Effect of Fermentation on the Anti-Nutritional Factors and

The effect of fermentation on the anti-nutritional factors and mineral composition of melon seed varieties for Ogiri production was studied. Melon seed varieties such as Citrullus vulgaris, Citrulluslanatus, Colocynthiscitrullus, Cucurbita pepo, Cucurmeropisedulis were respectively sorted, washed, boiled wrapped seed were then boiled again for 2 hours, drained, cooled and allowed to ferment naturally for 86 hours (primary fermentation). The primary fermented sees were then pounded and wrapped in little portions with “ofoala” leaf (Icacinatrichantha olive) and kept in wire mesh near a heat source for another 144 hours (secondary fermentation). Samples were drawn from the raw, boiled and fermented melon seed varieties for the quantitative analysis of mineral content and anti-nutritional prepared with the raw and primary fermented samples. Raw seed of Citrulluslanatus had the highest mineral analysis showed a decline in the boiled samples and secondary fermented sample, compared with the raw and mineral composition ranging from potassium, magnesium, cacium, iron and zinc of 1.21, 1.06, 0.89, 0.45 and 0.41mg/100g respectively followed by raw Citrullus Vulgaris with potassium, magenesium, calcium, iron and zinc of 1.18, 1.02, 0.55, 0.44 and 0.38 mg/100g respectively and 1.11, 0.94, 0.81, 0.38 and 0.31 mg/100gof potassium, magenesium, calcium, iron and zinc respectively in the primary fermented product. Statistical analysis of anti-nutrients revealed a significant reduction (p<0.05) in all the processed melon. There was a significant difference in all the processed melon with lowest anti-nutrient content ranging from alkaloid, saponin, HCN, phytate, tannin and flavonoid (0.00, 0.00, 0.00, 0.00, 0.03and 0.09 respectively) and Colocynthiscitrullus had the highest anti-nutrient content in the secondary fermentation. Keywords:Fermentation, anti-nutritional factors, Ogiri, mineral content,melon seed varieties

Marburg Virus Infection Detected in a Common African Bat

Marburg and Ebola viruses can cause large hemorrhagic fever (HF) outbreaks with high case fatality (80–90%) in human and great apes. Identification of the natural reservoir of these viruses is one of the most important topics in this field and a fundamental key to understanding their natural history. Despite the discovery of this virus family almost 40 years ago, the search for the natural reservoir of these lethal pathogens remains an enigma despite numerous ecological studies. Here, we report the discovery of Marburg virus in a common species of fruit bat (Rousettus aegyptiacus) in Gabon as shown by finding virus-specific RNA and IgG antibody in individual bats. These Marburg virus positive bats represent the first naturally infected non-primate animals identified. Furthermore, this is the first report of Marburg virus being present in this area of Africa, thus extending the known range of the virus. These data imply that more areas are at risk for MHF outbreaks than previously realized and correspond well with a recently published report in which three species of fruit bats were demonstrated to be likely reservoirs for Ebola virus

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Open Source Software for Evaluation of Applications and Traffic Measurement in an Experimental Testbed for Converged Networks

In this paper we present the development and validation of an experimental platform based in open source software and the development of a package for monitoring purposes. The software package with its components is described and validated through a group of tests and examples. These results are the initial support for future research which includes new ideas in the NGN (Next Generation Networks) topics, such as QoS (Quality of Service) mechanisms, traffic characterization and MPLS (Multi-protocol Label Switching) hybrid routing. The procedures shown in this paper may give other research groups an overview of the primary steps for the implementation of a research lab with similar interests. Keywords- convergence, networks, QoS monitoring, MOS, traffic characterization, traffic measurement