bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of oligofructose on appetite profile, glucagon-like peptide 1 and peptide YY3-36 concentrations and energy intake
Authors: Verhoef Sanne P. M. , Meyer Diederick , Westerterp Klaas R. 
Published: June 2011

In rats, oligofructose has been shown to stimulate satiety hormone secretion, reduce energy intake and promote weight loss. The present study aimed to examine the effect of oligofructose supplementation on appetite profiles, satiety hormone concentrations and energy intake in human subjects. A total of thirty-one healthy subjects (ten men and twenty-one women) aged 28 (sem 3) years with a BMI of 24·8 (sem 0·3) kg/m2 were included in a randomised double-blind, cross-over study. The subjects received 10 g oligofructose, 16 g oligofructose or 16 g placebo (maltodextrin) daily for 13 d, with a 2-week washout period between treatments. Appetite profile, active glucagon-like peptide 1 (GLP-1) and peptide YY3-36 (PYY) concentrations and energy intake were assessed on days 0 and 13 of the treatment period. Time × treatment interaction revealed a trend of reduction in energy intake over days 0–13 by oligofructose (P = 0·068). Energy intake was significantly reduced (11 %) over time on day 13 compared with day 0 with 16 g/d oligofructose (2801 (sem 301) v. 3217 (sem 320) kJ, P < 0·05). Moreover, energy intake was significantly lower with 16 g/d oligofructose compared with 10 g/d oligofructose on day 13 (2801 (sem 301) v. 3177 (sem 276) kJ, P < 0·05). Area under the curve (AUC) for GLP-1 on day 13 was significantly higher with 16 g/d oligofructose compared with 10 g/d oligofructose (45 (sem 4) v. 41 (sem 3) pmol/l × h, P < 0·05). In the morning until lunch, AUC0–230 min for PYY on day 13 was significantly higher with 16 g/d oligofructose compared with 10 g/d oligofructose and placebo (409 (sem 35) v. 222 (sem 19) and 211 (sem 20) pg/ml × h, P < 0·01). In conclusion, 16 g/d and not 10 g/d oligofructose may be an effective dose to reduce energy intake, possibly supported by higher GLP-1 and PYY concentrations.
Importance of early nutritional screening in patients with gastric cancer
Authors: Gavazzi Cecilia , Colatruglio Silvia , Sironi Alessandro , Mazzaferro Vincenzo , Miceli Rosalba 
Published: June 2011

In the present study, we evaluated the relationship between nutritional status, disease stage and quality of life (QoL) in 100 patients recently diagnosed with gastric carcinoma. The patients' nutritional status was investigated with anthropometric, biochemical, inflammatory and functional variables; and we also evaluated the nutritional risk with the Nutritional Risk Screening 2002. Oncological staging was standard. QoL was evaluated using the Functional Assessment of Anorexia/Cachexia Therapy questionnaire. The statistical correlation between nutritional risk score (NRS) and oncological characteristics or QoL was evaluated using both univariable and multivariable analyses. Weight loss and reduction of food intake were the most frequent pathological nutritional indicators, while biochemical, inflammatory and functional variables were in the normal range. According to NRS, thirty-six patients were malnourished or at risk for malnutrition. Patients with NRS ≥ 3 presented a significantly greater percentage of stage IV gastric cancer and pathological values of C-reactive protein, while no correlation was found with the site of tumour. NRS was negatively associated with QoL (P < 0·001) and this relation was independent from oncological and inflammatory variables as confirmed by multivariable analysis. In the present study, we found that in patients with gastric cancer malnutrition is frequent at diagnosis and this is likely due to reduction in food intake. Moreover, NRS is directly correlated with tumour stage and inversely correlated with QoL, which makes it a useful tool to identify patients in need of an early nutritional intervention during oncological treatments.
Replication in two independent population-based samples that childhood maltreatment and cannabis use synergistically impact on psychosis risk
Authors: Konings M. , Stefanis N. , Kuepper R. , de Graaf R. , Have M. ten , van Os J. , Bakoula C. , Henquet C. 
Published: June 2011

BackgroundThere may be biological plausibility to the notion that cannabis use and childhood trauma or maltreatment synergistically increase the risk for later development of psychotic symptoms. To replicate and further investigate this issue, prospective data from two independent population-based studies, the Greek National Perinatal Study (n=1636) and The Netherlands Mental Health Survey and Incidence Study (NEMESIS) (n=4842), were analyzed.MethodTwo different data sets on cannabis use and childhood maltreatment were used. In a large Greek population-based cohort study, data on cannabis use at age 19 years and childhood maltreatment at 7 years were assessed. In addition, psychotic symptoms were assessed using the Community Assessment of Psychic Experiences (CAPE). In NEMESIS, the Composite International Diagnostic Interview (CIDI) was used to assess psychotic symptoms at three different time points along with childhood maltreatment and lifetime cannabis use.ResultsA significant adjusted interaction between childhood maltreatment and later cannabis use was evident in both samples, indicating that the psychosis-inducing effects of cannabis were stronger in individuals exposed to earlier sexual or physical mistreatment [Greek National Perinatal Study: test for interactionF(2, 1627)=4.18,p=0.02; NEMESIS: test for interaction χ2(3)=8.08,p=0.04].ConclusionsCross-sensitivity between childhood maltreatment and cannabis use may exist in pathways that shape the risk for expression of positive psychotic symptoms.
Revolutionary versus Traditionalist Approaches to Kant: Some Aspects of the Debate
Authors: Baiasu Sorin , Grier Michelle 
Published: June 2011

AbstractThe interpretation of Kant's Critical philosophy as a version of traditional idealism has a long history. In spite of Kant's and his commentators’ various attempts to distinguish between traditional and transcendental idealism, his philosophy continues to be construed as committed (whether explicitly or implicitly and whether consistently or inconsistently) to various features usually associated with the traditional idealist project. As a result, most often, the accusation is that his Critical philosophy makes too strong metaphysical and epistemological claims.In his The Revolutionary Kant, Graham Bird engages in a systematic and thorough evaluation of the traditionalist interpretation, as part of perhaps the most comprehensive and compelling defence of a revolutionary reading of Kant's thought. In the third part of this special issue, the exchanges between, on the one hand, Graham Bird and, on the other, Gary Banham, Gordon Brittan, Manfred Kuehn, Adrian Moore and Kenneth Westphal focus on specific aspects of Bird's interpretation of Kant's first Critique. More exactly, the emphasis is on specific aspects of Bird's interpretation of the Introduction, Analytic of Principles and Transcendental Dialectic of Kant's first Critique.The second part of the special issue is devoted to discussions of particular topics in Bird's construal of the remaining significant parts of the first Critique, namely, of the Transcendental Aesthetic and the Analytic of Concepts. Written by Sorin Baiasu and Michelle Grier, these articles examine specific issues in these two remaining parts of the Critique, from the perspective of the debate between the traditionalist and revolutionary interpretation. The special issue begins with an Introduction by the guest co-editors. This provides a summary of the exchanges between Bird and his critics, with a particular focus on the debates stemming from the differences between traditional and revolutionary interpretations of Kant.
ccSVM: correcting Support Vector Machines for confounding factors in biological data classification
Authors: Li Limin , Rakitsch Barbara , Borgwardt Karsten 
Published: June 2011

Abstract
Motivation: Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.
Results: In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors. This is achieved by minimizing the statistical dependence between the classifier and the confounding factors. We prove that this formulation can be transformed into a standard Support Vector Machine with rescaled input data. In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.
Availability: A ccSVM-implementation in MATLAB is available from http://webdav.tuebingen.mpg.de/u/karsten/Forschung/ISMB11_ccSVM/.
Contact: limin.li@tuebingen.mpg.de; karsten.borgwardt@tuebingen.mpg.de
Error correction of high-throughput sequencing datasets with non-uniform coverage
Authors: Medvedev Paul , Scott Eric , Kakaradov Boyko , Pevzner Pavel 
Published: June 2011

Abstract
Motivation: The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.
Results: In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data.
Availability: http://www.cs.toronto.edu/~pashadag.
Contact: pmedvedev@cs.ucsd.edu
Construction of co-complex score matrix for protein complex prediction from AP-MS data
Authors: Xie Zhipeng , Kwoh Chee Keong , Li Xiao-Li , Wu Min 
Published: June 2011

Abstract
Motivation: Protein complexes are of great importance for unraveling the secrets of cellular organization and function. The AP-MS technique has provided an effective high-throughput screening to directly measure the co-complex relationship among multiple proteins, but its performance suffers from both false positives and false negatives. To computationally predict complexes from AP-MS data, most existing approaches either required the additional knowledge from known complexes (supervised learning), or had numerous parameters to tune.
Method: In this article, we propose a novel unsupervised approach, without relying on the knowledge of existing complexes. Our method probabilistically calculates the affinity between two proteins, where the affinity score is evaluated by a co-complexed score or C2S in brief. In particular, our method measures the log-likelihood ratio of two proteins being co-complexed to being drawn randomly, and we then predict protein complexes by applying hierarchical clustering algorithm on the C2S score matrix.
Results: Compared with existing approaches, our approach is computationally efficient and easy to implement. It has just one parameter to set and its value has little effect on the results. It can be applied to different species as long as the AP-MS data are available. Despite its simplicity, it is competitive or superior in performance over many aspects when compared with the state-of-the-art predictions performed by supervised or unsupervised approaches.
Availability: The predicted complex sets in this article are available in the Supplementary information or by sending email to asckkwoh@ntu.edu.sg
Contact: xlli@i2r.a-star.edu.sg
Supplementary information: Supplementary data are available at Bioinformatics online.
Physical Module Networks: an integrative approach for reconstructing transcription regulation
Authors: Novershtern Noa , Regev Aviv , Friedman Nir 
Published: June 2011

Abstract
Motivation: Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.
Results: Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein–protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.
Availability: The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.
Contact: aregev@broad.mit.edu; nirf@cs.huji.ac.il
Mapping ancestral genomes with massive gene loss: A matrix sandwich problem
Authors: Gavranović Haris , Chauve Cedric , Salse Jérôme , Tannier Eric 
Published: June 2011

Abstract
Motivation: Ancestral genomes provide a better way to understand the structural evolution of genomes than the simple comparison of extant genomes. Most ancestral genome reconstruction methods rely on universal markers, that is, homologous families of DNA segments present in exactly one exemplar in every considered species. Complex histories of genes or other markers, undergoing duplications and losses, are rarely taken into account. It follows that some ancestors are inaccessible by these methods, such as the proto–monocotyledon whose evolution involved massive gene loss following a whole genome duplication.
Results: We propose a mapping approach based on the combinatorial notion of ‘sandwich consecutive ones matrix’, which explicitly takes gene losses into account. We introduce combinatorial optimization problems related to this concept, and propose a heuristic solver and a lower bound on the optimal solution. We use these results to propose a configuration for the proto-chromosomes of the monocot ancestor, and study the accuracy of this configuration. We also use our method to reconstruct the ancestral boreoeutherian genomes, which illustrates that the framework we propose is not specific to plant paleogenomics but is adapted to reconstruct any ancestral genome from extant genomes with heterogeneous marker content.
Availability: Upon request to the authors.
Contact: haris.gavranovic@gmail.com; eric.tannier@inria.fr
TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages
Authors: Parikh Ankur P. , Wu Wei , Curtis Ross E. , Xing Eric P. 
Published: June 2011

AbstractMotivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.Results: We propose a novel algorithm, Treegl, an ℓ1 plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.Availability: Software will be available at http://www.sailing.cs.cmu.edu/.Contact: epxing@cs.cmu.edu
Piecewise linear approximation of protein structures using the principle of minimum message length
Authors: Konagurthu Arun S. , Allison Lloyd , Stuckey Peter J. , Lesk Arthur M. 
Published: June 2011

Abstract
Simple and concise representations of protein-folding patterns provide powerful abstractions for visualizations, comparisons, classifications, searching and aligning structural data. Structures are often abstracted by replacing standard secondary structural features—that is, helices and strands of sheet—by vectors or linear segments. Relying solely on standard secondary structure may result in a significant loss of structural information. Further, traditional methods of simplification crucially depend on the consistency and accuracy of external methods to assign secondary structures to protein coordinate data. Although many methods exist automatically to identify secondary structure, the impreciseness of definitions, along with errors and inconsistencies in experimental structure data, drastically limit their applicability to generate reliable simplified representations, especially for structural comparison.
This article introduces a mathematically rigorous algorithm to delineate protein structure using the elegant statistical and inductive inference framework of minimum message length (MML). Our method generates consistent and statistically robust piecewise linear explanations of protein coordinate data, resulting in a powerful and concise representation of the structure. The delineation is completely independent of the approaches of using hydrogen-bonding patterns or inspecting local substructural geometry that the current methods use. Indeed, as is common with applications of the MML criterion, this method is free of parameters and thresholds, in striking contrast to the existing programs which are often beset by them.
The analysis of results over a large number of proteins suggests that the method produces consistent delineation of structures that encompasses, among others, the segments corresponding to standard secondary structure.
Availability: http://www.csse.monash.edu.au/~karun/pmml.
Contact: arun.konagurthu@monash.edu; lloyd.allison@monesh.edu
Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering
Authors: Alexandrov Theodore , Kobarg Jan Hendrik 
Published: June 2011

Abstract
Motivation: Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability.
Methods: We segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra).
Results: The proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory.
Contact: theodore@math.uni-bremen.de
The Örebro prevention programme revisited: a cluster‐randomized effectiveness trial of programme effects on youth drinking
Authors: Bodin Maria C. , Strandberg Anna K. 
Published: June 2011

ABSTRACTAims  This study aimed to assess the effectiveness of the Örebro prevention programme (ÖPP), an alcohol misuse prevention programme that aims to reduce youth drinking by changing parental behaviour.Design  Cluster‐randomized trial, with schools assigned randomly to the ÖPP or no intervention.Setting  Forty municipal schools in 13 counties in Sweden.Participants  A total of 1752 students in the 7th grade and 1314 parents were assessed at baseline. Students' follow‐up rates in the 8th and 9th grades were 92.1% and 88.4%, respectively.Measurements  Classroom questionnaires to students and postal questionnaires to parents were administered before randomization and 12 and 30 months post‐baseline.Findings  Two‐level logistic regression models, under four different methods of addressing the problem of loss to follow‐up, revealed a statistically significant programme effect for only one of three drinking outcomes under one loss‐to‐follow‐up method, and that effect was observed only at the 12‐month follow‐up.Conclusions  The Örebro prevention programme as currently delivered in Sweden does not appear to reduce or delay youth drunkenness.