bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systematic interaction analysis of human lipocalin-type prostaglandin D synthase with small lipophilic ligands
Authors: Kume Satoshi , Lee Young-Ho , Miyamoto Yuya , Fukada Harumi , Goto Yuji , Inui Takashi 
Published: June 2012

L-PGDS [lipocalin-type PG (prostaglandin) D synthase] is a multi-functional protein, acting as a PGD2-producing enzyme and a lipid-transporter. In the present study, we focus on the function of L-PGDS as an extracellular transporter for small lipophilic molecules. We characterize the binding mechanism of human L-PGDS for the molecules, especially binding affinity stoichiometry and driving force, using tryptophan fluorescence quenching, ICD (induced circular dichroism) and ITC (isothermal titration calorimetry). The tryptophan fluorescence quenching measurements revealed that haem metabolites such as haemin, biliverdin and bilirubin bind to L-PGDS with significantly higher affinities than the other small lipophilic ligands examined, showing dissociation constant (Kd) values from 17.0 to 20.9 nM. We focused particularly on the extra-specificities of haem metabolites and L-PGDS. The ITC and ICD data revealed that two molecules of the haem metabolites bind to L-PGDS with high and low affinities, showing Kd values from 2.8 to 18.1 nM and from 0.209 to 1.63 μM respectively. The thermodynamic parameters for the interactions revealed that the contributions of enthalpy and entropy change were considerably different for each haem metabolite even when the Gibbs energy change was the same. Thus we believe that the binding energy of haem metabolites to L-PGDS is optimized by balancing enthalpy and entropy change.
Oxytetracycline Pharmacokinetics in Rainbow Trout during and after an Orally Administered Medicated Feed Regimen
Authors: Miller Ron A. , Pelsor Francis R. , Kane Andrew S. , Reimschuessel Renate 
Published: June 2012

Abstract
The pharmacokinetic–pharmacodynamic predictor of antimicrobial activity for tetracyclines is reported to be the area under the concentration–time curve at steady state (AUCss) divided by the minimal inhibitory concentration of the targeted pathogen. Here, we estimate AUCss values for oxytetracycline (OTC) in serum of rainbow trout Oncorhynchus mykiss by using a destructive sampling study design. Seventy-two rainbow trout were fed OTC-medicated feed at 74.7 ± 1.5 mg/kg (mean ± SD) body weight (BW) by oral gavage for 10 consecutive days. Serum was collected from nine fish at 1, 3, 6, 8, 10, 12, 15, and 22 d after dosing began. Serum OTC concentrations were measured by high-performance liquid chromatography with a 0.01-μg/mL limit of detection. The average OTC AUCss was 29.2 μg × h/mL and was estimated using nonlinear mixed-effects modeling and bootstrap resampling techniques. The elimination half-life was estimated as 85.0 h, and the fraction of steady state achieved was estimated as 0.85. The calculated AUCss (24.8 μg × h/mL) following 10 d of oral dosing with 75 mg OTC/kg BW was less than the estimated AUCss. Results suggest that the pharmacokinetics of OTC exposure, including the AUCss, is better evaluated by using multiday dosimetry than by using a standard single-dose protocol.
Received September 29, 2011; accepted January 30, 2012
Recognition models to predict DNA-binding specificities of homeodomain proteins
Authors: Christensen Ryan G. , Enuameh Metewo Selase , Noyes Marcus B. , Brodsky Michael H. , Wolfe Scot A. , Stormo Gary D. 
Published: June 2012

Abstract
Motivation: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C2H2 zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes.
Results: Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.
Contact: stormo@wustl.edu
Matching experiments across species using expression values and textual information
Authors: Wise Aaron , Oltvai Zoltán N. , Bar−Joseph Ziv 
Published: June 2012

Abstract
Motivation: With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease-related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species.
Results: To enable better cross-species comparisons, we developed methods for automatically identifying pairs of similar expression datasets across species. Our method uses a co-training algorithm to combine a model of expression similarity with a model of the text which accompanies the expression experiments. The co-training method outperforms previous methods based on expression similarity alone. Using expert analysis, we show that the new matches identified by our method indeed capture biological similarities across species. We then use the matched expression pairs between human and mouse to recover known and novel cycling genes as well as to identify genes with possible involvement in diabetes. By providing the ability to identify novel candidate genes in model organisms, our method opens the door to new models for studying diseases.
Availability: Source code and supplementary information is available at: www.andrew.cmu.edu/user/aaronwis/cotrain12.
Contact: zivbj@cs.cmu.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model
Authors: Polishko Anton , Ponts Nadia , Le Roch Karine G. , Lonardi Stefano 
Published: June 2012

Abstract
Motivation: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or ‘fuzzy’ in the sequenced cell sample.
Results: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes.
Availability: Visit http://www.cs.ucr.edu/~polishka
Contact: stelo@cs.ucr.edu or polishka@cs.ucr.edu
MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins
Authors: Disfani Fatemeh Miri , Hsu Wei-Lun , Mizianty Marcin J. , Oldfield Christopher J. , Xue Bin , Dunker A. Keith , Uversky Vladimir N. , Kurgan Lukasz 
Published: June 2012

Abstract
Motivation: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains.
Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues.
Availability: http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf
Contact: lkurgan@ece.ualberta.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
A single source k-shortest paths algorithm to infer regulatory pathways in a gene network
Authors: Shih Yu-Keng , Parthasarathy Srinivasan 
Published: June 2012

Abstract
Motivation: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene.
Results: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines.
Availability: All data and codes are freely available upon request.
Contact: srini@cse.ohio-state.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores
Authors: Hochbaum Dorit S. , Hsu Chun-Nan , Yang Yan T. 
Published: June 2012

Abstract
Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut—normalized cut prime (FABS-NC′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.
Results: We compare the performance of FABS-NC′ to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC′ also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC′ consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC′: In some cases FABS-NC′ produces over half correctly predicted ranking experiment trials than FABS-SVM.
Availability: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.
Contact: yxy128@berkeley.edu
DELISHUS: an efficient and exact algorithm for genome-wide detection of deletion polymorphism in autism
Authors: Aguiar Derek , Halldórsson Bjarni V. , Morrow Eric M. , Istrail Sorin 
Published: June 2012

AbstractMotivation: The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A considerable portion of autism appears to be correlated with copy number variation, which is not directly probed by single nucleotide polymorphism (SNP) array or sequencing technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem partly due to the inability of algorithms to detect them.Results: In this article, we present an algorithmic framework, which we term DELISHUS, that implements three exact algorithms for inferring regions of hemizygosity containing genomic deletions of all sizes and frequencies in SNP genotype data. We implement an efficient backtracking algorithm—that processes a 1 billion entry genome-wide association study SNP matrix in a few minutes—to compute all inherited deletions in a dataset. We further extend our model to give an efficient algorithm for detecting de novo deletions. Finally, given a set of called deletions, we also give a polynomial time algorithm for computing the critical regions of recurrent deletions. DELISHUS achieves significantly lower false-positive rates and higher power than previously published algorithms partly because it considers all individuals in the sample simultaneously. DELISHUS may be applied to SNP array or sequencing data to identify the deletion spectrum for family-based association studies.Availability: DELISHUS is available at http://www.brown.edu/Research/Istrail_Lab/.Contact: Eric_Morrow@brown.edu and Sorin_Istrail@brown.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Exploring the Needs of Socially Excluded Young Men
Authors: Rondón Janeet , Campbell James , Galway Karen , Leavey Gerard 
Published: June 2012

In recent years, social exclusion has gained the attention of governments around the world. This paper reports on a qualitative study involving 27 young men aged 14–19 years experiencing particular forms of exclusion in the context of Northern Irish society. The study used a focus group methodology to elicit their views with the aim of exploring both emotional and psychological needs and the structural factors that may contribute to their experiences of social exclusion. The findings reveal a range of unmet social and psychological needs, the impact of sectarianism and segregation, and a paucity of support systems in place. The paper concludes by making recommendations about how policy‐makers and practitioners could find more imaginative ways of engaging such vulnerable young men to reduce social exclusion.
Seasonal interactions between photoperiod and maternal effects determine offspring phenotype inFranklin's gull
Authors: Clark Mark E. , Reed Wendy L. 
Published: June 2012

Summary1.When predictable seasonal changes affect offspring fitness, we expect offspring to evolve phenotypes that minimize the costs of seasonal variation in timing of breeding. For species with parental care during embryonic development, offspring receive seasonal cues of the environment from parents that are biased by their parent's fitness (which is not equivalent to offspring fitness). Therefore, mechanisms enabling offspring to detect environmental cues independent of parents should be strongly favoured.2.We experimentally evaluated the ability of avian embryos to integrate cues of season from photoperiod and maternal environments present in eggs to produce seasonal variation in phenotypes among Franklin's gull (Leucophaeus pipixcan) hatchlings. Eggs were collected early and late in the season and some were separated into their component parts and others were incubated under short (early season) and long (late season) photoperiods. After hatching, we measured the structural size of the chicks and the amount of yolk sac reserves.3.We found that hatchling size, a phenotype linked with fitness, is sensitive to both egg contents provided by mothers and photoperiod, and development time decreases across the season. The effects of integrating cues of season from eggs and photoperiod on offspring phenotype are complex, and when cues of season from eggs are mismatched with cues of season from photoperiod, alternate phenotypes are created.4.We also found that seasonal variation in egg size, yolk, albumen or shell content of the eggs do not account for the seasonal maternal egg effect on hatchling size. This seasonal maternal effect could be a result of other egg constituents or reflect heritable variation in timing of breeding that is linked with offspring size.5.Changes in breeding phenology of adults could result in a mismatch between cues from parents and photoperiod cues of season. For example, if breeding seasons advanced such that late season birds initiate breeding at an early season photoperiod, offspring would then be integrating maternal cues of late season with photoperiod cues of early season and alter their phenotypes. We expect our results to initiate new studies on how vertebrate embryos integrate environmental cues with maternal effects and offspring responses to optimize the expression of offspring phenotype.
Characteristics of gelling agent substituted fruit jam: studies on the textural, optical, physicochemical and sensory properties
Authors: Javanmard Marjan , Chin Nyuk Ling , Mirhosseini Seyed Hamed , Endan Johari 
Published: June 2012

SummaryA series of mango jams were prepared using different hydrocolloid solutions (high methoxyl pectin (HMP), carboxymethyl cellulose (CMC), and sago starch) as gelling agents to investigate the effect of hydrocolloid type, hydrocolloid concentration, and freeze‐thaw treatment on the physicochemical, optical, textural, and sensory characteristics. The optical properties results revealed that the total colour change (ΔE) of the sago starch‐based mango jam samples displayed higher values in comparison with samples prepared with HMP and CMC. The textural parameters (hardness, work of shear, stickiness, and work of adhesion) increased with hydrocolloid concentration but decreased with the freeze‐thaw treatment except for sago starch‐based mango jams. The overall acceptability of mango jams containing 6% sago starch was rated similarly to the samples prepared with 0.7% HMP and 0.5% CMC. Principle component analysis revealed that it could be used as an effective tool to segregate characteristics related to fruit jam manufacture.
Nitrite and nitrate in fresh meats: a contribution to the estimation of admissible maximum limits to introduce in directive 95/2/EC
Authors: Iammarino Marco , Di Taranto Aurelia 
Published: June 2012

SummaryNitrite and nitrate are food preservatives not allowed in fresh meat preparations. Two hundred fresh meat samples were analysed to evaluate the natural presence of nitrite and nitrate and estimate the maximum concentrations to considerate as ‘natural’ and to admit in fresh meat preparations. The analyses were carried out by a validated ion chromatography method, and the ‘positive’ samples were confirmed by an alternative ion chromatography method. Nitrite residues were not detected. Quantifiable nitrate concentrations, ranging from 10.2 to 36.5 mg kg−1, were found in nineteen samples. In chicken meat samples, nitrate residues were not detected. Taking into account the distribution of nitrate concentrations, it is possible to suggest a maximum admissible limit of 30.0 mg kg−1 in beef and pork meats; in equine meats, this limit may be increased to 40.0 mg kg−1. Below these values, the samples can be considered as ‘compliant’.