Aerotaxis in the closest relatives of animals

As the closest unicellular relatives of animals, choanoflagellates serve as useful model organisms for understanding the evolution of animal multicellularity. An important factor in animal evolution was the increasing ocean oxygen levels in the Precambrian, which are thought to have influenced the emergence of complex multicellular life. As a first step in addressing these conditions, we study here the response of the colony-forming choanoflagellate $\textit{Salpingoeca rosetta}$ to oxygen gradients. Using a microfluidic device that allows spatio-temporal variations in oxygen concentrations, we report the discovery that $\textit{S. rosetta}$ displays positive aerotaxis. Analysis of the spatial population distributions provides evidence for logarithmic sensing of oxygen, which enhances sensing in low oxygen neighborhoods. Analysis of search strategy models on the experimental colony trajectories finds that choanoflagellate aerotaxis is consistent with stochastic navigation, the statistics of which are captured using an effective continuous version based on classical run-and-tumble chemotaxis.Engineering and Physical Sciences Research Council, St John’s College, European Research Council (Advanced Investigator Grant ID: 247333), Wellcome Trust (Senior Investigator Award

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Screening for body dysmorphic disorder in patients with acne

Body Dysmorphic Disorder (BDD), is a mental health disorder that is difficult to diagnose, causes much suffering and is a challenge to treat. The main symptoms are the preoccupation with the perceived defect and the actions taken to reduce accompanying feelings of distress. Prevalence of BDD is between 9% and 12% in dermatology patients. Although the onset of BDD is usually during adolescence, patients are usually diagnosed after many years of suffering, in part because patients are too ashamed to talk about their symptoms. The main objective of this multicenter study carried out in Spain, was to screen patients diagnosed with acne vulgaris for BDD by asking appearance-specific questions. A total of 403 patients were screened..

Persistent Homology Analysis of Brain Artery Trees.

New representations of tree-structured data objects, using ideas from topological data analysis, enable improved statistical analyses of a population of brain artery trees. A number of representations of each data tree arise from persistence diagrams that quantify branching and looping of vessels at multiple scales. Novel approaches to the statistical analysis, through various summaries of the persistence diagrams, lead to heightened correlations with covariates such as age and sex, relative to earlier analyses of this data set. The correlation with age continues to be significant even after controlling for correlations from earlier significant summaries

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data and provides new directions for the visual exploration of joint and individual structures. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types. Data and software are available at https://genome.unc.edu/jive/Comment: Published in at http://dx.doi.org/10.1214/12-AOAS597 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Genomic organization and evolution of the ULBP genes in cattle

BACKGROUND: The cattle UL16-binding protein 1 (ULBP1) and ULBP2 genes encode members of the MHC Class I superfamily that have homology to the human ULBP genes. Human ULBP1 and ULBP2 interact with the NKG2D receptor to activate effector cells in the immune system. The human cytomegalovirus UL16 protein is known to disrupt the ULBP-NKG2D interaction, thereby subverting natural killer cell-mediated responses. Previous Southern blotting experiments identified evidence of increased ULBP copy number within the genomes of ruminant artiodactyls. On the basis of these observations we hypothesized that the cattle ULBPs evolved by duplication and sequence divergence to produce a sufficient number and diversity of ULBP molecules to deliver an immune activation signal in the presence of immunogenic peptides. Given the importance of the ULBPs in antiviral immunity in other species, our goal was to determine the copy number and genomic organization of the ULBP genes in the cattle genome. RESULTS: Sequencing of cattle bacterial artificial chromosome genomic inserts resulted in the identification of 30 cattle ULBP loci existing in two gene clusters. Evidence of extensive segmental duplication and approximately 14 Kbp of novel repetitive sequences were identified within the major cluster. Ten ULBPs are predicted to be expressed at the cell surface. Substitution analysis revealed 11 outwardly directed residues in the predicted extracellular domains that show evidence of positive Darwinian selection. These positively selected residues have only one residue that overlaps with those proposed to interact with NKG2D, thus suggesting the interaction with molecules other than NKG2D. CONCLUSION: The ULBP loci in the cattle genome apparently arose by gene duplication and subsequent sequence divergence. Substitution analysis of the ULBP proteins provided convincing evidence for positive selection on extracellular residues that may interact with peptide ligands. These results support our hypothesis that the cattle ULBPs evolved under adaptive diversifying selection to avoid interaction with a UL16-like molecule whilst preserving the NKG2D binding site. The large number of ULBPs in cattle, their extensive diversification, and the high prevalence of bovine herpesvirus infections make this gene family a compelling target for studies of antiviral immunity

A Fast and Compact Quantum Random Number Generator

We present the realization of a physical quantum random number generator based on the process of splitting a beam of photons on a beam splitter, a quantum mechanical source of true randomness. By utilizing either a beam splitter or a polarizing beam splitter, single photon detectors and high speed electronics the presented devices are capable of generating a binary random signal with an autocorrelation time of 11.8 ns and a continuous stream of random numbers at a rate of 1 Mbit/s. The randomness of the generated signals and numbers is shown by running a series of tests upon data samples. The devices described in this paper are built into compact housings and are simple to operate.Comment: 23 pages, 6 Figs. To appear in Rev. Sci. Inst

Smoke gets in your eyes:what is sociological about cigarettes?

Contemporary public health approaches increasingly draw attention to the unequal social distribution of cigarette smoking. In contrast, critical accounts emphasize the importance of smokers’ situated agency, the relevance of embodiment and how public health measures against smoking potentially play upon and exacerbate social divisions and inequality. Nevertheless, if the social context of cigarettes is worthy of such attention, and sociology lays a distinct claim to understanding the social, we need to articulate a distinct, positive and systematic claim for smoking as an object of sociological enquiry. This article attempts to address this by situating smoking across three main dimensions of sociological thinking: history and social change; individual agency and experience; and social structures and power. It locates the emergence and development of cigarettes in everyday life within the project of modernity of the nineteenth and twentieth centuries. It goes on to assess the habituated, temporal and experiential aspects of individual smoking practices in everyday lifeworlds. Finally, it argues that smoking, while distributed in important ways by social class, also works relationally to render and inscribe it

Creating Porcine Biomedical Models Through Recombineering

Recent advances in genomics provide genetic information from humans and other mammals (mouse, rat, dog and primates) traditionally used as models as well as new candidates (pigs and cattle). In addition, linked enabling technologies, such as transgenesis and animal cloning, provide innovative ways to design and perform experiments to dissect complex biological systems. Exploitation of genomic information overcomes the traditional need to choose naturally occurring models. Thus, investigators can utilize emerging genomic knowledge and tools to create relevant animal models. This approach is referred to as reverse genetics. In contrast to ‘forward genetics’, in which gene(s) responsible for a particular phenotype are identified by positional cloning (phenotype to genotype), the ‘reverse genetics’ approach determines the function of a gene and predicts the phenotype of a cell, tissue, or organism (genotype to phenotype). The convergence of classical and reverse genetics, along with genomics, provides a working definition of a ‘genetic model’ organism (3). The recent construction of phenotypic maps defining quantitative trait loci (QTL) in various domesticated species provides insights into how allelic variations contribute to phenotypic diversity. Targeted chromosomal regions are characterized by the construction of bacterial artificial chromosome (BAC) contigs to isolate and characterize genes contributing towards phenotypic variation. Recombineering provides a powerful methodology to harvest genetic information responsible for phenotype. Linking recombineering with gene-targeted homologous recombination, coupled with nuclear transfer (NT) technology can provide ‘clones’ of genetically modified animals