VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search

Text-based Person Search (TBPS) aims to retrieve images of target pedestrian indicated by textual descriptions. It is essential for TBPS to extract fine-grained local features and align them crossing modality. Existing methods utilize external tools or heavy cross-modal interaction to achieve explicit alignment of cross-modal fine-grained features, which is inefficient and time-consuming. In this work, we propose a Vision-Guided Semantic-Group Network (VGSG) for text-based person search to extract well-aligned fine-grained visual and textual features. In the proposed VGSG, we develop a Semantic-Group Textual Learning (SGTL) module and a Vision-guided Knowledge Transfer (VGKT) module to extract textual local features under the guidance of visual local clues. In SGTL, in order to obtain the local textual representation, we group textual features from the channel dimension based on the semantic cues of language expression, which encourages similar semantic patterns to be grouped implicitly without external tools. In VGKT, a vision-guided attention is employed to extract visual-related textual features, which are inherently aligned with visual cues and termed vision-guided textual features. Furthermore, we design a relational knowledge transfer, including a vision-language similarity transfer and a class probability transfer, to adaptively propagate information of the vision-guided textual features to semantic-group textual features. With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction. Experimental results on two challenging benchmarks demonstrate its superiority over state-of-the-art methods.Comment: Accepted to IEEE TI

MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions

This paper strives for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects. Existing referring video object datasets typically focus on salient objects and use language expressions that contain excessive static attributes that could potentially enable the target object to be identified in a single frame. These datasets downplay the importance of motion in video content for language-guided video object segmentation. To investigate the feasibility of using motion expressions to ground and segment objects in videos, we propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments. We benchmarked 5 existing referring video object segmentation (RVOS) methods and conducted a comprehensive comparison on the MeViS dataset. The results show that current RVOS methods cannot effectively address motion expression-guided video segmentation. We further analyze the challenges and propose a baseline approach for the proposed MeViS dataset. The goal of our benchmark is to provide a platform that enables the development of effective language-guided video segmentation algorithms that leverage motion expressions as a primary cue for object segmentation in complex video scenes. The proposed MeViS dataset has been released at https://henghuiding.github.io/MeViS.Comment: ICCV 2023, Project Page: https://henghuiding.github.io/MeViS

Comparative studies of the anti-thrombotic effects of saffron and HongHua based on network pharmacology

Purpose: To investigate the comparative anti-thrombotic effects of saffron and Honghua, and also to explore possible mechanisms in thrombosis based on network pharmacology. Methods: A network pharmacology model was used for bioactive components, targets and pathways for saffron and HongHua via Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), PharmMapper, Genecard, Uniprot and KEGG databases. In animal experiments, 72 rats were randomly divided into 9 groups: normal control group (NC), model control group (MC), crocetin groups (80, 40, 20 mg/kg), hydroxysafflor yellow A(HSYA) groups (80, 40, 20 mg/kg), and aspirin group (40 mg/kg). Using in vitro thrombosis models and an acute blood stasis model in vivo, the anti-thrombotic effects of these treatments on clotting time, hemorheology parameters, Thromboxane B2 (TXB2), plasmin activator inhibitor (PAI), protein C (PC), protein S (PS), and thrombinantithrombin complex (TAT) were determined and comparisons made for saffron and HongHua. Results: Five potential compounds, 16 anti-thrombotic targets and 27 pathways were predicted for saffron, while 22 compounds, 37 disease targets and 35 pathways were found for HongHua (p < 0.05). Pharmacological experiments revealed that crocetin and HSYA had significant effects on thrombus length, thrombus wet/dry mass, whole blood viscosity (WBV), erythrocyte aggregation index (EAI), clotting time and D-dimer for the high and middle groups. Unlike HSYA, crocetin also had significant and dose-dependent effects on PAI, prothrombin fragment 1+2 (F1+2) and PS and had highly significant effects on TXB2 and TAT. Conclusion: This research provides a systematic, comprehensive and comparative analysis of component, target and anti-thrombotic pathways of saffron and HongHua based on network pharmacology, and also shows that saffron has more significant anti-thrombotic effect than HongHua. Keywords: Saffron; HongHua; Network pharmacology; Anti-thrombosis; Network mode

MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

Video object segmentation (VOS) aims at segmenting a particular object throughout the entire video clip sequence. The state-of-the-art VOS methods have achieved excellent performance (e.g., 90+% J&F) on existing datasets. However, since the target objects in these existing datasets are usually relatively salient, dominant, and isolated, VOS under complex scenes has rarely been studied. To revisit VOS and make it more applicable in the real world, we collect a new VOS dataset called coMplex video Object SEgmentation (MOSE) to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. The target objects in the videos are commonly occluded by others and disappear in some frames. To analyze the proposed MOSE dataset, we benchmark 18 existing VOS methods under 4 different settings on the proposed MOSE dataset and conduct comprehensive comparisons. The experiments show that current VOS algorithms cannot well perceive objects in complex scenes. For example, under the semi-supervised VOS setting, the highest J&F by existing state-of-the-art VOS methods is only 59.4% on MOSE, much lower than their ~90% J&F performance on DAVIS. The results reveal that although excellent performance has been achieved on existing benchmarks, there are unresolved challenges under complex scenes and more efforts are desired to explore these challenges in the future. The proposed MOSE dataset has been released at https://henghuiding.github.io/MOSE.Comment: MOSE Dataset Repor

Denatured-State Conformation As Regulator of Amyloid Assembly Pathways?

Additional file 1: Figure S1. Disease incidence of ginger bacterial wilt. Figure S2. The rarefaction curve of samples. Table S1. Soil Physicochemical Data. Table S2. The top ten Phyla of samples. Dataset S1. Discriminative taxa analyzed by LEfSe in all samples

Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. Nonetheless, remarkable changes in associated atomic sites and binding pose can provide vital information in understanding the process of drug binding. Based on this mechanism, molecular dynamics (MD) simulations were invented as a useful tool for investigating the dynamic properties of a molecular system. However, the computational expenditure limits the growth and application of protein trajectory-related studies, thus hindering the possibility of supervised learning. To tackle this obstacle, we present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two specially designed self-supervised learning tasks: an atom-level prompt-based denoising generative task and a conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen, i.e., the binding affinity prediction and the ligand efficacy prediction, to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. We observe a huge improvement from current state-of-the-art methods, with a decrease of 4.3% in RMSE for the binding affinity problem and an average increase of 13.8% in AUROC and AUPRC for the ligand efficacy problem. The results demonstrate valuable insight into a strong correlation between the magnitude of conformation's motion in the 3D space (i.e., flexibility) and the strength with which the ligand binds with its receptor

Plant Density and Nitrogen Supply Affect the Grain-Filling Parameters of Maize Kernels Located in Different Ear Positions

Although yield output of maize (Zea mays L.) has improved markedly over the last century, procedures for improving the grain-filling process remain elusive. Our aim in this study was to relate grain-filling variation in maize (including kernels in apical and middle positions in the ears) to plant density and nitrogen (N) application rate using a crossed experimental design. We also investigated changes in zeatin riboside (ZR), indole-3-acetic acid (IAA), abscisic acid (ABA), and gibberellic acid (GA) in the kernels during the grain-filling period. Two high-yield maize varieties cultivated extensively in China were field grown under normal (67,500 pl ha-1) and high (97,500 pl ha-1) densities, and supplied with low, normal and high (0, 180, and 360 kg N ha-1) concentrations of N. Kernel weight (KW), the maximum grain-filling rate (Gmax), the average grain-filling rate (Gave), and the kernel weight increment achieving Gmax (Wmax) were all significantly depressed under high density (HD) conditions, but increased N supply partially offset the losses. The apical kernels were more sensitive to density and N application rate than middle kernels. Correlation analysis indicated that plant density and N rate affected KW mainly by influencing the grain-filling rate. Variation in ZR, IAA, and ABA content tracked the variation in KW, but variation in GA content did not. Furthermore, the grain-filling parameters (closely related to TKW) had strong canonical correlation with the content of all hormones across the filling period and ZR content had the strongest relationship. Based on our study, high N supply is beneficial to optimize grain-filling parameters and improve KW of maize kernels under crowded condition

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public’s view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children’s Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1–6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6–12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research