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Abstract In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to streamflow forecasting for input variable selection (IVS), comparative analysis of different random forest-based IVS (RF-IVS) methods is yet absent. Here, we investigate performance of five RF-IVS methods in four data-driven models (RF, support vector regression (SVR), Gaussian process regression (GP), and long short-term memory (LSTM)). A case study is implemented in the contiguous United States for one-month-ahead streamflow forecasting. Results indicate that RF-IVS methods enable to acquire enhanced performance in comparison to widely used partial Pearson correlation and conditional mutual information. Meanwhile, performance-based RF-IVS methods appear to be superior to test-based methods, and the test-based methods tend to select redundant variables. The RF with a forward selection strategy is finally recommended to connect with GP model as a promising combination having potential to yield favorable performance.

Objectives: We aimed to explore the correlation between thyroid function and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: A total of 7516 T2DM patients were enrolled and grouped according to DKD status. Clinical parameters, including blood glucose parameters, thyroid function, and indicators of renal impairment, were collected and compared between the DKD and Non-DKD groups. Correlation analysis and univariate/multivariate logistic regression analyses were performed. Results: Age, T2DM duration, the use of insulin and lipid-lowering drugs, systolic and diastolic blood pressure, body mass index, and fasting blood glucose levels were greater in the DKD group than in the Non-DKD group (p < 0.001). Notably, compared with those in the Non-DKD group, patients in the DKD group had lower triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), and free thyroxine (FT4) levels and higher thyrotropin levels (p < 0.001). Univariate logistic regression analysis revealed that T3, T4, FT3, and FT4 levels were negatively correlated with the risk of DKD. Spearman correlation analysis confirmed that T3, T4, FT3, and FT4 levels were negatively correlated with blood urea nitrogen levels, blood creatinine levels, and the urinary albumin-to-creatinine ratio (p < 0.05). Multivariate logistic regression analysis revealed that a greater FT4 level was a protective factor against DKD in T2DM patients, especially in males, with a cut-off value of 13.35 pmol/L (area under the curve = 0.604). Conclusions: Thyroid hormone levels, especially FT4 levels, were significantly negatively correlated with DKD in T2DM patients.

In the quest to scientifically develop power systems increasingly reliant on renewable energy sources, the potential and temporal complementarity of wind and solar power in China’s northwestern provinces necessitated a systematic assessment. Using ERA5 reanalysis data for wind speed and solar irradiance, an evaluation was carried out to determine the potential and spatial distribution of wind and solar power across these provinces. Land use types and terrestrial surface slopes were considered in gauging this potential. Theoretical wind and solar power outputs were then compared to understand their complementarity on annual, monthly, and hourly temporal scales. This exploration utilized methodologies including rank correlation coefficients, crossover frequency analysis, and standard deviation complementarity rates. Areas such as the Tarim Basin, Jungar Basin, and the northeastern part of Xinjiang, northwestern Qinghai, and northern Gansu were identified as having significant wind and solar power potential, with wind power densities reaching as high as 600 W/m2 and solar irradiance surpassing 2000 kWh/m2. In these energy-rich areas, the distinct complementarity between theoretical wind and solar outputs was discerned. On an annual scale, the complementarity appeared weakest, with only 7.48% of the combined provinces’ area showing medium-level complementarity. On a monthly scale, conversely, a pronounced complementarity was displayed, especially during the March–May and October–November periods. When evaluated on an hourly basis, an impressive 63.63% of the total output duration exhibited complementary characteristics.

Accumulated evidence reminds one that abrupt transitions between dry and wet spells, though attracting less attention, have harmful influences upon global continents as extensively investigated droughts and floods. This study attempts to incorporate dryness–wetness transitions into the current hazard assessment framework through bivariate frequency analysis and causal attribution from a teleconnection perspective. In the study, regional dry and wet conditions are monitored using the multivariate standardized drought index (MSDI) which facilitates the integrated evaluation of water deficits/surplus from a combined viewpoint of precipitation (largely denoting the received atmospheric water) and runoff (representing an important source of surface water). On such a basis, a copula-based method is subsequently utilized to calculate joint return periods of dryness–wetness combinations in three (i.e., moderate, severe and extreme) severity scenarios. The changing frequency of diverse dryness–wetness combinations is also estimated under a changing climate using a 25-year time window. Furthermore, the cross-wavelet transform is applied to attribute variations in dry and wet conditions to large-scale climate indices, which benefits the early warning of dryness–wetness combinations by providing predictive information. A case study conducted during 1952–2010 in the Huai River basin (HRB)—a typical climatic transition zone in China—shows that the HRB is subject to prolonged dryness with the highest frequency, followed by the abrupt transition from dryness to wetness. Spatially, abrupt dryness–wetness transitions are more likely to occur in the southern and central parts of the HRB than in the rest of the proportion. The past half-century has witnessed the dominantly higher frequency of occurrence of dryness–wetness combinations under three severity scenarios. In particular, the occurrence of the continued dry/wetness escalates more rapidly than transition events under climate change. Moreover, a preliminary attribution analysis discloses the link of the dry and wet conditions in the HRB with climate indices, such as the El Niño southern oscillation, the Pacific decadal oscillation and the Arctic oscillation, as well as sunspot activities. The results of the study enrich the current atlas of water-related hazards, which may benefit more effective hazard mitigation and adaptation.

Cocoa powder is rich in flavonoids, which have many beneficial effects on human health, including antioxidative and anti-inflammatory effects. The aim of our study was to investigate whether the intake of cocoa powder has any influence on hyperlipidemia and atherosclerosis and examine the underlying molecular mechanisms. We fed apoE knockout mice a Western diet supplemented with either 0.2% (low group) or 2% (high group) cocoa powder for 12 weeks. The groups fed dietary cocoa powder showed a significant reduction in both plasma cholesterol levels and aortic atherosclerosis compared to the control group. Analysis of mRNA profiling of aortic atherosclerotic lesions revealed that the expression of several genes related to apoptosis, lipid metabolism, and inflammation was significantly reduced, while the antiapoptotic gene Bcl2 was significantly increased in the cocoa powder group compared to the control. RT-PCR analysis along with Western blotting revealed that a diet containing cocoa powder inhibited the expression of hepatic endoplasmic reticulum stress. These data suggest that cocoa powder intake improves hyperlipidemia and atherosclerosis, and such beneficial effects are possibly mediated through the suppression of hepatic endoplasmic reticulum stress.