Spikformer: When Spiking Neural Network Meets Transformer

We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It is intuitively promising to explore the marriage between them. In this paper, we consider leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer). The SSA mechanism in Spikformer models the sparse visual feature by using spike-form Query, Key, and Value without softmax. Since its computation is sparse and avoids multiplication, SSA is efficient and has low computational energy consumption. It is shown that Spikformer with SSA can outperform the state-of-the-art SNNs-like frameworks in image classification on both neuromorphic and static datasets. Spikformer (66.3M parameters) with comparable size to SEW-ResNet-152 (60.2M,69.26%) can achieve 74.81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models

Cryptanalysis of LU Decomposition-based Key Pre-distribution Scheme for Wireless Sensor Networks

S. J. Choi and H. Y. Youn proposed a key pre-distribution scheme for Wireless Sensor Networks based on LU decomposition of symmetric matrix, and later many researchers did works based on this scheme. Nevertheless, we find a mathematical relationship of L and U matrixes decomposed from symmetric matrix, by using which we can calculate one matrix from another regardless of their product -- the key matrix K. This relationship would profoundly harm the secure implementation of this decomposition scheme in the real world. In this paper, we first present and prove the mathematical theorem. Next we give samples to illustrate how to break the networks by using this theorem. Finally, we state the conclusion and some directions for improving the security of the key pre-distribution scheme

JWA Deficiency Suppresses Dimethylbenz[a]Anthracene-Phorbol Ester Induced Skin Papillomas via Inactivation of MAPK Pathway in Mice

Our previous studies indicated that JWA plays an important role in DNA damage repair, cell migration, and regulation of MAPKs. In this study, we investigated the role of JWA in chemical carcinogenesis using conditional JWA knockout (JWAΔ2/Δ2) mice and two-stage model of skin carcinogenesis. Our results indicated that JWAΔ2/Δ2 mice were resistant to the development of skin papillomas initiated by 7, 12-dimethylbenz(a)anthracene (DMBA) followed by promotion with 12-O-tetradecanoylphorbol-13-acetate (TPA). In JWAΔ2/Δ2 mice, the induction of papilloma was delayed, and the tumor number and size were reduced. In primary keratinocytes from JWAΔ2/Δ2 mice, DMBA exposure induced more intensive DNA damage, while TPA-promoted cell proliferation was reduced. The further mechanistic studies showed that JWA deficiency blocked TPA-induced activation of MAPKs and its downstream transcription factor Elk1 both in vitro and in vivo. JWAΔ2/Δ2 mice are resistance to tumorigenesis induced by DMBA/TPA probably through inhibition of transcription factor Elk1 via MAPKs. These results highlight the importance of JWA in skin homeostasis and in the process of skin tumor development

3D printed milk protein food simulant: improving the printing performance of milk protein concentration by incorporating whey protein isolate

This paper aimed to establish a milk protein based 3D printing food simulant and investigated the effect of whey protein isolate (WPI) concentration on the printing performance of milk protein concentrate (MPC). WPI and MPC powders at different ratios were prepared in paste (35 wt%, total dry matter content). The rheological properties and water distribution of protein matrix prepared with different MPC/WPI ratios were characterized with a rheometer and low field nuclear magnetic resonance (LF-NMR), respectively. Moreover, the variations in the microstructure of printed objects were observed with a scanning electron microscope (SEM). The printed objects showed different appearance and physical properties; the printing fidelity was also evaluated by measuring the geometric accuracy of printed objects. The rheological and texture data showed that the presence of WPI could reduce the apparent viscosity and soften the MPC paste, benefiting the printing process. The results showed that the milk powder paste mixture prepared with MPC/WPI at a ratio of 5/2 was the most desirable material for extrusion-based 3D printing, which could be successfully printed and matched the designed 3D model best. Industrial relevance: 3D printing in food sector has been an attractive and emerging technology owing to its potential advantages, such as customized food designs, personalized and digitalized nutrition, simplifying supply chain and so on. This paper established a high protein food simulant for 3D printing, optimized its printing performance with whey protein isolate, and studied the physicochemical property of prepared protein pastes. The overall results indicated that milk protein powders could be the promising materials for the application in food 3D printing. In flowing studies or practical production, the glycerol could be replaced by ingredients such as syrup, honey etc. This study may give more insights into 3D printing applied in food sector and facilitate the further developments of 3D food printing

Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures

Background: Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers.Methods: Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR.Results: 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (p-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4+ T cell, CD8+ T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8+ T cells, resting memory CD4+ T cells, M1 macrophages and naive B cells.Conclusion: We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis

Improving the microbial and nutritional quality of skim milk using microfiltration combined with thermal and nonthermal techniques

The effectiveness of microfiltration (MF), alone or combined with high temperature short time (HTST) pasteurisation, ultraviolet-C (UV-C), and ultrasonication (US) in improving the microbial and nutritional quality of skim milk was evaluated in comparison with commercial ultra-pasteurisation (UP). Compared with untreated skim milk, MF combined with UV-C and US retained more bioactive proteins, which made these techniques superior to MF combined with HTST. Almost no bioactive proteins survived after UP. MF alone reduced the bacterial load by 2.5 log; however, MF combined with HTST (MH), UV-C (MUV), or US (MUS) can reduce the bacterial load to an undetectable level and extend the microbial shelf-life of skim milk to 40 days. In addition, MUV and MUS did not induce significant protein oxidation. Even though the skim milk after MUV or MUS showed only minor microbial growth, the shelf-life was limited by proteolysis through plasmin activity

Current progress of emerging technologies in human and animals’ milk processing : Retention of immune-active components and microbial safety

Human milk and commercial dairy products play a vital role in humans, as they can provide almost all essential nutrients and immune-active components for the development of children. However, how to retain more native immune-active components of milk during processing remains a big question for the dairy industry. Nonthermal technologies for milk processing are gaining increasing interest in both academic and industrial fields, as it is known that thermal processing may negatively affect the quality of milk products. Thermosensitive components, such as lactoferrin, immunoglobulins (Igs), growth factors, and hormones, are highly important for the healthy development of newborns. In addition to product quality, thermal processing also causes environmental problems, such as high energy consumption and greenhouse gas (GHG) emissions. This review summarizes the recent advances of UV-C, ultrasonication (US), high-pressure processing (HPP), and other emerging technologies for milk processing from the perspective of immune-active components retention and microbial safety, focusing on human, bovine, goat, camel, sheep, and donkey milk. Also, the detailed application, including the instrumental design, technical parameters, and obtained results, are discussed. Finally, future prospects and current limitations of nonthermal techniques as applied in milk processing are discussed. This review thereby describes the current state-of-the-art in nonthermal milk processing techniques and will inspire the development of such techniques for in-practice applications in milk processing