bluuebunny/crossref_metadata_2025_split huggingface dataset:
Significance of serum adiponectin levels in patients with chronic liver disease
Authors: Balmer Maria Luisa , Joneli Jeannine , Schoepfer Alain , Stickel Felix , Thormann Wolfgang , Dufour Jean-François 
Published: June 2010

Adiponectin, which plays a pivotal role in metabolic liver diseases, is reduced in concentration in patients with NASH (non-alcoholic steatohepatitis). The aim of the present study was to determine adiponectin concentrations in patients with different forms and stages of chronic liver diseases. Serum adiponectin concentrations were measured in 232 fasting patients with chronic liver disease: 64 with NAFLD (non-alcoholic fatty liver disease), 123 with other chronic liver disease (e.g. viral hepatitis, n=71; autoimmune disease, n=18; alcohol-induced liver disease, n=3; or elevated liver enzymes of unknown origin, n=31) and 45 with cirrhosis. Circulating adiponectin levels were significantly lower in patients with NAFLD in comparison with patients with other chronic liver disease (4.8±3.5 compared with 10.4±6.3 μg/ml respectively; P&lt;0.0001). Circulating adiponectin levels were significantly higher in patients with cirrhosis in comparison with patients without cirrhosis (18.6±14.5 compared with 8.4±6.1 μg/ml respectively; P&lt;0.0001). Adiponectin concentrations correlated negatively with body weight (P&lt;0.001), serum triacylglycerols (triglycerides) (P&lt;0.001) and, in women, with BMI (body mass index) (P&lt;0.001). Adiponectin concentrations correlated positively with serum bile acids (P&lt;0.001), serum hyaluronic acid (P&lt;0.001) and elastography values (P&lt;0.001). Adiponectin levels were decreased in patients with NAFLD. In conclusion, adiponectin levels correlate positively with surrogate markers of hepatic fibrosis (transient elastography, fasting serum bile acids and hyaluronate) and are significantly elevated in cases of cirrhosis.
Lexical and Grammatical Associations in Sequential Bilingual Preschoolers
Authors: Kohnert Kathryn , Kan Pui Fong , Conboy Barbara T. 
Published: June 2010

PurposeThe authors investigated potential relationships between traditional linguistic domains (words, grammar) in the first (L1) and second (L2) languages of young sequential bilingual preschool children.MethodParticipants were 19 children, ages 2;11 (years;months) to 5;2 (M= 4;3) who began learning Hmong as the L1 from birth and English as the L2 during early childhood. Measures were the number of different words (NDW) and mean length of utterance (MLU) produced during a story retell task and scores on picture identification, an independent measure of receptive vocabulary. Correlations were conducted to determine relationships among measures.ResultsIn English, there were robust positive relationships between MLU and lexical measures (NDW, Picture Identification). In Hmong, more modest cross-domain associations were evident between lexical measures and MLU. There were positive cross-language links for NDW but more limited cross-domain correspondences between the L1 and the L2.ConclusionsIn English, relationships between words and grammar were similar to those found in previous studies with monolingual and simultaneous bilingual toddlers. Weaker cross-domain associations in the L1 may reflect participants' greater development in Hmong or typological differences between the L1 and the L2.
Perception of Phonemic Length and Its Relation to Reading and Spelling Skills in Children With Family Risk for Dyslexia in the First Three Grades of School
Authors: Pennala Riitta , Eklund Kenneth , Hämäläinen Jarmo , Richardson Ulla , Martin Maisa , Leiwo Matti , Leppänen Paavo H. T. , Lyytinen Heikki 
Published: June 2010

Purpose
To examine the ability to discriminate phonemic length and the association of this ability with reading accuracy, reading speed, and spelling accuracy in Finnish children throughout Grades 1–3.

Method

Reading-disabled (RDFR,
n
= 35) and typically reading children (TRFR,
n
= 69) with family risk for dyslexia and typically reading control children (TRC,
n
= 80) were tested once in each grade of Grades 1–3 using a phonemic length discrimination task. Reading, spelling, IQ, verbal short-term memory, phonological memory, and naming speed were assessed.

Results

The RDFR group made more errors in phonemic length discrimination than the TRC group in Grades 2 and 3. After taking into account variance in verbal short-term memory, phonological memory, and naming speed, discrimination ability explained unique variance of spelling accuracy in Grades 2 and 3 and reading accuracy in Grade 3 in the RDFR group. At the individual level, in Grade 2, 31.4% of the RDFR group and 14.7% of the TRFR group performed below −1.25
SD
s in the phonemic length discrimination task.

