401 research outputs found

    Statistical methods to study heterogeneity of treatment effects

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    Indiana University-Purdue University Indianapolis (IUPUI)Randomized studies are designed to estimate the average treatment effect (ATE) of an intervention. Individuals may derive quantitatively, or even qualitatively, different effects from the ATE, which is called the heterogeneity of treatment effect. It is important to detect the existence of heterogeneity in the treatment responses, and identify the different sub-populations. Two corresponding statistical methods will be discussed in this talk: a hypothesis testing procedure and a mixture-model based approach. The hypothesis testing procedure was constructed to test for the existence of a treatment effect in sub-populations. The test is nonparametric, and can be applied to all types of outcome measures. A key innovation of this test is to build stochastic search into the test statistic to detect signals that may not be linearly related to the multiple covariates. Simulations were performed to compare the proposed test with existing methods. Power calculation strategy was also developed for the proposed test at the design stage. The mixture-model based approach was developed to identify and study the sub-populations with different treatment effects from an intervention. A latent binary variable was used to indicate whether or not a subject was in a sub-population with average treatment benefit. The mixture-model combines a logistic formulation of the latent variable with proportional hazards models. The parameters in the mixture-model were estimated by the EM algorithm. The properties of the estimators were then studied by the simulations. Finally, all above methods were applied to a real randomized study in a low ejection fraction population that compared the Implantable Cardioverter Defibrillator (ICD) with conventional medical therapy in reducing total mortality

    Is adhesion superficial? Silicon wafers as a model system to study van der Waals interactions

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    Adhesion is a key issue for researchers of various fields, it is therefore of uppermost importance to understand the parameters that are involved. Commonly, only surface parameters are employed to determine the adhesive forces between materials. Yet, van der Waals forces act not only between atoms in the vicinity of the surface, but also between atoms in the bulk material. In this review, we describe the principles of van der Waals interactions and outline experimental and theoretical studies investigating the influence of the subsurface material on adhesion. In addition, we present a collection of data indicating that silicon wafers with native oxide layers are a good model substrate to study van der Waals interactions with coated materials

    DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments

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    Small non-coding RNAs (ncRNAs) such as microRNAs, snoRNAs and tRNAs are a diverse collection of molecules with several important biological functions. Current methods for high-throughput sequencing for the first time offer the opportunity to investigate the entire ncRNAome in an essentially unbiased way. However, there is a substantial need for methods that allow a convenient analysis of these overwhelmingly large data sets. Here, we present DARIO, a free web service that allows to study short read data from small RNA-seq experiments. It provides a wide range of analysis features, including quality control, read normalization, ncRNA quantification and prediction of putative ncRNA candidates. The DARIO web site can be accessed at http://dario.bioinf.uni-leipzig.de/

    SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments

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    <p>Abstract</p> <p>Background</p> <p>High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.</p> <p>Results</p> <p>Existing software solutions provide static and well-established algorithms in a restrictive package. However as high throughput sequencing is a rapidly evolving field, such static approaches lack the ability to readily adopt the latest advances and techniques which are often required by researchers. We have used a loosely coupled, service-oriented infrastructure to develop SeqAdapt. This system streamlines data management and allows for rapid integration of novel algorithms. Our approach also allows computational biologists to focus on developing and applying new methods instead of writing boilerplate infrastructure code.</p> <p>Conclusion</p> <p>The system is based around the Addama service architecture and is available at our website as a demonstration web application, an installable single download and as a collection of individual customizable services.</p

    Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

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    Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine)

    Optical response of finite-length carbon nanotubes

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    Optical response of finite-length metallic carbon nanotubes is calculated including effects of induced edge charges in a self-consistent manner. The results show that the main resonance corresponding to excitation of the fundamental plasmon mode with wave vector π/l\pi/l with ll being the tube length is quite robust and unaffected. This arises because the strong electric field associated with edge charges is screened and decays rapidly inside the nanotube. For higher-frequency resonances, the field starts to be mixed and tends to shift resonances to higher frequencies.Comment: 10 pages, 9 figures, to be published in J. Phys. Soc. Jp

    Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters

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    Transcription of long noncoding RNAs (lncRNAs) within gene regulatory elements can modulate gene activity in response to external stimuli, but the scope and functions of such activity are not known. Here we use an ultrahigh-density array that tiles the promoters of 56 cell-cycle genes to interrogate 108 samples representing diverse perturbations. We identify 216 transcribed regions that encode putative lncRNAs, many with RT-PCR–validated periodic expression during the cell cycle, show altered expression in human cancers and are regulated in expression by specific oncogenic stimuli, stem cell differentiation or DNA damage. DNA damage induces five lncRNAs from the CDKN1A promoter, and one such lncRNA, named PANDA, is induced in a p53-dependent manner. PANDA interacts with the transcription factor NF-YA to limit expression of pro-apoptotic genes; PANDA depletion markedly sensitized human fibroblasts to apoptosis by doxorubicin. These findings suggest potentially widespread roles for promoter lncRNAs in cell-growth control.National Institutes of Health (U.S.)National Institute of Arthritis and Musculoskeletal and Skin Diseases (U.S.) (NIAMS) (K08-AR054615))National Cancer Institute (U.S.) (NIH/(NCI) (R01-CA118750))National Cancer Institute (U.S.) (NIH/(NCI) R01-CA130795))Juvenile Diabetes Research Foundation InternationalAmerican Cancer SocietyHoward Hughes Medical Institute (Early career scientist)Stanford University (Graduate Fellowship)National Science Foundation (U.S.) (Graduate Research Fellowship)United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship
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