LunaNet: a Flexible and Extensible Lunar Exploration Communications and Navigation Infrastructure

NASA has set the ambitious goal of establishing a sustainable human presence on the Moon. Diverse commercial and international partners are engaged in this effort to catalyze scientific discovery, lunar resource utilization and economic development on both the Earth and at the Moon. Lunar development will serve as a critical proving ground for deeper exploration into the solar system. Space communications and navigation infrastructure will play an integral part in realizing this goal. This paper provides a high-level description of an extensible and scalable lunar communications and navigation architecture, known as LunaNet. LunaNet is a services network to enable lunar operations. Three LunaNet service types are defined: networking services, position, navigation and timing services, and science utilization services. The LunaNet architecture encompasses a wide variety of topology implementations, including surface and orbiting provider nodes. In this paper several systems engineering considerations within the service architecture are highlighted. Additionally, several alternative LunaNet instantiations are presented. Extensibility of the LunaNet architecture to the solar system internet is discussed

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology.

Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease. Liquid chromatography coupled tandem mass spectrometry (LC-MSE) proteomic profiling was applied comparing healthy and "disease state" in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as "development and differentiation", which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.This research was supported by the Stanley Medical Research Institute (SMRI) (R6123) and the NEWMEDS Innovative Medicines Initiative (FP7/2007-2013).This is the author accepted manuscript. The final version is available from Elsevier via https://doi.org/10.1016/j.schres.2016.06.01

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals

Funder: Stanley Medical Research Institute (SMRI); doi: https://doi.org/10.13039/100007123Abstract: Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals

Medium-Energy Gamma-Ray Astrophysics with the 3-DTI Gamma-Ray Telescope

Gamma-ray observations in the medium energy range (0.50-50.0 MeV) are central to unfolding many outstanding questions in astrophysics. The challenges of medium-energy gamma-ray observations, however, are the low photon statistics and large backgrounds. We review these questions, address the telescope technology requirements, and describe our development of the 3-Dimensional Track Imaging (3-DTI) Compton telescope and its performance for a new mediumenergy gamma-ray mission. The 3-DTI is a large-volume time projection chamber (TPC) with a 2-dimensional gas micro-well detector (MWD) readout

A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies

In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities, and use of “fuzzy” calls in association tests in order to make appropriate allowance for call uncertainty. We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing. We also find that, in the presence of different DNA sourcing, biases associated with missing data can increase the false-positive rate. Therefore, we propose the use of “fuzzy” calls to deal with uncertain genotypes that would otherwise be labeled as missing

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Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels, uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels

Pretreatment with phenoxybenzamine attenuates the radial artery's vasoconstrictor response to α-adrenergic stimuli

AbstractBackgroundAlthough the radial artery bypass conduit has excellent intermediate-term patency, it has a proclivity to vasospasm. We tested the hypothesis that brief pretreatment of a radial artery graft with the irreversible adrenergic antagonist phenoxybenzamine attenuates the vasoconstrictor response to the vasopressors phenylephrine and norepinephrine compared with the currently used papaverine/lidocaine.MethodsSegments of human radial artery grafts were obtained after a 30-minute intraoperative pretreatment with a solution containing 20 mL of heparinized blood, 0.4 mL of papaverine (30 mg/mL), and 1.6 mL of lidocaine (1%). The segments were transported to the laboratory and placed into a bath containing Krebs-Henseleit solution and 10, 100, or 1000 μmol/L phenoxybenzamine or vehicle. The segments were tested in organ chambers for contractile responses to increasing concentrations of phenylephrine and norepinephrine (0.5-15 μmol/L).ResultsContractile responses to 15 μmol/L phenylephrine in control radial artery segments averaged 44.2% ± 9.1% of the maximal contractile response to 30 mmol/L KCl. Papaverine/lidocaine modestly attenuated contraction to 15 μmol/L phenylephrine (32.1% ± 5.9%; P = .22), but 1000 μmol/L phenoxybenzamine completely abolished radial artery contraction (−7.2% ± 4.4%; P < .001). The effect of 10 and 100 μmol/L phenoxybenzamine on attenuating vasocontraction was intermediate between 1000 μmol/L phenoxybenzamine and papaverine/lidocaine. Responses to 15 μmol/L norepinephrine in control radial artery segments averaged 54.7% ± 7.5% of maximal contraction to 30 mmol/L KCl. Papaverine/lidocaine modestly attenuated the contraction response of radial artery segments (35.6% ± 5.1%; P = .04). In contrast, 1000 μmol/L phenoxybenzamine showed the greatest attenuation of norepinephrine-induced contraction (−10.5% ± 2.0%; P < .001).ConclusionsA brief pretreatment of the human radial artery bypass conduit with 1000 μmol/L phenoxybenzamine completely attenuates the vasoconstrictor responses to the widely used vasopressors norepinephrine and phenylephrine. Papaverine/lidocaine alone did not block vasoconstriction to these α-adrenergic agonists