Conclusion
Problems in phonemic length discrimination could be one of the accumulating risk factors affecting development leading to dyslexia.
Differentiating Cantonese-Speaking Preschool Children With and Without SLI Using MLU and Lexical Diversity (D)
Authors: Wong Anita M.-Y. , Klee Thomas , Stokes Stephanie F. , Fletcher Paul , Leonard Laurence B. 
Published: June 2010

Purpose
In this study, the authors examined the diagnostic accuracy of a composite clinical assessment measure based on mean length of utterance (MLU), lexical diversity (D), and age (Klee, Stokes, Wong, Fletcher, & Gavin, 2004) in a second, independent sample of 4-year-old Cantonese-speaking children with and without specific language impairment (SLI).

Method
The composite measure was calculated from play-based, conversational language samples of 15 children with SLI and 14 children without SLI. Scores were dichotomized and compared to diagnostic outcomes using a reference standard based on clinical judgment supported by test scores.

Results
Eleven of 15 children with SLI and 8 of 14 children with typical language skills were correctly classified by the dichotomized composite measure. The measure’s sensitivity in this second sample was 73.3% (95% confidence interval [CI] 48%–89%); specificity was 57.1% (95% CI 33%–79%); positive likelihood ratio was 1.71 (95% CI 0.87–3.37); and negative likelihood ratio was 0.47 (95% CI 0.18–1.21).

Conclusions
The diagnostic accuracy of the composite measure was substantially lower than in the original study, suggesting that it is unlikely to be informative for clinical use in its present form. The value of replication studies is discussed.
Markov dynamic models for long-timescale protein motion
Authors: Chiang Tsung-Han , Hsu David , Latombe Jean-Claude 
Published: June 2010

Abstract
Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.
Contact: chiangts@comp.nus.edu.sg
Sparse multitask regression for identifying common mechanism of response to therapeutic targets
Authors: Zhang Kai , Gray Joe W. , Parvin Bahram 
Published: June 2010

AbstractMotivation: Molecular association of phenotypic responses is an important step in hypothesis generation and for initiating design of new experiments. Current practices for associating gene expression data with multidimensional phenotypic data are typically (i) performed one-to-one, i.e. each gene is examined independently with a phenotypic index and (ii) tested with one stress condition at a time, i.e. different perturbations are analyzed separately. As a result, the complex coordination among the genes responsible for a phenotypic profile is potentially lost. More importantly, univariate analysis can potentially hide new insights into common mechanism of response.Results: In this article, we propose a sparse, multitask regression model together with co-clustering analysis to explore the intrinsic grouping in associating the gene expression with phenotypic signatures. The global structure of association is captured by learning an intrinsic template that is shared among experimental conditions, with local perturbations introduced to integrate effects of therapeutic agents. We demonstrate the performance of our approach on both synthetic and experimental data. Synthetic data reveal that the multi-task regression has a superior reduction in the regression error when compared with traditional L1-and L2-regularized regression. On the other hand, experiments with cell cycle inhibitors over a panel of 14 breast cancer cell lines demonstrate the relevance of the computed molecular predictors with the cell cycle machinery, as well as the identification of hidden variables that are not captured by the baseline regression analysis. Accordingly, the system has identified CLCA2 as a hidden transcript and as a common mechanism of response for two therapeutic agents of CI-1040 and Iressa, which are currently in clinical use.Contact: b_parvin@lbl.gov
Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM
Authors: Vaske Charles J. , Benz Stephen C. , Sanborn J. Zachary , Earl Dent , Szeto Christopher , Zhu Jingchun , Haussler David , Stuart Joshua M. 
Published: June 2010

AbstractMotivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference.Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.Availability:Source code available at http://sbenz.github.com/ParadigmContact: jstuart@soe.ucsc.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model
Authors: Yizhak Keren , Benyamini Tomer , Liebermeister Wolfram , Ruppin Eytan , Shlomi Tomer 
Published: June 2010

Abstract
Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.
Results: IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism.
Contacts: kerenyiz@post.tau.ac.il; tomersh@cs.technion.ac.il
Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate
Authors: Ye Ding , Fu Yan , Sun Rui-Xiang , Wang Hai-Peng , Yuan Zuo-Fei , Chi Hao , He Si-Min 
Published: June 2010

Abstract
Motivation: Identification of post-translationally modified proteins has become one of the central issues of current proteomics. Spectral library search is a new and promising computational approach to mass spectrometry-based protein identification. However, its potential in identification of unanticipated post-translational modifications has rarely been explored. The existing spectral library search tools are designed to match the query spectrum to the reference library spectra with the same peptide mass. Thus, spectra of peptides with unanticipated modifications cannot be identified.
Results: In this article, we present an open spectral library search tool, named pMatch. It extends the existing library search algorithms in at least three aspects to support the identification of unanticipated modifications. First, the spectra in library are optimized with the full peptide sequence information to better tolerate the peptide fragmentation pattern variations caused by some modification(s). Second, a new scoring system is devised, which uses charge-dependent mass shifts for peak matching and combines a probability-based model with the general spectral dot-product for scoring. Third, a target-decoy strategy is used for false discovery rate control. To demonstrate the effectiveness of pMatch, a library search experiment was conducted on a public dataset with over 40 000 spectra in comparison with SpectraST, the most popular library search engine. Additional validations were done on four published datasets including over 150 000 spectra. The results showed that pMatch can effectively identify unanticipated modifications and significantly increase spectral identification rate.
Availability: http://pfind.ict.ac.cn/pmatch/
Contact: yfu@ict.ac.cn; rxsun@ict.ac.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
Semi-automated ontology generation within OBO-Edit
Authors: Wächter Thomas , Schroeder Michael 
Published: June 2010

