A Regulator of Metabolic Reprogramming: MicroRNA Let-7

Let-7, a gene firstly known to control the timing of Caenorhabditis elegans larval development does not code for a protein but instead produces small non-coding RNAs, microRNAs. Higher animals have multiple isoforms of mature let-7 microRNAs. Mature let-7 family members share the same “seed sequence” and distinct from each other slightly by ‘non-seed’ sequence region. Let-7 has emerged as a central regulator of systemic energy homeostasis and it displays remarkable plasticity in metabolic responses to nutrients availability and physiological activities. In this review, we discuss recent studies highlighting post-transcriptional mechanisms that govern metabolic reprogramming in distinct cells by let-7. We focus on the participation of the let-7 clusters in immune cells, and suggest that tissue-specific regulation of the let-7 clusters by engineered mouse models might impact metabolic homeostasis and will be required to elucidate their physiological and pathological roles in the in vivo disease models

Text Data Mining for Uncovering the Influence of Religion on Ancient Greek Philosophical Thought with Optimization

Text data mining can provide valuable insights into the influence of religion on the development of ancient Greek philosophical thought. This paper presented text data mining techniques to perform feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. This paper explores the application of text data mining techniques, specifically feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. The FeCGO examined the relevant texts, including works by ancient Greek philosophers, religious texts, myths, and historical accounts. These texts are subjected to preprocessing steps, such as tokenization, stop word removal, stemming, and normalization, to ensure the data is prepared for analysis. The proposed FeCGO method combines the Gaussian Optimization algorithm with a classification model to optimize the classification accuracy and performance. Labeled data is used to train the FeCGO model, with texts categorized based on their religious or philosophical themes. The findings contribute to a deeper understanding of the interplay between religion and philosophy in ancient Greek society. The application of text data mining techniques, specifically FeCGO, demonstrates the potential of computational methods to extract valuable insights from large-scale textual datasets