Weak Signal Detection Based on Adaptive Cascaded Bistable Stochastic Resonance System

AbstractStochastic resonance system is an effective method to extract weak signal, however, system output is directly influenced by system parameters. Aiming to this, a method about weak periodic signal extraction was developed based on adaptive stochastic resonance. Firstly cascaded stochastic resonance system was established in order to achieve better low-pass filtering effect. And then, variance of zero point distance was chosen as measurement index of cascade system. It's able to overcome the shortage that traditional adaptive stochastic resonance system needs to know the signal frequency beforehand. Also, it could obtain optimum system parameters adaptively. Basing on these parameters, input signal will be handled, and optimum output could be obtained. Furthermore, different periodic signal have been recognized, and finally the validity of the method is verified through simulation experiments

DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization

Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions. Previous deep learning based models neglect two issues that are critical for practical applications, namely high resolution (HR) image processing and model comprehensibility. In this paper, we propose a novel Deep Comprehensible Color Filter (DCCF) learning framework for high-resolution image harmonization. Specifically, DCCF first downsamples the original input image to its low-resolution (LR) counter-part, then learns four human comprehensible neural filters (i.e. hue, saturation, value and attentive rendering filters) in an end-to-end manner, finally applies these filters to the original input image to get the harmonized result. Benefiting from the comprehensible neural filters, we could provide a simple yet efficient handler for users to cooperate with deep model to get the desired results with very little effort when necessary. Extensive experiments demonstrate the effectiveness of DCCF learning framework and it outperforms state-of-the-art post-processing method on iHarmony4 dataset on images' full-resolutions by achieving 7.63% and 1.69% relative improvements on MSE and PSNR respectively.Comment: ECCV 2022 (Oral

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Ciprofloxacin-Resistant Salmonella enterica Serotype Typhimurium, China

We characterized 44 Salmonella enterica serotype Typhimurium isolates from Tongji Hospital outpatients in Wuhan, China, May 2002–October 2005. All 31 ciprofloxacin-resistant isolates were also resistant to >8 other antimicrobial drugs and carried >2 mutations in GyrA and 1 mutation in ParC. Class 1 integrons were identified in 37 isolates

The IL-17 pathway intertwines with neurotrophin and TLR/IL-1R pathways since its domain shuffling origin

International audienceThe IL-17 pathway displays remarkably diverse functional modes between different subphyla, classes, and even orders, yet its driving factors remains elusive. Here, we demonstrate that the IL-17 pathway originated through domain shuffling between a Toll-like receptor (TLR)/IL-1R pathway and a neurotrophin-RTK (receptor-tyrosine-kinase) pathway (a Trunk-Torso pathway). Unlike other new pathways that evolve independently, the IL-17 pathway remains intertwined with its donor pathways throughout later evolution. This intertwining not only influenced the gains and losses of domains and components in the pathway but also drove the diversification of the pathway’s functional modes among animal lineages. For instance, we reveal that the crustacean female sex hormone, a neurotrophin inducing sex differentiation, could interact with IL-17Rs and thus be classified as true IL-17s. Additionally, the insect prothoracicotropic hormone, a neurotrophin initiating ecdysis in Drosophila by binding to Torso, could bind to IL-17Rs in other insects. Furthermore, IL-17R and TLR/IL-1R pathways maintain crosstalk in amphioxus and zebrafish. Moreover, the loss of the Death domain in the pathway adaptor connection to IκB kinase and stress-activated protein kinase (CIKSs) dramatically reduced their abilities to activate nuclear factor-kappaB (NF-κB) and activator protein 1 (AP-1) in amphioxus and zebrafish. Reinstating this Death domain not only enhanced NF-κB/AP-1 activation but also strengthened anti-bacterial immunity in zebrafish larvae. This could explain why the mammalian IL-17 pathway, whose CIKS also lacks Death, is considered a weak signaling activator, relying on synergies with other pathways. Our findings provide insights into the functional diversity of the IL-17 pathway and unveil evolutionary principles that could govern the pathway and be used to redesign and manipulate it

A metagenome-wide association study of gut microbiota in type 2 diabetes

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes