bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fostering community understanding of sufficient benefit and early stopping for a phase 2B HIV prevention clinical trial in Africa
Authors: White Rhonda , Chileshe Modesta , Dawson Liza , Donnell Deborah , Hillier Sharon , Morar Neetha , Noguchi Lisa , Dixon Dennis 
Published: February 2011

Background Most trials of interventions are designed to address the traditional null hypothesis of no benefit. VOICE, a phase 2B HIV prevention trial funded by NIH and conducted in Africa, is designed to assess if the intervention will prevent a substantial fraction of infections. Planned interim analysis may provide conclusive evidence against the traditional null hypothesis without establishing substantial benefit. At this interim point, the Data and Safety Monitoring Board would then face the dilemma of knowing the product has some positive effect, but perhaps not as great an effect as the protocol has declared necessary. Purpose In March 2008, NIH program staff recommended that the VOICE protocol team discuss the stopping rules with stakeholders prior to initiating the protocol. The goals of the workshop were to inform community representatives about the potential ethical dilemma associated with stopping rules and engage in dialogue about these issues. We describe the resulting community consultation and summarize the outcomes. Methods A 2-day workshop was convened with the goal of having a clear and transparent consultation with the stakeholders around the question, ‘Given emerging evidence that a product could prevent some infections, would the community support a decision to continue accruing to the trial?’ Participants included research staff and community stakeholders. Lectures with visual aids, discussions, and exercises using interactive learning tasks were used, with a focus on statistics and interpreting data from trials, particularly interim data. Results Results of oral and written evaluations by participants were reviewed. The feedback was mostly positive, with some residual confusion regarding statistical concepts. However, discussions with attendees later revealed that not all felt prepared to engage fully in the workshop. Limitations This was the presenters’ first experience facilitating a formal discussion with an audience that had no advanced science, research, or mathematics training. Community representatives’ concern regarding speaking for their communities without consulting them also created a challenge for the workshop. Conclusions Open discussion around trial stopping rules requires that all discussants have an understanding of trial design concepts and feel a sense of empowerment to ask and answer questions. The VOICE CWG workshop was a first step toward the goal of open discussion regarding trial stopping rules and interim results for the study; however, ongoing education and dialogue must occur to ensure that all stakeholders fully participate in the process.
Designing clinical trials to test disease-modifying agents: application to the treatment trials of Alzheimer’s disease
Authors: Xiong Chengjie , van Belle Gerald , Miller J Philip , Morris John C 
Published: February 2011

Background Therapeutic trials of disease-modifying agents on Alzheimer’s disease (AD) require novel designs and analyses involving switch of treatments for at least a portion of subjects enrolled. Randomized start and randomized withdrawal designs are two examples of such designs. Crucial design parameters such as sample size and the time of treatment switch are important to understand in designing such clinical trials. Purpose The purpose of this article is to provide methods to determine sample sizes and time of treatment switch as well as optimum statistical tests of treatment efficacy for clinical trials of disease-modifying agents on AD. Methods A general linear mixed effects model is proposed to test the disease-modifying efficacy of novel therapeutic agents on AD. This model links the longitudinal growth from both the placebo arm and the treatment arm at the time of treatment switch for these in the delayed treatment arm or early withdrawal arm and incorporates the potential correlation on the rate of cognitive change before and after the treatment switch. Sample sizes and the optimum time for treatment switch of such trials as well as optimum test statistic for the treatment efficacy are determined according to the model. Results Assuming an evenly spaced longitudinal design over a fixed duration, the optimum treatment switching time in a randomized start or a randomized withdrawal trial is half way through the trial. With the optimum test statistic for the treatment efficacy and over a wide spectrum of model parameters, the optimum sample size allocations are fairly close to the simplest design with a sample size ratio of 1:1:1 among the treatment arm, the delayed treatment or early withdrawal arm, and the placebo arm. The application of the proposed methodology to AD provides evidence that much larger sample sizes are required to adequately power disease-modifying trials when compared with those for symptomatic agents, even when the treatment switch time and efficacy test are optimally chosen. Limitations The proposed method assumes that the only and immediate effect of treatment switch is on the rate of cognitive change. Conclusions Crucial design parameters for the clinical trials of disease-modifying agents on AD can be optimally chosen. Government and industry officials as well as academia researchers should consider the optimum use of the clinical trials design for disease-modifying agents on AD in their effort to search for the treatments with the potential to modify the underlying pathophysiology of AD.
Distinct platelet packaging, release, and surface expression of proangiogenic and antiangiogenic factors on different platelet stimuli
Authors: Chatterjee Madhumita , Huang Zhangsen , Zhang Wei , Jiang Lei , Hultenby Kjell , Zhu Linjing , Hu Hu , Nilsson Gunnar P. , Li Nailin 
Published: February 2011

