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Introduction: Global watershed sustainable development has experienced world-wide threats from continuing anthropogenic stressors, and the need to deepen and broaden research encompassing the intersection in global environmental change as well as environmentally oriented watershed sustainable development (EOWSD) has been noticed. However, there is not yet a widely recognized cognition on the applicability and scope of various EOWSD issues, and the zoning of global EOWSD issues is remains uncertain despite it is crucial for achieving global watershed sustainable development.Methods: This research was conducted to both clarify the zoning and evolution of various EOWSD issues around the world, and differentiate the relative impacts on EOWSD of climate change and human activities. The global EOWSD issues were summarized from 62 watersheds around the world as 6 categories associated with different aspects of global watershed sustainability. And the partition method, in which the spatial and temporal variations of global summer Normalized Difference Vegetation Index in summer were examined and the quantitative climate classification were conducted, indicates a clear and definite relationship between the zoning of EOWSD issues and 8 natural geographical zones. Meanwhile, we selected 34 watersheds either or both are the 100 most populous river basins and the 100 largest (by area) river basins in the world from the 62 watersheds to assess relative effects of human impact on watershed sustainability.Results: Results from the numerical analyses of baseline water stress (BWS) values, which was used to provide a robust measure of human impact and evaluate the impact and relative importance of human-induced changes on watershed sustainability, indicate that the human activities do not affect the zoning of EOWSD issues at global scale while the environmental change induced by water engineering development should be certain to affect that on the long-term.Discussion: Our findings present a new perspective to illustrate the relationship among global EOWSD, environmental change and human impacts, and will also provide a scientific basis on setting future emphasizes of global watershed sustainable development and furthering the related disciplines.

Background. The community transmission of COVID-19 has caused the breakdown of the regional emergency medical system (REMS), impacting the rights and care of regional patients with acute and severe conditions. This study proposes a model for the surge capacity of REMS to plan for readiness and preparedness during challenging events that overload capacity. Methods. The surge capacity of REMS during the COVID-19 pandemic was studied. The data collection included 26 hospitals that received the data. To simulate the dynamics of Taipei’s REMS surge capacity, we observed its ability to treat COVID-19 patients with moderate to severe acute respiratory distress syndrome (ARDS). This will involve monitoring the stock of ventilators, physicians, and nurses within the subsystem loops. Results. Healthcare managers and administrators can use the overload model and hypothetical scenarios to develop new scenarios with different demands on surge capacity. The REMS system capacity model can be used as an aid to guide planning and cross-checking for address Prepare to plan. Conclusions. We combined data regarding the availability of ventilators, physicians, nurses, specialized beds, and general acute care beds in our simulations. Thus, our simulations, with support from a well-established regional command and management structure, could help REMS achieve the optimal surge capacity.

BackgroundAlthough lung cancer incidence rates according to smoking status, sex, and detailed race/ethnicity have not been available, it is estimated that more than half of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked.MethodsWe calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race/ethnicity among females, focusing on AANHPI ethnic groups, and assessed relative incidence across racial/ethnic groups. We used a large-scale dataset that integrates data from electronic health records from 2 large health-care systems-Sutter Health in Northern California and Kaiser Permanente Hawai'i-linked to state cancer registries for incident lung cancer diagnoses between 2000 and 2013. The study population included 1 222 694 females (n = 244 147 AANHPI), 3297 of which were diagnosed with lung cancer (n = 535 AANHPI).ResultsIncidence of lung cancer among never-smoking AANHPI as an aggregate group was 17.1 per 100 000 (95% confidence interval [CI] = 14.9 to 19.4) but varied widely across ethnic groups. Never-smoking Chinese American females had the highest rate (22.8 per 100 000, 95% CI = 17.3 to 29.1). Except for Japanese American females, incidence among every never-smoking AANHPI female ethnic group was higher than that of never-smoking non-Hispanic White females, from 66% greater among Native Hawaiian females (incidence rate ratio = 1.66, 95% CI = 1.03 to 2.56) to more than 100% greater among Chinese American females (incidence rate ratio = 2.26, 95% CI = 1.67 to 3.02).ConclusionsOur study revealed high rates of lung cancer among most never-smoking AANHPI female ethnic groups. Our approach illustrates the use of innovative data integration to dispel the myth that AANHPI females are at overall reduced risk of lung cancer and demonstrates the need to disaggregate this highly diverse population.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system’s reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient’s reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.