Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops.

Spatial examination of social and environmental drivers of Middle East respiratory syndrome coronavirus (MERS-CoV) across Kenya.

Climate and agricultural land-use change has increased the likelihood of infectious disease emergence and transmissions, but these drivers are often examined separately as combined effects are ignored. Further, seldom are the influence of climate and agricultural land use on emerging infectious diseases examined in a spatially explicit way at regional scales. Our objective in this study was to spatially examine the climate, agriculture, and socio-demographic factors related to agro-pastoralism, and especially the combined effects of these variables that can influence the prevalence of Middle East respiratory syndrome coronavirus (MERS-CoV) in dromedary camels across northern Kenya.

Neighborhood Context as a Barrier to Sport Participation for Girls: Evidence From a Sport Diversity Index.

Background: Evidence suggests neighborhood contexts play a vital role in shaping the availability and diversity of youth sport and participation rates, especially for African American or Black girls. Currently, no index captures interscholastic sport opportunities (eg, sport diversity) within and across school districts and specifically applied to African American or Black girls.

Objective: To visualize the inequalities present in interscholastic sport opportunities for girls across school districts using a novel index in a selected study area of St Louis City and County, Missouri, and discuss the implications for African American or Black girls.

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review.

Introduction: As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types.

Personal Location as Health-Related Data: Public Knowledge, Public Concern, and Personal Action.

Objectives: Personal health information (PHI), including health status and behaviors, are often associated with personal locations. Smart devices and other technologies routinely collect personal location. Therefore, technologies collecting personal location do not just create generic questions of privacy, but specific concerns related to PHI.

Implementing Geospatial Science and Technology to Get to Zero New HIV Infections.

Purpose Of Review: Tremendous advancements have been made in HIV treatment and prevention during the last 40 years that zero new HIV cases has become an attainable goal declared by international agencies. However, new cases of HIV infection persist.

Recent Findings: The emerging field of geospatial science is positioned to play key role in the reduction of continued HIV incidence through technology-driven interventions and innovative research that gives insights into at-risk populations.

A practitioner's guide to geospatial analysis in a neuroimaging context.

Introduction: Health disparities arise from biological-environmental interactions. Neuroimaging cohorts are reaching sufficiently large sample sizes such that analyses could evaluate how the environment affects the brain. We present a practical guide for applying geospatial methods to a neuroimaging cohort.

UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.

Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secure vulnerable food systems. This study investigated the versatility of UAV-borne multisensory data fusion within a framework of multi-task deep learning for high-throughput phenotyping in maize. UAVs equipped with a set of miniaturized sensors including hyperspectral, thermal, and LiDAR were collected in an experimental corn field in Urbana, IL, USA during the growing season.

Spatial Optimization to Improve COVID-19 Vaccine Allocation.

Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.

Enhancement of river flooding due to global warming.

Human-induced climate change has increased the frequency and intensity of heavy precipitation. Due to the complexity of runoff generation and the streamflow process, the historical impact of human-induced climate change on river flooding remains uncertain. Here, we address the question of whether anthropogenic climate change has altered the probability of the extreme river flood events for the period 1951-2010 based on simulated river discharge derived from large ensemble climate experiments with and without human-induced climate change.