A novel algorithm to define infection tendencies in H1N1 cases in Mainland China

Incidences of H1N1 viral infections in Mainland China are collected by the Ministry of Health, the People’s Republic of China. The number of confirmed cases and the timing of these outbreaks from May 13 to July 22, 2009 were obtained and subjected to a novel mathematical model to simulate the infection profile (time vs number). The model was predicated upon the grey prediction theory which allows assignment of future trends using limited numbers of data points. During the period of our analysis, the number of confirmed H1N1 cases in Mainland China increased from 1 to 1772. The efficiency of our model to simulate these data points was evaluated using Sum of squares of error (SSE), Relative standard error (RSE), Mean absolute deviation (MAD) and Average relative error (ARE). Results from these analyses were compared to similar calculations based upon the grey prediction algorithm. Using our equation, defined herein as equation D–R, results showed that SSE = 6742.00, RSE = 10.69, MAD = 7.07, ARE = 2.47% were all consistent with the D–R algorithm performing well in the estimation of future trends of H1N1 cases in Mainland China. Calculations using the grey theory had no predictive value [ARE for GM(1,1) = 104.63%]. To validate this algorithm, we performed a second analysis using new data obtained from cases reported to the WHO and CDC in the US between April 26 and June 8, 2009. In like manner, the model was equally predictive. The success of the D–R mathematical model suggests that it may have broader application to other viral infections among the human population in China and may be modified for application to other regions of the worl

Coprinus leucostictus rediscovered after a century, epitypified, and its generic position in Hausknechtia resolved by multigene phylogenetic analysis of Psathyrellaceae

About a century after the first finding in northern Vietnam (1908), Coprinus leucostictus is rediscovered on 12 localities in southern India and southern to southeastern China, growing in evergreen subtropical or tropical forests. It is morphologically a rather unique species with coprinoid basidiomata, strongly branched and diverticulate veil hyphae, and a hymeniderm pileipellis. The BLAST search of ITS and tef-1a sequences showed its close relationship to Hausknechtia floriformis, which is not clear based on morphological characters. Multigene phylogenetic analysis of a concatenated dataset of ITS, LSU, tef-1a, and -tubulin sequences, revealed C. leucostictus and H. floriformis as separate, but sister species. Molecular phylogenetic relationships within the family Psathyrellaceae (including 17 genera) are presented in the phylogram. The genera Hausknechtia and Candolleomyces formed two well-supported lineages and were recovered as a monophyletic group. A total of 27 sequences from the genus Hausknechtia were newly generated in this study. Coprinus leucostictus is combined as Hausknechtia leucosticta, its epitype is designated, and the hitherto monotypic genus Hausknechtia is emended. A detailed morphological description of H. leucosticta supplemented with colour photographs and line drawings is provided

Phage Displayed Peptides to Avian H5N1 Virus Distinguished the Virus from Other Viruses

The purpose of the current study was to identify potential ligands and develop a novel diagnostic test to highly pathogenic avian influenza A virus (HPAI), subtype H5N1 viruses using phage display technology. The H5N1 viruses were used as an immobilized target in a biopanning process using a 12-mer phage display random peptide library. After five rounds of panning, three phages expressing peptides HAWDPIPARDPF, AAWHLIVALAPN or ATSHLHVRLPSK had a specific binding activity to H5N1 viruses were isolated. Putative binding motifs to H5N1 viruses were identified by DNA sequencing. In terms of the minimum quantity of viruses, the phage-based ELISA was better than antiserum-based ELISA and a manual, semi-quantitative endpoint RT-PCR for detecting H5N1 viruses. More importantly, the selected phages bearing the specific peptides to H5N1 viruses were capable of differentiating this virus from other avian viruses in enzyme-linked immunosorbent assays

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

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

A novel algorithm to define infection tendencies in H1N1 cases in Mainland China

Incidences of H1N1 viral infections in Mainland China are collected by the Ministry of Health, the People’s Republic of China. The number of confirmed cases and the timing of these outbreaks from May 13 to July 22, 2009 were obtained and subjected to a novel mathematical model to simulate the infection profile (time vs number). The model was predicated upon the grey prediction theory which allows assignment of future trends using limited numbers of data points. During the period of our analysis, the number of confirmed H1N1 cases in Mainland China increased from 1 to 1772. The efficiency of our model to simulate these data points was evaluated using Sum of squares of error (SSE), Relative standard error (RSE), Mean absolute deviation (MAD) and Average relative error (ARE). Results from these analyses were compared to similar calculations based upon the grey prediction algorithm. Using our equation, defined herein as equation D–R, results showed that SSE = 6742.00, RSE = 10.69, MAD = 7.07, ARE = 2.47% were all consistent with the D–R algorithm performing well in the estimation of future trends of H1N1 cases in Mainland China. Calculations using the grey theory had no predictive value [ARE for GM(1,1) = 104.63%]. To validate this algorithm, we performed a second analysis using new data obtained from cases reported to the WHO and CDC in the US between April 26 and June 8, 2009. In like manner, the model was equally predictive. The success of the D–R mathematical model suggests that it may have broader application to other viral infections among the human population in China and may be modified for application to other regions of the worl