Scene Adaptive Sparse Transformer for Event-based Object Detection

While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks, their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However, they display inadequate sparsity and adaptability when applied to event-based object detection, since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers, leading to reduced performance and efficiency. Furthermore, they lack scene-specific sparsity optimization, resulting in information loss and a lower recall rate. To overcome these limitations, we propose the Scene Adaptive Sparse Transformer (SAST). SAST enables window-token co-sparsification, significantly enhancing fault tolerance and reducing computational overhead. Leveraging the innovative scoring and selection modules, along with the Masked Sparse Window Self-Attention, SAST showcases remarkable scene-aware adaptability: It focuses only on important objects and dynamically optimizes sparsity level according to scene complexity, maintaining a remarkable balance between performance and computational cost. The evaluation results show that SAST outperforms all other dense and sparse networks in both performance and efficiency on two large-scale event-based object detection datasets (1Mpx and Gen1). Code: https://github.com/Peterande/SAS

Transfer Learning in General Lensless Imaging through Scattering Media

Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational imaging, DNNs can overcome several shortcomings in the conventional lensless imaging through scattering media methods, namely, high cost, poor quality, complex control, and poor anti-interference. However, for training, a large number of training samples on various datasets have to be collected, with a DNN trained on one dataset generally performing poorly for recovering images from another dataset. The underlying reason is that lensless imaging through scattering media is a high dimensional regression problem and it is difficult to obtain an analytical solution. In this work, transfer learning is proposed to address this issue. Our main idea is to train a DNN on a relatively complex dataset using a large number of training samples and fine-tune the last few layers using very few samples from other datasets. Instead of the thousands of samples required to train from scratch, transfer learning alleviates the problem of costly data acquisition. Specifically, considering the difference in sample sizes and similarity among datasets, we propose two DNN architectures, namely LISMU-FCN and LISMU-OCN, and a balance loss function designed for balancing smoothness and sharpness. LISMU-FCN, with much fewer parameters, can achieve imaging across similar datasets while LISMU-OCN can achieve imaging across significantly different datasets. What's more, we establish a set of simulation algorithms which are close to the real experiment, and it is of great significance and practical value in the research on lensless scattering imaging. In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs

Event-assisted Low-Light Video Object Segmentation

In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation. Event cameras, characterized by their high dynamic range and ability to capture motion information of objects, offer promise in enhancing object visibility and aiding VOS methods under such low-light conditions. This paper introduces a pioneering framework tailored for low-light VOS, leveraging event camera data to elevate segmentation accuracy. Our approach hinges on two pivotal components: the Adaptive Cross-Modal Fusion (ACMF) module, aimed at extracting pertinent features while fusing image and event modalities to mitigate noise interference, and the Event-Guided Memory Matching (EGMM) module, designed to rectify the issue of inaccurate matching prevalent in low-light settings. Additionally, we present the creation of a synthetic LLE-DAVIS dataset and the curation of a real-world LLE-VOS dataset, encompassing frames and events. Experimental evaluations corroborate the efficacy of our method across both datasets, affirming its effectiveness in low-light scenarios.Comment: CVPR 202

Identification and characterization of pathogenicity-related genes of Rhizoctonia solani AG3 during tobacco infection

Tobacco target spot disease is caused by a ubiquitous soil-borne phytopathogen Rhizoctonia solani; the pathogenic mechanisms underlying the effects of R. solani remain unclear. Deeper understanding of the functional responses to R. solani during host plant infection would help identify the molecular mechanisms essential for successful host invasion. In this study, we performed global transcriptional analysis of R. solani during various stages (12, 24, 48, 72, 96, and 120 h) of tobacco infection via an RNA sequencing method, while utilizing the pathosystem model R. solani AG3–tobacco (Nicotiana tabacum L.). After R. solani inoculation, the number of differentially expressed genes of R. solani differed at the various time points. Moreover, several gene ontology and Kyoto encyclopedia of genes and genomes pathways were unique in different infection stages, especially with respect to the genes involved in plant cell wall degradation and catalysis of biotransformation reactions, such as the pectin metabolic process and pectin catabolic process. The overexpressing-PD8 N. benthamiana plants enhanced the susceptibility to R. solani. In addition, we found that large amounts of reactive oxygen species (ROS) were generated in tobacco after infected by R. solani. R. solani encoding FAD/NAD binding oxidoreductase and peroxidase gene family to eliminating ROS and counteract oxidative stress. Moreover, Perox3 was validated that can enhance the ability of scavenging ROS by co-injecting. Overall, our findings show that pectin-degrading enzymes and cytochrome P450 genes are critical for plant infection. These results provide comprehensive insights into R. solani AG3 transcriptome responses during tobacco invasion

SirT1 modulates the estrogen–insulin-like growth factor-1 signaling for postnatal development of mammary gland in mice

INTRODUCTION: Estrogen and insulin-like growth factor-1 (IGF-1) play important roles in mammary gland development and breast cancer. SirT1 is a highly conserved protein deacetylase that can regulate the insulin/IGF-1 signaling in lower organisms, as well as a growing number of transcription factors, including NF-κB, in mammalian cells. Whether SirT1 regulates the IGF-1 signaling for mammary gland development and function, however, is not clear. In the present study, this role of SirT1 was examined by studying SirT1-deficient mice. METHODS: SirT1-deficient (SirT1(ko/ko)) mice were generated by crossing a new strain of mice harboring a conditional targeted mutation in the SirT1 gene (SirT1(co/co)) with CMV-Cre transgenic mice. Whole mount and histology analyses, immunofluorescence staining, immunohistochemistry, and western blotting were used to characterize mammary gland development in virgin and pregnant mice. The effect of exogenous estrogen was also examined by subcutaneous implantation of a slow-releasing pellet in the subscapular region. RESULTS: Both male and female SirT1(ko/ko )mice can be fertile despite the growth retardation phenotype. Virgin SirT1(ko/ko )mice displayed impeded ductal morphogenesis, whereas pregnant SirT1(ko/ko )mice manifested lactation failure due to an underdeveloped lobuloalveolar network. Estrogen implantation was sufficient to rescue ductal morphogenesis. Exogenous estrogen reversed the increased basal level of IGF-1 binding protein-1 expression in SirT1(ko/ko )mammary tissues, but not that of IκBα expression, suggesting that increased levels of estrogen enhanced the production of local IGF-1 and rescued ductal morphogenesis. Additionally, TNFα treatment enhanced the level of the newly synthesized IκBα in SirT1(ko/ko )cells. SirT1 deficiency therefore affects the cellular response to multiple extrinsic signals. CONCLUSION: SirT1 modulates the IGF-1 signaling critical for both growth regulation and mammary gland development in mice. SirT1 deficiency deregulates the expression of IGF-1 binding protein-1 and attenuates the effect of IGF-1 signals, including estrogen-stimulated local IGF-1 signaling for the onset of ductal morphogenesis. These findings suggest that the enzymatic activity of SirT1 may influence both normal growth and malignant growth of mammary epithelial cells

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

In vivo inhibitory effect of suberoylanilide hydroxamic acid combined with sorafenib on human hepatocellular carcinoma cells

The present study aimed to investigate the in vivo inhibitory effect of histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) combined with sorafenib on human hepatocellular carcinoma HCCLM3 cells. The nude mice transplanted with HCCLM3 cells were randomly divided into control, SAHA, sorafenib and SAHA+sorafenib groups. The nude mice in the later 3 groups were intragastrically administrated with SAHA (10 mg·kg-1·day-1), sorafenib (10 mg·kg-1·day-1) and SAHA (10 mg·kg-1·day-1) combined with sorafenib (10 mg·kg-1·day-1), respectively, for successive 20 days. Finally, the inhibition rate of tumor was measured. The expressions of MEK1/2, p-ERK1/2, Cyclin D1, Bcl-2, Bax, p53, MMP2, MMP-9 and uPA in tumor tissues were determined. Results showed that, compared with SAHA and Sorafenib groups, in SAHA+sorafenib groups the inhibition rate of tumor was significantly increased (P < 0.05), the expression levels of MEK1/2, p-ERK1/2, Cyclin D1, Bcl-2, MMP-2 and MMP-9 and uPA protein in tumor tissues were significantly decreased, respectively (P < 0.05), and the expression levels of Bax and p53 protein were significantly increased, respectively (P < 0.05). In conclusion, compared with single drug, SAHA combined with sorafenib can enhance the inhibitory effects on HCCLM3 xenografts in nude mice

Better and Faster: Adaptive Event Conversion for Event-Based Object Detection

Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect sparse event streams with many advantages. In this paper, we focus on building better and faster event-based object detectors. To this end, we first propose a computationally efficient event representation Hyper Histogram, which adequately preserves both the polarity and temporal information of events. Then we devise an Adaptive Event Conversion module, which converts events into Hyper Histograms according to event density via an adaptive queue. Moreover, we introduce a novel event-based augmentation method Shadow Mosaic, which significantly improves the event sample diversity and enhances the generalization ability of detection models. We equip our proposed modules on three representative object detection models: YOLOv5, Deformable-DETR, and RetinaNet. Experimental results on three event-based detection datasets (1Mpx, Gen1, and MVSEC-NIGHTL21) demonstrate that our proposed approach outperforms other state-of-the-art methods by a large margin, while achieving a much faster running speed (< 14 ms and < 4 ms for 50 ms event data on the 1Mpx and Gen1 datasets)