Bille McGrath (eggnogblue5)

Emerging viral diseases pose a major threat to public health worldwide. Nearly all emerging viruses, including Ebola, Dengue, Nipah, West Nile, Zika, and coronaviruses (including SARS-Cov2, the causative agent of the current COVID-19 pandemic), have zoonotic origins, indicating that animal-to-human transmission constitutes a primary mode of acquisition of novel infectious diseases. Why these viruses can cause profound pathologies in humans, while natural reservoir hosts often show little evidence of disease is not completely understood. Differences in the host immune response, especially within the innate compartment, have been suggested to be involved in this divergence. Natural killer (NK) cells are innate lymphocytes that play a critical role in the early antiviral response, secreting effector cytokines and clearing infected cells. In this review, we will discuss the mechanisms through which NK cells interact with viruses, their contribution towards maintaining equilibrium between the virus and its natural host, and their role in disease progression in humans and other non-natural hosts.The creation of humanized mice generally involves the reconstitution of immunodeficient mice with human immune constituents. Different methodologies have been employed, and significant progress has been made towards the development of robustly humanized mouse models. Some of the techniques used include the injection of mature human immune cells, the injection of human hematopoietic stem cells (HSCs) capable of reconstituting radiation-depleted murine bone marrow, and the implantation of human fetal liver and thymus fragments under the kidney capsule to create a thymic organoid that can support thympoiesis. This review will serve as a brief introduction to the three most commonly utilized humanized mouse models for the study of gammaherpesvirus-driven pathogenesis, and highlight some of the critical discoveries these models have enabled.To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH4) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH4 generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH4 generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH4 prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH4 generation prediction.In the current scenario, used paper cups are disposed of without any proper treatment, thereby damaging the environment. Hence, the vermicomposting technique is preferred for managing these wastes. The ability of bacterial strains on cellulase enzyme (Endoglucanase, exoglucanase and β-glucosidase) production at altered pH and temperatures were focused in this study. Among nine bacterial strains Acinetobacter baumannii was fo