Identification and characterization of molecular entities differentially expressed in bacteria genome upon treatment with glyphosate shock.

Antimetabolites developed from enzymes in the shikimate pathway are appealing targets. There are, however, certain unidentified molecular entities that show bacterial sensitivity to glyphosate shock. This study aims to identify the expression pattern of such entities following treatment with glyphosate shock and to characterize them structurally and functionally. Understanding such entities' catalytic structure and modulatory role guides the design and development of novel antibiotics. This study's functional profiling of 16S rRNA sequencing data and transcriptome analysis of glyphosate-exposedE. coli revealed that two genes were upregulated and twenty-eight were downregulated after glyphosate shock. We discovered the differential expression of some processes based on functional gene analysis, such as global and overview maps (4.2195 on average), carbohydrate metabolism (0.6858 on average), amino acid metabolism (0.5032 on average), and co-factor and vitamin metabolism (0.5032 on average) (0.2876 on average). After examining the two data sets, we discovered that some unidentified proteins were strongly expressed after glyphosate treatment. After examining the two datasets, we discovered a protein with no unique features expressed when treated with glyphosate. The Ecs2020 model looks to be the most stable in structural modeling investigations, while the catalytic residues sought in drug development are anticipated. Furthermore, biological processes and cellular component enrichment analysis revealed that the differentially expressed genes were strongly related to the trehalose manufacturing process and represented the cell membrane's outer membrane component. To estimate the functional gene content of soil sample metagenomics based on 16S rRNA, predictive functional analysis was done with R using the Tax4Fun2 package. On the other hand, transcriptome analysis was carried out using the R tool GEO2R. The results could be a good starting point for making new antibiotic medicines.

Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach.

The dynamical nature of COVID-19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID-19 cases based on past infections, (b) predict current COVID-19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K-nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k-nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID-19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID-19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.

Impact of COVID -19 pandemic lockdown on distribution of inorganic pollutants in selected cities of Nigeria.

The COVID-19 global pandemic has necessitated some drastic measures to curb its spread. Several countries around the world instituted partial or total lockdown as part of the control measures for the pandemic. This presented a unique opportunity to study air pollution under reduced human activities. In this study, we investigated the impact of the lockdown on air pollution in three highly populated and industrious cities in Nigeria. Compared with historical mean values, NO2 levels increased marginally by 0.3% and 12% in Lagos and Kaduna respectively. However, the city of Port Harcourt saw a decrease of 1.1% and 215.5% in NO2 and SO2 levels respectively. Elevated levels of O3 were observed during the period of lockdown. Our result suggests that there are other sources of air pollution apart from transportation and industrial sources. Our findings showed that the COVID-19-induced lockdown was responsible for a decrease in NO2 levels in two of the locations studied. These results presents an opportunity for country wide policies to mitigate the impact of air pollution on the health of citizens.