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

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

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

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

Outcomes of off-label drug uses in hospitals: a multicentric prospective study

Purpose: The study aims to assess the clinical evidence, outcome and cost of off-label use of medicines in the hospital setting. Methods: A multicentric prospective cohort study of patients treated with off-label medicines was carried out in five tertiary hospitals from May 2011 to May 2012. Information on clinical characteristics of patients, drugs, outcomes and costs was collected. Patients were followed up to 6 months, and information was assessed by reviewing clinical records and interviewing physicians. Results: A total of 226 patients were included. The median (interquartile range (IQR)) age of patients was 46 (3362) years; 59 % were women. Patients had received a median of three previous treatments, and a lack of response (or suboptimal) was the main reason for off-label use (72.1 %). A total of 232 off-label medicines were administered for 102 different indications. The most frequent medicines were rituximab (49; 21.1 %), botulinum toxin (25; 10.7 %) and omalizumab (14; 6.0 %). In 117 (51.8 %) cases, the level of clinical evidence for their use was low. A partial clinical response was observed in 82 patients (36.3 %), complete response in 71 (31.4 %) and stabilization in 11 (4.9 %). A total of 58 (26.5 %) patients had adverse effects, which in 11 (4.9 %) were severe. The median (IQR) cost per patient was 2,943.07 (541.95,872.54). Conclusions: There was a high variability of off-label medicines and indications. Although the clinical evidence of off-label medicines was often low, clinical response was observed in many patients with previous multiple treatment failure, but at the expense of some adverse effects and a high cost. Registers of patients would be helpful for clinical decisions, although clinical trials are needed

SigFuge: Single gene clustering of RNA-seq reveals differential isoform usage among cancer samples

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor

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

Subtype-Selective Small Molecule Inhibitors Reveal a Fundamental Role for Nav1.7 in Nociceptor Electrogenesis, Axonal Conduction and Presynaptic Release.

Human genetic studies show that the voltage gated sodium channel 1.7 (Nav1.7) is a key molecular determinant of pain sensation. However, defining the Nav1.7 contribution to nociceptive signalling has been hampered by a lack of selective inhibitors. Here we report two potent and selective arylsulfonamide Nav1.7 inhibitors; PF-05198007 and PF-05089771, which we have used to directly interrogate Nav1.7's role in nociceptor physiology. We report that Nav1.7 is the predominant functional TTX-sensitive Nav in mouse and human nociceptors and contributes to the initiation and the upstroke phase of the nociceptor action potential. Moreover, we confirm a role for Nav1.7 in influencing synaptic transmission in the dorsal horn of the spinal cord as well as peripheral neuropeptide release in the skin. These findings demonstrate multiple contributions of Nav1.7 to nociceptor signalling and shed new light on the relative functional contribution of this channel to peripheral and central noxious signal transmission.The funder provided support in the form of salaries for authors [AA, AB, MC, JT, MM, AW, EP, AG, PJC, RD, DP, ZL, BM, CW, NS, RS, PS, NC, DK, RB, ES], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section

A framework for orthology assignment from gene rearrangement data

Abstract. Gene rearrangements have successfully been used in phylogenetic reconstruction and comparative genomics, but usually under the assumption that all genomes have the same gene content and that no gene is duplicated. While these assumptions allow one to work with organellar genomes, they are too restrictive when comparing nuclear genomes. The main challenge is how to deal with gene families, specifically, how to identify orthologs. While searching for orthologies is a common task in computational biology, it is usually done using sequence data. We approach that problem using gene rearrangement data, provide an optimization framework in which to phrase the problem, and present some preliminary theoretical results.