Pathogenicity and diagnostics of non-group A porcine rotaviruses

Rotavirus-associated diarrhea is a common enteric disease in piglets. Group A, B, and C rotaviruses have been implicated in US swine. While group A rotaviruses have been widely studied and attributed as a major cause of the disease, little known about group B and C rotaviruses with respect to their pathogenicity/pathogenesis as well as diagnostics and prevention/control. To address such shortfalls, three studies were carried out. The objective of the first study was producing monoclonal antibodies (MAbs) against PoRV A, B, and C. To that goal, full-length VP6 protein gene of each serogroup was cloned from feces positive for respective virus and expressed in a baculovirus system using Bac-to-Bac cloning and expression kits. The recombinant proteins, purified in their native conditions, were used to immunize mice. A VP6-based ELISA and an indirect fluorescent antibody test using Sf9 cells expressing VP6 of PoRV A, B or C were used to screen hybridomas. The protein specificity of selected MAbs were further verified by Western immunoblot, and the isotype of each MAb was determined using a commercial murine antibody isotyping kit. Based on all these evaluations, MAb 10A11, 10B1 and 11H3, which were of IgG isotype, were selected for PoRV A, B and C, respectively. The MAbs specific for PoRV A and C were proven to be useful for immunohistochemical staining to detect these viruses in formalin-fixed intestinal tissues, which can aid more accurate diagnostic investigation of rotavirus-associated diarrhea. The second study was to compare the pathogenicity of porcine rotavirus (PoRV) A, B and C individually or in combinations in immunologically naïve newborn piglets. Forty-eight one-day-old Cesarean-Derived Colostrum-Deprived (CDCD) pigs were divided into eight groups. Pigs in each group were challenged with rotaviruses that belong to individual group A, B, C or all combinations. Clinical

A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition

Panoptic segmentation in agriculture is an advanced computer vision technique that provides a comprehensive understanding of field composition. It facilitates various tasks such as crop and weed segmentation, plant panoptic segmentation, and leaf instance segmentation, all aimed at addressing challenges in agriculture. Exploring the application of panoptic segmentation in agriculture, the 8th Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA) hosted the challenge of hierarchical panoptic segmentation of crops and weeds using the PhenoBench dataset. To tackle the tasks presented in this competition, we propose an approach that combines the effectiveness of the Segment AnyThing Model (SAM) for instance segmentation with prompt input from object detection models. Specifically, we integrated two notable approaches in object detection, namely DINO and YOLO-v8. Our best-performing model achieved a PQ+ score of 81.33 based on the evaluation metrics of the competition.Comment: Technical report of NYCU-WEED team for the challenge of hierarchical panoptic segmentation of crops and weeds using the PhenoBench dataset at the 8th Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA) - International Conference on Computer Vision (ICCV) 202

Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs

This paper presents a knowledge graph construction method for legal case documents and related laws, aiming to organize legal information efficiently and enhance various downstream tasks. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment. First, the data crawler collects a large corpus of legal case documents and related laws from various sources, providing a rich database for further processing. Next, the information extraction step employs natural language processing techniques to extract entities such as courts, cases, domains, and laws, as well as their relationships from the unstructured text. Finally, the knowledge graph is deployed, connecting these entities based on their extracted relationships, creating a heterogeneous graph that effectively represents legal information and caters to users such as lawyers, judges, and scholars. The established baseline model leverages unsupervised learning methods, and by incorporating the knowledge graph, it demonstrates the ability to identify relevant laws for a given legal case. This approach opens up opportunities for various applications in the legal domain, such as legal case analysis, legal recommendation, and decision support.Comment: ISAILD@KSE 202

Leveraging the Learnable Vertex-Vertex Relationship to Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes

We present MeshLeTemp, a powerful method for 3D human pose and mesh reconstruction from a single image. In terms of human body priors encoding, we propose using a learnable template human mesh instead of a constant template as utilized by previous state-of-the-art methods. The proposed learnable template reflects not only vertex-vertex interactions but also the human pose and body shape, being able to adapt to diverse images. We conduct extensive experiments to show the generalizability of our method on unseen scenarios