AbstractMotivation: Ontologies and taxonomies have proven highly beneficial for biocuration. The Open Biomedical Ontology (OBO) Foundry alone lists over 90 ontologies mainly built with OBO-Edit. Creating and maintaining such ontologies is a labour-intensive, difficult, manual process. Automating parts of it is of great importance for the further development of ontologies and for biocuration.Results: We have developed the Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG), a system which supports the creation and extension of OBO ontologies by semi-automatically generating terms, definitions and parent–child relations from text in PubMed, the web and PDF repositories. DOG4DAG is seamlessly integrated into OBO-Edit. It generates terms by identifying statistically significant noun phrases in text. For definitions and parent–child relations it employs pattern-based web searches. We systematically evaluate each generation step using manually validated benchmarks. The term generation leads to high-quality terms also found in manually created ontologies. Up to 78% of definitions are valid and up to 54% of child–ancestor relations can be retrieved. There is no other validated system that achieves comparable results.By combining the prediction of high-quality terms, definitions and parent–child relations with the ontology editor OBO-Edit we contribute a thoroughly validated tool for all OBO ontology engineers.Availability: DOG4DAG is available within OBO-Edit 2.1 at http://www.oboedit.orgContact: thomas.waechter@biotec.tu-dresden.de;Supplementary Information: Supplementary data are available at Bioinformatics online.
Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation
Authors: Yang Rendong , Su Zhen 
Published: June 2010

Abstract
Motivation: Circadian rhythms are prevalent in most organisms. Identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Such genes are largely detected by searching periodic patterns in microarray data. However, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise. This makes circadian rhythmic analysis of temporal microarray data very challenging.
Results: We propose an algorithm named ARSER, which combines time domain and frequency domain analysis for extracting and characterizing rhythmic expression profiles from temporal microarray data. ARSER employs autoregressive spectral estimation to predict an expression profile's periodicity from the frequency spectrum and then models the rhythmic patterns by using a harmonic regression model to fit the time-series. ARSER describes the rhythmic patterns by four parameters: period, phase, amplitude and mean level, and measures the multiple testing significance by false discovery rate q-value. When tested on well defined periodic and non-periodic short time-series data, ARSER was superior to two existing and widely-used methods, COSOPT and Fisher's G-test, during identification of sinusoidal and non-sinusoidal periodic patterns in short, noisy and non-stationary time-series. Finally, analysis of Arabidopsis microarray data using ARSER led to identification of a novel set of previously undetected non-sinusoidal periodic transcripts, which may lead to new insights into molecular mechanisms of circadian rhythms.
Availability: ARSER is implemented by Python and R. All source codes are available from http://bioinformatics.cau.edu.cn/ARSER
Contact: zhensu@cau.edu.cn
Automated tracking and analysis of centrosomes in early Caenorhabditis elegans embryos
Authors: Jaensch Steffen , Decker Markus , Hyman Anthony A. , Myers Eugene W. 
Published: June 2010

Abstract
Motivation: The centrosome is a dynamic structure in animal cells that serves as a microtubule organizing center during mitosis and also regulates cell-cycle progression and sets polarity cues. Automated and reliable tracking of centrosomes is essential for genetic screens that study the process of centrosome assembly and maturation in the nematode Caenorhabditis elegans.
Results: We have developed a fully automatic system for tracking and measuring fluorescently labeled centrosomes in 3D time-lapse images of early C.elegans embryos. Using a spinning disc microscope, we monitor the centrosome cycle in living embryos from the 1- up to the 16-cell stage at imaging intervals between 30 and 50 s. After establishing the centrosome trajectories with a novel method involving two layers of inference, we also automatically detect the nuclear envelope breakdown in each cell division and recognize the identities of the centrosomes based on the invariant cell lineage of C.elegans. To date, we have tracked centrosomes in over 500 wild type and mutant embryos with almost no manual correction required.
Availability: The centrosome tracking software along with test data is freely available at http://publications.mpi-cbg.de/itemPublication.html?documentId=4082
Contact: jaensch@mpi-cbg.de
A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images
Authors: Lin Yu-Shi , Lin Chung-Chih , Tsai Yuh-Show , Ku Tien-Chuan , Huang Yi-Hung , Hsu Chun-Nan 
Published: June 2010

Abstract
Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise.
Results: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A.
Availability: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/.
Contact: chunnan@iis.sinica.edu.tw
Supplementary information: Supplementary data are available at Bioinformatics online.