A Lagrangian Identification of the Main Sources of Moisture Affecting Northeastern Brazil during Its Pre-Rainy and Rainy Seasons

This work examines the sources of moisture affecting the semi-arid Brazilian Northeast (NEB) during its pre-rainy and rainy season (JFMAM) through a Lagrangian diagnosis method. The FLEXPART model identifies the humidity contributions to the moisture budget over a region through the continuous computation of changes in the specific humidity along back or forward trajectories up to 10 days period. The numerical experiments were done for the period that spans between 2000 and 2004 and results were aggregated on a monthly basis. Results show that besides a minor local recycling component, the vast majority of moisture reaching NEB area is originated in the south Atlantic basin and that the nearby wet Amazon basin bears almost no impact. Moreover, although the maximum precipitation in the “Poligono das Secas” region (PS) occurs in March and the maximum precipitation associated with air parcels emanating from the South Atlantic towards PS is observed along January to March, the highest moisture contribution from this oceanic region occurs slightly later (April). A dynamical analysis suggests that the maximum precipitation observed in the PS sector does not coincide with the maximum moisture supply probably due to the combined effect of the Walker and Hadley cells in inhibiting the rising motions over the region in the months following April

Production of thermostable β-glucosidase and CMCase by Penicillium sp. LMI01 isolated from the Amazon region

Background: Cellulolytic enzymes of microbial origin have great industrial importance because of their wide application in various industrial sectors. Fungi are considered the most efficient producers of these enzymes. Bioprospecting survey to identify fungal sources of biomass-hydrolyzing enzymes from a high-diversity environment is an important approach to discover interesting strains for bioprocess uses. In this study, we evaluated the production of endoglucanase (CMCase) and β-glucosidase, enzymes from the lignocellulolytic complex, produced by a native fungus. Penicillium sp. LMI01 was isolated from decaying plant material in the Amazon region, and its performance was compared with that of the standard isolate Trichoderma reesei QM9414 under submerged fermentation conditions. Results: The effectiveness of LMI01 was similar to that of QM9414 in volumetric enzyme activity (U/mL); however, the specific enzyme activity (U/mg) of the former was higher, corresponding to 24.170 U/mg of CMCase and 1.345 U/mg of β-glucosidase. The enzymes produced by LMI01 had the following physicochemical properties: CMCase activity was optimal at pH 4.2 and the β-glucosidase activity was optimal at pH 6.0. Both CMCase and β-glucosidase had an optimum temperature at 60°C and were thermostable between 50 and 60°C. The electrophoretic profile of the proteins secreted by LMI01 indicated that this isolate produced at least two enzymes with CMCase activity, with approximate molecular masses of 50 and 35 kDa, and β-glucosidases with molecular masses between 70 and 100 kDa. Conclusions: The effectiveness and characteristics of these enzymes indicate that LMI01 can be an alternative for the hydrolysis of lignocellulosic materials and should be tested in commercial formulations

Production of thermostable \u3b2-glucosidase and CMCase by Penicillium sp. LMI01 isolated from the Amazon region

Background: Cellulolytic enzymes of microbial origin have great industrial importance because of their wide application in various industrial sectors. Fungi are considered the most efficient producers of these enzymes. Bioprospecting survey to identify fungal sources of biomass-hydrolyzing enzymes from a high-diversity environment is an important approach to discover interesting strains for bioprocess uses. In this study, we evaluated the production of endoglucanase (CMCase) and \u3b2-glucosidase, enzymes from the lignocellulolytic complex, produced by a native fungus. Penicillium sp. LMI01 was isolated from decaying plant material in the Amazon region, and its performance was compared with that of the standard isolate Trichoderma reesei QM9414 under submerged fermentation conditions. Results: The effectiveness of LMI01was similar to that of QM9414 in volumetric enzymeactivity (U/mL); however, the specific enzyme activity (U/mg) of the former was higher, corresponding to 24.170 U/mg of CMCase and 1.345 U/mg of \u3b2-glucosidase. The enzymes produced by LMI01 had the following physicochemical properties: CMCase activity was optimal at pH 4.2 and the \u3b2-glucosidase activity was optimal at pH 6.0. Both CMCase and \u3b2-glucosidase had an optimum temperature at 60\ub0C and were thermostable between 50 and 60\ub0C. The electrophoretic profile of the proteins secreted by LMI01 indicated that this isolate produced at least two enzymes with CMCase activity, with approximate molecular masses of 50 and 35 kDa, and \u3b2-glucosidases with molecular masses between 70 and 100 kDa. Conclusions: The effectiveness and characteristics of these enzymes indicate that LMI01 can be an alternative for the hydrolysis of lignocellulosic materials and should be tested in commercial formulations

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

Factors influencing terrestriality in primates of the Americas and Madagascar

Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (bodymass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs