Analysis of a Thrust Vectoring Fighter Aircraft Using Realtime Flight Simulation

A real-time flight simulation model based on the McDonnell Douglas F/A-18 Hornet was created. This model was then modified with two different styles of thrust vectoring so that a study of each system\u27s effect on the aircraft\u27s pitch performance could be conducted in order to determine the best system. At present, work is proceeding on the development of active flight controls for the aircraft. After that effort is completed the thrust vectoring study can begin

The taxonomic significance of species that have only been observed once : the genus Gymnodinium (Dinoflagellata) as an example

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS ONE 7 (2012): e44015, doi:10.1371/journal.pone.0044015.Taxonomists have been tasked with cataloguing and quantifying the Earth’s biodiversity. Their progress is measured in code-compliant species descriptions that include text, images, type material and molecular sequences. It is from this material that other researchers are to identify individuals of the same species in future observations. It has been estimated that 13% to 22% (depending on taxonomic group) of described species have only ever been observed once. Species that have only been observed at the time and place of their original description are referred to as oncers. Oncers are important to our current understanding of biodiversity. They may be validly described species that are members of a rare biosphere, or they may indicate endemism, or that these species are limited to very constrained niches. Alternatively, they may reflect that taxonomic practices are too poor to allow the organism to be re-identified or that the descriptions are unknown to other researchers. If the latter are true, our current tally of species will not be an accurate indication of what we know. In order to investigate this phenomenon and its potential causes, we examined the microbial eukaryote genus Gymnodinium. This genus contains 268 extant species, 103 (38%) of which have not been observed since their original description. We report traits of the original descriptions and interpret them in respect to the status of the species. We conclude that the majority of oncers were poorly described and their identity is ambiguous. As a result, we argue that the genus Gymnodinium contains only 234 identifiable species. Species that have been observed multiple times tend to have longer descriptions, written in English. The styles of individual authors have a major effect, with a few authors describing a disproportionate number of oncers. The information about the taxonomy of Gymnodinium that is available via the internet is incomplete, and reliance on it will not give access to all necessary knowledge. Six new names are presented – Gymnodinium campbelli for the homonymous name Gymnodinium translucens Campbell 1973, Gymnodinium antarcticum for the homonymous name Gymnodinium frigidum Balech 1965, Gymnodinium manchuriensis for the homonymous name Gymnodinium autumnale Skvortzov 1968, Gymnodinium christenum for the homonymous name Gymnodinium irregulare Christen 1959, Gymnodinium conkufferi for the homonymous name Gymnodinium irregulare Conrad & Kufferath 1954 and Gymnodinium chinensis for the homonymous name Gymnodinium frigidum Skvortzov 1968.This work was funded by grants from the John D and Catherine T MacArthur Foundation and the Alfred P Sloan Foundation to the Encyclopedia of Life and the National Science Foundation Data Net Program 0830976 and Global Names Project DBI-1062387

The influence of droplet size and biodegradation on the transport of subsurface oil droplets during the Deepwater Horizon: a model sensitivity study

A better understanding of oil droplet formation, degradation, and dispersal in deep waters is needed to enhance prediction of the fate and transport of subsurface oil spills. This research evaluates the influence of initial droplet size and rates of biodegradation on the subsurface transport of oil droplets, specifically those from the Deepwater Horizon oil spill. A three-dimensional coupled model was employed with components that included analytical multiphase plume, hydrodynamic and Lagrangian models. Oil droplet biodegradation was simulated based on first order decay rates of alkanes. The initial diameter of droplets (10–300 μm) spanned a range of sizes expected from dispersant-treated oil. Results indicate that model predictions are sensitive to biodegradation processes, with depth distributions deepening by hundreds of meters, horizontal distributions decreasing by hundreds to thousands of kilometers, and mass decreasing by 92–99% when biodegradation is applied compared to simulations without biodegradation. In addition, there are two- to four-fold changes in the area of the seafloor contacted by oil droplets among scenarios with different biodegradation rates. The spatial distributions of hydrocarbons predicted by the model with biodegradation are similar to those observed in the sediment and water column, although the model predicts hydrocarbons to the northeast and east of the well where no observations were made. This study indicates that improvement in knowledge of droplet sizes and biodegradation processes is important for accurate prediction of subsurface oil spills.National Science Foundation (U.S.) (RAPID: Deepwater Horizon Grant OCE-1048630)National Science Foundation (U.S.) (RAPID: Deepwater Horizon Grant OCE-1044573)National Science Foundation (U.S.) (RAPID: Deepwater Horizon Grant CBET-1045831)Gulf of Mexico Research Initiativ

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.

BACKGROUND: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery. METHODS AND FINDINGS: ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS). CONCLUSIONS: We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem

Microglial autophagy-associated phagocytosis is essential for recovery from neuroinflammation

Multiple sclerosis (MS) is a leading cause of incurable progressive disability in young adults caused by inflammation and neurodegeneration in the central nervous system (CNS). The capacity of microglia to clear tissue debris is essential for maintaining and restoring CNS homeostasis. This capacity diminishes with age, and age strongly associates with MS disease progression, although the underlying mechanisms are still largely elusive. Here, we demonstrate that the recovery from CNS inflammation in a murine model of MS is dependent on the ability of microglia to clear tissue debris. Microglia-specific deletion of the autophagy regulator Atg7, but not the canonical macroautophagy protein Ulk1, led to increased intracellular accumulation of phagocytosed myelin and progressive MS-like disease. This impairment correlated with a microglial phenotype previously associated with neurodegenerative pathologies. Moreover, Atg7-deficient microglia showed notable transcriptional and functional similarities to microglia from aged wild-type mice that were also unable to clear myelin and recover from disease. In contrast, induction of autophagy in aged mice using the disaccharide trehalose found in plants and fungi led to functional myelin clearance and disease remission. Our results demonstrate that a noncanonical form of autophagy in microglia is responsible for myelin degradation and clearance leading to recovery from MS-like disease and that boosting this process has a therapeutic potential for age-related neuroinflammatory conditions.Swedish Research CouncilSwedish Brain FoundationSwedish Association for Persons with Neurological DisabilitiesStockholm County Council (ALF project)AstraZeneca (AstraZeneca-Science for Life Laboratory collaboration)European Union Horizon 2020/European Research Council Consolidator Grant (Epi4MS)Knut and Alice Wallenbergs FoundationMargeretha af Ugglas FoundationAlltid Litt SterkereFoundation of Swedish MS researchNEURO SwedenKarolinska InstitutetAccepte

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org

Collective electrical oscillations of a diatom population induced by dark stress

Diatoms are photosynthetic microalgae, a group with a major environmental role on the planet due to the biogeochemical cycling of silica and global fixation of carbon. However, they can evolve into harmful blooms through a resourceful communication mechanism, not yet fully understood. Here, we demonstrate that a population of diatoms under darkness show quasi-periodic electrical oscillations, or intercellular waves. The origin is paracrine signaling, which is a feedback, or survival, mechanism that counteracts changes in the physicochemical environment. The intracellular messenger is related to Ca2+ ions since spatiotemporal changes in their concentration match the characteristics of the intercellular waves. Our conclusion is supported by using a Ca2+ channel inhibitor. The transport of Ca2+ ions through the membrane to the extracellular medium is blocked and the intercellular waves disappear. The translation of microalgae cooperative signaling paves the way for early detection and prevention of harmful blooms and an extensive range of stress-induced alterations in the aquatic ecosystem.Portuguese Foundation for Science and Technology (FCT) [SFRH/BPD/91518/2012, UID/Multi/04326/2013]; SNMB - INOV: Innovation for a more competitive shellfish sector; Operational Program (OP); European Union through the European Structural Funds and Investment Funds (FEEI); European Maritime and Fisheries Fund (EMFF)info:eu-repo/semantics/publishedVersio

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 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics