Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of gari. This domain remains unexplored thus far. A total of 196 graters were studied. Key variables studied included tooth diameter (TD), tooth height (TH), inter-tooth spacing (ITS), drum speed (DS), clearance (C), and moisture content of cassava (MC). Geometric mean diameter (GMD) represented mash quality. Feature importance rankings emphasized TH (0.488784), C (0.243284), TD (0.112682), ITS (0.103547), DS (0.036261), and MC (0.015442) in determining particle size (GMD) of grated mash. Machine learning models efficiently interpreted these attributes, including gradient boost regressor, linear regression, neural network, and random forest. The gradient boost regressor was the best predictive model, achieving 95.34% accuracy, RMSE (0.3291), and MAE (0.2303). The study provides a GMD predictive equation and optimized parameters for specific gari size production, offering valuable insights for tailored machinery in the cassava grating activity.

The analysis of curated genomic, metagenomic, and proteomic data are of paramount importance in the fields of biology, medicine, education, and bioinformatics. Although this type of data is usually hosted in raw form in free international repositories, its access requires plenty of computing, storage, and processing capacities for the domestic user. The purpose of the study is to offer a comprehensive set of genomic and proteomic reference data, in an accessible and easy-to-use form to the scientific community. A representative type material set of genomes, proteomes and metagenomes were directly downloaded from the site: https://www.ncbi.nlm.nih.gov/assembly/ and from Genome Taxonomy Database, associated with the major groups of Bacteria, Archaea, Virus, and Fungi. Sketched databases were subsequently created and stored on handy raw reduced representations, by using Mash software. Our dataset contains near to 100 GB of space disk reduced to 585.78 MB and represents 87,476 genomics/proteomic records from eight informative contexts, which have been prefiltered to make them accessible, usable, and user-friendly with computational resources. Potential uses of this dataset include but are not limited to, microbial species delimitation, estimation of genomic distances, genomic novelties, paired comparisons between proteomes, genomes, and metagenomes.

Background: Progression of metabolic dysfunction associated steatotic liver disease (MASLD) to steatohepatitis (MASH) is driven by stress-inducing lipids that promote liver inflammation and fibrosis. MASH can lead to cirrhosis and hepatocellular carcinoma. We showed coordinated defenses regulated by transcription factors, nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2), protect against hepatic lipid stress. Here, we investigated protective effects of hepatocyte Nrf1 and Nrf2 against MASLD-induced liver fibrosis and tumorigenesis. Methods: Using mice fed MASH diet for 24-52 weeks, we examined MASLD in mice with hepatocyte specific Nrf1, Nrf2, or combined deletion, and compared this to control. In a separate study, mice received weekly injections of carbon tetrachloride to induce liver fibrosis. From week 16-24, mice were treated with Nrf2 activating drug bardoxolone, hepatocyte overexpression of human NRF1 (hNRF1), or both, and groups were compared to control. Results: Hepatocyte Nrf2 deficiency had no effect. Hepatocyte Nrf1 and combined deficiency caused MASH but only hepatocyte Nrf1 deficiency in male mice increased tumor number. Bardoxolone reduced liver steatosis, fibrosis, inflammation, and proliferation, and this effect when combined with hNRF1 was greater than bardoxolone alone. Conclusion: Physiologic Nrf1 delays MASLD progression, Nrf2-induction alleviates MASH, and combined enhancement synergistically protects against steatosis.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

A large part of our understanding of the biological substrates of sex-recognition and mate-recognition is derived by studying animal models. In performing those tasks, rodents rely mostly of pheromones and other olfactory cues, whereas humans rely mostly on visual cues. That may hinder the translation of rodents’ biology to humans’ biology, especially at the neural-networks level, where those cues traverse different networks in humans and rodents brains. That may be called the “pheromonal-visual gap”. A theoretical model presented here addresses those issues. The model merges observations from humans and model-animals, as reported in specific scientific reports, and general biological principles that are accepted by the scientific community. The model suggests that the voices of men and women are the innate cues based on which humans learn to use visual cues in sex-recognition and mate-recognition. Children learn the two tasks in associative learning mechanisms, by being immersed in their community, and observing adult role-models in innocuous, non-sexual scenarios. The model proposes that the human medial-geniculate-nucleus (MGN) is the analog of the rodents’ accessory-olfactory-bulb (AOB) and the main-olfactory-bulb (MOB), and that the human MASH pathway (MGN, amygdala, bnST, hypothalamus) is the analog of the rodents’ VNOP (Vomeronasal-organ-pathway). Considering the differences in the pathways should facilitate the translation from rodents’ brain nuclei and tracks to humans’. Also, the model hypothesizes that innate direct and indirect connections between auditory centers, e.g., MGN, and sex-control centers, e.g., hypothalamus, vary across three groups of children, and those variations determine the individual’s mate-recognition that emerges at puberty.