bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nitric oxide gas phase release in human small airway epithelial cells
Authors: Jiang Jingjing , Malavia Nikita , Suresh Vinod , George Steven C 
Published: January 2009

Abstract

Background
Asthma is a chronic airway inflammatory disease characterized by an imbalance in both Th1 and Th2 cytokines. Exhaled nitric oxide (NO) is elevated in asthma, and is a potentially useful non-invasive marker of airway inflammation. However, the origin and underlying mechanisms of intersubject variability of exhaled NO are not yet fully understood. We have previously described NO gas phase release from normal human bronchial epithelial cells (NHBEs, tracheal origin). However, smaller airways are the major site of morbidity in asthma. We hypothesized that IL-13 or cytomix (IL-1β, TNF-α, and IFN-γ) stimulation of differentiated small airway epithelial cells (SAECs, generation 10–12) and A549 cells (model cell line of alveolar type II cells) in culture would enhance NO gas phase release.

Methods
Confluent monolayers of SAECs and A549 cells were cultured in Transwell plates and SAECs were allowed to differentiate into ciliated and mucus producing cells at an air-liquid interface. The cells were then stimulated with IL-13 (10 ng/mL) or cytomix (10 ng/mL for each cytokine). Gas phase NO release in the headspace air over the cells was measured for 48 hours using a chemiluminescence analyzer.

Results
In contrast to our previous result in NHBE, baseline NO release from SAECs and A549 is negligible. However, NO release is significantly increased by cytomix (0.51 ± 0.18 and 0.29 ± 0.20 pl.s-1.cm-2, respectively) reaching a peak at approximately 10 hours. iNOS protein expression increases in a consistent pattern both temporally and in magnitude. In contrast, IL-13 only modestly increases NO release in SAECs reaching a peak (0.06 ± 0.03 pl.s-1.cm-2) more slowly (30 to 48 hours), and does not alter NO release in A549 cells.

Conclusion
We conclude that the airway epithelium is a probable source of NO in the exhaled breath, and intersubject variability may be due, in part, to variability in the type (Th1 vs Th2) and location (large vs small airway) of inflammation.
STAMP: Extensions to the STADEN sequence analysis package for high throughput interactive microsatellite marker design
Authors: Kraemer Lars , Beszteri Bánk , Gäbler-Schwarz Steffi , Held Christoph , Leese Florian , Mayer Christoph , Pöhlmann Kevin , Frickenhaus Stephan 
Published: January 2009

Abstract

Background
Microsatellites (MSs) are DNA markers with high analytical power, which are widely used in population genetics, genetic mapping, and forensic studies. Currently available software solutions for high-throughput MS design (i) have shortcomings in detecting and distinguishing imperfect and perfect MSs, (ii) lack often necessary interactive design steps, and (iii) do not allow for the development of primers for multiplex amplifications. We present a set of new tools implemented as extensions to the STADEN package, which provides the backbone functionality for flexible sequence analysis workflows. The possibility to assemble overlapping reads into unique contigs (provided by the base functionality of the STADEN package) is important to avoid developing redundant markers, a feature missing from most other similar tools.

Results
Our extensions to the STADEN package provide the following functionality to facilitate microsatellite (and also minisatellite) marker design: The new modules (i) integrate the state-of-the-art tandem repeat detection and analysis software PHOBOS into workflows, (ii) provide two separate repeat detection steps – with different search criteria – one for masking repetitive regions during assembly of sequencing reads and the other for designing repeat-flanking primers for MS candidate loci, (iii) incorporate the widely used primer design program PRIMER 3 into STADEN workflows, enabling the interactive design and visualization of flanking primers for microsatellites, and (iv) provide the functionality to find optimal locus- and primer pair combinations for multiplex primer design. Furthermore, our extensions include a module for storing analysis results in an SQLite database, providing a transparent solution for data access from within as well as from outside of the STADEN Package.

Conclusion
The STADEN package is enhanced by our modules into a highly flexible, high-throughput, interactive tool for conventional and multiplex microsatellite marker design. It gives the user detailed control over the workflow, enabling flexible combinations of manual and automated analysis steps. The software is available under the OpenBSD License [1, 2]. The high efficiency of our automated marker design workflow has been confirmed in three microsatellite development projects.
ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules
Authors: Sun Hong , De Bie Tijl , Storms Valerie , Fu Qiang , Dhollander Thomas , Lemmens Karen , Verstuyf Annemieke , De Moor Bart , Marchal Kathleen 
Published: January 2009

Abstract

Background
The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.

Results
We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.

Conclusion
We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.
Co-evolution positions and rules for antigenic variants of human influenza A/H3N2 viruses
Authors: Huang Jhang-Wei , King Chwan-Chuen , Yang Jinn-Moon 
Published: January 2009

Abstract

Background
In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The "antigenic variants" often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development

Results
We developed a method for identifying antigenic critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to measure the score of an amino acid position on hemagglutinin (HA) for discriminating between antigenic variants and similar viruses. A position with high IG and entropy implied that this position is highly correlated to an antigenic drift. Nineteen positions with high IG and high genetic diversity are identified as antigenic critical positions on the HA proteins. Most of these antigenic critical positions are located on five epitopes or on the surface based on the HA structure. Based on IG values and entropies of these 19 positions on the HA, the decision tree was applied to create a rule-based model and to identify rules for predicting antigenic variants of a given two HA sequences which are often a vaccine strain and a circulating strain. The predicting accuracies of this model on two sets, which consist of a training set (181 hemagglutination inhibition (HI) assays) and an independent test set (31,878 HI assays), are 91.2% and 96.2% respectively.

Conclusion
Our method is able to identify critical positions, rules, and co-mutated positions on HA for predicting the antigenic variants. The information gains and the entropies of HA positions provide insight to the antigenic drift and co-evolution positions for influenza seasons. We believe that our method is robust and is potential useful for studying influenza virus evolution and vaccine development.
Predicting disordered regions in proteins using the profiles of amino acid indices
Authors: Han Pengfei , Zhang Xiuzhen , Feng Zhi-Ping 
Published: January 2009

Abstract

Background
Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures.

Results
In this paper, algorithms are presented to predict long and short disordered regions in proteins, namely the long disordered region prediction algorithm DRaai-L and the short disordered region prediction algorithm DRaai-S. These algorithms are developed based on the Random Forest machine learning model and the profiles of amino acid indices representing various physiochemical and biochemical properties of the 20 amino acids.

Conclusion
Experiments on DisProt3.6 and CASP7 demonstrate that some sets of the amino acid indices have strong association with the ordered and disordered status of residues. Our algorithms based on the profiles of these amino acid indices as input features to predict disordered regions in proteins outperform that based on amino acid composition and reduced amino acid composition, and also outperform many existing algorithms. Our studies suggest that the profiles of amino acid indices combined with the Random Forest learning model is an important complementary method for pinpointing disordered regions in proteins.
Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity
Authors: Jia Yi , Huan Jun , Buhr Vincent , Zhang Jintao , Carayannopoulos Leonidas N 
Published: January 2009

AbstractBackgroundAutomatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the "twilight-" or "midnight-" zones where pair-wise sequence identities to known sequences fall below 25% and sequence-based functional annotations often fail.ResultsHere we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification. The biologic motivation of our study is to recognize common structure patterns in "immunoevasins", proteins mediating virus evasion of host immune defense. Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method.ConclusionWe present a theoretic framework, offer a practical software implementation for incorporating prior domain knowledge, such as substitution matrices as studied here, and devise an efficient algorithm to identify approximate matched frequent subgraphs. By doing so, we significantly expanded the analytical power of sophisticated data mining algorithms in dealing with large volume of complicated and noisy protein structure data. And without loss of generality, choice of appropriate compatibility matrices allows our method to be easily employed in domains where subgraph labels have some uncertainty.
A computational analysis of SARS cysteine proteinase-octapeptide substrate interaction: implication for structure and active site binding mechanism
Authors: Phakthanakanok Krongsakda , Ratanakhanokchai Khanok , Kyu Khin Lay , Sompornpisut Pornthep , Watts Aaron , Pinitglang Surapong 
Published: January 2009

Abstract

Background
SARS coronavirus main proteinase (SARS CoVMpro) is an important enzyme for the replication of Severe Acute Respiratory Syndrome virus. The active site region of SARS CoVMpro is divided into 8 subsites. Understanding the binding mode of SARS CoVMpro with a specific substrate is useful and contributes to structural-based drug design. The purpose of this research is to investigate the binding mode between the SARS CoVMpro and two octapeptides, especially in the region of the S3 subsite, through a molecular docking and molecular dynamics (MD) simulation approach.

Results
The one turn α-helix chain (residues 47–54) of the SARS CoVMpro was directly involved in the induced-fit model of the enzyme-substrate complex. The S3 subsite of the enzyme had a negatively charged region due to the presence of Glu47. During MD simulations, Glu47 of the enzyme was shown to play a key role in electrostatic bonding with the P3Lys of the octapeptide.

Conclusion
MD simulations were carried out on the SARS CoVMpro-octapeptide complex. The hypothesis proposed that Glu47 of SARS CoVMpro is an important residue in the S3 subsite and is involved in binding with P3Lys of the octapeptide.
Quality assessment of tandem mass spectra using support vector machine (SVM)
Authors: Zou An-Min , Wu Fang-Xiang , Ding Jia-Rui , Poirier Guy G 
Published: January 2009

Abstract

Background
Tandem mass spectrometry has become particularly useful for the rapid identification and characterization of protein components of complex biological mixtures. Powerful database search methods have been developed for the peptide identification, such as SEQUEST and MASCOT, which are implemented by comparing the mass spectra obtained from unknown proteins or peptides with theoretically predicted spectra derived from protein databases. However, the majority of spectra generated from a mass spectrometry experiment are of too poor quality to be interpreted while some of spectra with high quality cannot be interpreted by one method but perhaps by others. Hence a filtering algorithm that removes those spectra with poor quality prior to the database search is appealing.

Results
This paper proposes a support vector machine (SVM) based approach to assess the quality of tandem mass spectra. Each mass spectrum is mapping into the 16 proposed features to describe its quality. Based the results from SEQUEST, four SVM classifiers with the input of the 16 features are trained and tested on ISB data and TOV data, respectively. The superior performance of the proposed SVM classifiers is illustrated both by the comparison with the existing classifiers and by the validation in terms of MASCOT search results.

Conclusion
The proposed method can be employed to effectively remove the poor quality spectra before the spectral searching, and also to find the more peptides or post-translational peptides from spectra with high quality using different search engines or de novo method.
Comparative analysis of the JAK/STAT signaling through erythropoietin receptor and thrombopoietin receptor using a systems approach
Authors: Won Hong-Hee , Park Inho , Lee Eunjung , Kim Jong-Won , Lee Doheon 
Published: January 2009

Abstract

Background
The Janus kinase-signal transducer and activator of transcription (JAK/STAT) pathway is one of the most important targets for myeloproliferative disorder (MPD). Although several efforts toward modeling the pathway using systems biology have been successful, the pathway was not fully investigated in regard to understanding pathological context and to model receptor kinetics and mutation effects.

Results
We have performed modeling and simulation studies of the JAK/STAT pathway, including the kinetics of two associated receptors (the erythropoietin receptor and thrombopoietin receptor) with the wild type and a recently reported mutation (JAK2V617F) of the JAK2 protein.

Conclusion
We found that the different kinetics of those two receptors might be important factors that affect the sensitivity of JAK/STAT signaling to the mutation effect. In addition, our simulation results support clinically observed pathological differences between the two subtypes of MPD with respect to the JAK2V617F mutation.
Extract interaction detection methods from the biological literature
Authors: Wang Hongning , Huang Minlie , Zhu Xiaoyan 
Published: January 2009

Abstract

Background
Considerable efforts have been made to extract protein-protein interactions from the biological literature, but little work has been done on the extraction of interaction detection methods. It is crucial to annotate the detection methods in the literature, since different detection methods shed different degrees of reliability on the reported interactions. However, the diversity of method mentions in the literature makes the automatic extraction quite challenging.

Results
In this article, we develop a generative topic model, the Correlated Method-Word model (CMW model) to extract the detection methods from the literature. In the CMW model, we formulate the correlation between the different methods and related words in a probabilistic framework in order to infer the potential methods from the given document. By applying the model on a corpus of 5319 full text documents annotated by the MINT and IntAct databases, we observe promising results, which outperform the best result reported in the BioCreative II challenge evaluation.

Conclusion
From the promising experiment results, we can see that the CMW model overcomes the issues caused by the diversity in the method mentions and properly captures the in-depth correlations between the detection methods and related words. The performance outperforming the baseline methods confirms that the dependence assumptions of the model are reasonable and the model is competent for the practical processing.
Maximum independent sets of commuting and noninterfering inversions
Authors: Swenson Krister M , To Yokuki , Tang Jijun , Moret Bernard ME 
Published: January 2009

AbstractBackgroundGiven three signed permutations, an inversion median is a fourth permutation that minimizes the sum of the pairwise inversion distances between it and the three others. This problem is NP-hard as well as hard to approximate. Yet median-based approaches to phylogenetic reconstruction have been shown to be among the most accurate, especially in the presence of long branches. Most existing approaches have used heuristics that attempt to find a longest sequence of inversions from one of the three permutations that, at each step in the sequence, moves closer to the other two permutations; yet very little is known about the quality of solutions returned by such approaches.ResultsRecently, Arndt and Tang took a step towards finding longer such sequences by using sets of commuting inversions. In this paper, we formalize the problem of finding such sequences of inversions with what we call signatures and provide algorithms to find maximum cardinality sets of commuting and noninterfering inversions.ConclusionOur results offer a framework in which to study the inversion median problem, faster algorithms to obtain good medians, and an approach to study characteristic events along an evolutionary path.
Identifying disease associations via genome-wide association studies
Authors: Huang Wenhui , Wang Pengyuan , Liu Zhen , Zhang Liqing 
Published: January 2009

Abstract

Background
Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels – the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.

Results
Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels.

Conclusion
Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach.
Triplet supertree heuristics for the tree of life
Authors: Lin Harris T , Burleigh J Gordon , Eulenstein Oliver 
Published: January 2009

AbstractBackgroundThere is much interest in developing fast and accurate supertree methods to infer the tree of life. Supertree methods combine smaller input trees with overlapping sets of taxa to make a comprehensive phylogenetic tree that contains all of the taxa in the input trees. The intrinsically hard triplet supertree problem takes a collection of input species trees and seeks a species tree (supertree) that maximizes the number of triplet subtrees that it shares with the input trees. However, the utility of this supertree problem has been limited by a lack of efficient and effective heuristics.ResultsWe introduce fast hill-climbing heuristics for the triplet supertree problem that perform a step-wise search of the tree space, where each step is guided by an exact solution to an instance of a local search problem. To realize time efficient heuristics we designed the first nontrivial algorithms for two standard search problems, which greatly improve on the time complexity to the best known (naïve) solutions by a factor ofnandn2(the number of taxa in the supertree). These algorithms enable large-scale supertree analyses based on the triplet supertree problem that were previously not possible. We implemented hill-climbing heuristics that are based on our new algorithms, and in analyses of two published supertree data sets, we demonstrate that our new heuristics outperform other standard supertree methods in maximizing the number of triplets shared with the input trees.ConclusionWith our new heuristics, the triplet supertree problem is now computationally more tractable for large-scale supertree analyses, and it provides a potentially more accurate alternative to existing supertree methods.