NASA's Solar System Exploration Research Virtual Institute: Science and Technology for Lunar Exploration

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on research at the intersection of science and exploration, training the next generation of lunar scientists, and development and support of the international community. As part of its mission, SSERVI acts as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. The nine domestic SSERVI teams that comprise the U.S. complement of the Institute engage with the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships. SSERVI represents a close collaboration between science, technology and exploration enabling a deeper, integrated understanding of the Moon and other airless bodies as human exploration moves beyond low Earth orbit. SSERVI centers on the scientific aspects of exploration as they pertain to the Moon, Near Earth Asteroids (NEAs) and the moons of Mars, with additional aspects of related technology development, including a major focus on human exploration-enabling efforts such as resolving Strategic Knowledge Gaps (SKGs). The Institute focuses on interdisciplinary, exploration-related science focused on airless bodies targeted as potential human destinations. Areas of study represent the broad spectrum of lunar, NEA, and Martian moon sciences encompassing investigations of the surface, interior, exosphere, and near-space environments as well as science uniquely enabled from these bodies. This research profile integrates investigations of plasma physics, geology/geochemistry, technology integration, solar system origins/evolution, regolith geotechnical properties, analogues, volatiles, ISRU and exploration potential of the target bodies. New opportunities for both domestic and international partnerships are continually generated through these research and community development efforts, and SSERVI can further serve as a model for joint international scientific efforts through its creation of bridges across disciplines and between countries. Since the inception of the NASA Lunar Science Institute (SSERVIs predecessor), it has and will continue to contribute in many ways toward the advancement of lunar science and the eventual human exploration of the Moon

Commercial Off-the-Shelf (COTS) Components and Enterprise Component Information System (eCIS)

The purpose of the project was to develop the processes for using commercial off-the-shelf (COTS) parts for WR production and to put in place a system for implementing the data management tools required to disseminate, store, track procurement, and qualify vendors. Much of the effort was devoted to determining if the use of COTS parts was possible. A basic question: How does the Nuclear Weapons Complex (NWC) begin to use COTS in the weapon Stockpile Life Extension Programs with high reliability, affordability, while managing risk at acceptable levels? In FY00, it was determined that a certain weapon refurbishment program could not be accomplished without the use of COTS components. The elements driving the use of COTS components included decreased cost, greater availability, and shorter delivery time. Key factors that required implementation included identifying the best suppliers and components, defining life cycles and predictions of obsolescence, testing the feasibility of using COTS components with a test contractor to ensure capability, as well as quality and reliability, and implementing the data management tools required to disseminate, store, track procurement, and qualify vendors. The primary effort of this project then was to concentrate on the risks involved in the use of COTS and address the issues of part and vendor selection, procurement and acceptance processes, and qualification of the parts via part and sample testing. The Enterprise Component Information System (eCIS) was used to manage the information generated by the COTS process. eCIS is a common interface for both the design and production of NWC components and systems integrating information between SNL National Laboratory (SNL) and the Kansas City Plant (KCP). The implementation of COTS components utilizes eCIS from part selection through qualification release. All part related data is linked across an unclassified network for access by both SNL and KCP personnel. The system includes not only NWC part information but also includes technical reference data for over 25 Million electronic and electromechanical commercial and military parts via a data subscription. With the capabilities added to the system through this project, eCIS provides decision support, parts list/BOM analysis, editing, tracking, workflows, reporting, and history/legacy information integrating manufacturer reference, company technical, company business, and design data

An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease

Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions. Genet. Epidemiol . 2007. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56137/1/20225_ftp.pd

Dietary iron depletion at weaning imprints low microbiome diversity and this is not recovered with oral nano Fe(III)

Alterations in the gut microbiota have been recently linked to oral iron. We conducted two feeding studies including an initial diet-induced iron-depletion period followed by supplementation with nanoparticulate tartrate-modified ferrihydrite (Nano Fe(III): considered bioavailable to host but not bacteria) or soluble ferrous sulfate (FeSO4: considered bioavailable to both host and bacteria). We applied denaturing gradient gel electrophoresis and fluorescence in situ hybridization for study-1 and 454-pyrosequencing of fecal 16S rRNA in study-2. In study-1, the within-community microbial diversity increased with FeSO4 (P = 0.0009) but not with Nano Fe(III) supplementation. This was confirmed in study-2, where we also showed that iron depletion at weaning imprinted significantly lower within- and between-community microbial diversity compared to mice weaned onto the iron-sufficient reference diet (P < 0.0001). Subsequent supplementation with FeSO4 partially restored the within-community diversity (P = 0.006 in relation to the continuously iron-depleted group) but not the between-community diversity, whereas Nano Fe(III) had no effect. We conclude that (1) dietary iron depletion at weaning imprints low diversity in the microbiota that is not, subsequently, easily recovered; (2) in the absence of gastrointestinal disease iron supplementation does not negatively impact the microbiota; and (3) Nano Fe(III) is less available to the gut microbiota

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Predictive Role Of Body Composition Parameters In Operable Breast Cancer Patients Treated With Neoadjuvant Chemotherapy.

BACKGROUND: Fat tissue is strongly involved in BC tumorigenesis inducing insulin resistance, chronic inflammation and hormonal changes. Computed tomography (CT) imaging instead of body mass index (BMI) gives a reliable measure of skeletal muscle mass and body fat distribution. The impact of body composition parameters (BCPs) on chemosensitivity is still debated. We examined the associations between BCPs and tumor response to neoadjuvant chemotherapy (NC) in patients treated for operable breast cancer (BC). METHODS: A retrospective review of BC patients treated with NC in Modena Cancer Center between 2005 and 2017 was performed. BCPs, such as subcutaneous fat area (SFA), visceral fat area (VFA), lumbar skeletal muscle index (LSMI) and liver-to-spleen (L/S) ratio were calculated by Advance workstation (General Electric), software ADW server 3.2 or 4.7. BMI and BCPs were correlated with pathological complete response (pCR) and survival outcomes. RESULTS: 407 patients were included in the study: 55% with BMI &lt; 25 and 45% with BMI 65 25. 137 of them had pre-treatment CT scan imagines. Overweight was significantly associated with postmenopausal status and older age. Hormonal receptor positive BC was more frequent in overweight patients (p&lt;0.05). Postmenopausal women had higher VFA, fatty liver disease and obesity compared to premenopausal patients. No association between BMI classes and tumor response was detected. High VFA and liver steatosis were negative predictive factors for pCR (pCR rate: 36% normal VFA vs 20% high VFA, p= 0.048; no steatosis 32% vs steatosis 13%, p=0.056). Neither BMI classes nor BCPs significantly influenced overall survival and relapse-free survival. CONCLUSION: Visceral adiposity as well as steatosis were closely involved in chemosensitivity in BC patients treated with NC. Their measures from clinically acquired CT scans provide significant predictive information that outperform BMI value. More research is required to evaluate the relationship among adiposity site and survival outcomes