Detection of porcine enteric viruses (Kobuvirus, Mamastrovirus and Sapelovirus) in domestic pigs in Corsica, France

Many enteric viruses are found in pig farms around the world and can cause death of animals or important production losses for breeders. Among the wide spectrum of enteric viral species, porcine Sapelovirus (PSV), porcine Kobuvirus (PKoV) and porcine Astrovirus (PAstV) are frequently found in pig feces. In this study we investigated sixteen pig farms in Corsica, France, to evaluate the circulation of three enteric viruses (PKoV, PAstV-1 and PSV). In addition to the three viruses studied by RT–qPCR (908 pig feces samples), 26 stool samples were tested using the Next Generation Sequencing method (NGS). Our results showed viral RNA detection rates (i) of 62.0% [58.7–65.1] (n = 563/908) for PSV, (ii) of 44.8% [41.5–48.1] (n = 407/908) for PKoV and (iii) of 8.6% [6.8–10.6] (n = 78/908) for PAstV-1. Significant differences were observed for all three viruses according to age (P-value = 2.4e–13 for PAstV-1; 2.4e–12 for PKoV and 0.005 for PSV). The type of breeding was significantly associated with RNA detection only for PAstV-1 (P-value = 9.6e–6). Among the 26 samples tested with NGS method, consensus sequences corresponding to 10 different species of virus were detected. This study provides first insight on the presence of three common porcine enteric viruses in France. We also showed that they are frequently encountered in pigs born and bred in Corsica, which demonstrates endemic local circulation.

Speeded detection of spectrotemporal modulations of sound

We tested whether joint spectrotemporal sensitivity follows from spectrotemporal separability for normal-hearing conditions and for impaired-hearing simulations. In a manual reaction-time task, normal-hearing listeners had to detect the onset of a ripple (with density between 0-8 cycles/octave and a fixed modulation depth of 50%), that moved up or down the log-frequency axis at constant velocity (between 0-64 Hz), in an otherwise-unmodulated broadband white-noise. Spectral and temporal modulations elicited band-pass filtered sensitivity characteristics, with fastest detection rates around 1 cycle/oct and 32 Hz for normal-hearing conditions. These results closely resemble data from other studies that typically used the modulation-depth threshold as a sensitivity measure for spectral-temporal modulations. To simulate hearing-impairment, stimuli were processed with a 6-channel cochlear-implant vocoder, and a hearing-aid simulation that introduced spectral smearing and low-pass filtering. Reaction times were always much slower compared to normal hearing, especially for the highest spectral densities. Binaural performance was predicted well by the benchmark race model of statistical facilitation of independent monaural channels. For the impaired-hearing simulations this implied a "best-of-both-worlds" principle in which the listeners relied on the hearing-aid ear to detect spectral modulations, and on the cochlear-implant ear for temporal-modulation detection. Although singular-value decomposition indicated that the joint spectrotemporal sensitivity matrix could be largely reconstructed from independent temporal and spectral sensitivity functions, in line with time-spectrum separability, a significant inseparable spectral-temporal interaction was present in all hearing conditions. These results imply that the reaction-time task yields a solid and effective objective measure of acoustic spectrotemporal modulation sensitivity, which may also be applicable to hearing-impaired individuals.

A rapid influenza diagnostic test based on detection of viral neuraminidase activity

Current methods used for diagnosis of acute infection of pathogens rely on detection of nucleic acids, antigens, or certain classes of antibodies such as IgM. Here we report a virus enzyme assay as an alternative to these methods for detection of acute viral infection. In this method, we used a luciferin derivative as the substrate for detection of the enzyme activity of influenza viral neuraminidase as a means for diagnosis of influenza. The resulting commercial test, the qFLU Dx Test, uses a different supply chain that does not compete with those for the current tests. The assay reagents were formulated as a master mix that accommodated both the neuraminidase and luciferase reactions, thereby enabling rapid and prolonged production of stable light signal in the presence of influenza virus in the sample. The assay was evaluated using depository throat swab specimens. As expected, the assay exhibited similar detection rates for all influenza types and subtypes except for A(H7N9), which exhibited lower detection rate due to lower viral titer in the specimens. When throat swab specimens were diluted with the sample buffer of the test kit and tested with the qFLU Dx Test. The sensitivity and specificity were 82.41% (95% confidence interval: 79.66–85.84%) and 95.39% (95% confidence interval: 94.32–96.46%), respectively, for these diluted specimens in comparison to a real-time polymerase chain reaction assay. The uniqueness of the qFLU Dx Test as an enzymatic assay makes it highly complementary with currently available methods.

Detection Rate of Bird Species and What It Depends on: Tips for Field Surveys

Bird counting inevitably suffers from imperfect detection, which varies across species, habitats, period of the day, and seasons. Although various modeling techniques have recently been developed to account for this phenomenon, the biological basis of natural variation in detection remains insufficiently known. This study examined the bird species’ detection rate throughout the day, considering their body mass and diet type, concerning the environment and weather characteristics. Species detection rates were significantly affected by the number of individuals of that species but were unrelated to body mass. Overall, species with the highest detection rate were Corn bunting, Blackbird, European robin, House sparrow and Common chiffchaff. Granivores-insectivores and insectivores showed significant differences in detection rates throughout the day among habitats, with higher detection rates in grasslands during the afternoon. Insectivores showed higher detection rates in farmland during midday (warmest time of the day). Granivores, omnivores and scavengers did not show changes in detection rates in different day periods. Such patterns in daily detection rates were significant even when considering abundance and total species richness in each community. Finally, cloudiness was unrelated to the overall detection rate of birds, while temperature and wind affected detection rates in some guilds. Our findings provide some advice for choosing a suitable ornithological sampling method by considering the avian communities composition in combination with the type of environment, the diet of bird species, and the period of the day.

Overview of Cancer Control in Armenia and Policy Implications

Cancer is the second leading cause of death in Armenia. Over the past two decades, the country has seen a significant rise in cancer morbidity and mortality. This review aims to provide up-to-date info about the state of cancer control in Armenia and identify priority areas of research. The paper analyzes published literature and local and international statistical reports on Armenia and similar countries to put numbers into context. While cancer detection, diagnosis, and treatment are improving, the prevalence of risk factors is still quite high and smoking is widespread. Early detection rates are low and several important screening programs are absent. Diagnosis and treatment methods are not standardized; there is a lack of treatment accessibility due to insufficient government coverage and limited availability of essential medicines. Overall, there is room for improvement in this sector, as research is limited and multidisciplinary approaches to the topic are rare.

Epidemiological characteristics of Epstein–Barr virus infection

Introduction. The Epstein–Barr virus (EBV) is one of the most common pathogens — it infects 90% of the world’s population. However, specific characteristics of the EBV infection epidemic process remain unidentified. The previous studies focusing on assessment of incidence rates for infectious mononucleosis (IM) tend to ignore the serological status of the population.The aim of the study was to identify epidemiological characteristics and assess the prevalence of serological markers for EBV infection for further epidemic control measures development.Materials and methods. In Moscow, the thorough analysis was performed using the data on IM incidence (Form 2 "Data on Infectious and Parasitic Diseases") and test results for 138,232 people checked for presence of VCA IgG, EBNA IgG, VCA IgM, EA IgG, and EBV DNA in their blood and saliva in 2011–2020.Results. The periodic pattern of IM incidence was discovered, demonstrating the repetitive peaks every 9 to 11 years and a strong direct correlative relationship with detection rates for active EBV infection markers. The intra-annual dynamics of IM incidence is characterized by a seasonal upswing during cold seasons of the year, reaching its peaks in October, November, or February and associated with a marked decrease in the VCA IgG and EBNA IgG seroprevalence. Children within the 1 to 17-year age range are groups at risk for acquiring primary infection, demonstrating significantly lower detection rates for chronic EBV infection (VCA IgG and EBNA IgG) markers and higher rates for VCA IgM and EBV DNA markers in blood compared to adults. The contribution of adult population to the epidemic process is formed through reactivation of chronic infection, which is observed primarily among women.Conclusion. The identified characteristics are essential for comprehensive understanding of the EBV infection epidemic process and can be used for developing preventive and anti-epidemic measures.

