Abstract. Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22 560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved ∼ 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.

Background Tuberculosis (TB) remains one of the infectious diseases with a leading cause of death among adults worldwide. Metformin, a first-line medication for the treatment of type 2 diabetes, may have potential for treating TB. The aims of the present systematic review were to evaluate the impact of metformin prescription on the risk of tuberculosis diseases, the risk of latent TB infection (LTBI) and treatment outcomes of tuberculosis among patients with diabetic mellitus. Methods Databases were searched through March 2019. Observational studies reporting the effect of metformin prescription on the risk and treatment outcomes of TB were included in the systematic review. We qualitatively analyzed results of included studies, and then pooled estimate effects with 95% confidence intervals (CIs) of different outcome using random-effect meta-analyses. Results This systematic review included 6980 cases from 12 observational studies. The meta-analysis suggested that metformin prescription could decrease the risk of TB among diabetics (pooled odds ratio [OR], 0.38; 95%CI, 0.21 to 0.66). Metformin prescription was not related to a lower risk of LTBI (OR, 0.73; 95%CI, 0.30 to 1.79) in patients with diabetes. Metformin medication during the anti-tuberculosis treatment is significantly associated with a smaller TB mortality (OR, 0.47; 95%CI, 0.27 to 0.83), and a higher probability of sputum culture conversion at 2 months of TB disease (OR, 2.72; 95%CI, 1.11 to 6.69) among patients with diabetes. The relapse of TB was not statistically reduced by metformin prescription (OR, 0.55; 95%CI, 0.04 to 8.25) in diabetics. Conclusions According to current observational evidence, metformin prescription significantly reduced the risk of TB in patients with diabetes mellitus. Treatment outcomes of TB disease could also be improved by the metformin medication among diabetics.

Disasters accompanied by heavy casualties and huge economic losses directly result in the disruption or delay of economic development. Considering the urgent need for reducing losses and accelerating the process of social recovery, international nongovernment organizations (INGOs) and local NGOs (LNGOs) with different resource endowments should achieve organizational coordination to improve the relief efficiency and sustainability of the humanitarian supply chain. Due to conflicting interests and expectations, this coordination is hard to achieve. In this study, we first establish an evolutionary game model between INGOs and LNGOs to determine the influencing factors and explore the interaction of NGOs in a dynamic way. Our results show that: (1) coordination by resource sharing can improve the sustainability of the humanitarian supply chain; (2) coordination willingness is affected by the behavior of other players, which can nevertheless achieve equilibrium under certain conditions; and (3) the important factors and optimal strategies of players are highlighted in the dynamic model. This study provides several insights into the theory of organizational coordination in the humanitarian supply chain regarding sustainability.

Background and Objective: Epidemiological studies suggested that the frequency of tooth brushing might be associated with the risk of diabetes mellitus (DM), but the results were inconsistent, and no systematic review was conducted to focus on this topic. In this meta-analysis, we synthesized available observational epidemiological evidences to identify the association between tooth brushing and DM risk and investigate the potential dose-response relationship of them. Methods: We searched PubMed and Embase from their inception throughDecember 2017 to identify observational studies examining the association between tooth brushing and the risk of DM. Reference lists from retrieved articles were also reviewed. We quantitatively combined results of the included studies using a random-effects model. Dose-response meta-analysis was conducted to further examine the effect of tooth brushing frequency on DM risk. Results:We identified 20 relevant studies (one cohort study, 14 case-control studies, and 5 cross-sectional studies) involving 161 189 participants and 10 884 patients with DM. Compared with the highest tooth brushing frequency, the lowest level was significantly associated with an increased risk of DM (OR 1.32; 95% CI, 1.19-1.47), and there was no significant heterogeneity across the included studies (p = 0.119, I 2 = 28.1%).Exclusion of any single study did not materially alter the combined risk estimate. The dose-response analysis indicated that the summary odds of DM for an increment of one time of tooth brushing per day was 1.20 (95% CI, 1.16-1.24).Conclusions: Integrated epidemiological evidence supports the hypothesis that low frequency of tooth brushing may be a risk factor of DM, and lower frequencies of tooth brushing were significantly associated with higher risk of DM. KEYWORDS diabetes, meta-analysis, tooth brushingAbbreviations: CI, confidence interval; DM, Diabetes mellitus; MOOSE, the meta-analysis of observational studies in epidemiology; OR, odds ratio; RR, relative risk Wenning Fu and Chuanzhu Lv are co-first author.

Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods.

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Lake water storage changes are important factors that influence the climate, hydrological cycle, and environments. However, long‐term estimation of global lake storage changes is challenging because historical in‐situ hydrological observations worldwide are rarely available. Benefiting from the laser altimeter ICESat and ICESat‐2, we comprehensively assessed water level and volume changes in global natural lakes larger than 10 km2 during 2003–2020. The 6,567 lakes observable by ICESat/ICESat‐2, which account for ∼94% of the total global lake volume, showed a total water storage increase of 10.88 ± 16.45 Gt/yr during 2003–2020, and the estimate reaches 16.12 ± 20.41 Gt/yr when also taking account of the remaining unobserved lakes. Despite water gains in most natural lakes, large lakes under dry and high water‐stress conditions experienced dramatic water loss in general. Presumably, these drying lakes may continue to shrink with a warming climate and continuously increasing water demands in the future without further action.