bluuebunny/crossref_metadata_2025_split huggingface dataset:
Which variables are associated with pain intensity and treatment response in advanced cancer patients?— Implications for a future classification system for cancer pain
Authors: Knudsenl Anne Kari , Brunellil Cinzia , Kaasal Stein , Apolonel Giovanni , Corlil Oscar , Montanaril Mauro , Fainsingerl Robin , Aassl Nina , Fayersl Peter , Caracenil Augusto , Klepstadl Pål ,   On behalf of the European Palliative Care Research Collaborative (EPCRC) and the European Pharmacogenetic Study (EPOS)
Published: September 2010

AbstractBackground: This study is part of a research program to reach consensus on an international cancer pain classification system. A confirmative and explorative approach was applied to investigate which of the variables identified in the literature, by experts and patients that are associated with pain.Methods: Data from an international, multicentre, cross‐sectional study of cancer patients treated with opioids were investigated. Dependent variables were: average pain, worst pain, and pain relief (11‐point Numerical Rating Scales). Forty‐six independent variables were chosen based upon previous studies. Bivariate analyses identified independent variables associated with at least one of the dependent ones; 21 were included in multivariate linear regression analyses.Results: Two thousand two hundred and seventy‐eight patients were investigated; 52% males, mean age 62 years, mean Karnofsky Performance Status 59%, mean daily opioid oral equivalent dose 341 mg. Fifty‐eight percent had breakthrough pain. Mean pain scores were: average pain 3.5, worst pain 5.3 and pain relief 74%. Variables most strongly associated with these three dependent variables were: breakthrough pain, psychological distress, sleep, and opioid dose.Conclusions: Breakthrough pain and psychological distress were confirmed as key variables of a future classification system. Candidate variables were: sleep, opioid dose, pain mechanism, use of non‐opioids, pain localisation, cancer diagnosis, location of metastases, and addiction.
Comparison of the TVT SECUR System “hammock” and “U” tape positions for management of stress urinary incontinence
Authors: Liapis Angelos , Bakas Panagiotis , Creatsas Georgios 
Published: September 2010

AbstractObjectiveTo assess the efficacy and complications associated with use of the TVT SECUR System device with placement of the tape in either a “hammock” or “U” position for management of stress urinary incontinence (SUI).MethodsA prospective study of patients with SUI allocated into one of two groups: “hammock” or “U” tape placement. Preoperative urodynamic results were compared with results at the 6‐month and 1‐year follow up. Outcome measures were objective cough test assessment and subjective patient responses to a questionnaire at follow up.ResultsOf 82 patients included in the study, 43 comprised the “hammock” group and 39 comprised the “U” group. The objective cure rate at 1‐year follow up was 62.8% (n = 27) in the “hammock” group and 71.8% (n = 28) in the “U” group. At 1‐year follow up, the subjective cure, improvement, and failure rates for the “hammock” group were 60.5%, 13.9%, and 25.7% respectively, and 69.2%, 12.8%, and 17.9% respectively, for the “U” group.ConclusionThe efficacy of the TVT SECUR System was lower (< 72%) than the cure rates reported for other TVT procedures; further studies are required.
Pharmacokinetics and first-pass metabolism of astaxanthin in rats
Authors: Choi Hye Duck , Kang Hee Eun , Yang Si Hyung , Lee Myung Gull , Shin Wan Gyoon 
Published: September 2010

Astaxanthin is a carotenoid with antioxidant, anti-cancer and anti-inflammatory properties. The pharmacokinetics of astaxanthin after its intravenous (5, 10, and 20 mg/kg) and oral (100 and 200 mg/kg) administration and its first-pass extraction ratios after its intravenous, intraportal or intragastric (20 mg/kg) administration were evaluated in rats. The pharmacokinetic parameters of astaxanthin were dose dependent after its intravenous administration, due to the saturable hepatic metabolism of astaxanthin, but dose independent after oral administration. The gastrointestinal absorption of astaxanthin followed the flip-flop model. The hepatic and gastrointestinal first-pass extraction ratios of astaxanthin were approximately 0·490 and 0·901, respectively. Astaxanthin was metabolised primarily by hepatic cytochromeP-450 1A1/2 in rats. Astaxanthin was unstable up to 4 h incubation in four rat gastric juices and up to 24 h incubation in various buffer solutions having a pH of 1–13. The tissue/plasma ratios of astaxanthin at 8 and 24 h after its oral administration (100 mg/kg) were greater than unity for all tissues studied, except in the heart, at 8 h, indicating that the rat tissues studied had high affinity for astaxanthin.
Provenance of detrital zircons from the late Neoproterozoic to Ordovician sandstones of South China: implications for its continental affinity
Authors: WU LONG , JIA DONG , LI HAIBIN , DENG FEI , LI YIQUAN 
Published: September 2010

