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The quest to decipher the intricate workings of the human brain continues to captivate scientists and researchers worldwide. In this endeavor, the Latin American Workshop on Computational Neuroscience (LAWCN) plays a pivotal role by fostering a vibrant community committed to understanding the intersection of neuroscience, computational approaches, and cutting-edge technological innovations. LAWCN was born in 2012 at the Federal University of Rio Grande do Sul (UFRGS) where it later formally became a biannual event in 2017. Since its beginning, its objective has been to support collaborative learning and knowledge sharing through events hosted in unique Brazilian cities such as Porto Alegre (2017), São João del-Rei (2019), and São Luís de Maranhão (2021).

Colorectal cancer is one of the leading death-related diseases worldwide, usually induced by a multifactorial and complex process, including genetic and epigenetic abnormalities and the impact of diet and lifestyle. In the present study, we evaluated the biological impact of two of the main coffee polyphenols, chlorogenic acid (CGA) and caffeic acid (CA), as well as two polyphenol-rich coffee extracts (green coffee extract and toasted coffee Extract) against SW480 and SW620 colorectal cancer cells. First, the total phenolic content and the antioxidant capability of the extracts were determined. Then, cytotoxicity was evaluated by MTT and SBR. Finally, a wound healing assay was performed to determine the impact on the cell migration process. The results showed a cytotoxic effect of all treatments in a time and dose-dependent manner, which decreased the viability in both cell lines at 24 h and 48 h; likewise, the migration capability of cells decreased with low doses of treatments. These results suggest the potential of coffee to modulate biological mechanisms involved in colorectal cancer development; however, more studies are required to understand the mechanistic insights of these observations.

Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r2 = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r2 = 0.60, RMSE = 48.06 mg/m3, MAE = 37.98 mg/m3), and TSS (r2 = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r2 = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r2 = 0.71, RMSE = 6.16 mg/m3, MAE = 4.97 mg/m3) with Landsat-8 radiometric data, while TSS (r2 = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r2 values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems.