University of California, Santa Cruz

About: University of California, Santa Cruz is a education organization based out in Santa Cruz, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 15541 authors who have published 44120 publications receiving 2759983 citations. The organization is also known as: UCSC & UC, Santa Cruz.

Knowledge-based analysis of microarray gene expression data by using support vector machines

Abstract: We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Quantum statistical mechanics in a closed system

Abstract: A closed quantum-mechanical system with a large number of degrees of freedom does not necessarily give time averages in agreement with the microcanonical distribution. For systems where the different degrees of freedom are uncoupled, situations are discussed that show a violation of the usual statistical-mechanical rules. By adding a finite but very small perturbation in the form of a random matrix, it is shown that the results of quantum statistical mechanics are recovered. Expectation values in energy eigenstates for this perturbed system are also discussed, and deviations from the microcanonical result are shown to become exponentially small in the number of degrees of freedom.

Profiles of dark haloes: evolution, scatter and environment

Abstract: We study dark-matter halo density profiles in a high-resolution N-body simulation of aCDM cosmology. Our statistical sample contains �5000 haloes in the range 10 11 10 14 h −1 M⊙ and the resolution allows a study of subhaloes inside host haloes. The profiles are parameterized by an NFW form with two parameters, an inner radius rs and a virial radius Rvir, and we define the halo concentration cvirRvir/rs. We find that, for a given halo mass, the redshift dependence of the median concentration is cvir / (1 + z) −1 . This corresponds to rs(z) � constant, and is contrary to earlier suspicions that cvir does not vary much with redshift. The implications are that high- redshift galaxies are predicted to be more extended and dimmer than expected before. Second, we find that the scatter in halo profiles is large, with a 1� �(logcvir) = 0.18 at a given mass, corresponding to a scatter in maximum rotation velocities of �Vmax/Vmax = 0.12. We discuss implications for modelling the Tully-Fisher relation, which has a smaller reported intrinsic scatter. Third, subhaloes and haloes in dense environments tend to be more concentrated than isolated haloes, and show a larger scatter. These results suggest that cvir is an essential parameter for the theory of galaxy modelling, and we briefly discuss implications for the universality of the Tully- Fisher relation, the formation of low surface brightness galaxies, and the origin of the Hubble sequence. We present an improved analytic treatment of halo formation that fits the measured relations between halo parameters and their redshift dependence, and can thus serve semi-analytic studies of galaxy formation.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

Abstract: The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

The Identification, Conservation, and Management of Estuarine and Marine Nurseries for Fish and Invertebrates

Abstract: Michael W. Beck, Kenneth L. Heck, Jr., Kenneth W. Able, Daniel L. Childers, David B. Eggleston, Bronwyn M. Gillanders, Benjamin Halpern, Cynthia G. Hays, Kaho Hoshino, Thomas J. Minello, Robert J. Orth, Peter F. Sheridan and Michael P. Weinstein