Using Caterpillar to Nibble Small-Scale Images

Recently, MLP-based models have become popular and attained significant performance on medium-scale datasets (e.g., ImageNet-1k). However, their direct applications to small-scale images remain limited. To address this issue, we design a new MLP-based network, namely Caterpillar, by proposing a key module of Shifted-Pillars-Concatenation (SPC) for exploiting the inductive bias of locality. SPC consists of two processes: (1) Pillars-Shift, which is to shift all pillars within an image along different directions to generate copies, and (2) Pillars-Concatenation, which is to capture the local information from discrete shift neighborhoods of the shifted copies. Extensive experiments demonstrate its strong scalability and superior performance on popular small-scale datasets, and the competitive performance on ImageNet-1K to recent state-of-the-art methods

A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models

Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input can mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, research on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. In this paper, we present a unified framework for detecting malicious examples and protecting the inference results of Deep Learning models. This framework is based on our observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we repurpose and revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This can enhance our understanding about the inherent features of these two attacks, as well as the defense opportunities

Comprehensive profiling of serotypes, antimicrobial resistance and virulence of Salmonella isolates from food animals in China, 2015–2021

IntroductionSalmonella is a ubiquitous foodborne pathogen and mainly transmitted to human farm-to-fork chain through contaminated foods of animal origin.MethodsIn this study, we investigated the serotypes, antimicrobial resistance and virulence of Salmonella from China.ResultsA total of 617 Salmonella isolates were collected from 4 major food animal species across 23 provi nces in China from 2015-2021. Highest Salmonella prevalence were observed in Guangdong (44.4%) and Sandong (23.7%). Chickens (43.0%) was shown to be the major source of Salmonella contamination, followed by pigs (34.5%) and ducks (18.5%). The number of Salmonella increased significantly from 5.51% to 27.23% during 2015–2020. S. Derby (17.3%), S. Enteritidis (13.1%) and S. Typhimurium (11.4%) were the most common serotypes among 41 serotypes identifiedin this study. Antibiotic susceptibility testing showing that the majority of the Salmonella isolates were resistant to neomycin (99.7%), tetracycline (98.1%), ampicillin (97.4%), sulfadiazine/trimethoprim (97.1%), nalidixic acid (89.1%), doxycycline (83.1%), ceftria xone (70.3%), spectinomycin (67.7%), florfenicol (60.0%), cefotaxime (52.0%) and lomefloxacin (59.8%). The rates of resistance to multiple antibiotics in S. Derby and S.Typhimurium were higher than that in S. Enteritidis. However, the rate of resistance to fosfomycin were observed from higher to lower by S. Derby, S. Enteritidis, and S. Typhimurium. Biofilm formation ability analysis found that 88.49%of the Salmonella were able to produce biofilms, of which 236 Salmonella isolates were strong biofilm producer. Among the 26 types of antibiotics resistance genes (ARGs) were identified in this study, 4 ARGs (tetB,sul2,aadA2, and aph(3’)-IIa) were highly prevalent. In addition, 5 β-lactam resistance genes (blaTEM, blaSHV, blaCMY-2, blaCTX-M, and blaOXA) and 7 quinolone resistance genes (oqxA, oqxB, qnrB, qnrC, qnrD, qnrS, and qeqA) were detected among these isolates. 12 out of 17 virulence genes selected in this study were commonly presented in the chromosomes of tested isolate, with a detection rate of over 80%, including misL, spiA, stn, pagC, iroN, fim, msgA, sopB, prgH, sitC, ttrC, spaN.DiscussionThis study provided a systematical updating on surveillance on prevalence of Salmonella from food animals in China, shedding the light on continued vigilance for Salmonella in food animals

The Sirtuin Family Members SIRT1, SIRT3 and SIRT6: Their Role In Vascular Biology and Atherogenesis

The sirtuins, silent mating-type information regulation 2 (SIRTs), are a family of nicotinamide adenine dinucleotide (NAD+)-dependent histone deacetylases with important roles in regulating energy metabolism and senescence. Activation of SIRTs appears to have beneficial effects on lipid metabolism and antioxidants, prompting investigation of the roles of these proteins in atherogenesis. Although clinical data are currently limited, the availability and safety of SIRT activators such as metformin and resveratrol provide an excellent opportunity to conduct research to better understand the role of SIRTs in human atherosclerosis. Encouraging observations from preclinical studies necessitate rigorous large, prospective, randomized clinical trials to determine the roles of SIRT activators on the progression of atherosclerosis and ultimately on cardiac outcomes, such as myocardial infarction and mortality

Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension

<p>Abstract</p> <p>Background</p> <p>The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.</p> <p>Methods</p> <p>The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.</p> <p>Results</p> <p>By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.</p> <p>Conclusion</p> <p>The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.</p

Metabolic profiling of femoral muscle from rats at different periods of time after death.

Clarification of postmortem metabolite changes can help characterize the process of biological degradation and facilitate investigations of forensic casework, especially in the estimation of postmortem interval (PMI). Metabolomics can provide information on the molecular profiles of tissues, which can aid in investigating postmortem metabolite changes. In this study, liquid chromatography-mass spectrometric (LC-MS) analysis was performed to identify the metabolic profiles of rat femoral muscle at ten periods of time after death within 168 h. The results obtained by reversed-phase liquid chromatography (RPLC)- and hydrophilic interaction liquid chromatography (HILIC)- electrospray ionization (ESI±) have revealed more than 16,000 features from all four datasets. Furthermore, 915 of these features were identified using an in-house database. Principal component analysis (PCA) demonstrated the time-specific features of molecular profiling at each period of time after death. Moreover, results from partial least squares projection to latent structures-discriminant analysis (PLS-DA) disclosed a strong association of metabolic alterations of at least 59 metabolites with the time since death, especially within 48 h after death, which expounds these metabolites as potential indicators in PMI estimation. Altogether, our results illustrate the potentiality of metabolic profiling in the evaluation of PMI and provide candidate metabolite markers with strong correlation with time since death for forensic purpose

Regulation of Tyrosinase Gene Expression by Retinoic Acid Pathway in the Pacific Oyster <i>Crassostrea gigas</i>

Retinoic acid (RA) plays important roles in various biological processes in animals. RA signaling is mediated by two types of nuclear receptors, namely retinoic acid receptor (RAR) and retinoid x receptor (RXR), which regulate gene expression by binding to retinoic acid response elements (RAREs) in the promoters of target genes. Here, we explored the effect of all-trans retinoic acid (ATRA) on the Pacific oyster Crassostera gigas at the transcriptome level. A total of 586 differentially expressed genes (DEGs) were identified in C. gigas upon ATRA treatment, with 309 upregulated and 277 downregulated genes. Bioinformatic analysis revealed that ATRA affects the development, metabolism, reproduction, and immunity of C. gigas. Four tyrosinase genes, including Tyr-6 (LOC105331209), Tyr-9 (LOC105346503), Tyr-20 (LOC105330910), and Tyr-12 (LOC105320007), were upregulated by ATRA according to the transcriptome data and these results were verified by real-time quantitative polymerase chain reaction (RT-qPCR) analysis. In addition, increased expression of Tyr (a melanin-related TYR gene in C. gigas) and Tyr-2 were detected after ATRA treatment. The yeast one-hybrid assay revealed the DNA-binding activity of the RA receptors CgRAR and CgRXR, and the interaction of CgRAR with RARE present in the Tyr-2 promoter. These results provide evidence for the further studies on the role of ATRA and the mechanism of RA receptors in mollusks

Serum connective tissue growth factor is a highly discriminatory biomarker for the diagnosis of rheumatoid arthritis

Background: Our previous proteomic study indicated that connective tissue growth factor (CTGF) may be a potential biomarker for rheumatoid arthritis (RA) diagnosis. The aim was to assess the performance of CTGF as a biomarker of RA. Method: Serum and synovial fluid CTGF was detected using a direct high sensitivity sandwich ELISA kit. Serum CTGF levels were tested for discriminatory capacity and optimal assay cutoffs determined in a training cohort of 98 cases of RA with 103 healthy controls. The assay performance was then validated in a further cohort of 572 patients (with RA (n = 217), ankylosing spondylitis (n = 92), gout (n = 74), osteoarthritis (n = 52), systemic lupus erythematosus (n = 72), or primary Sjögren’s syndrome (pSS) (n = 65)). Results: Significant elevation of synovial fluid CTGF concentration was found in RA patients, demonstrating excellent diagnostic ability to predict RA (area under the curve (AUC) = 0.97). Similar results were found in serum CTGF detection. At the optimal cutoff value 88.66 pg/mL, the sensitivity, specificity, and the AUC was 0.86, 0.92, and 0.92, respectively, in the training cohort. Similar performance was observed in the validation cohort, with sensitivity, specificity, positive likelihood, and negative likelihood of 0.82, 0.91, 5.74, and 0.12, respectively. Stronger discriminatory capacity was seen with the combination of CTGF and anti-citrullinated protein antibody (ACPA) (AUC = 0.96) than with either ACPA or rheumatoid factor (RF) alone (AUC = 0.80 or 0.79, respectively). The discriminatory performance of serum CTGF was consistent across all inflammatory conditions tested (AUC >0.92 in all cases), with the sole exception of pSS. Serum CTGF did not vary with symptom duration or disease activity. Conclusions: Serum CTGF is a promising diagnostic biomarker for RA, with performance in the current study better than either ACPA or RF