Evaluation of beryllium for space shuttle components

Application of beryllium to specific full-scale space shuttle structural components and assemblies was studied. Material evaluations were conducted to check the mechanical properties of as-received material to gain design information on characteristics needed for the material in the space shuttle environment, and to obtain data needed for evaluating component and panel tests. Four beryllium structural assemblies were analyzed and designed. Selected components of these assemblies, representing areas of critical loading or design/process uncertainty, were designed and tested, and two panel assemblies were fabricated. Trends in cost and weight factors were determined by progressive estimation at key points of preliminary design, final design, and fabrication to aid in a cost/weight evaluation of the use of beryllium

coping strategies in melanoma patients

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The Predictive Brain Must Have a Limitation in Short-Term Memory Capacity

Traditionally, short-term memory (STM) has been assessed by asking participants to remember words, visual objects, or numbers for a short amount of time before their recall or recognition of those items is tested. However, this focus on memory for past sensory input might have obscured potential theoretical insights into the function of this cognitive faculty. Here, we suggest that STM may have an important role in predicting future sensory input. This reconceptualization of STM may provide a functional explanation for its capacity limitation

Guest Artist Recital: Roy E. McLuen, Saxophone; Joan Trapp Fish, Piano; October 18, 1975

Centennial East Recital HallSaturday EveningOctober 18, 19758:15 p.m

MpTCP1 controls cell proliferation and redox processes in Marchantia polymorpha

TCP transcription factors are key regulators of angiosperm cell proliferation processes. It is unknown whether their regulatory growth capacities are conserved across land plants, which we examined in liverworts, one of the earliest diverging land plant lineages. We generated knockout mutants for MpTCP1, the single TCP‐P clade gene in Marchantia polymorpha, and characterized its function conducting cell proliferation and morphological analyses as well as mRNA expression, transcriptome, chemical and DNA binding studies. Mptcp1ge lines show a reduced vegetative thallus growth and extra tissue formation in female reproductive structures. Additionally, mutant plants reveal increased H2O2 levels and an enhanced pigmentation in the thallus caused by formation of secondary metabolites, such as aminochromes. MpTCP1 proteins interact redox‐dependently with DNA and regulate the expression of a comprehensive redox network, comprising enzymes involved in H2O2 metabolism. MpTCP1 regulates Marchantia growth context‐dependently. Redox sensitivity of the DNA binding capacity of MpTCP1 proteins provides a mechanism to respond to altered redox conditions. Our data suggest that MpTCP1 activity could thereby have contributed to diversification of land plant morphologies and to adaptations to abiotic and biotic challenges, experienced by liverworts during early land plant colonization

autonomic nervous tone in vitiligo patients a case control study

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Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks

While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in memory. In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference. In particular, by using GPs as leaves of an SPN we obtain a novel flexible prior over functions, which implicitly represents an exponentially large mixture of local GPs. Exact and efficient posterior inference in this model can be done in a natural interplay of the inference mechanisms in GPs and SPNs. Thereby, each GP is -- similarly as in a mixture of experts approach -- responsible only for a subset of data points, which effectively reduces inference cost in a divide and conquer fashion. We show that integrating GPs into the SPN framework leads to a promising probabilistic regression model which is: (1) computational and memory efficient, (2) allows efficient and exact posterior inference, (3) is flexible enough to mix different kernel functions, and (4) naturally accounts for non-stationarities in time series. In a variate of experiments, we show that the SPN-GP model can learn input dependent parameters and hyper-parameters and is on par with or outperforms the traditional GPs as well as state of the art approximations on real-world data