Multi-Task Pruning for Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks. There are a large number of works to compress and accelerate deep neural networks in the classification task (e.g., ResNet-50 on ImageNet), but they cannot be straightforwardly applied to the semantic segmentation network that involves an implicit multi-task learning problem. To boost the segmentation performance, the backbone of semantic segmentation network is often pre-trained on a large scale classification dataset (e.g., ImageNet), and then optimized on the desired segmentation dataset. Hence to identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter w.r.t the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we develop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods. Notably, we can obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$ mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on Cityscapes dataset, respectively

Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

AU Humans: Please confirm that all heading levels are represented correctly are able to adapt to the fast-changing world by estimating : statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.</p

TinySAM: Pushing the Envelope for Efficient Segment Anything Model

Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pretrained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive computational capacity, which hinders the further application of SAM on computation constrained edge devices. To this end, in this paper we propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with hard prompt sampling and hard mask weighting strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reduce the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by $2\times$ with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods. Pre-trained models and codes are available at https://github.com/xinghaochen/TinySAM and https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors

Anti-BP180 Autoantibodies Are Present in Stroke and Recognize Human Cutaneous BP180 and BP180-NC16A

Objective: Current evidence has revealed a significant association between bullous pemphigoid (BP) and neurological diseases (ND), including stroke, but the incidence of BP autoantibodies in patients with stroke has not previously been investigated. Our study aimed to assess BP antigen-specific antibodies in stroke patients.Design: One hundred patients with stroke and 100 matched healthy controls were randomly selected for measurement of anti-BP180/BP230 IgG autoantibodies by enzyme-linked immunosorbent assay (ELISA), salt-split indirect immunofluorescence (IIF), and immunoblotting against human cutaneous BP180 and BP180-NC16A.Results: Anti-BP180 autoantibodies were found in 14 (14.0%) patients with stroke and 5 (5.0%) of controls by ELISA (p &lt; 0.05). Sera from 13 (13.0%) patients with stroke and 3 (3.0%) controls reacted with 180-kDa proteins from human epidermal extract (p &lt; 0.05). 11 (11.0%) of stroke and 2 (2.0%) of control sera recognized the human recombinant full length BP180 and NC16A (p &lt; 0.05). The anti-BP180-positive patients were significantly younger than the negative patients at the time of stroke (p &lt; 0.001).Conclusion: Development of anti-BP180 autoantibodies occurs at a higher frequency after stroke, suggesting BP180 as a relatively common autoantigen after stroke and providing novel insights into BP pathogenesis in aging

The status and influencing factors of lung ventilation function in employees exposed to dust in enterprises of the XPCC, China

BackgroundOccupational health is closely related to harmful factors in the workplace. Dust is the primary contributing factor causing impaired lung ventilation function among employees with dust exposure, and their lung ventilation function may also be influenced by other factors. We aimed at assessing the status and influencing factors of lung ventilation function among employees exposed to dust in the enterprises of the Eighth Division located in the Xinjiang Production and Construction Corps (XPCC), China.MethodsEmployees exposed to dust in enterprises of the Eighth Division located in the XPCC in 2023 were selected as the subjects of this cross-sectional study. Their lung ventilation function indicators were extracted from health examination records, and an on-site electronic questionnaire survey was conducted among them. Binary logistic regression analyses were conducted to evaluate the factors influencing lung ventilation function.ResultsAccording to the fixed value criteria, the abnormal rates of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC were 31.6, 1.4, and 0.4%, respectively. The lower limit of normal (LLN) criteria could overestimate the rate of abnormal lung ventilation function. Several factors were related to impaired lung ventilation function, including gender, age, education level, marital status, body mass index (BMI), smoking status, physical activity, the type of dust, industry, enterprise scale, occupation, length of service, working shift, monthly income, and respiratory protection.ConclusionsA relatively low abnormal rate of lung ventilation function was observed among employees exposed to dust in enterprises of the Eighth Division, XPCC, and their lung ventilation function was associated with various factors. Effective measures should be taken urgently to reduce the effects of adverse factors on lung ventilation function, thereby further protecting the health of the occupational population

p38β MAPK mediates ULK1-dependent induction of autophagy in skeletal muscle of tumor-bearing mice

Muscle wasting is the key manifestation of cancer-associated cachexia, a lethal metabolic disorder seen in over 50% of cancer patients. Autophagy is activated in cachectic muscle of cancer hosts along with the ubiquitin-proteasome pathway (UPP), contributing to accelerated protein degradation and muscle wasting. However, established signaling mechanism that activates autophagy in response to fasting or denervation does not seem to mediate cancer-provoked autophagy in skeletal myocytes. Here, we show that p38β MAPK mediates autophagy activation in cachectic muscle of tumor-bearing mice via novel mechanisms. Complementary genetic and pharmacological manipulations reveal that activation of p38β MAPK, but not p38α MAPK, is necessary and sufficient for Lewis lung carcinoma (LLC)-induced autophagy activation in skeletal muscle cells. Particularly, muscle-specific knockout of p38β MAPK abrogates LLC tumor-induced activation of autophagy and UPP, sparing tumor-bearing mice from muscle wasting. Mechanistically, p38β MAPK-mediated activation of transcription factor C/EBPβ is required for LLC-induced autophagy activation, and upregulation of autophagy-related genes LC3b and Gabarapl1. Surprisingly, ULK1 activation (phosphorylation at S555) by cancer requires p38β MAPK, rather than AMPK. Activated ULK1 forms a complex with p38β MAPK in myocytes, which is markedly increased by a tumor burden. Overexpression of a constitutively active p38β MAPK in HEK293 cells increases phosphorylation at S555 and other amino acid residues of ULK1, but not several of AMPK-mediated sites. Finally, ULK1 activation is abrogated in tumor-bearing mice with muscle-specific knockout of p38β MAPK. Thus, p38β MAPK appears a key mediator of cancer-provoked autophagy activation, and a therapeutic target of cancer-induced muscle wasting

Perceived stigma among discharged patients of COVID-19 in Wuhan, China: A latent profile analysis

BackgroundPerceived stigma has greatly influenced the life quality of the COVID-19 patients who recovered and were discharged (RD hereafter). It is essential to understand COVID-19 stigma of RD and its related risk factors. The current study aims to identify the characteristics of perceived COVID-19 stigma in RD using latent profile analysis (LPA), to explore its psycho-social influencing factors, and to determine the cut-off point of the stigma scale using receiver operating characteristic (ROC) analysis.MethodsA cross-sectional study was conducted among COVID-19 RD in 13 communities in Jianghan District, Wuhan City, Hubei Province, China from June 10 to July 25, 2021, enrolling total 1,297 participants. Data were collected on demographic characteristics, COVID-19 perceived stigma, post-traumatic stress disorder (PTSD), anxiety, depression, sleep disorder, fatigue, resilience, social support, and peace of mind. LPA was performed to identify different profiles of perceived COVID-19 stigma level. Univariate analysis and multinominal logistic regression analysis were conducted to explore the influencing factors in different profiles. ROC analyses was carried out to identify the cut-off value of perceived stigma.ResultsAmong the participants, three profiles of perceived stigma were identified: “low perceived COVID-19 stigma” (12.8%), “moderate perceived COVID-19 stigma” (51.1%), and “severe perceived COVID-19 stigma” (36.1%). Multinominal logistic regression analysis revealed that older age, living with other people, anxiety, and sleep disorder were positively associated with moderate perceived COVID-19 stigma, while higher educational level was negatively associated with moderate perceived COVID-19 stigma. Female, older age, living with other people, anxiety, and sleep disorder were positively associated with severe perceived COVID-19 stigma, while higher educational level, social support, and peace of mind were negatively associated with severe perceived COVID-19 stigma. ROC curve of the Short Version of COVID-19 Stigma Scale (CSS-S) for screening perceived COVID-19 stigma showed that the optimal cut-off value was ≥ 20.ConclusionThe study focuses on the issue of perceived COVID-19 stigma and its psycho-socio influencing factors. It provides evidence for implementing relevant psychological interventions to COVID-19 RD