Exploration of Hydrogeochemical Characterization and Assessment of Organic Pollution Characteristics of Shallow Groundwater near a Chemical Plant That Discharged Sewage Illegally

Groundwater plays a significant role in domestic use and agricultural irrigation in rural areas of northern China. The untreated wastewater from the chemical plant was directly discharged into a seepage well, resulting in the pollution of groundwater. Assessing characteristics of groundwater organic pollution and identifying evolutionary mechanisms of hydrogeochemistry are beneficial for groundwater protection and sustainable management. Statistical methods (correlation analysis (CA) and principal component analysis (PCA)) combined with hydrogeochemical methods including Piper, Gibbs, Gaillardet, and ions binary diagrams and the chloride alkalinity index were employed to explore hydrogeochemical characteristics and evolutionary mechanisms. The results showed that cations were predominantly located at the Ca2+ end and anions were mostly close to the SO42− and Cl− end. The ion concentrations of groundwater were mainly affected by water–rock interactions. The weathering or dissolution of silicate (i.e., aluminosilicate minerals), evaporite (i.e., halite and gypsum), carbonate minerals (i.e., calcite and dolomite), cation exchange, and anthropogenic activities contribute to the chemical compositions of groundwater. Based on CA and PCA, the dissolution of halide minerals and the use of pesticides and fertilizers were the main factors controlling water chemistry. Additionally, the dissolution of sulfur-bearing minerals and gypsum was the key factor controlling the concentrations of Ca2+ and Mg2+. Application of mathematical statistical methods characterized that the exceedance rate of seven organic compounds with high detection rates were as follows: carbon tetrachloride (39.83%) > 1,1,2-trichloroethane (28.81%) > chloroform (10.17%) > trichloroethene (6.78%) > 1,1,2,2-tetrachloroethane (5.93%) > perchloroethylene (5.08%) > trichlorofluoromethane (0.85%). Simultaneously, pollution under the influence of volatilization and diffusion was significantly less than that in the direction of groundwater runoff.

Fusobacterium nucleatum and Bacteroides fragilis detection in colorectal tumours: Optimal target site and correlation with total bacterial load

Background Mucosal infiltration by certain bacterial species may contribute to the development and progression of colorectal cancer (CRC). There is considerable variation in reported detection rates in human CRC samples and the extent to which bacterial infiltration varies across regions of the primary tumour is unknown. This study aimed to determine if there is an optimal site for bacterial detection within CRC tumours. Methods Presence of target bacterial species was assessed by quantitative real-time PCR (qPCR) in 42 human CRC tumours. Abundance in primary tumour regions, normal epithelium and at metastatic sites was investigated in an expanded cohort of 51 patients. Species presence/absence was confirmed by diversity profiling in five patients. Correlation with total bacterial load and clinicopathological features was assessed. Results Fusobacterium nucleatum and Bacteroides fragilis were detected in tumours from 43% and 24% of patients, respectively (17% positive for both species). The optimal detection site was the tumour luminal surface (TLS). Patients testing positive at the TLS frequently tested negative at other sites, including central tumour and invasive margin. F. nucleatum was detected at a higher frequency in tumour versus normal epithelium (p < 0.01) and was associated with more advanced disease (p = 0.01). Detection of both species correlated with total bacterial load. However, corroboration of qPCR results via diversity profiling suggests detection of these species may indicate a specific microbial signature. Conclusions This study supports a role for F. nucleatum in CRC development. Presence of F. nucleatum and B. fragilis varies across primary tumour regions, with the TLS representing the optimal site for bacterial detection.

Profile of Environmental Chemicals in the Korean Population—Results of the Korean National Environmental Health Survey (KoNEHS) Cycle 3, 2015–2017

The Korean National Environmental Health Survey (KoNEHS) program provides useful information on chemical exposure, serves as the basis for environmental health policies, and suggests appropriate measures to protect public health. Initiated on a three-year cycle in 2009, it reports the concentrations of major environmental chemicals among the representative Korean population. KoNEHS Cycle 3 introduced children and adolescents into the analysis, where the blood and urine samples of 6167 participants were measured for major metals, phthalates, phenolics, and other organic compounds. Lead, mercury, cadmium, metabolites of DEHP and DnBP, and 3-phenoxybenzoic acid levels of the Korean adult population tended to decrease compared to previous survey cycles but remained higher than those observed in the US or Canada. Both bisphenol A (BPA) and trans,trans-muconic acid concentrations have increased over time. Heavy metal concentrations (blood lead, and cadmium) in children and adolescents were approximately half that of adults, while some organic substances (e.g., phthalates and BPA) were high. BPA showed higher levels than in the US or Canada, whereas BPF and BPS showed lower detection rates in this cycle; however, as these are increasingly used as a substitute for BPA, further research is necessary. As environmental chemicals may affect childhood health and development, additional analyses should assess exposure sources and routes through continuous observations.

Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.