Ethanol (EtOH) is a substantial stressor for Saccharomyces cerevisiae. Data integration from strains with different phenotypes, including EtOH stress-responsive lncRNAs, are still not available. We covered these issues seeking systems modifications that drive the divergences between higher (HT) and lower (LT) EtOH tolerant strains under their highest stress conditions. We showed that these phenotypes are neither related to high viability nor faster population rebound after stress relief. LncRNAs work on many stress-responsive systems in a strain-specific manner promoting the EtOH tolerance. Cells use membraneless RNA/protein storage and degradation systems to endure the stress harming, and lncRNAs jointly promote EtOH tolerance. CTA1 and longevity are primer systems promoting phenotype-specific gene expression. The lower cell viability and growth under stress is a byproduct of sphingolipids and inositol phosphorylceramide dampening, acerbated in HTs by sphinganine, ERG9, and squalene overloads; LTs diminish this harm by accumulating inositol 1-phosphate. The diauxic shift drives an EtOH buffering by promoting an energy burst under stress, mainly in HTs. Analysis of mutants showed genes and lncRNAs in three strains critical for their EtOH tolerance. Finally, longevity, peroxisome, energy and lipid metabolisms, RNA/protein degradation and storage systems are the main pathways driving the EtOH tolerance phenotypes.

Variability of the HIV reverse transcriptase (RT) and protease (PR) genes has been used as indicators of drug resistance and as a mean to evaluate phylogenetic relationships among circulating virus. However, these studies have been carried in HIV mono-infected populations. The goal of this study was to evaluate, for the first time, the HIV PR and RT sequences from HIV/HBV and HIV/HCV co-infected patients. HIV PR and RT genes were amplificated and sequenced to resistance analysis. The bioinformatics analysis was performed to infer about sequences clustering and molecular evolution. The results showed that the most frequent amino acid substitutions in RT were L214F (67.6%), I135T (55.9%), and in PR was V15I (41.2%). The molecular clock analysis showed that the HIV circulating in co-infected patients were separated in two clusters in the years 1999–2000. Some patients included as HIV mono-infected according patients’ medical records and inside the co-infected cluster were, in fact, co-infected by PCR analysis. Analysis of the decision trees showed susceptibility to lamivudine and emtricitabine were important attribute to characterize co-infected patients. In conclusion, the results obtained in this study suggest, for the first time, that HIV RT and PR genes variability could be a genetic biomarker to coinfection.

Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.

Conflict of interest: noneObjectives: to evaluate and indicate the procedure to be followed in the health unit, both for diagnosis and the treatment of acute exogenous intoxications by carbamates or organophosphates. Methods: a descriptive study based on retrospective analysis of the clinical history of patients diagnosed with intoxication by carbamates or organophosphates admitted at the emergency unit of the Hospital de Urgências de Sergipe Governador João Alves (HUSE) between January and December of 2012. Some criteria were evaluated, such as: intoxicating agent; patient's age and gender; place of event, cause, circumstances and severity of the intoxication; as well as signs and symptoms of the muscarinic, nicotinic and neurological effects. Results: seventy patients (average age: 25±19.97) formed the study's population. It was observed that 77.14% of them suffered carbamate intoxication. However, organophosphate intoxications were more severe, with 68.75% of patients presenting moderate to severe forms. Suicide attempt was the leading cause of poisoning, with 62 cases (88.57% of total). Atropine administration was an effective therapeutic approach for treating signs and symptoms, which included sialorrhea (p=0.0006), nausea (p=0. 0029) and emesis (p <0.0001). The use of activated charcoal was shown effective, both in combating the signs and symptoms presented by both patient groups (p <0.0001). Conclusion: it is concluded that the use of atropine and activated charcoal is highly effective to treat the signs and symptoms developed by patients presenting acute exogenous intoxication by carbamates or organophosphates.

Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making.