SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer

As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices. More recently, the most advanced SNN, Spikformer, combines the self-attention module from Transformer with SNN to achieve remarkable performance. However, it adopts larger channel dimensions in MLP layers, leading to an increased number of redundant model parameters. To effectively decrease the computational complexity and weight parameters of the model, we explore the Lottery Ticket Hypothesis (LTH) and discover a very sparse ($\ge$90%) subnetwork that achieves comparable performance to the original network. Furthermore, we also design a lightweight token selector module, which can remove unimportant background information from images based on the average spike firing rate of neurons, selecting only essential foreground image tokens to participate in attention calculation. Based on that, we present SparseSpikformer, a co-design framework aimed at achieving sparsity in Spikformer through token and weight pruning techniques. Experimental results demonstrate that our framework can significantly reduce 90% model parameters and cut down Giga Floating-Point Operations (GFLOPs) by 20% while maintaining the accuracy of the original model

DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics

Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma

Introduction: Altered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC. Methods: In total, 496 individuals of ESCC (n=114), precancerosis (n=187) and controls (n=195) from the discovery population (n=348) and validation population (n=148) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score. Results: In the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 (95% CI: 3.03-5.36, P \u3c 0.001), 0.69 (95% CI: 0.55-0.87, P \u3c 0.001), 0.56 (95% CI: 0.45-0.69, P \u3c 0.001), 0.52 (95% CI: 0.41-0.65, P \u3c 0.001), 7.17 (95% CI: 4.77-10.79, P \u3c 0.001), and 2.86 (95% CI: 2.33-3.53, P \u3c 0.001), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 (95% CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 (95% CI: 0.758-0.864). Discussion: Our study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression

Molecular Cloning and Copy Number Variation of a Ferritin Subunit (Fth1) and Its Association with Growth in Freshwater Pearl Mussel Hyriopsis cumingii

Iron is one of the most important minor elements in the shells of bivalves. This study was designed to investigate the involvement of ferritin, the principal protein for iron storage, in shell growth. A novel ferritin subunit (Fth1) cDNA from the freshwater pearl mussel (Hyriopsis cumingii) was isolated and characterized. The complete cDNA contained 822 bp, with an open reading frame (ORF) of 525 bp, a 153 bp 5′ untranslated region (UTR) and a 144 bp 3′ UTR. The complete genomic DNA was 4125 bp, containing four exons and three introns. The ORF encoded a protein of 174 amino acids without a signal sequence. The deduced ferritin contained a highly conserved motif for the ferroxidase center comprising seven residues of a typical vertebrate heavy-chain ferritin. It contained one conserved iron associated residue (Try27) and iron-binding region signature 1 residues. The mRNA contained a 27 bp iron-responsive element with a typical stem-loop structure in the 5′-UTR position. Copy number variants (CNVs) of Fth1 in two populations (PY and JH) were detected using quantitative real-time PCR. Associations between CNVs and growth were also analyzed. The results showed that the copy number of the ferritin gene of in the diploid genome ranged from two to 12 in PY, and from two to six in JH. The copy number variation in PY was higher than that in JH. In terms of shell length, mussels with four copies of the ferritin gene grew faster than those with three copies (P<0.05), suggesting that CNVs in the ferritin gene are associated with growth in shell length and might be a useful molecular marker in selective breeding of H. cumingii

Two-stage data-driven dispatch for integrated power and natural gas systems by using stochastic model predictive control

The optimal dispatch of the integrated power and natural gas systems can increase the utilization rate of renewable energy and energy efficiency while decreasing operation costs. The common prediction errors of wind power and electric load have the potential to negatively impact the normal operation of the integrated power and natural gas systems. A two-stage data-driven dispatch strategy is proposed to reduce this effect, consisting of the day-ahead dispatch stage and the intraday rolling dispatch stage using stochastic model predictive control (MPC). In the day-ahead dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators are built, and the day-ahead tie-line power is obtained and regarded as input parameters to the intraday dispatch stage. In the intraday dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators with the latest rolling prediction data are built, and the remaining control variables are obtained. The distribution characteristics of the stochastic prediction errors of wind power and electric load are captured and described by the variational Bayesian Gaussian mixture model with massive historical data. Then the original stochastic mixed-integer nonlinear programming problem is converted to a tractable deterministic one by the quantile-based analytical reformulation and convex relaxation technique. Finally, the proposed strategy is verified by the numerical experiments based on a modified IEEE 33-bus system integrated with a 10-node natural gas system and a micro hydrogen system. The numerical results demonstrate that the proposed strategy reduces the actual costs and decreases the violation rate caused by the stochastic prediction errors of wind power and electric load

Arsenic Trioxide in Synergy with Vitamin D Rescues the Defective VDR-PPAR-γ Functional Module of Autophagy in Rheumatoid Arthritis

Dysregulated autophagy leads to autoimmune diseases including rheumatoid arthritis (RA). Arsenic trioxide (ATO) is a single agent used for the treatment of acute promyelocytic leukemia and is highly promising for other malignancies but is also attractive for RA, although its relationship with autophagy remains to be further clarified and its application optimized. For the first time, we report a defective functional module of autophagy comprising the Vitamin D receptor (VDR), PPAR-γ, microtubule-associated protein 1 light-chain 3 (LC3), and p62 which appears in RA synovial fibroblasts. ATO alleviated RA symptoms by boosting effective autophagic flux through significantly downregulating p62, the inflammation and catabolism protein. Importantly, low-dose ATO synergizes with Vitamin D in RA treatment

Overview of Strategies to Improve Therapy against Tumors Using Natural Killer Cell

NK cells are lymphocytes with antitumor properties and can directly lyse tumor cells in a non-MHC-restricted manner. However, the tumor microenvironment affects the immune function of NK cells, which leads to immune evasion. This may be related to the pathogenesis of some diseases. Therefore, great efforts have been made to improve the immunotherapy effect of natural killer cells. NK cells from different sources can meet different clinical needs, in order to minimize the inhibition of NK cells and maximize the response potential of NK cells, for example, modification of NK cells can increase the number of NK cells in tumor target area, change the direction of NK cells, and improve their targeting ability to malignant cells. Checkpoint blocking is also a promising strategy for NK cells to kill tumor cells. Combination therapy is another strategy for improving antitumor ability, especially in combination with oncolytic viruses and nanomaterials. In this paper, the mechanisms affecting the activity of NK cells were reviewed, and the therapeutic potential of different basic NK cell strategies in tumor therapy was focused on. The main strategies for improving the immune function of NK cells were described, and some new strategies were proposed

Data‐driven chance‐constrained dispatch for integrated power and natural gas systems considering wind power prediction errors

Abstract Stochastic wind power prediction errors hurt the normal operation of integrated power and natural gas systems (IPGS). First, the data‐driven stochastic chance‐constrained programming method is applied to deal with wind power prediction errors, and its probability distribution is accurately fitted by variational Bayesian Gaussian mixture model with massive historical data. In addition, the data‐driven chance constraints of tie‐line power and reserve capacity of gas turbine are built. Next, to utilize wind power more reasonably, the operational characteristics and optimal commitment of power‐to‐hydrogen devices are considered and modelled in proposed strategy to reflect the actual situation of IPGS. Then, the original complicated dispatch problem is converted into a tractable second‐order cone programming problem via convex relaxation and quantile‐based analytical reformulation techniques. Finally, the effectiveness of the proposed strategy is validated by numerical experiments based on a modified IEEE 33‐bus system integrated with a 10‐node natural gas system and a micro hydrogen system