bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adjusted intraclass correlation coefficients for binary data: methods and estimates from a cluster-randomized trial in primary care
Authors: Yelland Lisa N , Salter Amy B , Ryan Philip , Laurence Caroline O 
Published: February 2011

Background Accurate estimates of the intraclass correlation coefficient (ICC) are important for calculating appropriate sample sizes for cluster-randomized trials. The ICC and hence the sample size may be reduced through adjustment for baseline covariates. A method exists for calculating adjusted ICCs for binary outcomes based on the logit link, used to calculate odds ratios. Recent interest in presenting relative risks rather than odds ratios indicates that a method based on the log link is needed. Purpose To determine and evaluate a method for calculating adjusted ICCs based on the log link, and to provide and compare unadjusted and adjusted ICCs from a cluster-randomized trial in primary care based on the logit and log link. Methods Two methods are proposed for calculating adjusted ICCs for the log link based on a first-order Taylor series expansion and properties of the lognormal distribution. The methods are evaluated by simulation. Unadjusted and adjusted ICCs are calculated for binary outcomes from the Point of Care Testing (PoCT) Trial using the logit and log link. Results The methods for calculating adjusted ICCs for the log link produced similar results unless the between cluster variance was large. Unadjusted ICCs for the PoCT Trial ranged from 0.001 to 0.048. The impact of adjustment on the ICC varied between outcomes and link functions, ranging from a 59% reduction to an 89% increase. Limitations The true ICC was unknown for the simulation study. Adjustment was made for age and gender only for the PoCT Trial. Conclusions The method for calculating adjusted ICCs for binary outcomes depends on the link function. For the log link, the method based on the lognormal distribution is recommended. This method will be useful for cluster-randomized trials where the relative risk, rather than the odds ratio, is the effect measure of interest.
cpsf1 is required for definitive HSC survival in zebrafish
Authors: Bolli Niccolò , Payne Elspeth M. , Rhodes Jennifer , Gjini Evisa , Johnston Adam B. , Guo Feng , Lee Jeong-Soo , Stewart Rodney A. , Kanki John P. , Chen Aye T. , Zhou Yi , Zon Leonard I. , Look A. Thomas 
Published: February 2011

AbstractA comprehensive understanding of the genes and pathways regulating hematopoiesis is needed to identify genes causally related to bone marrow failure syndromes, myelodysplastic syndromes, and hematopoietic neoplasms. To identify novel genes involved in hematopoiesis, we performed an ethyl-nitrosourea mutagenesis screen in zebrafish (Danio rerio) to search for mutants with defective definitive hematopoiesis. We report the recovery and analysis of the grechetto mutant, which harbors an inactivating mutation in cleavage and polyadenylation specificity factor 1 (cpsf1), a gene ubiquitously expressed and required for 3′ untranslated region processing of a subset of pre-mRNAs. grechetto mutants undergo normal primitive hematopoiesis and specify appropriate numbers of definitive HSCs at 36 hours postfertilization. However, when HSCs migrate to the caudal hematopoietic tissue at 3 days postfertilization, their numbers start decreasing as a result of apoptotic cell death. Consistent with Cpsf1 function, c-myb:EGFP+ cells in grechetto mutants also show defective polyadenylation of snrnp70, a gene required for HSC development. By 5 days postfertilization, definitive hematopoiesis is compromised and severely decreased blood cell numbers are observed across the myeloid, erythroid, and lymphoid cell lineages. These studies show that cpsf1 is essential for HSC survival and differentiation in caudal hematopoietic tissue.
Overexpression of IL-3Rα on CD34+CD38− stem cells defines leukemia-initiating cells in Fanconi anemia AML
Authors: Du Wei , Li Xu-E , Sipple Jared , Pang Qishen 
Published: February 2011

AbstractPatients with Fanconi anemia (FA) have a high risk of developing acute myeloid leukemia (AML). In this study, we attempted to identify cell-surface markers for leukemia-initiating cells in FA-AML patients. We found that the IL-3 receptor-α (IL-3Rα) is a promising candidate as an leukemia-initiating cell-specific antigen for FA-AML. Whereas IL-3Rα expression is undetectable on normal CD34+CD38− HSCs, it is overexpressed on CD34+CD38− cells from FA patients with AML. We examined the leukemia-initiating cell activity of IL-3Rα–positive FA-AML cells in a “humanized” FA xenotransplant model in which we separated AML cells into IL-3Rα–positive and IL-3Rα–negative CD34 fractions and transplanted them into irradiated recipient mice. In all 3 FA-AML samples, only IL-3Rα–positive cells showed significant levels of engraftment and developed leukemia in the recipient mice. The FA CD34+IL-3Rα+ blasts isolated from leukemic mice exhibited hypersensitivity to IL-3 deprivation and JAK2-STAT5 overactivation after IL-3 treatment. Finally, treatment of FA CD34+IL-3Rα+ blasts with an IL-3Rα–neutralizing antibody inhibited IL-3–mediated proliferation and STAT5 activation. These results demonstrate that IL-3Rα is a cell-surface marker present on FA-AML leukemia-initiating cells and may be a valuable therapeutic target.
Toll-like receptor 2 mediates the activation of human monocytes and endothelial cells by antiphospholipid antibodies
Authors: Satta Nathalie , Kruithof Egbert K. O. , Fickentscher Céline , Dunoyer-Geindre Sylvie , Boehlen Françoise , Reber Guido , Burger Danielle , de Moerloose Philippe 
Published: February 2011

AbstractThe presence of antiphospholipid antibodies (aPLAs) is associated with arterial or venous thrombosis and/or recurrent fetal loss. The proposed pathogenic mechanisms for aPLA effects include the inflammatory activation of monocytes and endothelial cells. Toll-like receptors (TLRs) are candidate signaling intermediates. The aim of this study was to investigate the relative contribution of TLR2 and TLR4 in cell activation by aPLAs. Of 32 patient-derived aPLAs, 19 induced an inflammatory activation of human monocytes and umbilical vein endothelial cells (HUVECs). In HUVECs, inflammatory responses to these aPLAs were increased by TNF pretreatment, which increases the expression of TLR2 but not TLR4. Anti-TLR2 but not anti-TLR4 antibodies reduced the aPLA-induced activation of monocytes and HUVECs. aPLAs activated TLR2-expressing human embryonic kidney 293 (HEK293) cells but not TLR4-expressing cells. Binding studies demonstrated an interaction between aPLAs and TLR2 but not TLR4. A role for CD14, a coreceptor for TLR2 and TLR4, can be inferred by observations that anti-CD14 antibodies reduced responses to aPLAs in monocytes, and that responses in HEK293 cells expressing TLR2 and CD14 were greater than in HEK293 cells expressing TLR2 alone. Our results demonstrate a role for TLR2 and CD14 in human endothelial cell and monocyte activation by aPLAs.
An efficient algorithmic approach for mass spectrometry-based disulfide connectivity determination using multi-ion analysis
Authors: Murad William , Singh Rahul , Yen Ten-Yang 
Published: February 2011

Abstract

Background
Determining the disulfide (S-S) bond pattern in a protein is often crucial for understanding its structure and function. In recent research, mass spectrometry (MS) based analysis has been applied to this problem following protein digestion under both partial reduction and non-reduction conditions. However, this paradigm still awaits solutions to certain algorithmic problems fundamental amongst which is the efficient matching of an exponentially growing set of putative S-S bonded structural alternatives to the large amounts of experimental spectrometric data. Current methods circumvent this challenge primarily through simplifications, such as by assuming only the occurrence of certain ion-types (b-ions and y-ions) that predominate in the more popular dissociation methods, such as collision-induced dissociation (CID). Unfortunately, this can adversely impact the quality of results.

Method
We present an algorithmic approach to this problem that can, with high computational efficiency, analyze multiple ions types (a, b, b

o

, b

*

, c, x, y, y

o

, y

*

, and z) and deal with complex bonding topologies, such as inter/intra bonding involving more than two peptides. The proposed approach combines an approximation algorithm-based search formulation with data driven parameter estimation. This formulation considers only those regions of the search space where the correct solution resides with a high likelihood. Putative disulfide bonds thus obtained are finally combined in a globally consistent pattern to yield the overall disulfide bonding topology of the molecule. Additionally, each bond is associated with a confidence score, which aids in interpretation and assimilation of the results.

Results
The method was tested on nine different eukaryotic Glycosyltransferases possessing disulfide bonding topologies of varying complexity. Its performance was found to be characterized by high efficiency (in terms of time and the fraction of search space considered), sensitivity, specificity, and accuracy. The method was also compared with other techniques at the state-of-the-art. It was found to perform as well or better than the competing techniques. An implementation is available at: http://tintin.sfsu.edu/~whemurad/disulfidebond.

Conclusions
This research addresses some of the significant challenges in MS-based disulfide bond determination. To the best of our knowledge, this is the first algorithmic work that can consider multiple ion types in this problem setting while simultaneously ensuring polynomial time complexity and high accuracy of results.
A genetic algorithm-based boolean delay model of intracellular signal transduction in inflammation
Authors: Kang Chu Chun , Chuang Yung Jen , Tung Kai Che , Chao Chun Cheih , Tang Chuan Yi , Peng Shih Chi , Wong David Shan Hill 
Published: February 2011

Abstract

Background
Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.

Results
In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified.

Conclusions
We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.
GARNET – gene set analysis with exploration of annotation relations
Authors: Rho Kyoohyoung , Kim Bumjin , Jang Youngjun , Lee Sanghyun , Bae Taejeong , Seo Jihae , Seo Chaehwa , Lee Jihyun , Kang Hyunjung , Yu Ungsik , Kim Sunghoon , Lee Sanghyuk , Kim Wan Kyu 
Published: February 2011

Abstract

Background
Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information.

Results
GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules - gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations.

Conclusions
GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).
Reconstructing phylogeny from metabolic substrate-product relationships
Authors: Chang Che-Wei , Lyu Ping-Chiang , Arita Masanori 
Published: February 2011

Abstract

Background
Many approaches utilize metabolic pathway information to reconstruct the phyletic tree of fully sequenced organisms, but how metabolic networks can add information to original genomic annotations has remained open.

Methods
We translated enzyme reactions assigned in 1075 organisms into substrate-product relationships to represent the metabolic information at a finer resolution than enzymes and compounds. Each organism was represented as a vector of substrate-product relationships and the phyletic tree was reconstructed by a simple hierarchical method. Obtained results were compared with several other approaches that use genome information and network properties.

Results
Phyletic trees without consideration of network properties can already extract organisms in anomalous environments. This efficient method can add insights to traditional genome-based phylogenetic reconstruction.

Conclusions
Structural relationship among metabolites can highlight parasitic or symbiont species such as spirochaete and clamydia. The method assists understanding of species-environment interaction when used in combination with traditional phylogenetic methods.
Potent bace-1 inhibitor design using pharmacophore modeling, in silico screening and molecular docking studies
Authors: John Shalini , Thangapandian Sundarapandian , Sakkiah Sugunadevi , Lee Keun Woo 
Published: February 2011

Abstract

Background
Beta-site amyloid precursor protein cleaving enzyme (BACE-1) is a single-membrane protein belongs to the aspartyl protease class of catabolic enzymes. This enzyme involved in the processing of the amyloid precursor protein (APP). The cleavage of APP by BACE-1 is the rate-limiting step in the amyloid cascade leading to the production of two peptide fragments Aβ40 and Aβ42. Among two peptide fragments Aβ42 is the primary species thought to be responsible for the neurotoxicity and amyloid plaque formation that lead to memory and cognitive defects in Alzheimer’s disease (AD). AD is a ravaging neurodegenerative disorder for which no disease-modifying treatment is currently available. Inhibition of BACE-1 is expected to stop amyloid plaque formation and emerged as an interesting and attractive therapeutic target for AD.

Methods
Ligand-based computational approach was used to identify the molecular chemical features required for the inhibition of BACE-1 enzyme. A training set of 20 compounds with known experimental activity was used to generate pharmacophore hypotheses using 3D QSAR Pharmacophore Generation module available in Discovery studio. The hypothesis was validated by four different methods and the best hypothesis was utilized in database screening of four chemical databases like Maybridge, Chembridge, NCI and Asinex. The retrieved hit compounds were subjected to molecular docking study using GOLD 4.1 program.

Results
Among ten generated pharmacophore hypotheses, Hypo 1 was chosen as best pharmacophore hypothesis. Hypo 1 consists of one hydrogen bond donor, one positive ionizable, one ring aromatic and two hydrophobic features with high correlation coefficient of 0.977, highest cost difference of 121.98 bits and lowest RMSD value of 0.804. Hypo 1 was validated using Fischer randomization method, test set with a correlation coefficient of 0.917, leave-one-out method and decoy set with a goodness of hit score of 0.76. The validated Hypo 1 was used as a 3D query in database screening and retrieved 773 compounds with the estimated activity value <100 nM. These hits were docked into the active site of BACE-1 and further refined based on molecular interactions with the essential amino acids and good GOLD fitness score.

Conclusion
The best pharmacophore hypothesis, Hypo 1, with high predictive ability contains chemical features required for the effective inhibition of BACE-1. Using Hypo 1, we have identified two compounds with diverse chemical scaffolds as potential virtual leads which, as such or upon further optimization, can be used in the designing of new BACE-1 inhibitors.
GOChase-II: correcting semantic inconsistencies from Gene Ontology-based annotations for gene products
Authors: Park Yu Rang , Kim Jihun , Lee Hye Won , Yoon Young Jo , Kim Ju Han 
Published: February 2011

Abstract

Background
The Gene Ontology (GO) provides a controlled vocabulary for describing genes and gene products. In spite of the undoubted importance of GO, several drawbacks associated with GO and GO-based annotations have been introduced. We identified three types of semantic inconsistencies in GO-based annotations; semantically redundant, biological-domain inconsistent and taxonomy inconsistent annotations.

Methods
To determine the semantic inconsistencies in GO annotation, we used the hierarchical structure of GO graph and tree structure of NCBI taxonomy. Twenty seven biological databases were collected for finding semantic inconsistent annotation.

Results
The distributions and possible causes of the semantic inconsistencies were investigated using twenty seven biological databases with GO-based annotations. We found that some evidence codes of annotation were associated with the inconsistencies. The numbers of gene products and species in a database that are related to the complexity of database management are also in correlation with the inconsistencies. Consequently, numerous annotation errors arise and are propagated throughout biological databases and GO-based high-level analyses. GOChase-II is developed to detect and correct both syntactic and semantic errors in GO-based annotations.

Conclusions
We identified some inconsistencies in GO-based annotation and provided software, GOChase-II, for correcting these semantic inconsistencies in addition to the previous corrections for the syntactic errors by GOChase-I.
Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures
Authors: Saito Yutaka , Sato Kengo , Sakakibara Yasubumi 
Published: February 2011

Abstract

Background
Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.

Results
We describe a new similarity measure for the hierarchical clustering of ncRNAs. The idea is that the reliability of approximate algorithms can be improved by utilizing the information of suboptimal solutions in their dynamic programming frameworks. We approximate structural alignment in a more simplified manner than the existing methods. Instead, our method utilizes all possible sequence alignments and all possible secondary structures, whereas the existing methods only use one optimal sequence alignment and one optimal secondary structure. We demonstrate that this strategy can achieve the best balance between the computational cost and the quality of the clustering. In particular, our method can keep its high performance even when the sequence identity of family members is less than 60%.

Conclusions
Our method enables fast and accurate clustering of ncRNAs. The software is available for download at http://bpla-kernel.dna.bio.keio.ac.jp/clustering/.
TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources
Authors: Lenzi Luca , Facchin Federica , Piva Francesco , Giulietti Matteo , Pelleri Maria Chiara , Frabetti Flavia , Vitale Lorenza , Casadei Raffaella , Canaider Silvia , Bortoluzzi Stefania , Coppe Alessandro , Danieli Gian Antonio , Principato Giovanni , Ferrari Sergio , Strippoli Pierluigi 
Published: February 2011

Abstract

Background
Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.

Results
TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.

Conclusions
TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.