AbstractThe U–Pb geochronology of 687 detrital zircons from the voluminous Upper Neoproterozoic–Ordovician succession in the Wuyishan Fold Belt of South China reveals a common dominant c. 1200–950 Ma group, indicative of an outboard provenance terrane with a Grenville-age province to the southeast during the late Neoproterozoic–Early Palaeozoic. Compared with coeval samples from the Gondwanan and eastern Laurentian margins, our data show a scarcity of distinctive Gondwanan provenances (c. 650–500 Ma) and reveal some Laurentian signatures. These results argue against the peri-Gondwanan setting for South China during the late Neoproterozoic–Ordovician, instead implying a Laurentian affinity.
Spatial evolution of a q-Gaussian laser beam in relativistic plasma
Authors: Sharma A. , Kourakis I. 
Published: September 2010

AbstractIn a recent experimental study, the beam intensity profile of the Vulcan petawatt laser beam was measured; it was found that only 20% of the energy was contained within the full width at half maximum of 6.9 μm and 50% within 16 μm, suggesting a long-tailed non-Gaussian transverse beam profile. A q-Gaussian distribution function was suggested therein to reproduce this behavior. The spatial beam profile dynamics of a q-Gaussian laser beam propagating in relativistic plasma is investigated in this article. A non-paraxial theory is employed, taking into account nonlinearity via the relativistic decrease of the plasma frequency. We have studied analytically and numerically the dynamics of a relativistically guided beam and its dependence on the q-parameter. Numerical simulation results are shown to trace the dependence of the focusing length on the q-Gaussian profile.
A fast algorithm for exact sequence search in biological sequences using polyphase decomposition
Authors: Srikantha Abhilash , Bopardikar Ajit S. , Kaipa Kalyan Kumar , Venkataraman Parthasarathy , Lee Kyusang , Ahn TaeJin , Narayanan Rangavittal 
Published: September 2010

Abstract
Motivation: Exact sequence search allows a user to search for a specific DNA subsequence in a larger DNA sequence or database. It serves as a vital block in many areas such as Pharmacogenetics, Phylogenetics and Personal Genomics. As sequencing of genomic data becomes increasingly affordable, the amount of sequence data that must be processed will also increase exponentially. In this context, fast sequence search algorithms will play an important role in exploiting the information contained in the newly sequenced data. Many existing algorithms do not scale up well for large sequences or databases because of their high-computational costs. This article describes an efficient algorithm for performing fast searches on large DNA sequences. It makes use of hash tables of Q-grams that are constructed after downsampling the database, to enable efficient search and memory use. Time complexity for pattern search is reduced using beam pruning techniques. Theoretical complexity calculations and performance figures are presented to indicate the potential of the proposed algorithm.
Contact: s.abhilash@samsung.com; ajit.b@samsung.com
Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim
Authors: Balzer Susanne , Malde Ketil , Lanzén Anders , Sharma Animesh , Jonassen Inge 
Published: September 2010

Abstract
Motivation: The commercial launch of 454 pyrosequencing in 2005 was a milestone in genome sequencing in terms of performance and cost. Throughout the three available releases, average read lengths have increased to ∼500 base pairs and are thus approaching read lengths obtained from traditional Sanger sequencing. Study design of sequencing projects would benefit from being able to simulate experiments.
Results: We explore 454 raw data to investigate its characteristics and derive empirical distributions for the flow values generated by pyrosequencing. Based on our findings, we implement Flowsim, a simulator that generates realistic pyrosequencing data files of arbitrary size from a given set of input DNA sequences. We finally use our simulator to examine the impact of sequence lengths on the results of concrete whole-genome assemblies, and we suggest its use in planning of sequencing projects, benchmarking of assembly methods and other fields.
Availability: Flowsim is freely available under the General Public License from http://blog.malde.org/index.php/flowsim/
Contact: susanne.balzer@imr.no; ketil.malde@imr.no
Integrating genome assemblies with MAIA
Authors: Nijkamp Jurgen , Winterbach Wynand , van den Broek Marcel , Daran Jean-Marc , Reinders Marcel , de Ridder Dick 
Published: September 2010

