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  1. Astafurova YV, Proshchalykin MY, Schwarz M
    Zookeys, 2020;937:31-88.
    PMID: 32547298 DOI: 10.3897/zookeys.937.51708
    The available information about the cleptoparasitic bees of the genus Sphecodes in Southeast Asia is summarized. Thirty-one species are currently known from this area. Four new species are described: Sphecodes discoverlifei Astafurova & Proshchalykin, sp. nov. (Laos), S. engeli Astafurova & Proshchalykin, sp. nov. (Laos, Vietnam), S. ilyadadaria Astafurova, sp. nov. (Indonesia), and S. pseudoredivivus Astafurova & Proshchalykin, sp. nov. (Laos). Nine species are newly recorded from South East Asia: S. chaprensis Blüthgen, 1927 (Laos), S. howardi Cockerell, 1922 (Malaysia, Myanmar, Thailand), S. kershawi Perkins, 1921 (Indonesia, Malaysia, Myanmar, Thailand), S. laticeps Meyer, 1920 (Thailand, Vietnam), S. montanus Smith, 1879 (Laos), S. sauteri Meyer, 1925 (Laos), S. sikkimensis Blüthgen, 1927 (Laos, Myanmar), S. simlaensis Blüthgen, 1924 (Laos), and S. turneri Cockerell, 1916 (Laos). Based on type specimens, new synonymies have been proposed for Sphecodes kershawi Perkins, 1921 = S. javanensis Blüthgen, 1927, syn. nov.; S. simlaensis Blüthgen, 1924 = S. simlaellus Blüthgen, 1927, syn. nov.; S. laticeps Meyer, 1920 = S. biroi mariae Cockerell, 1930, syn. nov. Lectotypes are designated for Sphecodes biroi Friese, 1909, S. simlaellus Blüthgen, 1927, and S. laticeps Meyer, 1920. The female of Sphecodes sauteri Meyer, 1925, and the male of S. turneri Cockerell, 1916 are described for the first time.
  2. Barteit S, Sié A, Zabré P, Traoré I, Ouédraogo WA, Boudo V, et al.
    Front Public Health, 2023;11:1153559.
    PMID: 37304117 DOI: 10.3389/fpubh.2023.1153559
    BACKGROUND: Climate change significantly impacts health in low-and middle-income countries (LMICs), exacerbating vulnerabilities. Comprehensive data for evidence-based research and decision-making is crucial but scarce. Health and Demographic Surveillance Sites (HDSSs) in Africa and Asia provide a robust infrastructure with longitudinal population cohort data, yet they lack climate-health specific data. Acquiring this information is essential for understanding the burden of climate-sensitive diseases on populations and guiding targeted policies and interventions in LMICs to enhance mitigation and adaptation capacities.

    OBJECTIVE: The objective of this research is to develop and implement the Change and Health Evaluation and Response System (CHEERS) as a methodological framework, designed to facilitate the generation and ongoing monitoring of climate change and health-related data within existing Health and Demographic Surveillance Sites (HDSSs) and comparable research infrastructures.

    METHODS: CHEERS uses a multi-tiered approach to assess health and environmental exposures at the individual, household, and community levels, utilizing digital tools such as wearable devices, indoor temperature and humidity measurements, remotely sensed satellite data, and 3D-printed weather stations. The CHEERS framework utilizes a graph database to efficiently manage and analyze diverse data types, leveraging graph algorithms to understand the complex interplay between health and environmental exposures.

    RESULTS: The Nouna CHEERS site, established in 2022, has yielded significant preliminary findings. By using remotely-sensed data, the site has been able to predict crop yield at a household level in Nouna and explore the relationships between yield, socioeconomic factors, and health outcomes. The feasibility and acceptability of wearable technology have been confirmed in rural Burkina Faso for obtaining individual-level data, despite the presence of technical challenges. The use of wearables to study the impact of extreme weather on health has shown significant effects of heat exposure on sleep and daily activity, highlighting the urgent need for interventions to mitigate adverse health consequences.

    CONCLUSION: Implementing the CHEERS in research infrastructures can advance climate change and health research, as large and longitudinal datasets have been scarce for LMICs. This data can inform health priorities, guide resource allocation to address climate change and health exposures, and protect vulnerable communities in LMICs from these exposures.

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