Abstract
The present study characterized platelet secretion and surface expression of proangiogenic stromal cell-derived factor-1α (SDF-1α) and vascular endothelial growth factor (VEGF) and antiangiogenic PF4 and endostatin on activation. The angiogenic factors presented in randomly distributed granules in resting platelets, which were peripherized on activation. Confocal and immunogold electron microscopy demonstrated that SDF-1α/CXCL12 and PF4/CXCL4 mostly present in different granules. Platelet activation induced marked SDF-1α and endostatin but mild PF4 or no VEGF surface expression. PAR1-activating peptide (PAR1-AP), adenosine diphosphate (via P2Y1/P2Y12), and glycoprotein VI-targeting collagen-related peptide induced massive SDF-1α and VEGF but modest PF4 or no endostatin release. In contrast, PAR4-AP triggered marked PF4 and sole endostatin release but limited SDF-1α or VEGF secretion. Distinct platelet release of SDF-1α and endostatin involved different engagements of intracellular signaling pathways. In conclusion, different platelet stimuli evoke distinct secretion and surface expression of proangiogenic and antiangiogenic factors. PAR1, adenosine diphosphate, and glycoprotein VI stimulation favors proangiogenic, whereas PAR4 promotes antiangiogenic, factor release.
An ILP solution for the gene duplication problem
Authors: Chang Wen-Chieh , Burleigh Gordon J , Fernández-Baca David F , Eulenstein Oliver 
Published: February 2011

AbstractBackgroundThe gene duplication (GD) problem seeks a species tree that implies the fewest gene duplication events across a given collection of gene trees. Solving this problem makes it possible to use large gene families with complex histories of duplication and loss to infer phylogenetic trees. However, the GD problem is NP-hard, and therefore, most analyses use heuristics that lack any performance guarantee.ResultsWe describe the first integer linear programming (ILP) formulation to solve instances of the gene duplication problem exactly. With simulations, we demonstrate that the ILP solution can solve problem instances with up to 14 taxa. Furthermore, we apply the new ILP solution to solve the gene duplication problem for the seed plant phylogeny using a 12-taxon, 6, 084-gene data set. The unique, optimal solution, which places Gnetales sister to the conifers, represents a new, large-scale genomic perspective on one of the most puzzling questions in plant systematics.ConclusionsAlthough the GD problem is NP-hard, our novel ILP solution for it can solve instances with data sets consisting of as many as 14 taxa and 1, 000 genes in a few hours. These are the largest instances that have been solved to optimally to date. Thus, this work can provide large-scale genomic perspectives on phylogenetic questions that previously could only be addressed by heuristic estimates.
The G protein-coupled receptors in the pufferfish Takifugu rubripes
Authors: Sarkar Anita , Kumar Sonu , Sundar Durai 
Published: February 2011

AbstractBackgroundGuanine protein-coupled receptors (GPCRs) constitute a eukaryotic transmembrane protein family and function as “molecular switches” in the second messenger cascades and are found in all organisms between yeast and humans. They form the single, biggest drug-target family due to their versatility of action and their role in several physiological functions, being active players in detecting the presence of light, a variety of smells and tastes, amino acids, nucleotides, lipids, chemicals etc. in the environment of the cell. Comparative genomic studies on model organisms provide information on target receptors in humans and their function. The Japanese teleost Fugu has been identified as one of the smallest vertebrate genomes and a compact model to study the human genome, owing to the great similarity in its gene repertoire with that of human and other vertebrates. Thus the characterization of the GPCRs of Fugu would provide insights to the evolution of the vertebrate genome.ResultsWe classified the GPCRs in the Fugu genome and our analysis of its 316 membrane-bound receptors, available on the public databases as well as from literature, detected 298 GPCRs that were grouped into five main families according to the GRAFS classification system (namely, Glutamate, Rhodopsin, Adhesion, Frizzled and Secretin). We also identified 18 other GPCRs that could not be grouped under the GRAFS family and hence were classified as ‘Other 7TM’ receptors. On comparison of the GPCR information from the Fugu genome with those in the human and chicken genomes, we detected 96.83% (306/316) and 96.51% (305/316) orthology in GPCRs among the Fugu-human genomes and Fugu-chicken genomes, respectively.ConclusionsThis study reveals the position of pisces in vertebrate evolution from the GPCR perspective. Fugu can act as a reference model for the human genome for other protein families as well, going by the high orthology observed for GPCRs between Fugu and human. The evolutionary comparison of GPCR sequences between key vertebrate classes of mammals, birds and fish will help in identifying key functional residues and motifs so as to fill in the blanks in the evolution of GPCRs in vertebrates.
A study on the flexibility of enzyme active sites
Authors: Weng Yi-Zhong , Chang Darby Tien-Hao , Huang Yu-Feng , Lin Chih-Wei 
Published: February 2011

Abstract

Background
A common assumption about enzyme active sites is that their structures are highly conserved to specifically distinguish between closely similar compounds. However, with the discovery of distinct enzymes with similar reaction chemistries, more and more studies discussing the structural flexibility of the active site have been conducted.

Results
Most of the existing works on the flexibility of active sites focuses on a set of pre-selected active sites that were already known to be flexible. This study, on the other hand, proposes an analysis framework composed of a new data collecting strategy, a local structure alignment tool and several physicochemical measures derived from the alignments. The method proposed to identify flexible active sites is highly automated and robust so that more extensive studies will be feasible in the future. The experimental results show the proposed method is (a) consistent with previous works based on manually identified flexible active sites and (b) capable of identifying potentially new flexible active sites.

Conclusions
This proposed analysis framework and the former analyses on flexibility have their own advantages and disadvantage, depending on the cause of the flexibility. In this regard, this study proposes an alternative that complements previous studies and helps to construct a more comprehensive view of the flexibility of enzyme active sites.
iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis
Authors: Hsu Kai-Cheng , Chen Yen-Fu , Lin Shen-Rong , Yang Jinn-Moon 
Published: February 2011

Abstract

Background
Pharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.

Results
We developed an easy-to-use graphic environment, i GEMDOCK, integrating VS stages (from preparations to post-screening analysis). For post-screening analysis, i GEMDOCK provides biological insights by deriving the pharmacological interactions from screening compounds without relying on the experimental data of active compounds. The pharmacological interactions represent conserved interacting residues, which often form binding pockets with specific physico-chemical properties, to play the essential functions of a target protein. Our experimental results show that the pharmacological interactions derived by i GEMDOCK are often hot spots involving in the biological functions. In addition, i GEMDOCK provides the visualizations of the protein-compound interaction profiles and the hierarchical clustering dendrogram of the compounds for post-screening analysis.

Conclusions
We have developed i GEMDOCK to facilitate steps from preparations of target proteins and ligand libraries toward post-screening analysis. i GEMDOCK is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. We believe that i GEMDOCK is useful for understanding the ligand binding mechanisms and discovering lead compounds. i GEMDOCK is available at http://gemdock.life.nctu.edu.tw/dock/igemdock.php.
Query-based biclustering of gene expression data using Probabilistic Relational Models
Authors: Zhao Hui , Cloots Lore , Van den Bulcke Tim , Wu Yan , De Smet Riet , Storms Valerie , Meysman Pieter , Engelen Kristof , Marchal Kathleen 
Published: February 2011

AbstractBackgroundWith the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developedProBic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set.ResultsWe appliedProBic on a large scaleEscherichia colicompendium to extend partially described regulons with potentially novel members. We comparedProBic's performance with previously published query-based biclustering algorithms, namely ISA and QDB, from the perspective of bicluster expression quality, robustness of the outcome against noisy seed sets and biological relevance.This comparison learns thatProBic is able to retrieve biologically relevant, high quality biclusters that retain their seed genes and that it is particularly strong in handling noisy seeds.ConclusionsProBic is a query-based biclustering algorithm developed in a flexible framework, designed to detect biologically relevant, high quality biclusters that retain relevant seed genes even in the presence of noise or when dealing with low quality seed sets.
Coregulation of transcription factors and microRNAs in human transcriptional regulatory network
Authors: Chen Cho-Yi , Chen Shui-Tein , Fuh Chiou-Shann , Juan Hsueh-Fen , Huang Hsuan-Cheng 
Published: February 2011

Abstract

Background
MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level. Recent studies have suggested that miRNAs and transcription factors are primary metazoan gene regulators; however, the crosstalk between them still remains unclear.

Methods
We proposed a novel model utilizing functional annotation information to identify significant coregulation between transcriptional and post-transcriptional layers. Based on this model, function-enriched coregulation relationships were discovered and combined into different kinds of functional coregulation networks.

Results
We found that miRNAs may engage in a wider diversity of biological processes by coordinating with transcription factors, and this kind of cross-layer coregulation may have higher specificity than intra-layer coregulation. In addition, the coregulation networks reveal several types of network motifs, including feed-forward loops and massive upstream crosstalk. Finally, the expression patterns of these coregulation pairs in normal and tumour tissues were analyzed. Different coregulation types show unique expression correlation trends. More importantly, the disruption of coregulation may be associated with cancers.

Conclusion
Our findings elucidate the combinatorial and cooperative properties of transcription factors and miRNAs regulation, and we proposes that the coordinated regulation may play an important role in many biological processes.
Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases
Authors: Song Young Soo , Park Chan Hee , Chung Hee-Joon , Shin Hyunjung , Kim Jihun , Kim Ju Han 
Published: February 2011

Abstract

Background
Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions.

Methods
An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs.

Results
When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF.

Conclusions
We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited.
Repeat-aware modeling and correction of short read errors
Authors: Yang Xiao , Aluru Srinivas , Dorman Karin S 
Published: February 2011

Abstract

Background
High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of k mers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous k mer may be frequently observed if it has few nucleotide differences with valid k mers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content.

Results
We develop a statistical model and a computational method for error detection and correction in the presence of genomic repeats. We propose a method to infer genomic frequencies of k mers from their observed frequencies by analyzing the misread relationships among observed k mers. We also propose a method to estimate the threshold useful for validating k mers whose estimated genomic frequency exceeds the threshold. We demonstrate that superior error detection is achieved using these methods. Furthermore, we break away from the common assumption of uniformly distributed errors within a read, and provide a framework to model position-dependent error occurrence frequencies common to many short read platforms. Lastly, we achieve better error correction in genomes with high repeat content. Availability: The software is implemented in C++ and is freely available under GNU GPL3 license and Boost Software V1.0 license at “http://aluru-sun.ece.iastate.edu/doku.php?id=redeem”.

Conclusions
We introduce a statistical framework to model sequencing errors in next-generation reads, which led to promising results in detecting and correcting errors for genomes with high repeat content.
Ontology-based instance data validation for high-quality curated biological pathways
Authors: Jeong Euna , Nagasaki Masao , Ueno Kazuko , Miyano Satoru 
Published: February 2011

Abstract

Background
Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results
We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions
A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.