Analysis of miRNAs involved in mouse brain injury upon Coxsackievirus A6 infection

IntroductionCoxsackievirus A6 (CV-A6) has emerged as the predominant epidemic strain responsible for hand, foot and mouth disease (HFMD). CV-A6 infection can result in severe clinical manifestations, including encephalitis, meningitis, and potentially life-threatening central nervous system disorders. Our previous research findings demonstrated that neonatal mice infected with CV-A6 exhibited limb weakness, paralysis, and ultimately succumbed to death. However, the underlying mechanism of CV-A6-induced nervous system injury remains elusive. Numerous reports have highlighted the pivotal role of miRNAs in various viral infections.MethodsSeparately established infection and control groups of mice were used to create miRNA profiles of the brain tissues before and after CV-A6 transfection, followed by experimental verification, prediction, and analysis of the results.ResultsAt 2 days post-infection (dpi), 4 dpi, and 2dpi vs 4dpi, we identified 175, 198 and 78 significantly differentially expressed miRNAs respectively using qRT-PCR for validation purposes. Subsequently, we predicted target genes of these differentially expressed miRNAs and determined their potential targets through GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. Finally, we verified the miRNA-mRNA pairing via double luciferase experiments while confirming functional enrichment of target genes through Western Blotting analyses.DiscussionThe results from this study suggest that transcriptional regulation, neuronal necrosis, pro-inflammatory cytokine release, and antiviral immunity are all implicated in the pathogenesis of central nervous system injury in mice infected with CV-A6. Brain injury resulting from CV-A6 infection may involve multiple pathways, including glial cell activation, neuronal necrosis, synaptic destruction, degenerative diseases of the nervous system. It can even encompass destruction of the blood-brain barrier, leading to central nervous system injury. The dysregulated miRNAs and signaling pathways discovered in this study provide valuable insights for further investigations into the pathogenesis of CV-A6

Modern Datalog on the GPU

Modern deductive database engines (e.g., LogicBlox and Souffl\'e) enable their users to write declarative queries which compute recursive deductions over extensional data, leaving their high-performance operationalization (query planning, semi-na\"ive evaluation, and parallelization) to the engine. Such engines form the backbone of modern high-throughput applications in static analysis, security auditing, social-media mining, and business analytics. State-of-the-art engines are built upon nested loop joins over explicit representations (e.g., BTrees and tries) and ubiquitously employ range indexing to accelerate iterated joins. In this work, we present GDlog: a GPU-based deductive analytics engine (implemented as a CUDA library) which achieves significant performance improvements (5--10x or more) versus prior systems. GDlog is powered by a novel range-indexed SIMD datastructure: the hash-indexed sorted array (HISA). We perform extensive evaluation on GDlog, comparing it against both CPU and GPU-based hash tables and Datalog engines, and using it to support a range of large-scale deductive queries including reachability, same generation, and context-sensitive program analysis. Our experiments show that GDlog achieves performance competitive with modern SIMD hash tables and beats prior work by an order of magnitude in runtime while offering more favorable memory footprint

Topological triply-degenerate point with double Fermi arcs

Unconventional chiral particles have recently been predicted to appear in certain three dimensional (3D) crystal structures containing three- or more-fold linear band degeneracy points (BDPs). These BDPs carry topological charges, but are distinct from the standard twofold Weyl points or fourfold Dirac points, and cannot be described in terms of an emergent relativistic field theory. Here, we report on the experimental observation of a topological threefold BDP in a 3D phononic crystal. Using direct acoustic field mapping, we demonstrate the existence of the threefold BDP in the bulk bandstructure, as well as doubled Fermi arcs of surface states consistent with a topological charge of 2. Another novel BDP, similar to a Dirac point but carrying nonzero topological charge, is connected to the threefold BDP via the doubled Fermi arcs. These findings pave the way to using these unconventional particles for exploring new emergent physical phenomena

Research Progress in the Regulation Mechanisms of White and Brown Adipose Tissue in the Body by Functionally Active Factors

Brown adipose tissue (BAT) improves the metabolic level of the body by promoting energy expenditure, which can contribute to the prevention and treatment of metabolic diseases such as obesity and diabetes, and BAT has become a new target for the treatment of metabolic diseases. BAT activity enhancement in the body is a hot topic but also a challenge for researchers, and research and analysis of functionally active factors in foods that regulate BAT can help to develop new nutritional activators. In this paper, we summarize the development and thermogenesis of BAT and thermogenesis-related factors, and review active ingredients in foods that regulate brown fat and their mechanisms of action, and briefly introduce the effects of white adipose tissue (WAT) and BAT on the body’s health. We also discuss recent developments in understanding the role of BAT in regulating energy metabolic balance and various diseases in the body. We hope that the present review will provide a theoretical basis for future development of brown adipose nutritional activators and improvement of individualized healthy dietary management programs in order to prevent and treat various diseases

Enhanced Quadratic Video Interpolation

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge.Comment: Winning solution of AIM2020 VTSR Challenge (in conjunction with ECCV 2020

Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework

Machine learning is widely used to make decisions with societal impact such as bank loan approving, criminal sentencing, and resume filtering. How to ensure its fairness while maintaining utility is a challenging but crucial issue. Fairness is a complex and context-dependent concept with over 70 different measurement metrics. Since existing regulations are often vague in terms of which metric to use and different organizations may prefer different fairness metrics, it is important to have means of improving fairness comprehensively. Existing mitigation techniques often target at one specific fairness metric and have limitations in improving multiple notions of fairness simultaneously. In this work, we propose CFU (Comprehensive Fairness-Utility), a reinforcement learning-based framework, to efficiently improve the fairness-utility trade-off in machine learning classifiers. A comprehensive measurement that can simultaneously consider multiple fairness notions as well as utility is established, and new metrics are proposed based on an in-depth analysis of the relationship between different fairness metrics. The reward function of CFU is constructed with comprehensive measurement and new metrics. We conduct extensive experiments to evaluate CFU on 6 tasks, 3 machine learning models, and 15 fairness-utility measurements. The results demonstrate that CFU can improve the classifier on multiple fairness metrics without sacrificing its utility. It outperforms all state-of-the-art techniques and has witnessed a 37.5% improvement on average

ALA: Naturalness-aware Adversarial Lightness Attack

Most researchers have tried to enhance the robustness of DNNs by revealing and repairing the vulnerability of DNNs with specialized adversarial examples. Parts of the attack examples have imperceptible perturbations restricted by Lp norm. However, due to their high-frequency property, the adversarial examples can be defended by denoising methods and are hard to realize in the physical world. To avoid the defects, some works have proposed unrestricted attacks to gain better robustness and practicality. It is disappointing that these examples usually look unnatural and can alert the guards. In this paper, we propose Adversarial Lightness Attack (ALA), a white-box unrestricted adversarial attack that focuses on modifying the lightness of the images. The shape and color of the samples, which are crucial to human perception, are barely influenced. To obtain adversarial examples with a high attack success rate, we propose unconstrained enhancement in terms of the light and shade relationship in images. To enhance the naturalness of images, we craft the naturalness-aware regularization according to the range and distribution of light. The effectiveness of ALA is verified on two popular datasets for different tasks (i.e., ImageNet for image classification and Places-365 for scene recognition).Comment: 9 page

Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic

Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a sufficient amount of encrypted malicious data with correct labels. When ML models are trained with low-quality training data, they suffer degraded performance. In this paper, we aim at addressing a real-world low-quality training dataset problem, namely, detecting encrypted malicious traffic generated by continuously evolving malware. We develop RAPIER that fully utilizes different distributions of normal and malicious traffic data in the feature space, where normal data is tightly distributed in a certain area and the malicious data is scattered over the entire feature space to augment training data for model training. RAPIER includes two pre-processing modules to convert traffic into feature vectors and correct label noises. We evaluate our system on two public datasets and one combined dataset. With 1000 samples and 45% noises from each dataset, our system achieves the F1 scores of 0.770, 0.776, and 0.855, respectively, achieving average improvements of 352.6%, 284.3%, and 214.9% over the existing methods, respectively. Furthermore, We evaluate RAPIER with a real-world dataset obtained from a security enterprise. RAPIER effectively achieves encrypted malicious traffic detection with the best F1 score of 0.773 and improves the F1 score of existing methods by an average of 272.5%

Quantitative modeling of streamer discharge branching in air

Streamer discharges are the primary mode of electric breakdown of air in lightning and high voltage technology. Streamer channels branch many times, which determines the developing tree-like discharge structure. Understanding these branched structures is for example important to describe streamer coronas in lightning research. We simulate branching of positive streamers in air using a 3D fluid model where photoionization is included as a discrete and stochastic process. The probability and morphology of branching are in good agreement with dedicated experiments. This demonstrates that photoionization indeed provides the noise that triggers branching, and we show that branching is remarkably sensitive to the amount of photoionization. Our comparison is therefore one of the first sensitive tests for Zheleznyak's photoionization model, confirming its validity