An Architecture to Enable Autonomous Control of Spacecraft

Autonomy is required for manned spacecraft missions distant enough that light-time communication delays make ground-based mission control infeasible. Presently, ground controllers develop a complete schedule of power modes for all spacecraft components based on a large number of factors. The proposed architecture is an early attempt to formalize and automate this process using on-vehicle computation resources. In order to demonstrate this architecture, an autonomous electrical power system controller and vehicle Mission Manager are constructed. These two components are designed to work together in order to plan upcoming load use as well as respond to unanticipated deviations from the plan. The communication protocol was developed using "paper" simulations prior to formally encoding the messages and developing software to implement the required functionality. These software routines exchange data via TCP/IP sockets with the Mission Manager operating at NASA Ames Research Center and the autonomous power controller running at NASA Glenn Research Center. The interconnected systems are tested and shown to be effective at planning the operation of a simulated quasi-steady state spacecraft power system and responding to unexpected disturbances

Complete genome sequence of Mycobacterium xenopi type strain RIVM700367

Item does not contain fulltextMycobacterium xenopi is a slow-growing, thermophilic, water-related Mycobacterium species. Like other nontuberculous mycobacteria, M. xenopi more commonly infects humans with altered immune function, such as chronic obstructive pulmonary disease patients. It is considered clinically relevant in a significant proportion of the patients from whom it is isolated. We report here the whole genome sequence of M. xenopi type strain RIVM700367.1 juni 201

Structural and Functional Characterization of MppR, an Enduracididine Biosynthetic Enzyme from Streptomyces hygroscopicus: Functional Diversity in the Acetoacetate Decarboxylase-like Superfamily

The nonproteinogenic amino acid enduracididine is a critical component of the mannopeptimycins, cyclic glycopeptide antibiotics with activity against drug-resistant pathogens, including methicillin-resistant Staphylococcus aureus. Enduracididine is produced in Streptomyces hygroscopicus by three enzymes, MppP, MppQ, and MppR. On the basis of primary sequence analysis, MppP and MppQ are pyridoxal 5'-phosphate-dependent aminotransferases; MppR shares a low, but significant, level of sequence identity with acetoacetate decarboxylase. The exact reactions catalyzed by each enzyme and the intermediates involved in the route to enduracididine are currently unknown. Herein, we present biochemical and structural characterization of MppR that demonstrates a catalytic activity for this enzyme and provides clues about its role in enduracididine biosynthesis. Bioinformatic analysis shows that MppR belongs to a previously uncharacterized family within the acetoacetate decarboxylase-like superfamily (ADCSF) and suggests that MppR-like enzymes may catalyze reactions diverging from the well-characterized, prototypical ADCSF decarboxylase activity. MppR shares a high degree of structural similarity with acetoacetate decarboxylase, though the respective quaternary structures differ markedly and structural differences in the active site explain the observed loss of decarboxylase activity. The crystal structure of MppR in the presence of a mixture of pyruvate and 4-imidazolecarboxaldehyde shows that MppR catalyzes the aldol condensation of these compounds and subsequent dehydration. Surprisingly, the structure of MppR in the presence of "4-hydroxy-2-ketoarginine" shows the correct 4R enantiomer of "2-ketoenduracididine" bound to the enzyme. These data, together with bioinformatic analysis of MppR homologues, identify a novel family within the acetoacetate decarboxylase-like superfamily with divergent active site structure and, consequently, biochemical function.Keywords: Nosocomial infections, Mannopeptimycins, Active site, Gram positive bacteria, Viomycin biosynthesis, X-ray, Protein structure, Reporter group, Glycopeptide antibiotics, Ionization constan

Positively selected amino acid replacements within the RuBisCO enzyme of oak trees are associated with ecological adaptations

Phylogenetic analysis by maximum likelihood (PAML) has become the standard approach to study positive selection at the molecular level, but other methods may provide complementary ways to identify amino acid replacements associated with particular conditions. Here, we compare results of the decision tree (DT) model method with ones of PAML using the key photosynthetic enzyme RuBisCO as a model system to study molecular adaptation to particular ecological conditions in oaks (Quercus). We sequenced the chloroplast rbcL gene encoding RuBisCO large subunit in 158 Quercus species, covering about a third of the global genus diversity. It has been hypothesized that RuBisCO has evolved differentially depending on the environmental conditions and leaf traits governing internal gas diffusion patterns. Here, we show, using PAML, that amino acid replacements at the residue positions 95, 145, 251, 262 and 328 of the RuBisCO large subunit have been the subject of positive selection along particular Quercus lineages associated with the leaf traits and climate characteristics. In parallel, the DT model identified amino acid replacements at sites 95, 219, 262 and 328 being associated with the leaf traits and climate characteristics, exhibiting partial overlap with the results obtained using PAML

A Classification and regression trees (CART) model of parallel structure and long-term prediction prognosis of machine condition

This paper presents a combined prediction model involving the parallel of classification and regression trees (CART) model, namely p-CART, and a long-term direct prediction methodology of time series techniques to predict the future stages of the machine’s operating conditions. p-CART model consists of multiple CART models which are connected in parallel. Each sub-model in the p-CART is trained independently. Based on the observations, these sub-models are subsequently used to predict the future values of the machine’s operating conditions separately with the same embedding dimension but the different observations’ indices. Finally, the predicted results of sub-models are combined to produce the final results of the predicting process. Real trending data acquired from condition monitoring routine of low methane compressor are employed for evaluating the proposed method. A comparative study of the predicted results obtained from traditional CART and p-CART models is also carried out to appraise the prediction capability of proposed model. In addition, a further improvement in predicting capability of p-CART is proposed to ameliorate the accuracy and efficiency of this method