Abstract
Motivation: De novo assembly of a eukaryotic genome with next-generation sequencing data is still a challenging task. Over the past few years several assemblers have been developed, often suitable for one specific type of sequencing data. The number of known genomes is expanding rapidly, therefore it becomes possible to use multiple reference genomes for assembly projects. We introduce an assembly integrator that makes use of all available data, i.e. multiple de novo assemblies and mappings against multiple related genomes, by optimizing a weighted combination of criteria.
Results: The developed algorithm was applied on the de novo sequencing of the Saccharomyces cerevisiae CEN.PK 113-7D strain. Using Solexa and 454 read data, two de novo and three comparative assemblies were constructed and subsequently integrated, yielding 29 contigs, covering more than 12 Mbp; a drastic improvement compared with the single assemblies.
Availability: MAIA is available as a Matlab package and can be downloaded from http://bioinformatics.tudelft.nl
Contact: j.f.nijkamp@tudelft.nl
Discriminatory power of RNA family models
Authors: Siederdissen Christian Höner zu , Hofacker Ivo L. 
Published: September 2010

Abstract
Motivation: RNA family models group nucleotide sequences that share a common biological function. These models can be used to find new sequences belonging to the same family. To succeed in this task, a model needs to exhibit high sensitivity as well as high specificity. As model construction is guided by a manual process, a number of problems can occur, such as the introduction of more than one model for the same family or poorly constructed models. We explore the Rfam database to discover such problems.
Results: Our main contribution is in the definition of the discriminatory power of RNA family models, together with a first algorithm for its computation. In addition, we present calculations across the whole Rfam database that show several families lacking high specificity when compared to other families. We give a list of these clusters of families and provide a tentative explanation. Our program can be used to: (i) make sure that new models are not equivalent to any model already present in the database; and (ii) new models are not simply submodels of existing families.
Availability: www.tbi.univie.ac.at/software/cmcompare/. The code is licensed under the GPLv3. Results for the whole Rfam database and supporting scripts are available together with the software.
Contact: choener@tbi.univie.ac.at
Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
Authors: Cannistraci Carlo Vittorio , Ravasi Timothy , Montevecchi Franco Maria , Ideker Trey , Alessio Massimo 
Published: September 2010

Abstract
Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures—specifically dimension reduction (DR), coupled with clustering—provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space.
Methods: ‘Minimum Curvilinearity’ (MC) is a principle that—for small datasets—suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering.
Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin.
Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal.
Availability: https://sites.google.com/site/carlovittoriocannistraci/home
Contact: kalokagathos.agon@gmail.com; massimo.alessio@hsr.it
Supplementary information: Supplementary data are available at Bioinformatics online.
is-rSNP: a novel technique forin silicoregulatory SNP detection
Authors: Macintyre Geoff , Bailey James , Haviv Izhak , Kowalczyk Adam 
Published: September 2010

AbstractMotivation: Determining the functional impact of non-coding disease-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) is challenging. Many of these SNPs are likely to be regulatory SNPs (rSNPs): variations which affect the ability of a transcription factor (TF) to bind to DNA. However, experimental procedures for identifying rSNPs are expensive and labour intensive. Therefore, in silico methods are required for rSNP prediction. By scoring two alleles with a TF position weight matrix (PWM), it can be determined which SNPs are likely rSNPs. However, predictions in this manner are noisy and no method exists that determines the statistical significance of a nucleotide variation on a PWM score.Results: We have designed an algorithm for in silico rSNP detection called is-rSNP. We employ novel convolution methods to determine the complete distributions of PWM scores and ratios between allele scores, facilitating assignment of statistical significance to rSNP effects. We have tested our method on 41 experimentally verified rSNPs, correctly predicting the disrupted TF in 28 cases. We also analysed 146 disease-associated SNPs with no known functional impact in an attempt to identify candidate rSNPs. Of the 11 significantly predicted disrupted TFs, 9 had previous evidence of being associated with the disease in the literature. These results demonstrate that is-rSNP is suitable for high-throughput screening of SNPs for potential regulatory function. This is a useful and important tool in the interpretation of GWAS.Availability: is-rSNP software is available for use at: www.genomics.csse.unimelb.edu.au/is-rSNPContact: gmaci@csse.unimelb.edu.au; adam.kowalczyk@nicta.com.auSupplementary information: Supplementary data are available at Bioinformatics online.
A varying threshold method for ChIP peak-calling using multiple sources of information
Authors: Chen Kuan-Bei , Zhang Yu 
Published: September 2010

AbstractMotivation: Gene regulation commonly involves interaction among DNA, proteins and biochemical conditions. Using chromatin immunoprecipitation (ChIP) technologies, protein–DNA interactions are routinely detected in the genome scale. Computational methods that detect weak protein-binding signals and simultaneously maintain a high specificity yet remain to be challenging. An attractive approach is to incorporate biologically relevant data, such as protein co-occupancy, to improve the power of protein-binding detection. We call the additional data related with the target protein binding as supporting tracks.Results: We propose a novel but rigorous statistical method to identify protein occupancy in ChIP data using multiple supporting tracks (PASS2). We demonstrate that utilizing biologically related information can significantly increase the discovery of true protein-binding sites, while still maintaining a desired level of false positive calls. Applying the method to GATA1 restoration in mouse erythroid cell line, we detected many new GATA1-binding sites using GATA1 co-occupancy data.Availability: http://stat.psu.edu/∼yuzhang/pass2.tarContact: yuzhang@stat.psu.edu
De-correlating expression in gene-set analysis
Authors: Nam Dougu 
Published: September 2010

Abstract
Motivation: Group-wise pattern analysis of genes, known as gene-set analysis (GSA), addresses the differential expression pattern of biologically pre-defined gene sets. GSA exhibits high statistical power and has revealed many novel biological processes associated with specific phenotypes. In most cases, however, GSA relies on the invalid assumption that the members of each gene set are sampled independently, which increases false predictions.
Results: We propose an algorithm, termed DECO, to remove (or alleviate) the bias caused by the correlation of the expression data in GSAs. This is accomplished through the eigenvalue-decomposition of covariance matrixes and a series of linear transformations of data. In particular, moderate de-correlation methods that truncate or re-scale eigenvalues were proposed for a more reliable analysis. Tests of simulated and real experimental data show that DECO effectively corrects the correlation structure of gene expression and improves the prediction accuracy (specificity and sensitivity) for both gene- and sample-randomizing GSA methods.
Availability: The MATLAB codes and the tested data sets are available at ftp://deco.nims.re.kr/pub or from the author.
Contact: dougnam@unist.ac.kr
Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
Authors: Tari Luis , Anwar Saadat , Liang Shanshan , Cai James , Baral Chitta 
Published: September 2010

AbstractMotivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning.Results: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.Contact: luis.tari@roche.com
Improving disease gene prioritization using the semantic similarity of Gene Ontology terms
Authors: Schlicker Andreas , Lengauer Thomas , Albrecht Mario 
Published: September 2010

Abstract
Motivation: Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level.
Results: Here, we introduce MedSim, a novel approach for ranking candidate genes for a particular disease based on functional comparisons involving the Gene Ontology. MedSim uses functional annotations of known disease genes for assessing the similarity of diseases as well as the disease relevance of candidate genes. We benchmarked our approach with genes known to be involved in 99 diseases taken from the OMIM database. Using artificial quantitative trait loci, MedSim achieved excellent performance with an area under the ROC curve of up to 0.90 and a sensitivity of over 70% at 90% specificity when classifying gene products according to their disease relatedness. This performance is comparable or even superior to related methods in the field, albeit using less and thus more easily accessible information.
Availability: MedSim is offered as part of our FunSimMat web service (http://www.funsimmat.de).
Contact: mario.albrecht@mpi-inf.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Efficient parameter search for qualitative models of regulatory networks using symbolic model checking
Authors: Batt Gregory , Page Michel , Cantone Irene , Goessler Gregor , Monteiro Pedro , de Jong Hidde 
Published: September 2010

Abstract
Motivation: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior.
Results: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust.
Availability: GNA and the IRMA model are available at http://ibis.inrialpes.fr/
Contact: gregory.batt@inria.fr
Supplementary information: Supplementary data are available at Bioinformatics online.