Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009

Transfer matrix method to study electromagnetic shower

Transfer matrix method gives underlying dynamics of a multifractal. In the present studies transfer matrix method is applied to multifractal properties of Cherenkov image from which probabilities of electromagnetic components are obtained.Comment: 7 page

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art method

Different atmospheric moisture divergence responses to extreme and moderate El Niños

On seasonal and inter-annual time scales, vertically integrated moisture divergence provides a useful measure of the tropical atmospheric hydrological cycle. It reflects the combined dynamical and thermodynamical effects, and is not subject to the limitations that afflict observations of evaporation minus precipitation. An empirical orthogonal function (EOF) analysis of the tropical Pacific moisture divergence fields calculated from the ERA-Interim reanalysis reveals the dominant effects of the El Niño-Southern Oscillation (ENSO) on inter-annual time scales. Two EOFs are necessary to capture the ENSO signature, and regression relationships between their Principal Components and indices of equatorial Pacific sea surface temperature (SST) demonstrate that the transition from strong La Niña through to extreme El Niño events is not a linear one. The largest deviation from linearity is for the strongest El Niños, and we interpret that this arises at least partly because the EOF analysis cannot easily separate different patterns of responses that are not orthogonal to each other. To overcome the orthogonality constraints, a self-organizing map (SOM) analysis of the same moisture divergence fields was performed. The SOM analysis captures the range of responses to ENSO, including the distinction between the moderate and strong El Niños identified by the EOF analysis. The work demonstrates the potential for the application of SOM to large scale climatic analysis, by virtue of its easier interpretation, relaxation of orthogonality constraints and its versatility for serving as an alternative classification method. Both the EOF and SOM analyses suggest a classification of “moderate” and “extreme” El Niños by their differences in the magnitudes of the hydrological cycle responses, spatial patterns and evolutionary paths. Classification from the moisture divergence point of view shows consistency with results based on other physical variables such as SST

Structural insights into the production of 3-hydroxypropionic acid by aldehyde dehydrogenase from Azospirillum brasilense

3-Hydroxypropionic acid (3-HP) is an important platform chemical to be converted to acrylic acid and acrylamide. Aldehyde dehydrogenase (ALDH), an enzyme that catalyzes the reaction of 3-hydroxypropionaldehyde (3-HPA) to 3-HP, determines 3-HP production rate during the conversion of glycerol to 3-HP. To elucidate molecular mechanism of 3-HP production, we determined the first crystal structure of a 3-HP producing ALDH, alpha-ketoglutarate-semialdehyde dehydrogenase from Azospirillum basilensis (AbKGSADH), in its apo-form and in complex with NAD(+). Although showing an overall structure similar to other ALDHs, the AbKGSADH enzyme had an optimal substrate binding site for accepting 3-HPA as a substrate. Molecular docking simulation of 3-HPA into the AbKGSADH structure revealed that the residues Asn159, Gln160 and Arg163 stabilize the aldehyde-and the hydroxyl-groups of 3-HPA through hydrogen bonds, and several hydrophobic residues, such as Phe156, Val286, Ile288, and Phe450, provide the optimal size and shape for 3-HPA binding. We also compared AbKGSADH with other reported 3-HP producing ALDHs for the crucial amino acid residues for enzyme catalysis and substrate binding, which provides structural implications on how these enzymes utilize 3-HPA as a substrate

Count every newborn; a measurement improvement roadmap for coverage data.

