially regulated during diapause preparation and overwintering diapause maintenance phases in this mosquito, including major shifts in energy production and conversion, fatty acid metabolism, the citrate (TCA) cycle, and the cytoskeletal reorganization pathway. Our results provide novel insight into the molecular bases of diapause in mosquitoes and corroborate previously reported diapause-associated features in invertebrates. More interestingly, the phototransduction pathway exists in Cx . pipiens pallens , in particular, actin, rather than other proteins, appears to have substantial role in diapause regulation. In addition, the differential changes in calmodulin protein expression in each stage implicate its important regulatory role of the Cx . pipiens pallens biological clock. Finally, 24 proteins were selected for verification of differential expression using a parallel reaction monitoring strategy. The findings of this study provide a unique opportunity to explore the molecular modifications underlying diapause in mosquitoes and might therefore enable the future design and development of novel genetic tools for improving management strategies in mosquitoes.

The bacterium is a multispecies pathogen associated with meningitis-like disease that has been isolated from several amphibian species, including the bullfrog, but this is the first isolation in Guangxi. In the present study, the dominant bacteria were isolated from the brains of five bullfrogs with meningitis-like disease on a South China farm in Guangxi. The NFEM01 isolate was identified by Gram staining; morphological observations; , and -based phylogenetic tree analysis; and physiochemical characterization and was subjected to drug sensitivity and artificial infection testing. As a result of identification, the NFEM01 strain was found to be . An artificial infection experiment revealed that NFEM01 infected bullfrogs and could cause symptoms of typical meningitis-like disease. As a result of the bacterial drug sensitivity test, NFEM01 is highly sensitive to mequindox, rifampicin, enrofloxacin, nitrofural, and oxytetracycline and there was strong resistance to gentamicin, florfenicol, neomycin, penicillin, amoxicillin, doxycycline, and sulfamonomethoxine. This study provides a reference to further study the pathogenesis mechanism of -induced bullfrog meningitislike disease and its prevention and treatment.