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Climate change poses critical threats to water-related safety and sustainability in the Mekong River basin. Hydrological impact signals from earlier Coupled Model Intercomparison Project phase 3 (CMIP3)-based assessments, however, are highly uncertain and largely ignore hydrological extremes. This paper provides one of the first hydrological impact assessments using the CMIP5 climate projections. Furthermore, we model and analyse changes in river flow regimes and hydrological extremes (i.e. high-flow and low-flow conditions). In general, the Mekong's hydrological cycle intensifies under future climate change. The scenario's ensemble mean shows increases in both seasonal and annual river discharges (annual change between +5 and +16 %, depending on location). Despite the overall increasing trend, the individual scenarios show differences in the magnitude of discharge changes and, to a lesser extent, contrasting directional changes. The scenario's ensemble, however, shows reduced uncertainties in climate projection and hydrological impacts compared to earlier CMIP3-based assessments. We further found that extremely high-flow events increase in both magnitude and frequency. Extremely low flows, on the other hand, are projected to occur less often under climate change. Higher low flows can help reducing dry season water shortage and controlling salinization in the downstream Mekong Delta. However, higher and more frequent peak discharges will exacerbate flood risks in the basin. Climate-change-induced hydrological changes will have important implications for safety, economic development, and ecosystem dynamics and thus require special attention in climate change adaptation and water management.

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to + 9 degrees C) and precipitation (-50 to + 50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development

Objectives: This study systematically evaluates the diagnostic accuracy of dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values. Methods: The literature search started and ended on 10 June 2024. We searched MEDLINE, Cochrane Library, Pubmed, Science Direct, and Google Scholar. Our research question could be formulated as “In women with NME detected by MRI, how accurate are DCE and DWI in ruling in and ruling out malignancy when the diagnosis is compared to histopathology analysis with or without a clinical follow-up?”. The meta-analysis was conducted using the STATA 17 software with the “midas” commands. The study protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) database. Results: Fifty-four studies involving 6121 NME lesions were analyzed. The combined use of DCE-MRI and DWI demonstrated the highest diagnostic accuracy (AUC: 0.91; 95% CI: 0.88–0.93), followed by DWI alone (AUC: 0.85; 95% CI: 0.81–0.87) and ADC (AUC: 0.77; 95% CI: 0.74–0.81). DCE-MRI alone showed the lowest performance (AUC: 0.68; 95% CI: 0.64–0.72). Significant heterogeneity was observed across all modalities, with I2 values exceeding 95% in several analyses. The likelihood ratio scattergram indicated that no modality reliably confirmed or excluded malignancy. Conclusions: While the combination of DCE-MRI and DWI achieves the highest diagnostic accuracy, no modality can reliably differentiate benign from malignant NME lesions. Standardized imaging protocols and refined diagnostic descriptors are needed for clinical improvement.

Upward soil water flow is a vital supply of water to crops. The purpose of this study is to determine if upward flow and recirculated percolation water can be quantified separately, and to determine the contribution of capillary rise and recirculated water to crop yield and groundwater recharge. Therefore, we performed impact analyses of various soil water flow regimes on grass, maize and potato yields in the Dutch delta. Flow regimes are characterized by soil composition and groundwater depth and derived from a national soil database. The intermittent occurrence of upward flow and its influence on crop growth are simulated with the combined SWAP-WOFOST model using various boundary conditions. Case studies and model experiments are used to illustrate the impact of upward flow on yield and crop growth. This impact is clearly present in situations with relatively shallow groundwater levels (85 % of the Netherlands), where capillary rise is a well-known source of upward flow; but also in free-draining situations the impact of upward flow is considerable. In the latter case recirculated percolation water is the flow source. To make this impact explicit we implemented a synthetic modelling option that stops upward flow from reaching the root zone, without inhibiting percolation. Such a hypothetically moisture-stressed situation compared to a natural one in the presence of shallow groundwater shows mean yield reductions for grassland, maize and potatoes of respectively 26, 3 and 14 % or respectively about 3.7, 0.3 and 1.5 t dry matter per hectare. About half of the withheld water behind these yield effects comes from recirculated percolation water as occurs in free-drainage conditions and the other half comes from increased upward capillary rise. Soil water and crop growth modelling should consider both capillary rise from groundwater and recirculation of percolation water as this improves the accuracy of yield simulations. This also improves the accuracy of the simulated groundwater recharge: neglecting these processes causes overestimates of 17 % for grassland and 46 % for potatoes, or 63 and 34 mm yr−1, respectively.

Objective Atrial fibrillation (AF) is one of the most common arrhythmic disorders worldwide. This study aims to describe the prevalence of AF in various cities in Indonesia using single‐lead hand held electrocardiography linked to a smartphone‐based application. Methods This is a cross‐sectional epidemiological study conducted at tertiary hospital of major cities in Indonesia, between January 2018 and July 2019. The AliveCor Kardia™ Mobile system device was used as a screening tool for AF and confirmed its finding with clinical diagnoses made by cardiologists. Results A total of 9773 subjects were enrolled in this study. The prevalence of AF reported by the device and cardiologist was 3.2% and 3.5%, respectively. AF prevalences are equal in men than women. The majority of subjects (56%) are having low risk for stroke or systemic thromboembolism. Prevalence of risk factors such as heart failure, hypertension, diabetes, and history of stroke among AF patients were 50%, 22.9%, 31.9%, 13.5%, and 5.8%, respectively. Conclusion The prevalence of AF at referral hospitals visitors based on smartphone diagnosis in Indonesia is 3.2%. Smartphone based screening of AF in upper‐middle‐income country of Indonesia showed prevalence of 3.2%, most are low risk for stroke and systemic thromboembolism but tend to be younger aged.

Ductulography is a key imaging technique for assessing nipple discharge. Despite modern advancements such as mammography and MRI, the relevance of ductulography in diagnosing pathologic nipple discharge (PND) remains significant, particularly in low-resource settings. This review examines our single-center experience with ductulography in Indonesia, highlighting patient characteristics and procedural outcomes. This review is based on case-based experiences from a private tertiary hospital in Indonesia, a main referral center for ductulography. We describe 28 women who underwent ductulography between January 2019 and June 2024. This review is supplemented by the theories of ductulography and how we do it in our institution. The median age of the patients was 46.5 years (IQR: 42-53.5 years). Among the 28 women, 2 experienced non-spontaneous discharge, 12 had colorless discharge, 12 had bloody discharge, and the remaining had yellowish to brownish discharge. Laterality was almost evenly distributed with 14 right-sided, 13 left-sided, and 1 bilateral discharge. The Galactogram Imaging Classification System (GICS) outcomes were 2 patients with GICS 0, 7 with GICS 1, 17 with GICS 2, and 2 with GICS 4a. Histopathologic analyses were performed on three patients, revealing fibrocystic changes in two and intraductal papilloma with fibrocystic changes in one. While ductulography may be considered an older technique, it remains a valuable diagnostic tool in the modern imaging era, especially in settings with limited access to advanced imaging modalities. Our single-center experience supports the continued use of ductulography, providing crucial preoperative mapping and aiding in the accurate diagnosis of PND.

Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the \"static\" model, and one with endogenous crop growth simulation, the \"dynamic\" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.

For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, that can quantify the differences between years and also the effects of extreme weather events. Furthermore they would like a method that considers current farm management and that can distinguish three different causes of crop yield reduction: drought, saline conditions or too wet conditions causing oxygen shortage in the root zone. WaterVision Agriculture is based on the hydrological simulation model SWAP and the crop growth model WOFOST. SWAP simulates water transport in the unsaturated zone using meteorological data, boundary conditions (like groundwater level or drainage) and soil parameters. WOFOST simulates crop growth as a function of meteorological conditions and crop parameters. Using the combination of these process-based models we have derived a meta-model, i.e. a set of easily applicable simplified relations for assessing crop growth as a function of soil type and groundwater level. These relations are based on multiple model runs for at least 72 soil units and the possible groundwater regimes in the Netherlands. So far, we parameterized the model for the crops silage maize and grassland. For the assessment, the soil characteristics (soil water retention and hydraulic conductivity) are very important input parameters for all soil layers of these 72 soil units. These 72 soil units cover all soils in the Netherlands. This paper describes (i) the setup and examples of application of the process-based model SWAP-WOFOST, (ii) the development of the simplified relations based on this model and (iii) how WaterVision Agriculture can be used by farmers, regional government, water boards and others to assess crop yield reduction as a function of groundwater characteristics or as a function of the salt concentration in the root zone for the various soil types.

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Understanding climate change effects on forests is important considering the role forests play in mitigating climate change. We studied the effects of changes in temperature, rainfall, atmospheric carbon dioxide (CO2) concentration, solar radiation, and number of wet days (as a measure of rainfall intensity) on net primary productivity (NPP) of the Zambian Zambezi teak forests along a rainfall gradient. Using 1960–1989 as a baseline, we projected changes in NPP for the end of the 21st century (2070–2099). We adapted the parameters of the dynamic vegetation model, LPJ-GUESS, to simulate the growth of Zambian forests at three sites along a moisture gradient receiving annual rainfall of between 700 and more than 1000 mm. The adjusted plant functional type was tested against measured data. We forced the model with contemporary climate data (1960–2005) and with climatic forecasts of an ensemble of five general circulation models (GCMs) following Representative Concentration Pathways (RCPs) RCP4.5 and RCP8.5. We used local soil parameter values to characterize texture and measured local tree parameter values for maximum crown area, wood density, leaf longevity, and allometry. The results simulated with the LPJ-GUESS model improved when we used these newly generated local parameters, indicating that using local parameter values is essential to obtaining reliable simulations at site level. The adapted model setup provided a baseline for assessing the potential effects of climate change on NPP in the studied Zambezi teak forests. Using this adapted model version, NPP was projected to increase by 1.77 % and 0.69 % at the wetter Kabompo and by 0.44 % and 0.10 % at the intermediate Namwala sites under RCP8.5 and RCP4.5 respectively, especially caused by the increased CO2 concentration by the end of the 21st century. However, at the drier Sesheke site, NPP would respectively decrease by 0.01 % and 0.04 % by the end of the 21st century under RCP8.5 and RCP4.5. The projected decreased NPP under RCP8.5 at the Sesheke site results from the reduced rainfall coupled with increasing temperature. We thus demonstrated that differences in the amount of rainfall received in a site per year influence the way in which climate change will affect forest resources. The projected increase in CO2 concentration would thus have more effects on NPP in high rainfall receiving areas, while in arid regions, NPP would be affected more by the changes in rainfall and temperature. CO2 concentrations would therefore be more important in forests that are generally not temperature- or precipitation-limited; however, precipitation will continue to be the limiting factor in the drier sites.