bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surface modification of polydimethylsiloxane microchannel using air plasma for DNA capillary migration in polydimethylsiloxane–glass microfluidic devices
Authors: Alsadat Rad Maryam , Ibrahim Kamarulazizi , Mohamed Khairudin , Najimudin Nazalan 
Published: June 2013

Polydimethylsiloxane (PDMS) microchannel surfaces were modified by air plasma to improve their applicability in microfluidics. The procedure included an increase in air plasma duration from 10 to 30 s. This resulted in an increase of wettability which was demonstrated by the decrease of water contact angles from 105° to 8°. The surface modification‐assisted PDMS microchannel easily bonded to a glass surface, and a PDMS/glass microfluidic device was fabricated with a simplified process. Slight pressure applied directly over the PDMS microchannel (approximate dimensions of 2.5 µm deep and 8.8 µm wide) formed nanoslits with dimensions of 830 nm in width and 170 nm in height on the PDMS/glass interface. Nanoslit formation was directly correlated to the metastable collapse of PDMS microchannels on the glass surface after the plasma treatment. The fabricated microfluidic devices were successfully employed for λ ‐DNA capillary migration without any external driving force.
Empowerment as Contested Terrain
Authors: Pruijt Hans , Yerkes Mara A. 
Published: June 2013

ABSTRACT
Sociological analysis has mainly portrayed empowerment as a manipulative, masking discourse. However, various actors in society view it as the opposite of domination and espouse it as a goal. Empowerment can constitute a discursive field shaped by its internal contractions between autonomy and control, between ambition and risk of programmed failure, exacerbated by the emphasis on responsibility, and between focus and stigmatization. The paper presents a case study of controversies and interventions concerning employability in The Netherlands. Employability can be seen as empowerment in matters of career. The study is based on 41 interviews with policy makers, managers, union and employers' leaders and politicians. It shows that actors drawing on the principle of empowerment as a goal in itself can reset or reclaim a drifting empowerment project in its inceptive phase and add their own twist during execution, evaluation and efforts to engineer improvements.
Ventilator-Associated Tracheobronchitis Increases the Length of Intensive Care Unit Stay
Authors: Karvouniaris Marios , Makris Demosthenes , Manoulakas Efstratios , Zygoulis Paris , Mantzarlis Konstantinos , Triantaris Apostolos , Chatzi Maria , Zakynthinos Epaminondas 
Published: June 2013

Objective.To investigate prospectively the clinical course and risk factors for ventilator-associated tracheobronchitis (VAT) and the impact of VAT on intensive care unit (ICU) morbidity and mortality.Design.Prospective cohort study.Setting.University Hospital Larissa, Larissa, GreecePatients.Critical care patients who received mechanical ventilation for more than 48 hours were prospectively studied between 2009 and 2011.Methods.The modified Clinical Pulmonary Infection Score, white blood cell count, and C-reactive protein level were systematically assessed every 2 days for the first 2 weeks of ICU stay. Bronchial secretions were assessed daily. Quantitative cultures of endotracheal secretions were performed on the first ICU day for every patient and every 2 days thereafter for the first 2 weeks or more at the discretion of the attending physicians. Definition of VAT was based on previously published criteria.Results.A total of 236 patients were observed; 42 patients (18%) presented with VAT. Gram-negative pathogens, which were usually multidrug resistant, were responsible for 92.9% of cases. Patients with a neurosurgical admission presented with VAT significantly more often than did other ICU patients (28.5% vs 14.1%; P = .02). The occurrence of VAT was a significant risk factor for increased duration of ICU stay (OR [95% CI], 3.04 [1.35-6.85]; P = .01). Age (OR [95% CI], 1.04 [1.015-1.06]; P = .02), Acute Physiology and Chronic Health Evaluation II score (OR [95% CI], 1.08 [1.015-1.16]; P = .02), and C-reactive protein level at admission (OR [95% CI], 1.05 [1.011.1]; P = .02) were independent factors for ICU mortality.Conclusions.VAT is a nosocomial infection that might be associated with prolonged stay in the ICU, especially in neurocritical patients. VAT was not associated with increased mortality in our study.
Mucosal Barrier Injury Laboratory-Confirmed Bloodstream Infection: Results from a Field Test of a New National Healthcare Safety Network Definition
Authors: See Isaac , Iwamoto Martha , Allen-Bridson Kathy , Horan Teresa , Magill Shelley S. , Thompson Nicola D. 
Published: June 2013

Objective.To assess challenges to implementation of a new National Healthcare Safety Network (NHSN) surveillance definition, mucosal barrier injury laboratory-confirmed bloodstream infection (MBI-LCBI).Design.Multicenter field test.Setting.Selected locations of acute care hospitals participating in NHSN central line-associated bloodstream infection (CLABSI) surveillance.Methods.Hospital staff augmented their CLABSI surveillance for 2 months to incorporate MBI-LCBI: a primary bloodstream infection due to a selected group of organisms in patients with either neutropenia or an allogeneic hematopoietic stem cell transplant with gastrointestinal graft-versus-host disease or diarrhea. Centers for Disease Control and Prevention (CDC) staff reviewed submitted data to verify whether CLABSIs met MBI-LCBI criteria and summarized the descriptive epidemiology of cases reported.Results.Eight cancer, 2 pediatric, and 28 general acute care hospitals including 193 inpatient units (49% oncology/bone marrow transplant [BMT], 21% adult ward, 20% adult critical care, 6% pediatric, 4% step-down) conducted field testing. Among 906 positive blood cultures reviewed, 282 CLABSIs were identified. Of the 103 CLABSIs that also met MBI-LCBI criteria, 100 (97%) were reported from oncology/BMT locations. Agreement between hospital staff and CDC classification of reported CLABSIs as meeting the MBI-LCBI definition was high (90%; k= 0.82). Most MBI-LCBIs (91%) occurred in patients meeting neutropenia criteria. Some hospitals indicated that their laboratories' methods of reporting cell counts prevented application of neutropenia criteria; revised neutropenia criteria were created using data from field testing.Conclusions.Hospital staff applied the MBI-LCBI definition accurately. Field testing informed modifications for the January 2013 implementation of MBI-LCBI in the NHSN.
FuncISH: learning a functional representation of neural ISH images
Authors: Liscovitch Noa , Shalit Uri , Chechik Gal 
Published: June 2013

Abstract
Motivation: High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.
Results: Here, we present FuncISH—a method to learn functional representations of neural in situ hybridization (ISH) images. We represent images using a histogram of local descriptors in several scales, and we use this representation to learn detectors of functional (GO) categories for every image. As a result, each image is represented as a point in a low-dimensional space whose axes correspond to meaningful functional annotations. The resulting representations define similarities between ISH images that can be easily explained by functional categories. We applied our method to the genomic set of mouse neural ISH images available at the Allen Brain Atlas, finding that most neural biological processes can be inferred from spatial expression patterns with high accuracy. Using functional representations, we predict several gene interaction properties, such as protein–protein interactions and cell-type specificity, more accurately than competing methods based on global correlations. We used FuncISH to identify similar expression patterns of GABAergic neuronal markers that were not previously identified and to infer new gene function based on image–image similarities.
Contact: noalis@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Authors: Cannistraci Carlo Vittorio , Alanis-Lobato Gregorio , Ravasi Timothy 
Published: June 2013

Abstract
Motivation: Most functions within the cell emerge thanks to protein–protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.
Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.
Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.
Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules.
Availability: https://sites.google.com/site/carlovittoriocannistraci/home
Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa
Supplementary information: Supplementary data are available at Bioinformatics online.
Poly(A) motif prediction using spectral latent features from human DNA sequences
Authors: Xie Bo , Jankovic Boris R. , Bajic Vladimir B. , Song Le , Gao Xin 
Published: June 2013

AbstractMotivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ∼30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.Availability: http://sfb.kaust.edu.sa/Pages/Software.aspxContact: lsong@cc.gatech.edu or xin.gao@kaust.edu.saSupplementary information: Supplementary data are available at Bioinformatics online.
IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels
Authors: Peng Yu , Leung Henry C. M. , Yiu Siu-Ming , Lv Ming-Ju , Zhu Xin-Guang , Chin Francis Y. L. 
Published: June 2013

Abstract
Motivation: RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges.
Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.
Availability: http://www.cs.hku.hk/∼alse/idba_tran.
Contact: chin@cs.hku.hk
Supplementary information: Supplementary data are available at Bioinformatics online.
A graph kernel approach for alignment-free domain–peptide interaction prediction with an application to human SH3 domains
Authors: Kundu Kousik , Costa Fabrizio , Backofen Rolf 
Published: June 2013

Abstract
Motivation: State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains.
Results: Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices.
We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data.
The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs).
Availability: The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz.
Contact: backofen@informatik.uni-freiburg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancers
Authors: Orenstein Yaron , Shamir Ron 
Published: June 2013

Abstract
Motivation: Novel technologies can generate large sets of short double-stranded DNA sequences that can be used to measure their regulatory effects. Microarrays can measure in vitro the binding intensity of a protein to thousands of probes. Synthetic enhancer sequences inserted into an organism’s genome allow us to measure in vivo the effect of such sequences on the phenotype. In both applications, by using sequence probes that cover all k-mers, a comprehensive picture of the effect of all possible short sequences on gene regulation is obtained. The value of k that can be used in practice is, however, severely limited by cost and space considerations. A key challenge is, therefore, to cover all k-mers with a minimal number of probes. The standard way to do this uses the de Bruijn sequence of length . However, as probes are double stranded, when a k-mer is included in a probe, its reverse complement k-mer is accounted for as well.
Results: Here, we show how to efficiently create a shortest possible sequence with the property that it contains each k-mer or its reverse complement, but not necessarily both. The length of the resulting sequence approaches half that of the de Bruijn sequence as k increases resulting in a more efficient array, which allows covering more longer sequences; alternatively, additional sequences with redundant k-mers of interest can be added.
Availability: The software is freely available from our website http://acgt.cs.tau.ac.il/shortcake/.
Contact: rshamir@tau.ac.il
Predicting drug-target interactions using restricted Boltzmann machines
Authors: Wang Yuhao , Zeng Jianyang 
Published: June 2013

Abstract
Motivation: In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action.
Results: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process.
Availability: Software and datasets are available on request.
Contact: zengjy321@tsinghua.edu.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
Simple topological properties predict functional misannotations in a metabolic network
Authors: Liberal Rodrigo , Pinney John W. 
Published: June 2013

Abstract
Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation.
Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes).
Contact: j.pinney@imperial.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.