Insights into serum metabolic biomarkers for early detection of incident diabetic kidney disease in Chinese patients with type 2 diabetes by random forest.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide. Early detection is critical for the risk stratification and early intervention of progressive DKD. Serum creatinine (sCr) and urine output are used to assess kidney function, but these markers are limited by their delayed changes following kidney pathology, and lacking of both sensitivity and accuracy. Hence, it is essential to illustrate potential diagnostic indicators to enhance the precise prediction of early DKD. A total of 194 Chinese individuals include 30 healthy participants (Stage 0) and 164 incidents with type 2 diabetes (T2D) spanning from DKD's Stage 1a to 4 were recruited and their serums were subjected for untargeted metabolomic analysis. Random forest (RF), a machine learning approach, together with univariate linear regression (ULR) and multivariate linear regression (MvLR) analysis were applied to characterize the features of untargeted metabolites of DKD patients and to identify candidate DKD biomarkers. Our results indicate that 2-(α-D-mannopyranosyl)-L-tryptophan (ADT), succinyladenosine (SAdo), pseudouridine and N,N,N-trimethyl-L-alanyl-L-proline betaine (L-L-TMAP) were associated with the development of DKD, in particular, the latter three that were significantly elevated in Stage 2-4 T2D incidents. Each of the four metabolites in combination with sCr achieves better performance than sCr alone with area under the receiver operating characteristic curve (AUC) of 0.81-0.91 in predicting DKD stages. An average of 3.9 years follow-up study of another cohort including 106 Stage 2-3 patients suggested that "urinary albumin-to-creatinine ratio (UACR) + ADT + SAdo" can be utilized for better prognosis evaluation of early DKD (average AUC = 0.9502) than UACR without sexual difference.

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama.

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

Artemether Alleviates Diabetic Kidney Disease by Modulating Amino Acid Metabolism.

Diabetes is a worldwide metabolic disease with rapid growing incidence, characterized by hyperglycemia. Diabetic kidney disease (DKD), the leading cause of chronic kidney disease (CKD), has a high morbidity according to the constantly increasing diabetic patients and always develops irreversible deterioration of renal function. Though different in pathogenesis, clinical manifestations, and therapies, both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) can evolve into DKD. Since amino acids are both biomarkers and causal agents, rarely report has been made about its metabolism which lies in T1DM- and T2DM-related kidney disease. This study was designed to investigate artemether in adjusting renal amino acid metabolism in T1DM and T2DM mice. Artemether was applied as treatment in streptozotocin (STZ) induced T1DM mice and db/db T2DM mice, respectively. Artemether-treated mice showed lower FBG and HbA1c and reduced urinary albumin excretion, as well as urinary NAG. Both types of diabetic mice showed enlarged kidneys, as confirmed by increased kidney weight and the ratio of kidney weight to body weight. Artemether normalized kidney size and thus attenuated renal hypertrophy. Kidney tissue UPLC-MS analysis showed that branched-chain amino acids (BCAAs) and citrulline were upregulated in diabetic mice without treatment and downregulated after being treated with artemether. Expressions of glutamine, glutamic acid, aspartic acid, ornithine, glycine, histidine, phenylalanine and threonine were decreased in both types of diabetic mice whereas they increased after artemether treatment. The study demonstrates the initial evidence that artemether exerted renal protection in DKD by modulating amino acid metabolism.

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System.

Groundwater level fluctuations are affected by surface properties due to complex correlations of groundwater-surface water interaction and/or other surface processes, which are usually hard to be accurately quantified. Previous studies have assessed the relationship between groundwater level fluctuations and specific controlling factors. However, few studies have been conducted to explore the impact of the combination of multiple factors on the groundwater system. Hence, this paper tries to explore the localized and scale-specific multivariate relationships between the groundwater level and controlling factors (such as hydrologic and meteorological factors) using bivariate wavelet coherence and multiple wavelet coherence. The groundwater level fluctuations of two wells in areas covered by different plant densities (i.e., the riparian zone of the Colorado River, USA) are analyzed. Main findings include three parts. First, barometric pressure and river stage are the best factors to interpret the groundwater level fluctuations at small scales (<1 day) and large scales (>1 day) at the well of low-density plants stand, respectively. Second, at the well of high-density plants stand, the best predictors to control the groundwater level fluctuations include barometric pressure (<1 day), the combination of barometric pressure and temperature (1-7 days), temperature (7-30 days), and the combination of barometric pressure, temperature, and river stage (>30 days). The best predictor of groundwater head fluctuations depends on the variance of the vegetation coverage and hydrological processes. Third, these results provide a suite of factors to explain the groundwater level variations, which is an important topic in water-resource prediction and management.

Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations.

Groundwater systems affected by various factors can exhibit complex fractal behaviors, whose reliable characterization however is not straightforward. This study explores the fractal scaling behavior of the groundwater systems affected by plant water use and river stage fluctuations in the riparian zone, using multifractal detrended fluctuation analysis (MFDFA). The multifractal spectrum based on the local Hurst exponent is used to quantify the complexity of fractal nature. Results show that the water level variations at the riparian zone of the Colorado River, USA, exhibit multifractal characteristics mainly caused by the memory of time series of the water level fluctuations. The groundwater level at the monitoring well close to the river characterizes the season-dependent scaling behavior, including persistence from December to February and anti-persistence from March to November. For the site with high-density plants (Tamarisk ramosissima, which requires direct access to groundwater as its source of water), the groundwater level fluctuation becomes persistent in spring and summer, since the plants have the most significant and sustained influence on the groundwater in these seasons, which can result in stronger memory of the water level fluctuation. Results also show that the high-density plants weaken the complexity of the multifractal property of the groundwater system. In addition, the groundwater level variations at the site close to the river exhibit the most complex multifractality due to the influence of the river stage fluctuation.

Temporal Scaling Analytical Method to Identify Multi-Fractionality in Groundwater Head Fluctuations.

Natural dynamics such as groundwater head fluctuations may exhibit multi-fractionality, likely caused by multi-scale aquifer heterogeneity and other controlling factors, whose statistics requires efficient quantification methods. As a scaling exponent, the Hurst exponent can describe the temporal correlation or multifractal behavior in groundwater level fluctuation processes. However, the scaling behavior may change with time under natural conditions, likely due to the non-stationary evolution of internal and external conditions, which cannot be characterized by traditional methods using a single or several scaling exponents for the complex features of the overall process. This methods note quantifies the multi-fractionality using the timescale local Hurst exponent (TS-LHE) and then proposes a systematic statistical method to analyze groundwater head fluctuations. Time series of daily groundwater level fluctuations from three wells located in the lower Mississippi valley are analyzed, after removing the seasonal cycle, which leads to transient TS-LHE, implying multi-fractionality and multifractal-scaling behavior that changes with time and location. Therefore, the temporal scaling analysis proposed here may provide useful and quantitative information to understand the nature of dynamic hydrologic systems.