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Urbanization and agricultural activities are increasingly threatening karstic systems and the water resources they provide, which are crucial for the livelihood of rural communities. Over the past decade, the Río Grande de Comitán-Lagos de Montebello (RGC-LM) watershed in Chiapas, Mexico, has experienced significant deterioration in water quality. The objective of this study was to analyze the water quality dynamics within the RGC-LM watershed and their potential interaction with land use change. We conducted assessments of the physicochemical and microbiological parameters across the lotic and lentic systems within the watershed over eight monitoring campaigns (from 2013 to 2020). Cluster Analysis (CA) and Principal Component Analysis (PCA) statistical approaches were performed to identify the main drivers of surface water quality variability. This approach aims to enhance water quality assessments within environmental management strategies, with the goal of promoting sustainable land-use practices aimed at protecting and improving water resources within the watershed. Our findings indicate that there is a substantial difference between the upper and lower watershed water quality. The good water quality conditions observed in the higher altitude lakes (mountain lakes) can be attributed to favorable topographical features, specific land use patterns, and effective conservation measures. In contrast, the lotic system in the middle watershed and the lentic water bodies located at lower altitudes (plain lakes) exhibit signs of water quality degradation due to inefficient wastewater treatment and agricultural runoff. This study highlights the importance of implementing adequate environmental management strategies to address these water quality challenges effectively. Given the pressing issue of water quality degradation in karstic environments, we emphasize on the need for increased monitoring frequency of both physicochemical and microbiological parameters to capture seasonal variations and to further understand the vulnerability of water resources in karstic environments. Future assessment of nutrient concentrations, pesticide levels, and blue-green algae populations will also be crucial for evaluating the trophic state at a watershed scale.

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Background: The efficacy of transcranial direct current stimulation (tDCS) has been studied extensively. The cathodic (c‐tDCS), anodic (a‐tDCS), and bihemispheric stimulation have demonstrated efficacy in the management of the paretic upper extremity (UE) after stroke, but it has not been determined which stimulation polarity has, so far, shown the best results. Objective: To evaluate the available evidence to determine which tDCS polarity has the best results in improving UE motor function after stroke. Methods: PubMed, PEDro, Web of Science, EMBASE, and SCOPUS databases were searched. Different Medical Subject Headings (MeSH) terms were combined for the search strategy, to cover all studies that performed a comparison between different tDCS configurations focused on UE motor rehabilitation in people with lived experience of stroke. Results: Fifteen studies remained for qualitative analysis and 12 for quantitative analysis. Non‐significant differences with a 95% confidence interval (CI) were obtained for c‐tDCS versus a‐tDCS (g = 0.10, 95% CI = −0.13; 0.33, p =.39, N = 292), for a‐tDCS versus bihemispheric (g = 0.02, 95% CI = −0.46; 0.42, p =.93, N = 81), and for c‐tDCS versus bihemispheric (g = 0.09, 95% CI = −0.84;.66, p =.73, N = 100). No significant differences between the subgroups of the meta‐analysis were found. Conclusions: The results of the present meta‐analysis showed no evidence that a stimulation polarity is superior to the others in the rehabilitation of UE motor function after stroke. A non‐significant improvement trend was observed toward c‐tDCS compared to a‐tDCS. [ABSTRACT FROM AUTHOR]

Copyright of Ecologia Austral is the property of Asociacion Argentina de Ecologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

River systems provide multiple ecosystem services to society globally, but these are already degraded or threatened in many areas of the world due to water quality issues linked to diffuse and point-source pollutant inputs. Water quality evaluation is essential to develop remediation and management strategies. Computational tools such as machine learning based predictive models have been developed to improve monitoring network capabilities. The model's performance is reduced when datasets composed of reductant information are used for training, on the other hand, the selection of most representative and variable water quality scenarios could result in higher precision. This study analyzed historical water quality behavior in the Santiago River, Mexico, to identify the most variable and representative data available to train machine learning models (Adaptive Neuro Fuzzy Inference System – ANFIS, Artificial Neural Network – ANN, and Support Vector Machine - SVM). Thirteen monitoring sites were clustered according to their water quality variability from 2009 to 2022. Subsequently, a Time Series Analysis (TSA) was used to select the most representative monitoring station from each cluster. Data for 6/13 monitoring sites were retained for the Best Training Subset (BTS) used to train restricted models that performed with similar (ANN and SMV) or higher (ANFIS) prediction accuracy (in terms of RMSE, MAE, MSE and R2) for both training and testing. This study provides evidence of water quality data containing redundant information that is not useful to improve machine learning model performance, in turn leading to overtraining. Combined analytical approaches can maximize the representativeness and variability of data selected for machine learning applications, leading to improved prediction. [Display omitted] • Selection of the most variable scenarios for training can improve predictive models' precision. • The most representative water quality scenarios were efficiently selected by CA and TSA. • Training with a reduced dataset performed a better prediction than using a complete dataset. • Using larger dataset for training does not necessarily result in enhanced model performance. [ABSTRACT FROM AUTHOR]