Development and Validation of a Model for Hydrogen Reduction of JSC-1A

Hydrogen reduction of lunar regolith has been proposed as a viable technology for oxygen production on the moon. Hydrogen reduces FeO present in the lunar regolith to form metallic iron and water. The water may be electrolyzed to recycle the hydrogen and produce oxygen. Depending upon the regolith composition, FeO may be bound to TiO2 as ilmenite or it may be dispersed in glassy substrates. Some testing of hydrogen reduction has been conducted with Apollo-returned lunar regolith samples. However, due to the restricted amount of lunar material available for testing, detailed understanding and modeling of the reduction process in regolith have not yet been developed. As a step in this direction, hydrogen reduction studies have been carried out in more detail with lunar regolith simulants such as JSC-1A by NASA and other organizations. While JSC-1A has some similarities with lunar regolith, it does not duplicate the wide variety of regolith types on the moon, for example, it contains almost no ilmenite. Nonetheless, it is a good starting point for developing an understanding of the hydrogen reduction process with regolith-like material. In this paper, a model utilizing a shrinking core formulation coupled with the reactor flow is described and validated against experimental data on hydrogen reduction of JSC-1A

Receptor Tyrosine Kinase (RTK) Mediated Tyrosine Phosphor-Proteome from Drosophila S2 (ErbB1) Cells Reveals Novel Signaling Networks

Protein phosphorylation mediates many critical cellular responses and is essential for many biological functions during development. About one-third of cellular proteins are phosphorylated, representing the phosphor-proteome, and phosphorylation can alter a protein's function, activity, localization and stability. Tyrosine phosphorylation events mediated by aberrant activation of Receptor Tyrosine Kinase (RTK) pathways have been proven to be involved in the development of several diseases including cancer. To understand the systems biology of RTK activation, we have developed a phosphor-proteome focused on tyrosine phosphorylation events under insulin and EGF signaling pathways using the PhosphoScan® technique coupled with high-throughput mass spectrometry analysis. Comparative proteomic analyses of all these tyrosine phosphorylation events revealed that around 70% of these pY events are conserved in human orthologs and paralogs. A careful analysis of published in vivo tyrosine phosphorylation events from literature and patents revealed that around 38% of pY events from Drosophila proteins conserved on 185 human proteins are confirmed in vivo tyrosine phosphorylation events. Hence the data are validated partially based on available reports, and the credibility of the remaining 62% of novel conserved sites that are unpublished so far is very high but requires further follow-up studies. The novel pY events found in this study that are conserved on human proteins could potentially lead to the discovery of drug targets and biomarkers for the detection of various cancers and neurodegenerative diseases

Aristotelian Essentialism: Essence in the Age of Evolution

The advent of contemporary evolutionary theory ushered in the eventual decline of Aristotelian Essentialism (Æ) – for it is widely assumed that essence does not, and cannot have any proper place in the age of evolution. This paper argues that this assumption is a mistake: if Æ can be suitably evolved, it need not face extinction. In it, I claim that if that theory’s fundamental ontology consists of dispositional properties, and if its characteristic metaphysical machinery is interpreted within the framework of contemporary evolutionary developmental biology, an evolved essentialism is available. The reformulated theory of Æ offered in this paper not only fails to fall prey to the typical collection of criticisms, but is also independently both theoretically and empirically plausible. The paper contends that, properly understood, essence belongs in the age of evolution

Analyzing the regulation of metabolic pathways in human breast cancer

<p>Abstract</p> <p>Background</p> <p>Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.</p> <p>Methods</p> <p>For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.</p> <p>Results</p> <p>Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.</p> <p>Conclusion</p> <p>We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.</p

Phylostratigraphic tracking of cancer genes suggests a link to the emergence of multicellularity in metazoa

Background: Phylostratigraphy is a method used to correlate the evolutionary origin of founder genes (that is, functional founder protein domains) of gene families with particular macroevolutionary transitions. It is based on a model of genome evolution that suggests that the origin of complex phenotypic innovations will be accompanied by the emergence of such founder genes, the descendants of which can still be traced in extant organisms. The origin of multicellularity can be considered to be a macroevolutionary transition, for which new gene functions would have been required. Cancer should be tightly connected to multicellular life since it can be viewed as a malfunction of interaction between cells in a multicellular organism. A phylostratigraphic tracking of the origin of cancer genes should, therefore, also provide insights into the origin of multicellularity. Results: We find two strong peaks of the emergence of cancer related protein domains, one at the time of the origin of the first cell and the other around the time of the evolution of the multicellular metazoan organisms. These peaks correlate with two major classes of cancer genes, the 'caretakers', which are involved in general functions that support genome stability and the 'gatekeepers', which are involved in cellular signalling and growth processes. Interestingly, this phylogenetic succession mirrors the ontogenetic succession of tumour progression, where mutations in caretakers are thought to precede mutations in gatekeepers. Conclusions: A link between multicellularity and formation of cancer has often been predicted. However, this has not so far been explicitly tested. Although we find that a significant number of protein domains involved in cancer predate the origin of multicellularity, the second peak of cancer protein domain emergence is, indeed, connected to a phylogenetic level where multicellular animals have emerged. The fact that we can find a strong and consistent signal for this second peak in the phylostratigraphic map implies that a complex multi-level selection process has driven the transition to multicellularity

Predicting the Evolution of Sex on Complex Fitness Landscapes

Most population genetic theories on the evolution of sex or recombination are based on fairly restrictive assumptions about the nature of the underlying fitness landscapes. Here we use computer simulations to study the evolution of sex on fitness landscapes with different degrees of complexity and epistasis. We evaluate predictors of the evolution of sex, which are derived from the conditions established in the population genetic literature for the evolution of sex on simpler fitness landscapes. These predictors are based on quantities such as the variance of Hamming distance, mean fitness, additive genetic variance, and epistasis. We show that for complex fitness landscapes all the predictors generally perform poorly. Interestingly, while the simplest predictor, ΔVarHD, also suffers from a lack of accuracy, it turns out to be the most robust across different types of fitness landscapes. ΔVarHD is based on the change in Hamming distance variance induced by recombination and thus does not require individual fitness measurements. The presence of loci that are not under selection can, however, severely diminish predictor accuracy. Our study thus highlights the difficulty of establishing reliable criteria for the evolution of sex on complex fitness landscapes and illustrates the challenge for both theoretical and experimental research on the origin and maintenance of sexual reproduction

The interpretations and uses of fitness landscapes in the social sciences

__Abstract__ This working paper precedes our full article entitled “The evolution of Wright’s (1932) adaptive field to contemporary interpretations and uses of fitness landscapes in the social sciences” as published in the journal Biology & Philosophy (http://link.springer.com/article/10.1007/s10539-014-9450-2). The working paper features an extended literature overview of the ways in which fitness landscapes have been interpreted and used in the social sciences, for which there was not enough space in the full article. The article features an in-depth philosophical discussion about the added value of the various ways in which fitness landscapes are used in the social sciences. This discussion is absent in the current working paper. Th

DICE: A New Family of Bivariate Estimation of Distribution Algorithms based on Dichotomised Multivariate Gaussian Distributions

A new family of Estimation of Distribution Algorithms (EDAs) for discrete search spaces is presented. The proposed algorithms, which we label DICE (Discrete Correlated Estimation of distribution algorithms) are based, like previous bivariate EDAs such as MIMIC and BMDA, on bivariate marginal distribution models. However, bivariate models previously used in similar discrete EDAs were only able to exploit an O(d) subset of all the O(d2) bivariate variable dependencies between d variables. We introduce, and utilize in DICE, a model based on dichotomised multivariate Gaussian distributions. These models are able to capture and make use of all O(d2) bivariate variable interactions in binary and multary search spaces. This paper tests the performances of these new EDA models and algorithms on a suite of challenging combinatorial optimization problems, and compares their performances to previously used discrete-space bivariate EDA models. EDAs utilizing these new dichotomised Gaussian (DG) models exhibit significantly superior optimization performances, with the performance gap becoming more marked with increasing dimensionality