Advanced power sources for space missions

Approaches to satisfying the power requirements of space-based Strategic Defense Initiative (SDI) missions are studied. The power requirements for non-SDI military space missions and for civil space missions of the National Aeronautics and Space Administration (NASA) are also considered. The more demanding SDI power requirements appear to encompass many, if not all, of the power requirements for those missions. Study results indicate that practical fulfillment of SDI requirements will necessitate substantial advances in the state of the art of power technology. SDI goals include the capability to operate space-based beam weapons, sometimes referred to as directed-energy weapons. Such weapons pose unprecedented power requirements, both during preparation for battle and during battle conditions. The power regimes for these two sets of applications are referred to as alert mode and burst mode, respectively. Alert-mode power requirements are presently stated to range from about 100 kW to a few megawatts for cumulative durations of about a year or more. Burst-mode power requirements are roughly estimated to range from tens to hundreds of megawatts for durations of a few hundred to a few thousand seconds. There are two likely energy sources, chemical and nuclear, for powering SDI directed-energy weapons during the alert and burst modes. The choice between chemical and nuclear space power systems depends in large part on the total duration during which power must be provided. Complete study findings, conclusions, and eight recommendations are reported

A direct physical interaction between Nanog and Sox2 regulates embryonic stem cell self-renewal

Embryonic stem (ES) cell self-renewal efficiency is determined by the Nanog protein level. However, the protein partners of Nanog that function to direct self-renewal are unclear. Here, we identify a Nanog interactome of over 130 proteins including transcription factors, chromatin modifying complexes, phosphorylation and ubiquitination enzymes, basal transcriptional machinery members, and RNA processing factors. Sox2 was identified as a robust interacting partner of Nanog. The purified Nanog–Sox2 complex identified a DNA recognition sequence present in multiple overlapping Nanog/Sox2 ChIP-Seq data sets. The Nanog tryptophan repeat region is necessary and sufficient for interaction with Sox2, with tryptophan residues required. In Sox2, tyrosine to alanine mutations within a triple-repeat motif (S X T/S Y) abrogates the Nanog–Sox2 interaction, alters expression of genes associated with the Nanog-Sox2 cognate sequence, and reduces the ability of Sox2 to rescue ES cell differentiation induced by endogenous Sox2 deletion. Substitution of the tyrosines with phenylalanine rescues both the Sox2–Nanog interaction and efficient self-renewal. These results suggest that aromatic stacking of Nanog tryptophans and Sox2 tyrosines mediates an interaction central to ES cell self-renewal

The X-inactivation trans-activator Rnf12 is negatively regulated by pluripotency factors in embryonic stem cells

X-inactivation, the molecular mechanism enabling dosage compensation in mammals, is tightly controlled during mouse early embryogenesis. In the morula, X-inactivation is imprinted with exclusive silencing of the paternally inherited X-chromosome. In contrast, in the post-implantation epiblast, X-inactivation affects randomly either the paternal or the maternal X-chromosome. The transition from imprinted to random X-inactivation takes place in the inner cell mass (ICM) of the blastocyst from which embryonic stem (ES) cells are derived. The trigger of X-inactivation, Xist, is specifically downregulated in the pluripotent cells of the ICM, thereby ensuring the reactivation of the inactive paternal X-chromosome and the transient presence of two active X-chromosomes. Moreover, Tsix, a critical cis-repressor of Xist, is upregulated in the ICM and in ES cells where it imposes a particular chromatin state at the Xist promoter that ensures the establishment of random X-inactivation upon differentiation. Recently, we have shown that key transcription factors supporting pluripotency directly repress Xist and activate Tsix and thus couple Xist/Tsix control to pluripotency. In this manuscript, we report that Rnf12, a third X-linked gene critical for the regulation of X-inactivation, is under the control of Nanog, Oct4 and Sox2, the three factors lying at the heart of the pluripotency network. We conclude that in mouse ES cells the pluripotency-associated machinery exerts an exhaustive control of X-inactivation by taking over the regulation of all three major regulators of X-inactivation: Xist, Tsix, and Rnf12

Reduced Oct4 expression directs a robust pluripotent state with distinct signaling activity and increased enhancer occupancy by Oct4 and Nanog

10.1016/j.stem.2013.04.023Cell Stem Cell125531-54

Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags

Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback (Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP–based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis