Search

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.

Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some of its strains can produce toxins that significantly affect human health. To better control eutrophication and HCB, catchment managers need to continuously keep track of nitrogen (N) and phosphorus (P) in the water bodies. However, the high-frequency monitoring of these water quality indicators is not economical. In these cases, machine learning techniques may serve as viable alternatives since they can learn directly from the available surrogate data. In the present work, a random forest, extremely randomized trees (ET), extreme gradient boosting, k-nearest neighbors, a light gradient boosting machine, and bagging regressor-based virtual sensors were used to predict N and P in two catchments with contrasting land uses. The effect of data scaling and missing value imputation were also assessed, while the Shapley additive explanations were used to rank feature importance. A specification book, sensitivity analysis, and best practices for developing virtual sensors are discussed. Results show that ET, MinMax scaler, and a multivariate imputer were the best predictive model, scaler, and imputer, respectively. The highest predictive performance, reported in terms of R 2 , was 97% in the rural catchment and 82% in an urban catchment., Competing Interests: The authors declare no conflict of interest.

Coffee is one of the historical socioeconomic crops. It has received an increasing attention at the global level, due to its positive interlinkage with the economic growth and on the gross domestic product for most of the producing countries, particularly, developing and least developed countries. Saudi Arabia is one of the coffee producing countries that has a relative comparative advantage of coffee cultivation. Yet, coffee cultivation has not received as much attention in Saudi Arabia as that of producing countries around the world. This study aims to assess the current state of coffee cultivation in Saudi Arabia and to investigate the potential to optimize coffee cultivation in Saudi Arabia that maximizes the net national economic return and export earnings, given limitation of cultivated areas, local market activities, and international trade activities. The study statistically analyzed primary data collected from around (65) coffee farms and traders in the study regions at the south and southwest Saudi Arabia, and optimized coffee cultivation in Saudi Arabia using LINGO optimization software. Empirical results of the study revealed the great potential of Saudi Arabia to expand coffee cultivation at south and southwest regions to meet the escalating local demand and to increase its share at the world market up to 2%. Optimization of coffee cultivation in Saudi Arabia showed a high potential to meet the local demand for coffee by producing 80.07 thousand tons grown over 2861.78 hectares and to generate a net return equivalent to $395.72 million a year, which is equivalent to $138.28 thousand per hectare and $4.94 thousand per ton of coffee. Optimizing coffee cultivation will play a substantial role to increase market share of Saudi Arabia to about 1-2% of the world market by increasing its export volume, respectively, to about 69.66 and 112.56 thousand tons, the national net economic return by about $395.86 and $395.95 million a year, and the export earnings of coffee by about $219.43-354.57 million a year, which in turns, will serve the national strategic trend to diversify the economic base and lower the dependency of incomes generated from oil exportation.