A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that simultaneously trains synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher accuracies on various static and neuromorphic datasets than SNNs trained with two single-learning models of the synaptic learning (SL) and the threshold learning (TL). During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework (JDF). Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio

Potential molecular and cellular mechanisms of the effects of cuproptosis-related genes in the cardiomyocytes of patients with diabetic heart failure: a bioinformatics analysis

BackgroundDiabetes mellitus is an independent risk factor for heart failure, and diabetes-induced heart failure severely affects patients’ health and quality of life. Cuproptosis is a newly defined type of programmed cell death that is thought to be involved in the pathogenesis and progression of cardiovascular disease, but the molecular mechanisms involved are not well understood. Therefore, we aimed to identify biomarkers associated with cuproptosis in diabetes mellitus-associated heart failure and the potential pathological mechanisms in cardiomyocytes.MaterialsCuproptosis-associated genes were identified from the previous publication. The GSE26887 dataset was downloaded from the GEO database.MethodsThe consistency clustering was performed according to the cuproptosis gene expression. Differentially expressed genes were identified using the limma package, key genes were identified using the weighted gene co-expression network analysis(WGCNA) method, and these were subjected to immune infiltration analysis, enrichment analysis, and prediction of the key associated transcription factors. Consistency clustering identified three cuproptosis clusters. The differentially expressed genes for each were identified using limma and the most critical MEantiquewhite4 module was obtained using WGCNA. We then evaluated the intersection of the MEantiquewhite4 output with the three clusters, and obtained the key genes.ResultsThere were four key genes: HSDL2, BCO2, CORIN, and SNORA80E. HSDL2, BCO2, and CORIN were negatively associated with multiple immune factors, while SNORA80E was positively associated, and T-cells accounted for a major proportion of this relationship with the immune system. Four enriched pathways were found to be associated: arachidonic acid metabolism, peroxisomes, fatty acid metabolism, and dorsoventral axis formation, which may be regulated by the transcription factor MECOM, through a change in protein structure.ConclusionHSDL2, BCO2, CORIN, and SNORA80E may regulate cardiomyocyte cuproptosis in patients with diabetes mellitus-associated heart failure through effects on the immune system. The product of the cuproptosis-associated gene LOXL2 is probably involved in myocardial fibrosis in patients with diabetes, which leads to the development of cardiac insufficiency

The elicitor VP2 from Verticillium dahliae triggers defence response in cotton

Summary: Verticillium dahliae is a widespread and destructive soilborne vascular pathogenic fungus that causes serious diseases in dicot plants. Here, comparative transcriptome analysis showed that the number of genes upregulated in defoliating pathotype V991 was significantly higher than in the non‐defoliating pathotype 1cd3‐2 during the early response of cotton. Combined with analysis of the secretome during the V991–cotton interaction, an elicitor VP2 was identified, which was highly upregulated at the early stage of V991 invasion, but was barely expressed during the 1cd3‐2‐cotton interaction. Full‐length VP2 could induce cell death in several plant species, and which was dependent on NbBAK1 but not on NbSOBIR1 in N. benthamiana. Knock‐out of VP2 attenuated the pathogenicity of V991. Furthermore, overexpression of VP2 in cotton enhanced resistance to V. dahliae without causing abnormal plant growth and development. Several genes involved in JA, SA and lignin synthesis were significantly upregulated in VP2‐overexpressing cotton. The contents of JA, SA, and lignin were also significantly higher than in the wild‐type control. In summary, the identified elicitor VP2, recognized by the receptor in the plant membrane, triggers the cotton immune response and enhances disease resistance

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure

Effects of depth of straw returning on maize yield potential and greenhouse gas emissions

Appropriate straw incorporation has ample agronomic and environmental benefits, but most studies are limited to straw mulching or application on the soil surface. To determine the effect of depth of straw incorporation on the crop yield, soil organic carbon (SOC), total nitrogen (TN) and greenhouse gas emission, a total of 4 treatments were set up in this study, which comprised no straw returning (CK), straw returning at 15 cm (S15), straw returning at 25 cm (S25) and straw returning at 40 cm (S40). The results showed that straw incorporation significantly increased SOC, TN and C:N ratio. Compared with CK treatments, substantial increases in the grain yield (by 4.17~5.49% for S15 and 6.64~10.06% for S25) were observed under S15 and S25 treatments. S15 and S25 could significantly improve the carbon and nitrogen status of the 0-40 cm soil layer, thereby increased maize yield. The results showed that the maize yield was closely related to the soil carbon and nitrogen index of the 0-40 cm soil layer. In order to further evaluate the environmental benefits of straw returning, this study measured the global warming potential (GWP) and greenhouse gas emission intensity (GHGI). Compared with CK treatments, the GWP of S15, S25 and S40 treatments was increased by 9.35~20.37%, 4.27~7.67% and 0.72~6.14%, respectively, among which the S15 treatment contributed the most to the GWP of farmland. GHGI is an evaluation index of low-carbon agriculture at this stage, which takes into account both crop yield and global warming potential. In this study, GHGI showed a different trend from GWP. Compared with CK treatments, the S25 treatments had no significant difference in 2020, and decreased significantly in 2021 and 2022. This is due to the combined effect of maize yield and cumulative greenhouse gas emissions, indicating that the appropriate straw returning method can not only reduce the intensity of greenhouse gas emissions but also improve soil productivity and enhance the carbon sequestration effect of farmland soil, which is an ideal soil improvement and fertilization measure

Cloning and Functional Analysis of FLJ20420: A Novel Transcription Factor for the BAG-1 Promoter

BAG-1 is an anti-apoptotic protein that interacts with a variety of cellular molecules to inhibit apoptosis. The mechanisms by which BAG-1 interacts with other proteins to inhibit apoptosis have been extensively explored. However, it is currently unknown how BAG-1 expression is regulated at the molecular level, especially in cancer cells. Here we reported to clone a novel down-regulated BAG-1 expression gene named FLJ20420 using hBAG-1 promoter as a probe to screen Human Hela 5′ cDNA library by Southernwestern blot. The FLJ20420 gene encodes a ∼26-kDa protein that is localized in both the cytoplasm and nucleus. We proved that FLJ20420 protein can specially bind hBAG-1 promoter region by EMSA in vivo and ChIP assay in vivo. Northern blot analysis revealed a low level of FLJ20420 transcriptional expression in normal human tissues (i.e., brain, placenta, lung, liver, kidney, pancreas and cervix), except for heart and skeletal muscles, which showed higher levels. Furthermore, enhanced FLJ20420 expression was observed in tumor cell lines (i.e., MDA468, BT-20, MCF-7, C33A, HeLa and Caski). Knockdown of endogenous FLJ20420 expression significantly increased BAG-1 expression in A549 and L9981 cells, and also significantly enhanced their sensitivity to cisplatin-induced apoptosis. A microarray assay of the FLJ20420 siRNA –transfectants showed altered expression of 505 known genes, including 272 upregulated and 233 downregulated genes. Finally, our gene array studies in lung cancer tissue samples revealed a significant increase in FLJ20420 expression in primary lung cancer relative to the paired normal lung tissue controls (p = 0.0006). The increased expression of FLJ20420 corresponded to a significant decrease in BAG-1 protein expression in the primary lung cancers, relative to the paired normal lung tissue controls (p = 0.0001). Taken together, our experiments suggest that FLJ20420 functions as a down-regulator of BAG-1 expression. Its abnormal expression may be involved in the oncogenesis of human malignancies such as lung cancer

Fusing R features and local features with context-aware kernels for action recognition

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition