Immunoprotection against influenza H5N1 virus by oral administration of enteric-coated recombinant Lactococcus lactis mini-capsules

AbstractEdible vaccines that can be made widely available and easily administered could bring great benefit to the worldwide battle against pandemic viral infections. They can be used not only for the vaccination of humans and domesticated animals, but also for wild herds and live stock which are otherwise difficult to vaccinate. In this study, we report the development of an edible mini-capsule form of live, non-persisting, recombinant Lactococcus lactis (L. lactis) vaccine against the highly virulent influenza H5N1 strain. Recombinant L. lactis-based H5N1 HA antigen expression constructs were made and shown to be able to induce higher levels of HA-specific serum IgG and fecal IgA antibody production after oral administration. The vectors were then formulated into a mini-capsule dosage form and fed to mouse. Four doses of oral administration rendered complete protection of the mouse against lethal challenges of H5N1 virus

Approaching single temporal mode operation in twin beams generated by pulse pumped high gain spontaneous four wave mixing

By investigating the intensity correlation function, we study the spectral/temporal mode properties of twin beams generated by the pulse-pumped high gain spontaneous four wave mixing (SFWM) in optical fiber from both the theoretical and experimental aspects. The results show that the temporal property depends not only on the phase matching condition and the filters applied in the signal and idler fields, but also on the gain of SFWM. When the gain of SFWM is low, the spectral/temporal mode properties of the twin beams are determined by the phase matching condition and optical filtering and are usually of multi-mode nature, which leads to a value larger than 1 but distinctly smaller than 2 for the normalized intensity correlation function of individual signal/idler beam. However, when the gain of SFWM is very high, we demonstrate the normalized intensity correlation function of individual signal/idler beam approaches to 2, which is a signature of single temporal mode. This is so even if the frequencies of signal and idler fields are highly correlated so that the twin beams have multiple modes in low gain regime. We find that the reason for this behavior is the dominance of the fundamental mode over other higher order modes at high gain. Our investigation is useful for constructing high quality multi-mode squeezed and entangled states by using pulse-pumped spontaneous parametric down-conversion and SFWM

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Epidemiological and virological characteristics of pandemic influenza A (H1N1) 2009 in school outbreaks in China

Background: During the 2009 pandemic influenza H1N1 (2009) virus (pH1N1) outbreak, school students were at an increased risk of infection by the pH1N1 virus. However, the estimation of the attack rate showed significant variability. Methods: Two school outbreaks were investigated in this study. A questionnaire was designed to collect information by interview. Throat samples were collected from all the subjects in this study 6 times and sero samples 3 times to confirm the infection and to determine viral shedding. Data analysis was performed using the software STATA 9.0. Findings: The attack rate of the pH1N1 outbreak was 58.3% for the primary school, and 52.9% for the middle school. The asymptomatic infection rates of the two schools were 35.8% and 37.6% respectively. Peak virus shedding occurred on the day of ARI symptoms onset, followed by a steady decrease over subsequent days (p = 0.026). No difference was found either in viral shedding or HI titer between the symptomatic and the asymptomatic infectious groups. Conclusions: School children were found to be at a high risk of infection by the novel virus. This may be because of a heightened risk of transmission owing to increased mixing at boarding school, or a lack of immunity owing to socioeconomic status. We conclude that asymptomatically infectious cases may play an important role in transmission of the pH1N1 virus

Sex Differences in Antidepressant Effect of Sertraline in Transgenic Mouse Models

The main purpose of this study is to explore sex differences in the antidepressant effect of sertraline in genetic knockout or overexpression estrogen-synthesizing enzyme aromatase (Ar) gene mouse models in the forced swim test (FST). Our results demonstrated a significant reduction of depression-like behavior in the mice with overexpression of brain aromatase (Thy1-Ar) compared to sex- and age-matched Ar+/− mice or wild type control mice. Using HPLC analysis, we also found an association between the brain estrogen-related antidepressive behavior and the regulation of serotonin (5-HT) system. Interestingly, a single dose administration of sertraline (10 mg/kg, i.p.) induced reduction of immobility time was found in all genotypes, except male Ar+/− mice. While the underlying mechanisms of sex-specific response on antidepressive effect of sertraline remain to be investigated, our data showed that female mice appear to be more sensitive to sertraline-induced changes of 5-HT system than male mice in the prefrontal cortex (PFC) and the hippocampus (HPC). Further investigation of sex-specific effect of brain estrogen on antidepressant is needed

Analysis of the molecular composition of humic substances and their effects on physiological metabolism in maize based on untargeted metabolomics

IntroductionHumic substances (HSs), components of plant biostimulants, are known to influence plant physiological processes, nutrient uptake and plant growth, thereby increasing crop yield. However, few studies have focused on the impact of HS on overall plant metabolism, and there is still debate over the connection between HS’ structural characteristics and their stimulatory actions.MethodsIn this study, two different HSs (AHA, Aojia humic acid and SHA, Shandong humic acid) screened in a previous experiment were chosen for foliar spraying, and plant samples were collected on the tenth day after spraying (62 days after germination) to investigate the effects of different HSs on photosynthesis, dry matter accumulation, carbon and nitrogen metabolism and overall metabolism in maize leaf.Results and discussionThe results showed different molecular compositions for AHA and SHA and a total of 510 small molecules with significant differences were screened using an ESI-OPLC-MS techno. AHA and SHA exerted different effects on maize growth, with the AHA inducing more effective stimulation than the SHA doing. Untargeted metabolomic analysis revealed that the phospholipid components of maize leaves treated by SHA generally increased significantly than that in the AHA and control treatments. Additionally, both HS-treated maize leaves exhibited different levels of accumulation of trans-zeatin, but SHA treatment significantly decreased the accumulation of zeatin riboside. Compared to CK treatment, AHA treatment resulted in the reorganization of four metabolic pathways: starch and sucrose metabolism, TCA cycle, stilbenes, diarylheptanes, and curcumin biosynthesis, and ABC transport, SHA treatment modified starch and sucrose metabolism and unsaturated fatty acid biosynthesis. These results demonstrate that HSs exert their function through a multifaceted mechanism of action, partially connected to their hormone-like activity but also involving hormoneindependent signaling pathways

Charge Measurement of Cosmic Ray Nuclei with the Plastic Scintillator Detector of DAMPE

One of the main purposes of the DArk Matter Particle Explorer (DAMPE) is to measure the cosmic ray nuclei up to several tens of TeV or beyond, whose origin and propagation remains a hot topic in astrophysics. The Plastic Scintillator Detector (PSD) on top of DAMPE is designed to measure the charges of cosmic ray nuclei from H to Fe and serves as a veto detector for discriminating gamma-rays from charged particles. We propose in this paper a charge reconstruction procedure to optimize the PSD performance in charge measurement. Essentials of our approach, including track finding, alignment of PSD, light attenuation correction, quenching and equalization correction are described detailedly in this paper after a brief description of the structure and operational principle of the PSD. Our results show that the PSD works very well and almost all the elements in cosmic rays from H to Fe are clearly identified in the charge spectrum.Comment: 20 pages, 4 figure