Host-range shift of H3N8 canine influenza virus: a phylodynamic analysis of its origin and adaptation from equine to canine host

International audiencePrior to the emergence of H3N8 canine influenza virus (CIV) and the latest avian-origin H3N2 CIV, there was no evidence of a circulating canine-specific influenza virus. Molecular and epidemiological evidence suggest that H3N8 CIV emerged from H3N8 equine influenza virus (EIV). This host-range shift of EIV from equine to canine hosts and its subsequent establishment as an enzootic CIV is unique because this host-range shift was from one mammalian host to another. To further understand this host-range shift, we conducted a comprehensive phylodynamic analysis using all the available whole-genome sequences of H3N8 CIV. We found that (1) the emergence of H3N8 CIV from H3N8 EIV occurred in approximately 2002; (2) this interspecies transmission was by a reassortant virus of the circulating Florida-1 clade H3N8 EIV; (3) once in the canine species, H3N8 CIV spread efficiently and remained an enzootic virus; (4) H3N8 CIV evolved and diverged into multiple clades or sublineages, with intra and inter-lineage reassortment. Our results provide a framework to understand the molecular basis of host-range shifts of influenza viruses and that dogs are potential “mixing vessels” for the establishment of novel influenza viruses

Parameter identification of PEMFC via feedforward neural network-pelican optimization algorithm

Parameter identification is a critical task in the research of proton exchange membrane fuel cells (PEMFC), which provides the basis for establishing an accurate and reliable PEMFC model. However, the nonlinear characteristics of PEMFC model as well as inevitable noise data and insufficient measurement data often overwhelm traditional optimization techniques. In particular, noise data and inadequate measurement data can introduce bias or lead to data loss. To address this problem, a novel hybrid optimization strategy is proposed. Firstly, a feedforward neural network (FNN) is employed to preprocess the measured data (i.e., reducing noise data and enriching measurement data). Furthermore, Gaussian noise and Rayleigh noise with three signal-to-noise ratio levels are introduced to simulate various disturbances of noise. Then, the pelican optimization algorithm (POA) is used to identify the parameters of PEMFC based on preprocessed data. Lastly, the effectiveness of the proposed strategy named FNN-POA is verified by comparing it with seven advanced competitive algorithms. Simulation results demonstrate that FNN-POA has higher robustness and optimization quality by comparing original data and preprocessed data. For instance, the root-mean-square error obtained by FNN-POA is reduced by 99.44% under medium temperature and medium pressure through noise reduction

The adenosine A2A receptor antagonist KW6002 distinctly regulates retinal ganglion cell morphology during postnatal development and neonatal inflammation

Adenosine A2A receptors (A2ARs) appear early in the retina during postnatal development, but the roles of the A2ARs in the morphogenesis of distinct types of retinal ganglion cells (RGCs) during postnatal development and neonatal inflammatory response remain undetermined. As the RGCs are rather heterogeneous in morphology and functions in the retina, here we resorted to the Thy1-YFPH transgenic mice and three-dimensional (3D) neuron reconstruction to investigate how A2ARs regulate the morphogenesis of three morphologically distinct types of RGCs (namely Type I, II, III) during postnatal development and neonatal inflammation. We found that the A2AR antagonist KW6002 did not change the proportion of the three RGC types during retinal development, but exerted a bidirectional effect on dendritic complexity of Type I and III RGCs and cell type-specifically altered their morphologies with decreased dendrite density of Type I, decreased the dendritic field area of Type II and III, increased dendrite density of Type III RGCs. Moreover, under neonatal inflammation condition, KW6002 specifically increased the proportion of Type I RGCs with enhanced the dendrite surface area and volume and the proportion of Type II RGCs with enlarged the soma area and perimeter. Thus, A2ARs exert distinct control of RGC morphologies to cell type-specifically fine-tune the RGC dendrites during normal development but to mainly suppress RGC soma and dendrite volume under neonatal inflammation

The reliability of colorimetry is precise(ly) as expected.

Figure S6. Gene ontology (GO) analysis of the differentially expressed genes between C1 and C2. (JPG 403 kb

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Fiscal Decentralization, Green Technology Innovation, and Regional Air Pollution in China: An Investigation from the Perspective of Intergovernmental Competition

Fiscal decentralization (FD), as an institutional arrangement for the fiscal division between central and local governments, gives local governments the enthusiasm and autonomy to provide public products and services. With the dominance of environmental governance, how local governments can avoid intergovernmental &ldquo;race to the bottom&rdquo; issues through green technology innovation (GTI) is a matter of regional green development and continuous improvement of atmospheric environmental quality. Based on a sample of 30 provinces in China from 2003 to 2018, this paper uses the spatial Durbin model (SDM) to examine the relationship between FD, GTI, and regional air pollution and explores their spatial spillover effect and regional heterogeneity from the perspective of intergovernmental competition. The results indicate that the FD and GTI in various provinces had significant and regionally differentiated inhibitory effects on local air pollution. In Western China, due to the regional competition among local governments in terms of economic development, economic development-oriented fiscal expenditures crowd out environmental governance-oriented fiscal expenditures, which has led to the consequence that FD can intensify local air pollution and has a positive spillover effect, but the demonstration effect of green technological innovation can well moderate the effect of FD on air pollution. FD in the eastern region has played a positive role in promoting regional air quality improvement. However, its green technological innovation has not played a positive role in reducing emissions, and it plays a significant negative regulatory role in the emission reduction effect led by FD. Finally, the article puts forward policy recommendations in terms of a fiscal decentralization system, green technological innovation, and performance evaluation mechanism

Mechanism and Algorithm for Stable Trading Matching between Coal Mining and Power Generation Companies in China

This paper is concerned with stable trading between the coal mining and power generation companies in China. Under the current marketized coal and planned electricity price systems, barriers to price shifting between coal and electricity are created and conflicts between the two sectors are aggravated. The stable trading matching between coal mining and power generation companies is not only an effective means to resolve the conflict in the coal trading market, but also a ballast stone for price stabilization and supply guarantees in coal trading. Based on the two-sided matching theory, this paper starts from the micro market preference and matching willingness of coal mining and power generation companies, puts forward the conceptual framework of the pairwise stable matching of both sides, innovates a mechanism for trading between coal mining and power generation companies, and designs a stable trading matching algorithm. The algorithm has certain theoretical innovation significance from the matching problem of non-separable commodities to that of separable commodities considering the trading volume between coal mining and power generation companies. Furthermore, it is a complement and perfection of the existing coal–power trading platform in its transaction mechanism and trading function. The results reveal that the trading relations between coal mining and power generation companies under the stable matching mechanism are resistant to disintegration and that the pairwise stable matching result is sensitive