A comparative analysis of COVID-19 physical distancing policies in South Africa and Uganda.

COVID-19 responses internationally have depended on physical distancing policies to manage virus transmission, given the initial absence of treatments and limitations on vaccine availability. Different jurisdictions have different contexts affecting their responses such as past epidemic experience, ratings of epidemic preparedness, and income level. COVID-19 responses in African countries have not been well-studied. A qualitative multiple embedded case study design was used to examine the COVID-19 policies in South Africa and Uganda from January 2020 to November 2021. This study included a documentary review using government websites and reports, news articles, and peer-reviewed journal articles to obtain data on policy responses and contextual factors. Epidemiological data were collected from public sources. Key informant interviews with relevant stakeholders were used to confirm findings and cover missing information. A comparative analysis was conducted to explore differences in implementation of different types of physical distancing policies and potential consequences of lifting or prolonging public health measures. South African and Ugandan policy responses included physical distancing measures such as lockdown, international travel bans, school closures, public transportation measures, and curfew, in addition to socioeconomic relief programs and vaccinations. Differences between jurisdiction policy responses existed in terms of overarching strategy, timing, and stringency. This study provided in-depth comparisons of COVID-19 policy responses and relevant contextual factors in South Africa and Uganda. The study showed how contextual factors such as population age, geographic distribution, and recent epidemic response experience can influence COVID-19 transmission and response. The study also showed differences in overall strategy, timing, and strictness of epidemic management policies in these jurisdictions. These findings suggest it may be important to have sustained, strict measures to limit the spread of COVID-19 and manage the course of a pandemic, which need to be further explored alongside other important social and economic pandemic outcomes.

A suberized exodermis is required for tomato drought tolerance.

Plant roots integrate environmental signals with development using exquisite spatiotemporal control. This is apparent in the deposition of suberin, an apoplastic diffusion barrier, which regulates flow of water, solutes and gases, and is environmentally plastic. Suberin is considered a hallmark of endodermal differentiation but is absent in the tomato endodermis. Instead, suberin is present in the exodermis, a cell type that is absent in the model organism Arabidopsis thaliana. Here we demonstrate that the suberin regulatory network has the same parts driving suberin production in the tomato exodermis and the Arabidopsis endodermis. Despite this co-option of network components, the network has undergone rewiring to drive distinct spatial expression and with distinct contributions of specific genes. Functional genetic analyses of the tomato MYB92 transcription factor and ASFT enzyme demonstrate the importance of exodermal suberin for a plant water-deficit response and that the exodermal barrier serves an equivalent function to that of the endodermis and can act in its place.

Predictive Value of Blood Markers in Pediatric Brain Tumors Using Machine Learning.

BACKGROUND: The brain tumor is the most common solid tumor in children. Blood markers in most malignancies are altered due to the effect of inflammatory mediators on the bone marrow. OBJECTIVE: This study aimed to predict the malignancy of pediatric brain tumors using blood markers. METHODS: The pediatric brain tumors were divided into benign and malignant groups. Blood markers, including RBC, WBC, neutrophil, lymphocyte, monocyte, platelet, neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio, and derived neutrophil to lymphocyte ratio were extracted. Differences in blood markers between two groups were assessed using statistical analysis. The accuracy of machine learning to determine pediatric brain tumors' malignancy was evaluated using blood markers and demographic information. RESULTS: Among 113 patients, 55 patients were in the benign tumor group, and 58 patients were in the malignant tumor group. In the statistical study of blood markers in two groups, LMR was significantly different and positively correlated with malignancy. Other blood markers were not significantly different between two groups. This study showed that support-vector machines using blood markers, age, and sex can differentiate benign and malignant pediatric brain tumors with 71.6% accuracy. CONCLUSIONS: Despite the statistically significant differences in blood markers in different grades of brain tumors in adults, their differences in pediatric brain tumors, except LMR, were not significant. Machine learning using blood markers can differentiate between benign and malignant pediatric brain tumors with 71.6% accuracy.