Efficient Spiking Transformer Enabled By Partial Information

Spiking neural networks (SNNs) have received substantial attention in recent years due to their sparse and asynchronous communication nature, and thus can be deployed in neuromorphic hardware and achieve extremely high energy efficiency. However, SNNs currently can hardly realize a comparable performance to that of artificial neural networks (ANNs) because their limited scalability does not allow for large-scale networks. Especially for Transformer, as a model of ANNs that has accomplished remarkable performance in various machine learning tasks, its implementation in SNNs by conventional methods requires a large number of neurons, notably in the self-attention module. Inspired by the mechanisms in the nervous system, we propose an efficient spiking Transformer (EST) framework enabled by partial information to address the above problem. In this model, we not only implemented the self-attention module with a reasonable number of neurons, but also introduced partial-information self-attention (PSA), which utilizes only partial input signals, further reducing computational resources compared to conventional methods. The experimental results show that our EST can outperform the state-of-the-art SNN model in terms of accuracy and the number of time steps on both Cifar-10/100 and ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1 accuracy on the ImageNet dataset with only 16 time steps. In addition, our proposed PSA reduces flops by 49.8% with negligible performance loss compared to a self-attention module with full information

COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users' cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions

Urban-Rural Disparity in Helicobacter Pylori Infection–Related Upper Gastrointestinal Cancer in China and the Decreasing Trend in Parallel with Socioeconomic Development and Urbanization in an Endemic Area

Background: Globally China has the largest urban-rural disparity in socioeconomic development, and the urban-rural difference in upper gastrointestinal cancer (UGIC) is similar to the difference between developed and developing countries. Objectives: To describe urban-rural disparity in UGIC and to emphasize prevention by socioeconomic development and urbanization in China. Methods: Age-standardized incidence rates (ASRs) of cancers in 2012 were compared between urban Shijiazhuang city and rural Shexian County, and trends from 2000-2015 in Shexian County were analyzed. Findings: Compared with urban Shijiazhuang city, the ASR of gastroesophageal cancers in rural Shexian County was 5.3 times higher in men (234.1 vs 44.2/100,000, 'P' 'Helicobacter pylori' infection prevalence of 75% vs 50%. From 2000-2015, the GDP per capita in Shexian County increased from US$860 to US$3000, urbanization rate increased from 22.4% to 54.8%, and prevalence of 'H pylori' infection among 3- to 10-year-old children decreased from 60% to 46.1% ('P' 'gallbladder cancers and leukemia in both sexes and breast, ovary, thyroid, and kidney cancer in women increased significantly. Despite this offset, ASR of all cancers combined decreased 25% in men (from 378.2 to 283.0/100,000, 'P' '=' '.'00) and 19% in women (from 238.5 to 193.6/100,000, 'P' '=' '.'00). ConclusionsUrban-rural disparity in UGIC is related to inequity in socioeconomic development. Economic growth and urbanization is effective for prevention in endemic regions in China and should be a policy priority

Identification of a shared gene signature and biological mechanism between diabetic foot ulcers and cutaneous lupus erythemnatosus by transcriptomic analysis

Diabetic foot ulcers (DFU) and cutaneous lupus erythematosus (CLE) are both diseases that can seriously affect a patient’s quality of life and generate economic pressure in society. Symptomatically, both DLU and CLE exhibit delayed healing and excessive inflammation; however, there is little evidence to support a molecular and cellular connection between these two diseases. In this study, we investigated potential common characteristics between DFU and CLE at the molecular level to provide new insights into skin diseases and regeneration, and identify potential targets for the development of new therapies. The gene expression profiles of DFU and CLE were obtained from the Gene Expression Omnibus (GEO) database and used for analysis. A total of 41 common differentially expressed genes (DEGs), 16 upregulated genes and 25 downregulated genes, were identified between DFU and CLE. GO and KEGG analysis showed that abnormalities in epidermal cells and the activation of inflammatory factors were both involved in the occurrence and development of DFU and CLE. Protein-protein interaction network (PPI) and sub-module analysis identified enrichment in seven common key genes which is KRT16, S100A7, KRT77, OASL, S100A9, EPGN and SAMD9. Based on these seven key genes, we further identified five miRNAs(has-mir-532-5p, has-mir-324-3p,has-mir-106a-5p,has-mir-20a-5p,has-mir-93-5p) and7 transcription factors including CEBPA, CEBPB, GLI1, EP30D, JUN,SP1, NFE2L2 as potential upstream molecules. Functional immune infiltration assays showed that these genes were related to immune cells. The CIBERSORT algorithm and Pearson method were used to determine the correlations between key genes and immune cells, and reverse key gene-immune cell correlations were found between DFU and CLE. Finally, the DGIbd database demonstrated that Paquinimod and Tasquinimod could be used to target S100A9 and Ribavirin could be used to target OASL. Our findings highlight common gene expression characteristics and signaling pathways between DFU and CLE, indicating a close association between these two diseases. This provides guidance for the development of targeted therapies and mutual interactions

T cell senescence: a new perspective on immunotherapy in lung cancer

T cell senescence is an indication of T cell dysfunction. The ability of senescent T cells to respond to cognate antigens is reduced and they are in the late stage of differentiation and proliferation; therefore, they cannot recognize and eliminate tumor cells in a timely and effective manner, leading to the formation of the suppressive tumor microenvironment. Establishing methods to reverse T cell senescence is particularly important for immunotherapy. Aging exacerbates profound changes in the immune system, leading to increased susceptibility to chronic, infectious, and autoimmune diseases. Patients with malignant lung tumors have impaired immune function with a high risk of recurrence, metastasis, and mortality. Immunotherapy based on PD-1, PD-L1, CTLA-4, and other immune checkpoints is promising for treating lung malignancies. However, T cell senescence can lead to low efficacy or unsuccessful treatment results in some immunotherapies. Efficiently blocking and reversing T cell senescence is a key goal of the enhancement of tumor immunotherapy. This study discusses the characteristics, mechanism, and expression of T cell senescence in malignant lung tumors and the treatment strategies

Mitochondrial Ferritin Deletion Exacerbates β

Mitochondrial ferritin (FtMt) is a mitochondrial iron storage protein which protects mitochondria from iron-induced oxidative damage. Our previous studies indicate that FtMt attenuates β-amyloid- and 6-hydroxydopamine-induced neurotoxicity in SH-SY5Y cells. To explore the protective effects of FtMt on β-amyloid-induced memory impairment and neuronal apoptosis and the mechanisms involved, 10-month-old wild-type and Ftmt knockout mice were infused intracerebroventricularly (ICV) with Aβ25–35 to establish an Alzheimer’s disease model. Knockout of Ftmt significantly exacerbated Aβ25–35-induced learning and memory impairment. The Bcl-2/Bax ratio in mouse hippocampi was decreased and the levels of cleaved caspase-3 and PARP were increased. The number of neuronal cells undergoing apoptosis in the hippocampus was also increased in Ftmt knockout mice. In addition, the levels of L-ferritin and FPN1 in the hippocampus were raised, and the expression of TfR1 was decreased. Increased MDA levels were also detected in Ftmt knockout mice treated with Aβ25–35. In conclusion, this study demonstrated that the neurological impairment induced by Aβ25–35 was exacerbated in Ftmt knockout mice and that this may relate to increased levels of oxidative stress

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia

IntroductionAccurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation.Materials and methodsWe proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups.ResultsBy comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729.ConclusionIn sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy

Age-specific reference values for low psoas muscle index at the L3 vertebra level in healthy populations: A multicenter study

Background and aimsThe progressive and generalized loss of skeletal muscle mass, strength and physical function is defined as sarcopenia. Sarcopenia is closely related to the prognosis of patients. Accurate diagnosis and adequate management of sarcopenia are crucial. The psoas muscle mass index taken at the third lumbar vertebra (L3-PMI, cm2/m2) is one of the established methods for evaluating skeletal muscle mass. However, the cutoff values of L3-PMI for diagnosis of sarcopenia are not yet to be clarified in Asian populations. We attempted to establish reference values for low L3-PMI that would be suitable for defining sarcopenia in the Northern Chinese population.MethodsThis was a retrospective, multicenter cross-sectional study. A search of abdominal CT imaging reports was conducted in four representative cities in northern China. Transverse CT images were measured using the analysis software Slice-O-Matic. Low psoas muscle index was defined as the 5th percentile or mean-2SD of the study group.Results1,787 healthy individuals in the study were grouped by age. The sex and number of people in each group were similar. L3-PMI had a negative linear correlation with age, and a strong correlation with the skeletal muscle index taken at the third lumbar vertebrae (L3-SMI, cm2/m2). The L3-PMI reference values in males were 5.41 cm2/m2 for 20–29 years, 4.71 cm2/m2 for 30–39 years, 4.65 cm2/m2 for 40–49 years, 4.10 cm2/m2 for 50–59 years and 3.68 cm2/m2 for over 60 years by using 5th percentile threshold. Similarly, the reference values in females were 3.32, 3.40, 3.18, 2.91, and 2.62 cm2/m2. When using mean-2SD as the reference, the values for each age group were 4.57, 4.16, 4.03, 3.37, and 2.87 cm2/m2 for males and 2.79, 2.70, 2.50, 2.30, and 2.26 cm2/m2 for females, respectively.ConclusionWe defined the reference values of age-specific low skeletal muscle mass when simply evaluated by L3-PMI. Further studies about the association of sarcopenia using these reference values with certain clinical outcomes or diseases are needed

Research on the factors influencing the premium rate of the Inherent Defects Insurance

This paper introduces the composition of the premium rate of Inherent Defects Insurance, and analyzes the the factors influencing the premium rate of the Inherent Defects Insurance