Improving Bone-Health Monitoring in Astronauts: Recommended Use of Quantitative Computed Tomography [QCT] for Clinical and Operational Decisions by NASA

DXA measurement of areal bone mineral density [aBMD,g/cm2] is required by NASA for assessing skeletal integrity in astronauts. Due to the abundance of population-based data that correlate hip and spine BMDs to fragility fractures, BMD is widely applied as a predictor of fractures in the general aging population. In contrast, QCT is primarily a research technology that measures three-dimensional , volumetric BMD (vBMD,mg/cm3) of bone and is therefore capable of differentiating between cortical and trabecular components. Additionally, when combined with Finite Element Modeling [FEM], a computational tool, QCT data can be used to estimate the whole bone strength of the hip [FE strength] for a specific load vector. A recent report demonstrated that aBMD failed to correlate with incurred changes in FE strength (for fall and stance loading) by astronauts over typical 180-day ISS (International Space Station) missions. While there are no current guidelines for using QCT data in clinical practice, QCT increases the understanding of how bone structure and mineral content are affected by spaceflight and recovery on Earth. In order to understand/promote/consider the use of QCT, NASA convened a panel of clinicians specializing in osteoporosis. After reviewing the available, albeit limited, medical and research information from long-duration astronauts (e.g., data from DXA, QCT, FEM, biochemistry analyses, medical records and in-flight exercise performance) the panelists were charged with recommending how current and future research data and analyses could inform clinical and operational decisions. The Panel recommended that clinical bone tests on astronauts should include QCT (hip and lumbar spine) for occupational risk surveillance and for the estimation of whole hip bone strength as derived by FEM. FE strength will provide an improved index that NASA could use to select astronauts of optimal bone health for extended duration missions, for repeat missions or for specific mission operations

Challenges of Developing New Classes of NASA Self-Managing Mission

NASA is proposing increasingly complex missions that will require a high degree of autonomy and autonomicity. These missions pose hereto unforeseen problems and raise issues that have not been well-addressed by the community. Assuring success of such missions will require new software development techniques and tools. This paper discusses some of the challenges that NASA and the rest of the software development community are facing in developing these ever-increasingly complex systems. We give an overview of a proposed NASA mission as well as techniques and tools that are being developed to address autonomic management and the complexity issues inherent in these missions

Requirements of an Integrated Formal Method for Intelligent Swarms

NASA is investigating new paradigms for future space exploration, heavily focused on the (still) emerging technologies of autonomous and autonomic systems [47, 48, 49]. Missions that rely on multiple, smaller, collaborating spacecraft, analogous to swarms in nature, are being investigated to supplement and complement traditional missions that rely on one large spacecraft [16]. The small spacecraft in such missions would each be able to operate on their own to accomplish a part of a mission, but would need to interact and exchange information with the other spacecraft to successfully execute the mission

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

A framework for tracing timber following the Ukraine invasion

Scientific testing including stable isotope ratio analysis (SIRA) and trace element analysis (TEA) is critical for establishing plant origin, tackling deforestation and enforcing economic sanctions. Yet methods combining SIRA and TEA into robust models for origin verification and determination are lacking. Here we report a (1) large Eastern European timber reference database (Betula, Fagus, Pinus, Quercus) tailored to sanctioned products following the Ukraine invasion; (2) statistical test to verify samples against a claimed origin; (3) probabilistic model of SIRA, TEA and genus distribution data, using Gaussian processes, to determine timber harvest location. Our verification method rejects 40–60% of simulated false claims, depending on the spatial scale of the claim, and maintains a low probability of rejecting correct origin claims. Our determination method predicts harvest location within 180 to 230 km of true location. Our results showcase the power of combining data types with probabilistic modelling to identify and scrutinize timber harvest location claims

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets