Plasmodesmata mediate cell-to-cell transport of brassinosteroid hormones.

Brassinosteroids (BRs) are steroidal phytohormones that are essential for plant growth, development and adaptation to environmental stresses. BRs act in a dose-dependent manner and do not travel over long distances; hence, BR homeostasis maintenance is critical for their function. Biosynthesis of bioactive BRs relies on the cell-to-cell movement of hormone precursors. However, the mechanism of the short-distance BR transport is unknown, and its contribution to the control of endogenous BR levels remains unexplored. Here we demonstrate that plasmodesmata (PD) mediate the passage of BRs between neighboring cells. Intracellular BR content, in turn, is capable of modulating PD permeability to optimize its own mobility, thereby manipulating BR biosynthesis and signaling. Our work uncovers a thus far unknown mode of steroid transport in eukaryotes and exposes an additional layer of BR homeostasis regulation in plants.

Identifying the kind behind SMILES-anatomical therapeutic chemical classification using structure-only representations.

Anatomical Therapeutic Chemical (ATC) classification for compounds/drugs plays an important role in drug development and basic research. However, previous methods depend on interactions extracted from STITCH dataset which may make it depend on lab experiments. We present a pilot study to explore the possibility of conducting the ATC prediction solely based on the molecular structures. The motivation is to eliminate the reliance on the costly lab experiments so that the characteristics of a drug can be pre-assessed for better decision-making and effort-saving before the actual development. To this end, we construct a new benchmark consisting of 4545 compounds which is with larger scale than the one used in previous study. A light-weight prediction model is proposed. The model is with better explainability in the sense that it is consists of a straightforward tokenization that extracts and embeds statistically and physicochemically meaningful tokens, and a deep network backed by a set of pyramid kernels to capture multi-resolution chemical structural characteristics. Its efficacy has been validated in the experiments where it outperforms the state-of-the-art methods by 15.53% in accuracy and by 69.66% in terms of efficiency. We make the benchmark dataset, source code and web server open to ease the reproduction of this study.

CIRF: Coupled Image Reconstruction and Fusion Strategy for Deep Learning Based Multi-Modal Image Fusion.

Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network's feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods.

Lattice-Strained Bimetallic Nanocatalysts: Fundamentals of Synthesis and Structure.

Bimetallic nanostructured catalysts have shown great promise in the areas of energy, environment and magnetics. Tunable composition and electronic configurations due to lattice strain at bimetal interfaces have motivated researchers worldwide to explore them industrial applications. However, to date, the fundamentals of the synthesis of lattice-mismatched bimetallic nanocrystals are still largely uninvestigated for most supported catalyst materials. Therefore, in this work, we have conducted a detailed review of the synthesis and structural characterization of bimetallic nanocatalysts, particularly for renewable energies. In particular, the synthesis of Pt, Au and Pd bimetallic particles in a liquid phase has been critically discussed. The outcome of this review is to provide industrial insights of the rational design of cost-effective nanocatalysts for sustainable conversion technologies.

Three-Year Point Prevalence Survey of Antimicrobial Use in a Chinese University Hospital.

To evaluate the prevalence and quality of antimicrobial prescriptions using a Global Point Prevalence Survey (PPS) tool and help identify targets for improvement of antimicrobial prescribing and inform the development of antimicrobial stewardship activities. Antimicrobial prescriptions for inpatients staying at a hospital overnight were surveyed on one weekday in October 2018, November 2019, and November 2020. Data including basic patient information, antimicrobial drugs, quality evaluation of antimicrobial drug prescription, and the risk factors of nosocomial infection were collected from doctor network workstation. Patient information was anonymized and entered in the PPS Web application by physicians. A total of 720 patients (median age, 62 years) were surveyed. Of them, 246 (34.2%) were prescribed antimicrobials on the survey days. Hospital-wide antimicrobial use had a significantly decreasing trend (P < 0.001). The most commonly prescribed antimicrobial drugs were third-generation cephalosporins (40.5%), followed by quinolones (21.8%) and second-generation cephalosporin (12.5%). In our study, cefoperazone/sulbactam, ceftazidime, and levofloxacin were the most commonly used antimicrobials. The most common indication for antimicrobial use was pneumonia or lower respiratory tract infection (159/321, 49.5%). Antimicrobial for surgical prophylaxis represented 16.2% of the total antibiotic doses. Of those, 67.3% were administered for more than 24 h. The rate of adherence to antibiotic guidelines was 61.4%. The indications for antimicrobials were not documented in 54.5% of the prescriptions. Stop/review date was documented for 36.8% of prescriptions. The PPS tool is useful in identifying targets to enhance the quality of antimicrobial prescriptions to improve the adherence rate in hospitals. This survey can be used as a control to assess the rational application quality of antimicrobial after regular application of antimicrobial intervention.

