This paper presents the determination method of the exact geographical coordinates of aboveground nuclear tests (NT) epicenters based on the radioecological study results the example of the Semipalatinsk Nuclear Test Site. By testing the NT Epicenter software for determining the exact geographic coordinates of the NT centers, it was established that it is indeed possible to determine the exact coordinates of most of the aboveground NTs. Their locations are currently determined by the presence are currently determined by the presence of technogenic disturbance of the soil surface in the area of the alleged epicenter (the presence of a crater), as well as by comparing maps of radioactive contamination and a space image. The accuracy of the precise coordinates of the NT is highly dependent on the density of the auxiliary grid: the smaller the pitch of the auxiliary grid, the higher the accuracy of the NT epicenter.

UNASSIGNED: Cutaneous melanoma (CM) is a significant contributor to skin cancer-related mortality globally and in Canada. Despite the well-established link between ultraviolet (UV) radiation exposure and skin cancer risk, there remains a gap in population-level interventions and persistent misconceptions about sun exposure and impact of environment on individual behavior.
UNASSIGNED: The current study provides an ecological analysis using latest available data (2011-2017) to define geographic/environmental contributors to the CM landscape in Canada.
UNASSIGNED: Utilizing Canadian Cancer Registry and Canadian Urban Environmental Health Research Consortium data, we analyzed 39,605 CM cases occurring in Canada from 2011 to 2017. Environmental data, including UV radiation, greenspace (normalized difference vegetation index), temperature, heat events, and precipitation was used to evaluate the effect of environment on CM incidence rates across Forward Sortation Area postal codes.
UNASSIGNED: Forward Sortation Areas with increased CM incidence were associated with higher annual average temperature, snowfall, heat events, normalized difference vegetation index, and vitamin D-weighted UV exposure. Conversely, factors associated with decreased incidence included an increased annual highest temperature, rain precipitation, and a longer duration of heat events.
UNASSIGNED: This study is subject to ecological bias and findings should be interpreted with caution.
UNASSIGNED: This study further substantiates associations between specific environmental factors and CM incidence.

UNASSIGNED: The PIONEER III trial showed the 12-month safety and efficacy of the Supreme drug-eluting stent (DES) vs the durable polymer everolimus-eluting stent. We sought to assess whether the characteristics and clinical outcomes of the Supreme DES in PIONEER III were consistent among patients by enrollment location.
UNASSIGNED: This subgroup analysis of the PIONEER III trial compared the characteristics and outcomes of patients recruited from North America, Europe, and Japan and the relative differences in patient outcomes according to the site recruitment volume.
UNASSIGNED: From October 2017 to July 2019, 1629 patients were recruited in North America (816, 50.1%), Europe (650, 39.9%), and Japan (163, 10%). Procedural success was achieved in 1556 of 1611 procedures (96.6%), with no difference by the geographic location. Target lesion failure at 12 months for combined groups was observed in 84 of 1629 patients (5.2%), with no significant geographic differences (4.7%, 6.5%, and 2.5%, respectively; P =.08), with similar results in the Supreme DES group alone (4.4%, 6.8%, and 3.7%, respectively, P =.20). Cardiac death at 12 months occurred in 0.4%, 0.2%, and 0.0% (P =.79), target vessel-related myocardial infarction occurred in 2.2%, 4.7%, and 3.7%, (P =.10), and clinically driven target lesion revascularization was required in 2.1%, 3.1%, and 0%, respectively (P =.15). Compared with those from high-recruiting sites, results from low-recruiting sites were similar for target lesion failure, major adverse cardiac events, stent thrombosis, and mortality, with a nonsignificant trend for higher rates of myocardial infarction.
UNASSIGNED: Despite regional differences in patient characteristics, the clinical outcomes between Supreme DES and durable polymer everolimus-eluting stent in the PIONEER III trial were not different, supporting the generalizability and robustness of the findings from this multicenter controlled trial.

Olive leaves are a rich source of polyphenols with healthful properties and represent one of the most abundant waste products of olive oil production. The aims of this study were to explore the phenolic composition of olive leaves from the three main Tuscan cultivars (Leccino, Moraiolo and Frantoio) collected in Siena and Grosseto provinces and to investigate the possible use of these compounds as varietal and geographic origin markers. Discriminant factorial analysis (DFA) was used for distinguishing between different cultivars and locations. Apigenin and caffeoyl-secologanoside showed significant differences between cultivars. DFA showed that ligstroside, apigenin and luteolin have the most influence in determining the differences between sites, whereas total polyphenols, olacein and hydroxytyrosol acetate allowed for separation between leaves from the same province. The results of the present study indicate that concentrations of phenolic compounds, measured through high-resolution mass spectrometry, can be used as a marker for both the cultivar and of geographical origin of olive leaves, and possibly of olive-related products, as well as across small geographic scales (less than 50 km distance between sites).

BACKGROUND: Geographic variation in COVID-19 vaccination can create areas at higher risk of infection, complications, and death, exacerbating health inequalities. This ecological study examined geographic patterns of COVID-19 vaccine completion, using age and sociodemographic characteristics as possible explanatory mechanisms.
RESULTS: Using 2020-2022 data from the North Carolina COVID-19 Vaccination Management System and U.S. Census Bureau American Community Survey, at the Zip code-level, we evaluated completion of the primary COVID-19 vaccine series across age groups. We examined geographic clustering of age-specific completion by Zip code and evaluated similarity of the age-specific geographic patterns. Using unadjusted and adjusted spatial autoregressive models, we examined associations between sociodemographic characteristics and age-specific vaccine completion. COVID-19 vaccine completion was moderately geographically clustered in younger groups, with lower clustering in older groups. Urban areas had clusters of higher vaccine completion. Younger and middle-aged groups were the most similar in completion geographically, while the oldest group was most dissimilar to other age groups. Higher income was associated with higher completion in adjusted models across all age groups, while a higher percent of Black residents was associated with higher completion for some groups.
CONCLUSIONS: COVID-19 vaccination completion is more variable among younger age groups in North Carolina, and it is higher in urban areas with higher income. Higher completion in areas with more Black residents may reflect the success of racial equity efforts in the state. The findings show a need to reach younger populations and lower income areas that were not prioritized during early vaccination distribution.

UNASSIGNED: Socio-economic and political events of recent years have caused a significant increase in immigrants attempting to illegally cross the United States (US)-Mexico border. While a 30-foot border wall separates the US and Mexico, immigrants from around the world have used this location as their point of entry to the US. These border crossings have led to a dramatic increase in major trauma resulting in increased inpatient resource utilization and the need for comprehensive hospital services. The aim of this study was to describe the nationality of injured immigrants admitted to a Trauma Center serving a segment of the US-Mexico border wall and to report their ultimate destinations after discharge.
UNASSIGNED: We performed a retrospective review of patients admitted to an academic, Level 1 Trauma Center after injury at the US-Mexico border wall from 2021 to 2022. Demographic information was obtained from the trauma registry. The electronic medical record was searched to identify each patient\'s self-reported country of origin. Patients\' nationality was then stratified by region of the world to understand geographic representation of border injury admissions.
UNASSIGNED: We identified 597 patients injured while crossing the US-Mexico border wall representing 38 different countries. The mean age of patients was 32.2±10.4 years and 446 (75%) were male. Most patients (405, 67.8%) were Mexican, followed by 23 (3.9%) patients from Peru, 17 (2.8%) patients from India, 14 (2.3%) patients from El Salvador, 13 (2.2%) patients from Cuba and 12 (2.0%) patients from Jamaica. When considering regions of the world other than Mexico, patients were most commonly from Africa, South America and Central America.
UNASSIGNED: The increased volume of trauma associated with the US-Mexico border wall is a humanitarian and health crisis.(1) The diverse national origin of patients admitted after injury from border wall falls has shed new light on the social and interpreter services needed to care for these border injury patients and the challenges that exist in their post-discharge care.

Accurate geographical traffic forecasting plays a critical role in urban transportation planning, traffic management, and geospatial artificial intelligence (GeoAI). Although deep learning models have made significant progress in geographical traffic forecasting, they still face challenges in effectively capturing long-term temporal dependencies and modeling heterogeneous dynamic spatial dependencies. To address these issues, we propose a novel deep transformer-based heterogeneous spatiotemporal graph learning model for geographical traffic forecasting. Our model incorporates a temporal transformer that captures long-term temporal patterns in traffic data without simple data fusion. Furthermore, we introduce adaptive normalized graph structures within different graph layers, enabling the model to capture dynamic spatial dependencies and adapt to diverse traffic scenarios, especially for the heterogeneous relationship. We conduct comprehensive experiments and visualization on four primary public datasets and demonstrate that our model achieves state-of-the-art results in comparison to existing methods.

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs.
OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.
METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.
RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the \"NWO Navigate Stroke\" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes.
CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.

An increasing amount of California\'s landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.