Chronic Heat Stress Weakened the Innate Immunity and Increased the Virulence of Highly Pathogenic Avian Influenza Virus H5N1 in Mice

Chronic heat stress (CHS) can negatively affect immune response in animals. In this study we assessed the effects of CHS on host innate immunity and avian influenza virus H5N1 infection in mice. Mice were divided into two groups: CHS and thermally neutral (TN). The CHS treatment group exhibited reduced local immunity in the respiratory tract, including the number of pulmonary alveolar macrophages and lesions in the nasal mucosa, trachea, and lungs. Meanwhile, CHS retarded dendritic cells (DCs) maturation and reduced the mRNA levels of IL-6 and IFN-β significantly (P < .05). After the CHS treatment, mice were infected with H5N1 virus. The mortality rate and viral load in the lungs of CHS group were higher than those of TN group. The results suggest that the CHS treatment could suppress local immunity in the respiratory tract and innate host immunity in mice significantly and moderately increased the virulence in H5N1-infected mice

Ordering-Flexible Multi-Robot Coordination for MovingTarget Convoying Using Long-TermTask Execution

In this paper, we propose a cooperative long-term task execution (LTTE) algorithm for protecting a moving target into the interior of an ordering-flexible convex hull by a team of robots resiliently in the changing environments. Particularly, by designing target-approaching and sensing-neighbor collision-free subtasks, and incorporating these subtasks into the constraints rather than the traditional cost function in an online constraint-based optimization framework, the proposed LTTE can systematically guarantee long-term target convoying under changing environments in the n-dimensional Euclidean space. Then, the introduction of slack variables allow for the constraint violation of different subtasks; i.e., the attraction from target-approaching constraints and the repulsion from time-varying collision-avoidance constraints, which results in the desired formation with arbitrary spatial ordering sequences. Rigorous analysis is provided to guarantee asymptotical convergence with challenging nonlinear couplings induced by time-varying collision-free constraints. Finally, 2D experiments using three autonomous mobile robots (AMRs) are conducted to validate the effectiveness of the proposed algorithm, and 3D simulations tackling changing environmental elements, such as different initial positions, some robots suddenly breakdown and static obstacles are presented to demonstrate the multi-dimensional adaptability, robustness and the ability of obstacle avoidance of the proposed method

Acute, Multiple-Dose Dermal and Genetic Toxicity of Nu-3: A Novel Antimicrobial Agent

Nu-3 [butyl-phosphate-5′-thymidine-3′-phosphate-butyl] is a modified nucleotide that has been shown to have antimicrobial activity against a range of bacteria including Pseudomonas aeruginosa. However, data on the toxicological profile of Nu-3 are still lacking. In the present study, the toxicity of Nu-3 was evaluated by the following studies: acute oral toxicity, dermal and mucous membrane irritation, multiple-dose toxicity and genotoxicity in vivo and vitro. The acute oral toxicity test in mice showed that Nu-3 had an LD50 of 2001mg/kg body weight. The irritation tests on rats revealed that Nu-3 was not irritant, with an irritation scoring of 0. The multiple-dose toxicity study in rats showed that Nu-3 did not cause significant changes in histology, selected serum chemistry, and hematological parameters compared to the controls. Rats administrated with multiple-doses of Nu-3 showed no visible toxic symptoms. Both in vitro and in vivo, Nu-3 exhibited no notable genetic toxicity. Overall, the data suggest that Nu-3 is hypotoxic or nontoxic antimicrobial compound that warrants being further developed for treating Pseudomonas aeruginosa infection

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency. To address these issues, this paper presents a novel SFL approach, named Sliding Split Federated Learning (S$^2$FL), which adopts an adaptive sliding model split strategy and a data balance-based training mechanism. By dynamically dispatching different model portions to AIoT devices according to their computing capability, S$^2$FL can alleviate the low training efficiency caused by stragglers. By combining features uploaded by devices with different data distributions to generate multiple larger batches with a uniform distribution for back-propagation, S$^2$FL can alleviate the performance degradation caused by data heterogeneity. Experimental results demonstrate that, compared to conventional SFL, S$^2$FL can achieve up to 16.5\% inference accuracy improvement and 3.54X training acceleration

Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned faster than normal knowledge, and ii) trigger patterns have a greater effect on image classification than normal class patterns. Our approach generates the images with newly learned knowledge by identifying the differences between the old and new global models, and filters trigger images by evaluating the effect of these generated images. By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign. Comprehensive experiments demonstrate that our approach can defend against almost all the existing types of backdoor attacks and outperform all the seven state-of-the-art defense methods with both IID and non-IID scenarios. Especially, our approach can successfully defend against the backdoor attack even when 80\% of the clients are malicious

Efficient induction of CD25- iTreg by co-immunization requires strongly antigenic epitopes for T cells

Background: We previously showed that co-immunization with a protein antigen and a DNA vaccine coding for the same antigen induces CD40(low) IL-10(high) tolerogenic DCs, which in turn stimulates the expansion of antigenspecific CD4(+)CD25(-)Foxp3(+) regulatory T cells (CD25(-) iTreg). However, it was unclear how to choose the antigen sequence to maximize tolerogenic antigen presentation and, consequently, CD25(-) iTreg induction. Results: In the present study, we demonstrated the requirement of highly antigenic epitopes for CD25(-) iTreg induction. Firstly, we showed that the induction of CD25(-) iTreg by tolerogenic DC can be blocked by anti-MHC-II antibody. Next, both the number and the suppressive activity of CD25(-) iTreg correlated positively with the overt antigenicity of an epitope to activate T cells. Finally, in a mouse model of dermatitis, highly antigenic epitopes derived from a flea allergen not only induced more CD25(-) iTreg, but also more effectively prevented allergenic reaction to the allergen than did weakly antigenic epitopes. Conclusions: Our data thus indicate that efficient induction of CD25- iTreg requires highly antigenic peptide epitopes. This finding suggests that highly antigenic epitopes should be used for efficient induction of CD25- iTreg for clinical applications such as flea allergic dermatitis