BACKGROUND: The Every Newborn Action Plan (ENAP), launched in 2014, aims to end preventable newborn deaths and stillbirths, with national targets of ≤12 neonatal deaths per 1000 live births and ≤12 stillbirths per 1000 total births by 2030. This requires ambitious improvement of the data on care at birth and of small and sick newborns, particularly to track coverage, quality and equity. METHODS: In a multistage process, a matrix of 70 indicators were assessed by the Every Newborn steering group. Indicators were graded based on their availability and importance to ENAP, resulting in 10 core and 10 additional indicators. A consultation process was undertaken to assess the status of each ENAP core indicator definition, data availability and measurement feasibility. Coverage indicators for the specific ENAP treatment interventions were assigned task teams and given priority as they were identified as requiring the most technical work. Consultations were held throughout. RESULTS: ENAP published 10 core indicators plus 10 additional indicators. Three core impact indicators (neonatal mortality rate, maternal mortality ratio, stillbirth rate) are well defined, with future efforts needed to focus on improving data quantity and quality. Three core indicators on coverage of care for all mothers and newborns (intrapartum/skilled birth attendance, early postnatal care, essential newborn care) have defined contact points, but gaps exist in measuring content and quality of the interventions. Four core (antenatal corticosteroids, neonatal resuscitation, treatment of serious neonatal infections, kangaroo mother care) and one additional coverage indicator for newborns at risk or with complications (chlorhexidine cord cleansing) lack indicator definitions or data, especially for denominators (population in need). To address these gaps, feasible coverage indicator definitions are presented for validity testing. Measurable process indicators to help monitor health service readiness are also presented. A major measurement gap exists to monitor care of small and sick babies, yet signal functions could be tracked similarly to emergency obstetric care. CONCLUSIONS: The ENAP Measurement Improvement Roadmap (2015-2020) outlines tools to be developed (e.g., improved birth and death registration, audit, and minimum perinatal dataset) and actions to test, validate and institutionalise proposed coverage indicators. The roadmap presents a unique opportunity to strengthen routine health information systems, crosslinking these data with civil registration and vital statistics and population-based surveys. Real measurement change requires intentional transfer of leadership to countries with the greatest disease burden and will be achieved by working with centres of excellence and existing networks

Cyclin-dependent kinase 5 activity is required for T cell activation and induction of experimental autoimmune encephalomyelitis

Cyclin-dependent kinase 5 drives T cell activation and disease in EAE in part by regulating the actin binding protein coronin 1a

Progression to AIDS in South Africa Is Associated with both Reverting and Compensatory Viral Mutations

We lack the understanding of why HIV-infected individuals in South Africa progress to AIDS. We hypothesised that in end-stage disease there is a shifting dynamic between T cell imposed immunity and viral immune escape, which, through both compensatory and reverting viral mutations, results in increased viral fitness, elevated plasma viral loads and disease progression. We explored how T cell responses, viral adaptation and viral fitness inter-relate in South African cohorts recruited from Bloemfontein, the Free State (n = 278) and Durban, KwaZulu-Natal (n = 775). Immune responses were measured by γ-interferon ELISPOT assays. HLA-associated viral polymorphisms were determined using phylogenetically corrected techniques, and viral replication capacity (VRC) was measured by comparing the growth rate of gag-protease recombinant viruses against recombinant NL4-3 viruses. We report that in advanced disease (CD4 counts <100 cells/µl), T cell responses narrow, with a relative decline in Gag-directed responses (p<0.0001). This is associated with preserved selection pressure at specific viral amino acids (e.g., the T242N polymorphism within the HLA-B*57/5801 restricted TW10 epitope), but with reversion at other sites (e.g., the T186S polymorphism within the HLA-B*8101 restricted TL9 epitope), most notably in Gag and suggestive of “immune relaxation”. The median VRC from patients with CD4 counts <100 cells/µl was higher than from patients with CD4 counts ≥500 cells/µl (91.15% versus 85.19%, p = 0.0004), potentially explaining the rise in viral load associated with disease progression. Mutations at HIV Gag T186S and T242N reduced VRC, however, in advanced disease only the T242N mutants demonstrated increasing VRC, and were associated with compensatory mutations (p = 0.013). These data provide novel insights into the mechanisms of HIV disease progression in South Africa. Restoration of fitness correlates with loss of viral control in late disease, with evidence for both preserved and relaxed selection pressure across the HIV genome. Interventions that maintain viral fitness costs could potentially slow progression

Compartment-Restricted Biotinylation Reveals Novel Features of Prion Protein Metabolism in Vivo

A selective tagging method for detecting minor alternatively-localized populations of a protein is used to study a disease-associated transmembrane form of prion protein. The analysis reveals key features of transmembrane prion protein metabolism and one way this is altered by human disease-causing mutants

Raman spectroscopy: techniques and applications in the life sciences

Raman spectroscopy is an increasingly popular technique in many areas including biology and medicine. It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration