4,266 research outputs found
LTRo: Learning to Route Queries in Clustered P2P IR
Query Routing is a critical step in P2P Information Retrieval. In this paper, we consider learning to rank approaches for query routing in the clustered P2P IR architecture. Our formulation, LTRo, scores resources based on the number of relevant documents for each training query, and uses that information to build a model that would then rank promising peers for a new query. Our empirical analysis over a variety of P2P IR testbeds illustrate the superiority of our method against the state-of-the-art methods for query routing
The identification of informative genes from multiple datasets with increasing complexity
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.
Results
In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.
Conclusions
We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events
A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies
A promising direction in drug development is to exploit the ability of
natural killer cells to kill antibody-labeled target cells. Monoclonal
antibodies and drugs designed to elicit this effect typically bind cell-surface
epitopes that are overexpressed on target cells but also present on other
cells. Thus it is important to understand adhesion of cells by antibodies and
similar molecules. We present an equilibrium model of such adhesion,
incorporating heterogeneity in target cell epitope density and epitope
immobility. We compare with experiments on the adhesion of Jurkat T cells to
bilayers containing the relevant natural killer cell receptor, with adhesion
mediated by the drug alefacept. We show that a model in which all target cell
epitopes are mobile and available is inconsistent with the data, suggesting
that more complex mechanisms are at work. We hypothesize that the immobile
epitope fraction may change with cell adhesion, and we find that such a model
is more consistent with the data. We also quantitatively describe the parameter
space in which binding occurs. Our results point toward mechanisms relating
epitope immobility to cell adhesion and offer insight into the activity of an
important class of drugs.Comment: 13 pages, 5 figure
Ordinary Percolation with Discontinuous Transitions
Percolation on a one-dimensional lattice and fractals such as the Sierpinski
gasket is typically considered to be trivial because they percolate only at
full bond density. By dressing up such lattices with small-world bonds, a novel
percolation transition with explosive cluster growth can emerge at a nontrivial
critical point. There, the usual order parameter, describing the probability of
any node to be part of the largest cluster, jumps instantly to a finite value.
Here, we provide a simple example of this transition in form of a small-world
network consisting of a one-dimensional lattice combined with a hierarchy of
long-range bonds that reveals many features of the transition in a
mathematically rigorous manner.Comment: RevTex, 5 pages, 4 eps-figs, and Mathematica Notebook as Supplement
included. Final version, with several corrections and improvements. For
related work, see http://www.physics.emory.edu/faculty/boettcher
BRCA1 and BRCA2 mutations in a population-based study of male breast cancer
Background: The contribution of BRCA1 and BRCA2 to the incidence of male breast cancer (MBC)
in the United Kingdom is not known, and the importance of these genes in the increased risk of female
breast cancer associated with a family history of breast cancer in a male first-degree relative is unclear.
Methods: We have carried out a population-based study of 94 MBC cases collected in the UK. We
screened genomic DNA for mutations in BRCA1 and BRCA2 and used family history data from these
cases to calculate the risk of breast cancer to female relatives of MBC cases. We also estimated the
contribution of BRCA1 and BRCA2 to this risk.
Results: Nineteen cases (20%) reported a first-degree relative with breast cancer, of whom seven also
had an affected second-degree relative. The breast cancer risk in female first-degree relatives was 2.4
times (95% confidence interval [CI] = 1.4–4.0) the risk in the general population. No BRCA1 mutation
carriers were identified and five cases were found to carry a mutation in BRCA2. Allowing for a
mutation detection sensitivity frequency of 70%, the carrier frequency for BRCA2 mutations was 8%
(95% CI = 3–19). All the mutation carriers had a family history of breast, ovarian, prostate or
pancreatic cancer. However, BRCA2 accounted for only 15% of the excess familial risk of breast
cancer in female first-degree relatives.
Conclusion: These data suggest that other genes that confer an increased risk for both female and
male breast cancer have yet to be found
Kerr-AdS and its Near-horizon Geometry: Perturbations and the Kerr/CFT Correspondence
We investigate linear perturbations of spin-s fields in the Kerr-AdS black
hole and in its near-horizon geometry (NHEK-AdS), using the Teukolsky master
equation and the Hertz potential. In the NHEK-AdS geometry we solve the
associated angular equation numerically and the radial equation exactly. Having
these explicit solutions at hand, we search for linear mode instabilities. We
do not find any (non-)axisymmetric instabilities with outgoing boundary
conditions. This is in agreement with a recent conjecture relating the
linearized stability properties of the full geometry with those of its
near-horizon geometry. Moreover, we find that the asymptotic behaviour of the
metric perturbations in NHEK-AdS violates the fall-off conditions imposed in
the formulation of the Kerr/CFT correspondence (the only exception being the
axisymmetric sector of perturbations).Comment: 26 pages. 4 figures. v2: references added. matches published versio
Pulmonary Evaluation of Patients Presenting with Dermatological Manifestations of Sarcoidosis
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65674/1/j.1365-4362.1981.tb00826.x.pd
Discovering study-specific gene regulatory networks
This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets
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Women’s pelvic floor muscle strength and urinary and anal incontinence after childbirth: a cross-sectional study
Abstract OBJECTIVE To analyse pelvic floor muscle strength (PFMS) and urinary and anal incontinence (UI and AI) in the postpartum period. METHOD Cross-sectional study carried out with women in their first seven months after child birth. Data were collected through interviews, perineometry (Peritron™), and the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF). RESULTS 128 women participated in the study. The PFMS mean was 33.1 (SD=16.0) cmH2O and the prevalence of UI and AI was 7.8% and 5.5%, respectively. In the multiple analyses, the variables associated with PFMS were type of birth and cohabitation with a partner. Newborn’s weight, previous pregnancy, UI during pregnancy, and sexual activity showed an association with UI after child birth. Only AI prior to pregnancy was associated with AI after childbirth. CONCLUSION Vaginal birth predisposes to the reduction of PFMS, and caesarean section had a protective effect to its reduction. The occurrence of UI during pregnancy is a predictor of UI after childbirth, and women with previous pregnancies and newborns with higher weights are more likely to have UI after childbirth.AI prior to pregnancy is the only risk factor for its occurrence after childbirth. Associations between PFMS and cohabitation with a partner, and between UI and sexual activity do not make possible to conclude that these variables are directly associated
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