High-Energy Aqueous Magnesium Ion Batteries with Capacity-Compensation Evolved from Dynamic Copper Ion Redox.

The low specific capacity and low voltage plateau are significant challenges in the advancement of practical magnesium ion batteries (MIBs). Here, a superior aqueous electrolyte combining with a copper foam interlayer between anode and separator is proposed to address these drawbacks. Notably, with the dynamic redox of copper ions, the weakened solvation of Mg2+ cations in the electrolyte and the enhanced electronic conductivity of anode, which may offer effective capacity-compensation to the 3,4,9,10-perylenetetracarboxylic diimide (PTCDI)-Mg conversion reactions during the long-term cycles. As a result, the unique MIBs using expanded graphite cathode coupled with PTCDI anode demonstrate exceptional performance with an ultra-high capacity (205 mAh g-1 , 243 Wh kg-1 at 5 A g-1 ) as well as excellent cycling stability after 600 cycles and rate capability (138 mAh g-1 , 81 Wh kg-1 at 10 A g-1 ).

Update of Contrast-enhanced Ultrasound in Musculoskeletal Medicine: Clinical Perspectives - A Review.

Contrast-enhanced ultrasound (CEUS) uses an intravascular contrast agent to enhance blood flow signals and assess microcirculation in different parts of the human body. Over the past decade, CEUS has become more widely applied in musculoskeletal (MSK) medicine, and the current review aims to systematically summarize current research on the application of CEUS in the MSK field, focusing on 67 articles published between January 2001 and June 2021 in online databases including PubMed, Scopus, and Embase. CEUS has been widely used for the clinical assessment of muscle microcirculation, tendinopathy, fracture nonunions, sports-related injuries, arthritis, peripheral nerves, and tumors, and can serve as an objective and quantitative evaluation tool for prognosis and outcome prediction. Optimal CEUS parameters and diagnostic cut off values for each disease category remain to be confirmed.

Resveratrol inhibits the progression of premature senescence partially by regulating v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) and sirtuin 1 (SIRT1).

OBJECTIVE: To explore the effect of resveratrol in premature senescence and reveal its anti-premature senescence mechanisms through network pharmacology. METHODS: In this study, the H2O2-induced bone marrow mesenchymal stem cells (BMMSCs) premature senescence model is applied. Cell counting kit-8 assay, ß-galactosidase staining and flow cytometry are conducted to detect the proliferation, senescence and apoptosis of BMMSCs. Bioinformatics analyses are used to screen and validate molecular targets of resveratrol acting on premature senescence. Dual-luciferase reporter assay is conducted to verify the interaction between v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) and sirtuin 1 (SIRT1). RT-qPCR and western blot are adopted to detect mRNA and protein levels of RELA, SIRT1, senescence-related genes and apoptosis-related genes. RESULTS: First, we proved that resveratrol alleviated the H2O2-induced senescence of BMMSCs. Then, bioinformatics analysis revealed that RELA was the downstream target of resveratrol and SIRT1 was the downstream target of RELA, respectively, involved in premature aging. RELA/SIRT1 may be the potential target of resveratrol for premature senescence. Notably, rescue experiments indicated that resveratrol inhibited premature senescence partially through targeting regulation RELA/SIRT1. CONCLUSION: In our study, we confirm the functional role of the resveratrol-RELA- SIRT1 axis in the progression of premature senescence, which provides a latent target for premature senescence treatment.

Exploratory Investigation of the Plasma Proteome Associated with the Endotheliopathy of Trauma.

BACKGROUND: The endotheliopathy of trauma (EoT) is associated with increased mortality following injury. Herein, we describe the plasma proteome related to EoT in order to provide insight into the role of the endothelium within the systemic response to trauma. METHODS: 99 subjects requiring the highest level of trauma activation were included in the study. Enzyme-linked immunosorbent assays of endothelial and catecholamine biomarkers were performed on admission plasma samples, as well as untargeted proteome quantification utilizing high-performance liquid chromatography and tandem mass spectrometry. RESULTS: Plasma endothelial and catecholamine biomarker abundance was elevated in EoT. Patients with EoT (n = 62) had an increased incidence of death within 24 h at 21% compared to 3% for non-EoT (n = 37). Proteomic analysis revealed that 52 out of 290 proteins were differentially expressed between the EoT and non-EoT groups. These proteins are involved in endothelial activation, coagulation, inflammation, and oxidative stress, and include known damage-associated molecular patterns (DAMPs) and intracellular proteins specific to several organs. CONCLUSIONS: We report a proteomic profile of EoT suggestive of a surge of DAMPs and inflammation driving nonspecific activation of the endothelial, coagulation, and complement systems with subsequent end-organ damage and poor clinical outcome. These findings support the utility of EoT as an index of cellular injury and delineate protein candidates for therapeutic intervention.

The Impact of Rapid Infuser Use on the Platelet Count, Platelet Function, and Hemostatic Potential of Whole Blood.

BACKGROUND: Rapid infusion pumps employing filters, roller pumps, and heat exchangers for the administration of blood products are not approved for platelets or cryoprecipitate. This technology may decrease platelet count and degrade coagulation proteins. The effect of rapid infusers on the hemostatic potential of whole blood is unknown. METHODS: Five units of low titer O+ whole blood were obtained from anonymous donors. Each unit was subjected to infusion by five different techniques: (1) gravity infusion without a filter, (2) gravity infusion with a filter, (3) Belmont rapid infuser at 70 mL/min, (4) Belmont at 100 mL/min, and (5) pressurized infusion with a pneumatic pressure bag and filter. After infusion, platelet count, platelet function, thrombin generation, and hemostatic potential were measured for each aliquot. Infusion techniques were compared, using gravity infusion without a filter as the control. RESULTS: There was a significant decrease in platelet count from baseline (168,000) in the BELMONT70 (97,000) and BELMONT100 (94,000) groups (P < 0.05). However, there were no differences in platelet function (all P > 0.20). While there were no differences in thromboelastography parameters between control and infusion models (all P > 0.20), there were significant increases in thrombin generation parameters by CAT in both the BELMONT70 and BELMONT100 groups (all P < 0.05). CONCLUSIONS: The use of a rapid infuser decreases the platelet count of WB but does not decrease platelet function or overall hemostatic potential. In fact, thrombin generation and thrombin potential are actually increased. Rapid infusers are safe for the transfusion of WB.

Reservoir hosts prediction for COVID-19 by hybrid transfer learning model.

The recent outbreak of COVID-19 has infected millions of people around the world, which is leading to the global emergency. In the event of the virus outbreak, it is crucial to get the carriers of the virus timely and precisely, then the animal origins can be isolated for further infection. Traditional identifications rely on fields and laboratory researches that lag the responses to emerging epidemic prevention. With the development of machine learning, the efficiency of predicting the viral hosts has been demonstrated by recent researchers. However, the problems of the limited annotated virus data and imbalanced hosts information restrict these approaches to obtain a better result. To assure the high reliability of predicting the animal origins on COVID-19, we extend transfer learning and ensemble learning to present a hybrid transfer learning model. When predicting the hosts of newly discovered virus, our model provides a novel solution to utilize the related virus domain as auxiliary to help building a robust model for target virus domain. The simulation results on several UCI benchmarks and viral genome datasets demonstrate that our model outperforms the general classical methods under the condition of limited target training sets and class-imbalance problems. By setting the coronavirus as target domain and other related virus as source domain, the feasibility of our approach is evaluated. Finally, we show the animal reservoirs prediction of the COVID-19 for further analysing.

