Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Tracking using bio-inspired event cameras has drawn more and more attention in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The first category needs more cost for inference and the second one may be easily influenced by noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically, a teacher Transformer-based multi-modal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then, we design a new hierarchical knowledge distillation strategy which includes pairwise similarity, feature representation, and response maps-based knowledge distillation to guide the learning of the student Transformer network. Moreover, since existing event-based tracking datasets are all low-resolution ($346 \times 260$), we propose the first large-scale high-resolution ($1280 \times 720$) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pongs, etc. Extensive experiments on both low-resolution (FE240hz, VisEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method. The dataset, evaluation toolkit, and source code are available on \url{https://github.com/Event-AHU/EventVOT_Benchmark

Serine 129 Phosphorylation of α-Synuclein Cross-Links with Tissue Transglutaminase to Form Lewy Body-Like Inclusion Bodies

Intraneuronal depositions of α-synuclein have been implicated in the pathogenesis of Parkinsons's disease (PD). Previous reports have identified the crosslinking between α-synuclein and tTG (tissue transglutaminase) in both PD patients and the cellular model. However, no researches have been conducted to further investigate their interaction in physiological conditions. To address this question, we generated the SH-SY5Y cell line which stably expressed the wild-type or mutant (Ser129Ala) α-synuclein. After the treatment with okadaic acid, α-synuclein started forming aggregates upon the activation of tTG. Coimmunoprecipitation assays revealed a decreased interaction of the mutant α-synuclein S129A with tTG compared with the wild-type α-synuclein. Cells expressing the wild-type α-synuclein showed increased eosinophilic cytoplasmic inclusion bodies that resembled Lewy bodies compared with the mutant. Double immunofluorescence staining confirmed the colocalization of the phosphorylated α-synuclein and the tTG in the cells. The S129A mutant demonstrated a lesser degree of colocalization than the wild type

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

Combining the Color and Event cameras (also called Dynamic Vision Sensors, DVS) for robust object tracking is a newly emerging research topic in recent years. Existing color-event tracking framework usually contains multiple scattered modules which may lead to low efficiency and high computational complexity, including feature extraction, fusion, matching, interactive learning, etc. In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously. Given the event points and RGB frames, we first transform the points into voxels and crop the template and search regions for both modalities, respectively. Then, these regions are projected into tokens and parallelly fed into the unified Transformer backbone network. The output features will be fed into a tracking head for target object localization. Our proposed CEUTrack is simple, effective, and efficient, which achieves over 75 FPS and new SOTA performance. To better validate the effectiveness of our model and address the data deficiency of this task, we also propose a generic and large-scale benchmark dataset for color-event tracking, termed COESOT, which contains 90 categories and 1354 video sequences. Additionally, a new evaluation metric named BOC is proposed in our evaluation toolkit to evaluate the prominence with respect to the baseline methods. We hope the newly proposed method, dataset, and evaluation metric provide a better platform for color-event-based tracking. The dataset, toolkit, and source code will be released on: \url{https://github.com/Event-AHU/COESOT}

Finerenone in Patients with Chronic Kidney Disease and Type 2 Diabetes: FIDELIO-DKD subgroup from China

Background: This prespecified subgroup analysis of the FIDELIO-DKD trial aimed to evaluate the efficacy and safety of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) in China. Methods: Three hundred and seventy-two participants were recruited from 67 centers in China and randomized 1:1 to oral finerenone or placebo with standard therapy for T2DM. The primary composite outcome included kidney failure, sustained decrease of estimated glomerular filtration rate (eGFR) ≥ 40% from baseline over at least 4 weeks, or renal death. The key secondary composite outcome included death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Results: After a median follow-up of 30 months, the finerenone group showed a relative risk reduction (RRR) of 41% (hazard ratio [HR]=0.59, 95% confidence interval [CI], 0.39 to 0.88; p=0.009) for the primary composite outcome compared with placebo, consistent across its components with treatment benefits with finerenone. Based on an absolute between-group difference of 12.2% after 30 months, the number of patients who needed to be treated (NNT) with finerenone to prevent one primary outcome event was eight (95%CI: 4 to 84). For the key secondary composite outcome, the finerenone group showed a RRR of 25% (HR=0.75, 95% CI, 0.38 to 1.48; p=0.408). Adverse events were similar between the two groups. The effects of finerenone on blood pressure were modest. No gynecomastia events were reported in the study. Hyperkalemia leading to discontinuation occurred in eight (4.3%) and two (1.1%) participants in the finerenone and control groups, respectively. The incidence of acute kidney injury was comparable between the two groups (1.6% vs. 1.6%). Conclusions: Finerenone resulted in lower risks of CKD progression than placebo and a balanced safety profile in Chinese patients with CKD and T2DM

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049