Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.

Introduction: The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints.

Mobile phone data reveal the effects of violence on internal displacement in Afghanistan.

Nearly 50 million people globally have been internally displaced due to conflict, persecution and human rights violations. However, the study of internally displaced persons-and the design of policies to assist them-is complicated by the fact that these people are often underrepresented in surveys and official statistics. We develop an approach to measure the impact of violence on internal displacement using anonymized high-frequency mobile phone data.

The Correlation Between F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography Semiquantitative Parameters and the Clinical Features and Pathological Biological Indexes of Gastric Cancer.

This study explored the application value of the maximum standard uptake value (SUV) of F-fluorodeoxyglucose-positron emission tomography/computed tomography (F-FDG PET/CT) in gastric cancer. Data of 164 patients with gastric cancer who had undergoneF-FDG PET/CT before a biopsy were collected, and the correlation of SUV with clinical stage, pathological differentiation degree, human epidermal growth factor receptor-2 (HER-2) status, and Ki-67 index of gastric cancer was analyzed. The SUV of poorly differentiated adenocarcinoma was significantly higher than that of moderately differentiated adenocarcinoma and signet-ring cell carcinoma ( < 0.

Public mobility data enables COVID-19 forecasting and management at local and global scales.

Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility-collected by Google, Facebook, and other providers-can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases.

A Fully Automatic Method for Comparing Cartridge Case Images.

When a gun is fired, it leaves marks on cartridge cases that are thought to be unique to the gun. In current practice, firearms examiners inspect cartridge cases for "sufficient agreement," in which case they conclude that they come from the same gun, testifying in courts as such. A 2016 President's Council of Advisors on Science and Technology report questioned the scientific validity of such analysis (President's Committee of Advisors on Science and Technology, Washington, DC, Executive Office of the President).