Comparison of Clinical Outcomes Following Anterior Cervical Discectomy and Fusion with Zero-Profile Anchored Spacer-ROI-C-Fixation and Combined Intervertebral Cage and Anterior Cervical Discectomy and Fusion: A Retrospective Study from a Single Center.

BACKGROUND The aim of this study was to compare the outcomes following anterior cervical discectomy and fusion with zero-profile anchored spacer-ROI-C-fixation (ROI-C) vs combined intervertebral cage and anterior cervical discectomy and fusion (ACDF). MATERIAL AND METHODS We retrospectively analyzed 87 patients who underwent operations between January 2015 and January 2019, including 42 patients that underwent ROI-C treatment (group A) and 45 that were treated by the ACDF approach (group B). Operative duration, blood loss, dysphagia, Neck Disability Index scores (NDI), Japanese Orthopaedic Association scores (JOA), and other complications were compared between these groups. In addition, implant settlement, fusion, and cervical Cobb angle were assessed via imaging analyses. RESULTS Patients in group A and group B were followed for 22.6±3.3 months and 27.1±3.5 months, respectively (range: 13-30 months). Relative to preoperative values, JOA scores were increased and NDI scores were reduced in both groups following treatment (P<0.05), with comparable outcomes between groups (P>0.05). However, operative duration, intraoperative blood loss, and postoperative complications did differ significantly between these groups (P<0.05). Specifically, rates of short-term dysphagia were lower and recovery time was faster in group A relative to group B (P<0.05). CONCLUSIONS The findings from this study showed that ROI-C fixation achieved satisfactory outcomes, improved cervical curvature, restored intervertebral height, and was associated with shorter operative duration, reduced blood loss, and less dysphagia.

Iron foam combined ozonation for enhanced treatment of pharmaceutical wastewater.

In this study, iron foam combined ozonation was employed as an advanced oxidation process to treat the organic contaminants in real pharmaceutical wastewater. It was found that this procedure worked well in a wide range of pH, the existence of iron foam in ozonation system markedly elevated the mineralization level of organic contaminants. Within the reaction time of 120 min, iron foam combined ozonation achieved 53% of DOC removal percentage, which was 21% higher than that of ozone alone. Meanwhile, the biodegradability of the pharmaceutical wastewater was improved, a large part of the organic pollutants containing benzene rings and amino groups were effectively degraded, and a certain amount of phosphate and nitrogen also get removed. In iron foam combined ozonation, zero valent iron played the role as an activator. It was oxidized into iron oxides and oxyhydroxides, the electrons transferring among different valences of iron stimulated the decomposition of ozone and the generation of hydroxyl radicals, which accounted for most of the organic contaminants degradation.

CEP3 levels affect starvation-related growth responses of the primary root.

CEPs (C-TERMINALLY ENCODED PEPTIDEs) inhibit Arabidopsis primary root growth by unknown mechanisms. We investigated how CEP3 levels control primary root growth. CEP3 peptide application decreased cell division, S-phase cell number, root meristematic cell number, and meristem zone (MZ) size in a dose- and CEP RECEPTOR1-dependent manner. Grafting showed that CEP3-dependent growth inhibition requires root and shoot CEPR1. CEP3 induced mitotic quiescence in MZ cells significantly faster than that induced by nutrient limitation alone. CEP3 also inhibited the restoration of S-phase to mitotically quiescence cells by nutrient resupply without quantitatively reducing TARGET OF RAPAMYCIN (TOR) kinase activity. In contrast, cep3-1 had an increased meristem size and S-phase cell number under nitrogen (N)-limited conditions, but not under N-sufficient conditions. Furthermore, cep3-1 meristematic cells remained in S-phase longer than wild-type cells during a sustained carbon (C) and N limitation. RNA sequencing showed that CEP3 peptide down-regulated genes involved in S-phase entry, cell wall and ribosome biogenesis, DNA replication, and meristem expansion, and up-regulated genes involved in catabolic processes and proteins and peptides that negatively control meristem expansion and root growth. Many of these genes were reciprocally regulated in cep3-1. The results suggest that raising CEP3 induces starvation-related responses that curtail primary root growth under severe nutrient limitation.

Impact of TREM-2 gene silencing on inflammatory response of endotoxin-induced acute lung injury in mice.

Acute lung injury (ALI) is one of the critical clinical respiratory diseases, of which infection is the main cause and the first risk factor. This study investigated the impact of triggering receptor of myeloid cells expression (TREM)-2 gene silencing on inflammatory response of endotoxin-induced ALI in mice. Lentivirus-mediated TREM-2-shRNA was transfected into healthy male C57BL/6 mice, and the lipopolysaccharide-induced ALI model was established. The immunohistochemistry, immunofluorescence, fluorescence quantitative PCR, western blot, and ELISA were applied to detect the pathological changes of lung tissue and expressions of TREM-2, tumor necrosis factor-α (TNF-α), and interleukin 10 (IL-10) in bronchoalveolar lavage fluid. The lentivirus group, saline control group, ALI model group, blank control group, and negative control group were set up at the same time. Results found that, in lentivirus group, the pathological change of lung tissue was significantly lighter than ALI model group (P < 0.05), and the expression of TREM-2 was significantly reduced compared with all control groups (P < 0.05). The levels of TNF-α and IL-10 were significantly increased than all control groups (P < 0.05), while above indexes in negative control group and blank control group showed no significant difference with ALI group (P > 0.05). This study indicates that TREM-2 has a protective effect on inflammatory response of endotoxin-induced ALI in mice, which has provided new potential targets for prevention and treatment of ALI.

Reliability-Guided Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability. In this work, we propose a reliability-guided hierarchical memory network (RHMNet) for this task, which segments the target in a stepwise expanding strategy w.r.t. the memory reliability level. To be specific, RHMNet maintains a reliability-guided memory bank. It first uses the high-reliability memory to locate the region with high reliability belonging to the target, i.e., highly similar to the initial target scribble. Then, it expands the located high-reliability region to the entire target conditioned on the region itself and all existing memories. In addition, we propose a scribble-supervised learning mechanism to facilitate the model learning for dense prediction. It exploits the pixel-level relations within a single frame and the instance-level variations across multiple frames to take full advantage of the scribble annotations in sequence training samples. The favorable performance on four popular benchmarks demonstrates that our method is promising. Our project is available at: https://github.com/mkg1204/RHMNet-for-SSVOS.

Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting.

Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a simple-yet-effective modulating domain-specific knowledge network (MDKNet) to handle the domain bias issue in multidomain crowd counting. MDKNet is achieved by employing the idea of "modulating", enabling deep network balancing and modeling different distributions of diverse datasets with little bias. Specifically, we propose an instance-specific batch normalization (IsBN) module, which serves as a base modulator to refine the information flow to be adaptive to domain distributions. To precisely modulating the domain-specific information, the domain-guided virtual classifier (DVC) is then introduced to learn a domain-separable latent space. This space is employed as an input guidance for the IsBN modulator, such that the mixture distributions of multiple datasets can be well treated. Extensive experiments performed on popular benchmarks, including Shanghai-tech A/B, QNRF, and NWPU validate the superiority of MDKNet in tackling multidomain crowd counting and the effectiveness for multidomain learning. Code is available at https://github.com/csguomy/MDKNet.

Fine-Grained Accident Detection: Database and Algorithm.

This paper presents a novel fine-grained task for traffic accident analysis. Accident detection in surveillance or dashcam videos is a common task in the field of traffic accident analysis by using videos. However, common accident detection does not analyze the specific particulars of the accident, only identifies the accident's existence or occurrence time in a video. In this paper, we define the novel fine-grained accident detection task which contains fine-grained accident classification, temporal-spatial occurrence region localization, and accident severity estimation. A transformer-based framework combining the RGB and optical flow information of videos is proposed for fine-grained accident detection. Additionally, we introduce a challenging Fine-grained Accident Detection (FAD) database that covers multiple tasks in surveillance videos which places more emphasis on the overall perspective. Experimental results demonstrate that our model could effectively extract the video features for multiple tasks, indicating that current traffic accident analysis has limitations in dealing with the FAD task and that further research is indeed needed.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods.

VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows.

Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination (LI). Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking. In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under LI, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and source code have been released on: https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark.