bluuebunny/crossref_metadata_2025_split huggingface dataset:
Public health approach to detection of non-O157 Shiga toxin-producingEscherichia coli: summary of two outbreaks and laboratory procedures
Authors: SCHAFFZIN J. K. , CORONADO F. , DUMAS N. B. , ROOT T. P. , HALSE T. A. , SCHOONMAKER-BOPP D. J. , LURIE M. M. , NICHOLAS D. , GERZONICH B. , JOHNSON G. S. , WALLACE B. J. , MUSSER K. A. 
Published: May 2011

SUMMARYRoutine laboratory testing may not detect non-O157 Shiga toxin-producingEscherichia coli(STEC) reliably. Active clinical, epidemiological, environmental health, and laboratory collaboration probably influence successful detection and study of non-O157 STEC infection. We summarized two outbreak investigations in which such coordinated efforts identified non-O157 STEC disease and led to effective control measures. Outbreak 1 involved illness associated with consuming unpasteurized apple cider from a local orchard. Public health personnel were notified by a local hospital; stool specimens from ill persons contained O111 STEC. Outbreak 2 involved bloody diarrhoea at a correctional facility. Public health personnel were notified by the facility infection control officer; O45 STEC was the implicated agent. These reports highlight the ability of non-O157 STEC to cause outbreaks and demonstrate that a coordinated effort by clinicians, infection-control practitioners, clinical diagnostic laboratorians, and public health personnel can lead to effective identification, investigation, and prevention of non-O157 STEC disease.
Explaining Differences in Episodic Memory Performance among Older African Americans and Whites: The Roles of Factors Related to Cognitive Reserve and Test Bias
Authors: Fyffe Denise C. , Mukherjee Shubhabrata , Barnes Lisa L. , Manly Jennifer J. , Bennett David A. , Crane Paul K. 
Published: May 2011

AbstractOlder African Americans tend to perform poorly in comparison with older Whites on episodic memory tests. Observed group differences may reflect some combination of biological differences, measurement bias, and other confounding factors that differ across groups. Cognitive reserve refers to the hypothesis that factors, such as years of education, cognitive activity, and socioeconomic status, promote brain resilience in the face of pathological threats to brain integrity in late life. Educational quality, measured by reading test performance, has been postulated as an important aspect of cognitive reserve. Previous studies have not concurrently evaluated test bias and other explanations for observed differences between older African Americans and Whites. We combined data from two studies to address this question. We analyzed data from 273 African American and 720 White older adults. We assessed DIF using an item response theory/ordinal logistic regression approach. DIF and factors associated with cognitive reserve did not explain the relationship between race, and age- and sex-adjusted episodic memory test performance. However, reading level did explain this relationship. The results reinforce the importance of considering education quality, as measured by reading level, when assessing cognition among diverse older adults. (JINS, 2011,17, 625–638)
μ-EXAFS and μ-XRD study of selected regions of a Al–Fe–Cr–Ti complex alloy
Authors: Liotti E. , Hogg S. C. , Kirk C.A. , Quinn P. , Grant P. S. 
Published: May 2011

Al3Ti, Al13Cr2 and Al13Fe4 are important intermetallics in a number of Al alloy systems including complex ultra-high-strength systems with excellent elevated-temperature performance. A full knowledge of their properties and crystallographic structures is a key factor for the understanding of these complex alloys. In the present study samples of the three pure intermetallics were prepared and regions of interest identified in a billet of Al93Fe3Cr2Ti2 alloys and 20 × 10 × 2 µm samples extracted utilizing a Focussed Ion Beam Transmission Electron Microscopy (FIB TEM) sample preparation technique. Using the microfocus spectroscopy beamline I18 at Diamond Light Source we were able to examine 5 µm sections of the samples using X-ray Diffraction (μ-XRD) and Extended X-ray Absorption Fine Structure (μ-EXAFS) in an attempt to describe the local structure of the second-phase particles and characterized the microstructure of the FIBed samples to selectively illuminate the different phases.
Probabilistic divergence measures for detecting interspecies

recombination
Authors: Husmeier Dirk , Wright Frank 
Published: May 2011

Abstract
This paper proposes a graphical method for detecting interspecies recombination in multiple alignments of DNA sequences. A fixed-size window is moved along a given DNA sequence alignment. For every position, the marginal posterior probability over tree topologies is determined by means of a Markov chain Monte Carlo simulation. Two probabilistic divergence measures are plotted along the alignment, and are used to identify recombinant regions. The method is compared with established detection methods on a set of synthetic benchmark sequences and two real-world DNA sequence alignments.
Contact: dirk@bioss.ac.uk
SCOPE: a probabilistic model for scoring tandem mass spectra
against a peptide database
Authors: Bafna Vineet , Edwards Nathan 
Published: May 2011

Abstract
Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.
Contact: Vineet.Bafna@Celera.Com
Integrating genomic homology into gene structure prediction
Authors: Korf Ian , Flicek Paul , Duan Daniel , Brent Michael R. 
Published: May 2011

Abstract
Twinscan is a new gene-structure prediction system that directly extends the probability model of Genscan, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolutionary conservation. Twinscan is specifically designed for the analysis of high-throughput genomic sequences containing an unknown number of genes. In experiments on high-throughput mouse sequences, using homologous sequences from the human genome, Twinscan shows notable improvement over Genscan in exon sensitivity and specificity and dramatic improvement in exact gene sensitivity and specificity. This improvement can be attributed entirely to modeling the patterns of evolutionary conservation in genomic sequence.
Contact: ikorf@cs.wustl.edu; pflicek@cs.wustl.edu; duan@cs.wustl.edu; brent@cs.wustl.edu
Prediction of the coupling specificity of G protein coupled
receptors to their G proteins
Authors: Möller Steffen , Vilo Jaak , Croning Michael D.R. 
Published: May 2011

Abstract
G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo , from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silico characterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases. Available at: http://www.ebi.ac.uk/~croning/coupling.html
Contact: croning@ebi.ac.uk
Joint modeling of DNA sequence and physical properties to

improve eukaryotic promoter recognition
Authors: Ohler Uwe , Niemann Heinrich , Liao Guo-chun , Rubin Gerald M. 
Published: May 2011

Abstract
We present an approach to integrate physical properties of DNA, such as DNA bendability or GC content, into our probabilistic promoter recognition system McPromoter. In the new model, a promoter is represented as a sequence of consecutive segments represented by joint likelihoods for DNA sequence and profiles of physical properties. Sequence likelihoods are modeled with interpolated Markov chains, physical properties with Gaussian distributions. The background uses two joint sequence/profile models for coding and non-coding sequences, each consisting of a mixture of a sense and an anti-sense submodel. On a large Drosophila test set, we achieved a reduction of about 30% of false positives when compared with a model solely based on sequence likelihoods.
Contact: Uwe.Ohler@informatik.uni-erlangen.de
Designing better phages
Authors: Skiena Steven S. 
Published: May 2011

Abstract
We propose a method to engineer the genome of bacteriophages to increase their effectiveness as antibacterial agents. Specifically, we exploit the redundancy of the triplet code to design genomes that avoid restriction sites while producing the same proteins as wild-type phages. We give an efficient algorithm to minimize the number of restriction sites against sets of cutter sequences, and demonstrate that that phage genomes can be significantly protected against surprisingly large sets of enzymes with no loss of function. Finally, we develop a model to explain why evolution has failed to eliminate many possible restriction sites despite selective pressure, thus motivating the need for genome-level sequence engineering.
Contact: skiena@cs.sunysb.edu
0j.py: a software tool for low complexity proteins and protein

domains
Authors: Wise Michael J. 
Published: May 2011

Abstract
Low complexity proteins and protein domains have sequences which appear highly non-random. Over the years, these sequences have been routinely filtered out during sequence similarity searches because interest has been focused on globular proteins, and inclusion of these domains can severely skew search results. However, early work on these proteins and more recent studies of the related area of repeated protein sequences suggests that low complexity protein domains have function and therefore are in need of further investigation. 0j.py is a new tool for demarcating low complexity protein domains more accurately than has been possible to date. The paper describes 0j.py and its use in revealing proteins with repeated and poly-amino-acid peptides. Statistical methods are then employed to to examine the distribution of these proteins across species, while keyword clustering is used to suggest roles performed by proteins through the use of low complexity domains.
Contact: M.Wise@ccsr.cam.ac.uk
Protein-protein interaction map inference using interacting

domain profile pairs
Authors: Wojcik Jérôme , Schächter Vincent 
Published: May 2011

Abstract
A number of predictive methods have been designed to predict protein interaction from sequence or expression data. On the experimental front, however, high-throughput proteomics technologies are starting to yield large volumes of protein-protein interaction data. High-quality experimental protein interaction maps constitute the natural dataset upon which to build interaction predictions. Thus the motivation to develop the first interaction-based protein interaction map prediction algorithm.
A technique to predict protein-protein interaction maps across organisms is introduced, the ‘interaction-domain pair profile’ method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. We apply this approach to the prediction of an interaction map of Escherichia coli from the recently published interaction map of the human gastric pathogen Helicobacter pylori. Results are compared with predictions of a second inference method based only on full-length protein sequence similarity — the “naive” method. The domain-based method is shown to i) eliminate a significant amount of false-positives of the naive method that are the consequences of multi-domain proteins; ii) increase the sensitivity compared to the naive method by identifying new potential interactions.
Availability: Contact the authors.
Contact: vschachter@hybrigenics.fr
CLIFF: clustering of high-dimensional microarray data via

iterative feature filtering using normalized cuts
Authors: Xing Eric P. , Karp Richard M. 
Published: May 2011

Abstract
We present CLIFF, an algorithm for clustering biological samples using gene expression microarray data. This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high dimensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant. Our algorithm iterates between two computational processes, feature filtering and clustering. Given a reference partition that approximates the correct clustering of the samples, our feature filtering procedure ranks the features according to their intrinsic discriminability, relevance to the reference partition, and irredundancy to other relevant features, and uses this ranking to select the features to be used in the following round of clustering. Our clustering algorithm, which is based on the concept of a normalized cut, clusters the samples into a new reference partition on the basis of the selected features. On a well-studied problem involving 72 leukemia samples and 7130 genes, we demonstrate that CLIFF outperforms standard clustering approaches that do not consider the feature selection issue, and produces a result that is very close to the original expert labeling of the sample set.
Contact: epxing@cs.berkeley.edu
Molecular classification of multiple tumor types
Authors: Yeang Chen-Hsiang , Ramaswamy Sridhar , Tamayo Pablo , Mukherjee Sayan , Rifkin Ryan M. , Angelo Michael , Reich Michael , Lander Eric , Mesirov Jill , Golub Todd 
Published: May 2011

Abstract
Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.
Contact: chyeang@mit.edu
Visualizing associations between genome sequences and gene
expression data using genome-mean expression profiles
Authors: Chiang Derek Y. , Brown Patrick O. , Eisen Michael B. 
Published: May 2011

Abstract
The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP’s) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP’s calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP’s of motifs that are reverse complements, a result that supports the relationship between GMEP’s and transcriptional regulation. Hierarchical clustering of GMEP’s identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP’s of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors.
Software that computed GMEP’s from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/
Contact: mbeisen@lbl.gov
Using mixtures of common ancestors for estimating the
probabilities of discrete events in biological sequences
Authors: Eskin Eleazar , Grundy William N. , Singer Yoram 
Published: May 2011

Abstract
Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.
Contact: eeskin@cs.columbia.edu
Promoter prediction in the human genome
Authors: Hannenhalli Sridhar , Levy Samuel 
Published: May 2011

Abstract
Computational prediction of eukaryotic polII promoters has been one of the most elusive problems despite considerable effort devoted to the study. Researchers have looked for various types of signals around the transcriptional start site (TSS), viz. oligo-nucleotide statistics, potential binding sites for core factors, clusters of binding sites, proximity to CpG islands etc.. The proximity of CpG islands to gene starts is now a well established fact, although until recently, it was based on very little genomic data. In this work we explore the possibility of enhancing the promoter prediction accuracy by combining CpG island information with a few other, biologically motivated, seemingly independent signals, that cover most of the known knowledge. We benchmarked the method on a much larger genomic datasets compared to previous studies. We were able to improve slightly upon current prediction accuracy. Furthermore, we observe that CpG islands are the most dominant signals and the other signals do not improve the prediction. This suggests that the computational prediction of promoters for genes with no associated CpG-island (typically having tissue-specific expression) looking only at the immediate neighborhood of the TSS may not even be possible. We suggest some biological experiments and studies to better understand the biology of transcription.
Contact: Sridhar.Hannenhalli@celera.com; Samuel.Levy@celera.com
Disambiguating proteins, genes, and RNA in text: a machine
learning approach
Authors: Hatzivassiloglou Vasileios , Duboué Pablo 
   A. , Rzhetsky Andrey 
Published: May 2011

Abstract
We present an automated system for assigning protein, gene, or mRNA class labels to biological terms in free text. Three machine learning algorithms and several extended ways for defining contextual features for disambiguation are examined, and a fully unsupervised manner for obtaining training examples is proposed. We train and evaluate our system over a collection of 9 million words of molecular biology journal articles, obtaining accuracy rates up to 85%.
Contact: vh@cs.columbia.edu
A variance-stabilizing transformation for gene-expression
microarray data
Authors: Durbin B.P. , Hardin J.S. , Hawkins D.M. , Rocke D.M. 
Published: May 2011

Abstract
Motivation: Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background.
Results: We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data.
Contact: bpdurbin@wald.ucdavis.edu
Keywords: cDNA array; microarray; statistical analysis; transformation; normalization.
Microarray synthesis through multiple-use PCR primer design
Authors: Fernandes Rohan J. , Skiena Steven S. 
Published: May 2011

Abstract
A substantial percentage of the expense in constructing full-genome spotted microarrays comes from the cost of synthesizing the PCR primers to amplify the desired DNA. We propose a computationally-based method to substantially reduce this cost. Historically, PCR primers are designed so that each primer occurs uniquely in the genome. This condition is unnecessarily strong for selective amplification, since only the primer pair associated with each amplification need be unique. We demonstrate that careful design in a genome-level amplification project permits us to save the cost of several thousand primers over conventional approaches.
Contact: skiena@cs.sunysb.edu; rohan@cs.sunysb.edu
Keywords: PCR; primer design; microarray synthesis.