In Flight Performance of a Six Ampere-hour Nickel-cadmium Battery in Low Earth Orbit

Flight data for 17,000 orbital cycles are reviewed and summarized. The nickel cadmium battery system operated without failure or abnormality. Battery trend analysis used in determining the feasibility of extending mission life is discussed. The life test data for 20% depth of discharge indicates design life requirements would be reached even at a deeper depth of discharge

Nickel-hydrogen low-Earth-orbit test program

The incorporation of nickel-hydrogen technology for low-Earth-orbit spacecraft applications requires the establishment of a data base. An extensive test program was established to provide this data base. The test program is outlined and the preliminary test programs is presented

RAY-O-VAC BR2325 Lithium Carbon Monofluoride Cell Performance

RAY-O-VAC currently markets a 160 mAH lithium cell recommended for usage in watch and calculator products. The lithium carbon monofluoride cell offers an extended shelf life with no reduction in performance effectiveness. The BR2325 cell has aerospace applications for memory devices and telemetry systems. Over one hundred thirty (130) cells were purchased and tested for evaluation purposes. The test statistics and overall cell performance of the RAY-O-VOC BR2325 lithium carbon monofluoride cell is reviewed

Engineering Bacillus megaterium for production of functional intracellular materials

Background: Over the last 10-15 years, a technology has been developed to engineer bacterial polyhydroxybutyrate (PHB) inclusions as functionalized beads, for applications such as vaccines, diagnostics and enzyme immobilization. This has been achieved by translational fusion of foreign proteins to the PHB synthase (PhaC). The respective fusion protein mediates self-assembly of PHB inclusions displaying the desired protein function. So far, beads have mainly been produced in recombinant Escherichia coli which is problematic for some applications as the lipopolysaccharides (LPS) co-purified with such inclusions are toxic to humans and animals. Results: In this study, we have engineered the formation of functional PHB inclusions in the Gram-positive bacterium Bacillus megaterium, an LPS-free and established industrial production host. As B. megaterium is a natural PHB producer, the PHB-negative strain PHA05 was used to avoid any background PHB production. Plasmid-mediated T7 promoter-driven expression of the genes encoding β-ketothiolase (phaA), acetoacetyl-CoA-reductase (phaB) and PHB synthase (phaC) enabled/effected PHB production by B. megaterium PHA05. To produce functionalized PHB inclusions, the N- and C-terminus of PhaC was fused to four and two IgG binding Z-domains from Staphylococcus aureus, respectively. The ZZ-domain PhaC fusion protein was strongly overproduced at the surface of the PHB inclusions and the corresponding isolated ZZ-domain displaying PHB beads were found to purify IgG with a binding capacity of 40-50 mg IgG/g beads. As B. megaterium has the ability to sporulate and respective endospores could co-purify with cellular inclusions, a sporulation negative production strain was generated by disrupting the spoIIE gene in PHA05. This strain did not produce spores when tested under sporulation inducing conditions and it was still able to synthesize ZZ-domain displaying PHB beads. Conclusions: This study provides proof of concept for the successful genetic engineering of B. megaterium as a host for the production of functionalized PHB beads. Disruption of the spoIIE gene rendered B. megaterium incapable of sporulation but particularly suitable for production of functionalized PHB beads. This sporulation-negative mutant represents an improved industrial production strain for biotechnological processes otherwise impaired by the possibility of endospore formation.fals

Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications

Low-density genotype panel for both parentage verification and discovery in a multi-breed sheep population

peer-reviewedThe generally low usage of artificial insemination and single-sire mating in sheep, compounded by mob lambing (and lambing outdoors), implies that parentage assignment in sheep is challenging. The objective here was to develop a low-density panel of single nucleotide polymorphisms (SNPs) for accurate parentage verification and discovery in sheep. Of particular interest was where SNP selection was limited to only a subset of chromosomes, thereby eliminating the ability to accurately impute genome-wide denser marker panels. Data used consisted of 10,933 candidate SNPs on 9,390 purebred sheep. These data consisted of 1,876 validated genotyped sire–offspring pairs and 2,784 validated genotyped dam–offspring pairs. The SNP panels developed consisted of 87 SNPs to 500 SNPs. Parentage verification and discovery were undertaken using 1) exclusion, based on the sharing of at least one allele between candidate parent–offspring pairs, and 2) a likelihood-based approach. Based on exclusion, allowing for one discordant offspring–parent genotype, a minimum of 350 SNPs was required when the goal was to unambiguously identify the true sire or dam from all possible candidates. Results suggest that, if selecting SNPs across the entire genome, a minimum of 250 carefully selected SNPs are required to ensure that the most likely selected parent (based on the likelihood approach) was, in fact, the true parent. If restricting the SNPs to just a subset of chromosomes, the recommendation is to use at least a 300-SNP panel from at least six chromosomes, with approximately an equal number of SNPs per chromosome