bluuebunny/crossref_metadata_2025_split huggingface dataset:
LOCALIZING SAGITTARIUS A* AND M87 ON MICROARCSECOND SCALES WITH MILLIMETER VERY LONG BASELINE INTERFEROMETRY
Authors: Broderick Avery E. , Loeb Abraham , Reid Mark J. 
Published: June 2011

ABSTRACT
With the advent of the Event Horizon Telescope (EHT), a millimeter/submillimeter very long baseline interferometer (VLBI), it has become possible to image a handful of black holes with sub-horizon resolutions. However, these images do not translate into microarcsecond absolute positions due to the lack of absolute phase information when an external phase reference is not used. Due to the short atmospheric coherence time at these wavelengths, nodding between the source and phase reference is impractical. However, here we suggest an alternative scheme which makes use of the fact that many of the VLBI stations within the EHT are arrays in their own right. With this we show that it should be possible to absolutely position the supermassive black holes at the centers of the Milky Way (Sgr A*) and M87 relative to nearby objects with precisions of roughly 1 μas. This is sufficient to detect the perturbations to Sgr A*'s position resulting from interactions with the stars and stellar-mass black holes in the Galactic cusp on year timescales, and severely constrain the astrophysically relevant parameter space for an orbiting intermediate-mass black hole, implicated in some mechanisms for producing the young massive stars in the Galactic center. For M87, it allows the registering of millimeter images, in which the black hole may be identified by its silhouette against nearby emission, and existing larger-scale radio images, eliminating present ambiguities in the nature of the radio core and inclination, opening angle, and source of the radio jet.
RINQ: Reference-based Indexing for Network Queries
Authors: Gülsoy Günhan , Kahveci Tamer 
Published: June 2011

Abstract
We consider the problem of similarity queries in biological network databases. Given a database of networks, similarity query returns all the database networks whose similarity (i.e. alignment score) to a given query network is at least a specified similarity cutoff value. Alignment of two networks is a very costly operation, which makes exhaustive comparison of all the database networks with a query impractical. To tackle this problem, we develop a novel indexing method, named RINQ (Reference-based Indexing for Biological Network Queries). Our method uses a set of reference networks to eliminate a large portion of the database quickly for each query. A reference network is a small biological network. We precompute and store the alignments of all the references with all the database networks. When our database is queried, we align the query network with all the reference networks. Using these alignments, we calculate a lower bound and an approximate upper bound to the alignment score of each database network with the query network. With the help of upper and lower bounds, we eliminate the majority of the database networks without aligning them to the query network. We also quickly identify a small portion of these as guaranteed to be similar to the query. We perform pairwise alignment only for the remaining networks. We also propose a supervised method to pick references that have a large chance of filtering the unpromising database networks. Extensive experimental evaluation suggests that (i) our method reduced the running time of a single query on a database of around 300 networks from over 2 days to only 8 h; (ii) our method outperformed the state of the art method Closure Tree and SAGA by a factor of three or more; and (iii) our method successfully identified statistically and biologically significant relationships across networks and organisms.
Contact: ggulsoy@cise.ufl.edu; tamer@cise.ufl.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
The role of proteosome-mediated proteolysis in modulating potentially harmful transcription factor activity in Saccharomyces cerevisiae
Authors: Bonzanni Nicola , Zhang Nianshu , Oliver Stephen G. , Fisher Jasmin 
Published: June 2011

Abstract
Motivation: The appropriate modulation of the stress response to variable environmental conditions is necessary to maintain sustained viability in Saccharomyces cerevisiae. Particularly, controlling the abundance of proteins that may have detrimental effects on cell growth is crucial for rapid recovery from stress-induced quiescence.
Results: Prompted by qualitative modeling of the nutrient starvation response in yeast, we investigated in vivo the effect of proteolysis after nutrient starvation showing that, for the Gis1 transcription factor at least, proteasome-mediated control is crucial for a rapid return to growth. Additional bioinformatics analyses show that potentially toxic transcriptional regulators have a significantly lower protein half-life, a higher fraction of unstructured regions and more potential PEST motifs than the non-detrimental ones. Furthermore, inhibiting proteasome activity tends to increase the expression of genes induced during the Environmental Stress Response more than those in the rest of the genome. Our combined results suggest that proteasome-mediated proteolysis of potentially toxic transcription factors tightly modulates the stress response in yeast.
Contact: jasmin.fisher@microsoft.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity
Authors: Furlotte Nicholas A. , Kang Hyun Min , Ye Chun , Eskin Eleazar 
Published: June 2011

Abstract
Motivation: The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearson's correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearson's correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable.
Results: In this article, we present a statistical model for calculating gene coexpression called mixed model coexpression (MMC), which models coexpression within a mixed model framework. Confounding effects are expected to be encoded in the matrix representing the correlation between arrays, the inter-sample correlation matrix. By conditioning on the information in the inter-sample correlation matrix, MMC is able to produce gene coexpressions that are not influenced by global confounding effects and thus significantly reduce the number of spurious coexpressions observed. We applied MMC to both human and yeast datasets and show it is better able to effectively prioritize strong coexpressions when compared to a traditional Pearson's correlation and a Pearson's correlation applied to data corrected with surrogate variable analysis (SVA).
Availability: The method is implemented in the R programming language and may be found at http://genetics.cs.ucla.edu/mmc.
Contact: nfurlott@cs.ucla.edu; eeskin@cs.ucla.edu
Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning
Authors: Airoldi Edoardo M. , Heller Katherine A. , Silva Ricardo 
Published: June 2011

Abstract
Motivation: Proteins and protein complexes coordinate their activity to execute cellular functions. In a number of experimental settings, including synthetic genetic arrays, genetic perturbations and RNAi screens, scientists identify a small set of protein interactions of interest. A working hypothesis is often that these interactions are the observable phenotypes of some functional process, which is not directly observable. Confirmatory analysis requires finding other pairs of proteins whose interaction may be additional phenotypical evidence about the same functional process. Extant methods for finding additional protein interactions rely heavily on the information in the newly identified set of interactions. For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs. A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence.
Results: We develop RBSets, a computational approach to ranking protein interactions rooted in analogical reasoning; that is, the ability to learn and generalize relations between objects. Our approach is tailored to situations where the training set of protein interactions is small, and leverages the attributes of the individual proteins indirectly, in a Bayesian ranking setting that is perhaps closest to propensity scoring in mathematical psychology. We find that RBSets leads to good performance in identifying additional interactions starting from a small evidence set of interacting proteins, for which an underlying biological logic in terms of functional processes and signaling pathways can be established with some confidence. Our approach is scalable and can be applied to large databases with minimal computational overhead. Our results suggest that analogical reasoning within a Bayesian ranking problem is a promising new approach for real-time biological discovery.
Availability: Java code is available at: www.gatsby.ucl.ac.uk/~rbas.
Contact: airoldi@fas.harvard.edu; kheller@mit.edu; ricardo@stats.ucl.ac.uk
Automatic 3D neuron tracing using all-path pruning
Authors: Peng Hanchuan , Long Fuhui , Myers Gene 
Published: June 2011

Abstract
Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.
Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).
Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d.
Contact: pengh@janelia.hhmi.org
A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments
Authors: Mayhew Michael B. , Robinson Joshua W. , Jung Boyoun , Haase Steven B. , Hartemink Alexander J. 
Published: June 2011

Abstract
Motivation: To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cell's position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers.
Results: Here, we develop a new sampling model to accommodate an arbitrary number of different binary markers that characterize the progression of a population of dividing cells along a branching process. We engineer a strain of Saccharomyces cerevisiae with fluorescently labeled markers of cell cycle progression, and apply our new model to two image datasets we collected from the strain, as well as an independent dataset of different markers. We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell. The Java implementation is fast and extensible, and includes a graphical user interface. Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.
Availability: The software is available from: http://www.cs.duke.edu/~amink/software/cloccs/.
Contact: michael.mayhew@duke.edu; amink@cs.duke.edu
Optimally discriminative subnetwork markers predict response to chemotherapy
Authors: Dao Phuong , Wang Kendric , Collins Colin , Ester Martin , Lapuk Anna , Sahinalp S. Cenk 
Published: June 2011

Abstract
Motivation: Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.
Results: We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy.
Availability: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html
Contact: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com
The association between antipsychotic agents and the risk of myocardial infarction: a systematic review
Authors: Brauer Ruth , Douglas Ian , Smeeth Liam 
Published: June 2011

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• An unfavourable risk profile for myocardial infarction (MI) is associated with various lifestyle factors and co‐morbidities that are more prevalent among patients with severe mental illness.• Patient populations that are prescribed antipsychotic agents have a higher cardiovascular mortality rate than the general population.• The association between the use of antipsychotic agents and any increase in the risk of MI is unclear.WHAT THIS STUDY ADDS• The aim of this systematic literature review was to assess the relationship between antipsychotic agents and MI.• Substantial clinical and methodological heterogeneity between the five studies in this review led to an inconclusive answer to the question whether the use of antipsychotics is associated with the incidence of MI in adults.• The largest study had a favourable internal validity compared with the other studies and did not find an association between the use of antipsychotic agents and an increased risk of MI.AIM Patient populations that are prescribed antipsychotic agents have higher cardiovascular mortality rates. The risk of myocardial infarction is influenced by various factors that are more prevalent in patients with a mental illness. The aim of this review was to determine whether the use of antipsychotic agents is associated with the incidence of myocardial infarction in adults.METHODS Using multiple sources, all studies of antipsychotic agents using myocardial infarction as primary or secondary outcome measures were considered for inclusion. Study populations were adult subjects who had been prescribed an antipsychotic agent at least once in their medical history.RESULTS It total, five studies were identified. Four studies with small numbers of events reported a moderate to strong effect of typical antipsychotic agents on the risk of myocardial infarction. The largest study had a favourable internal validity compared with all other studies and reported no association between the risk of myocardial infarction and current use of either atypical (relative risk 0.98, 95% confidence interval [CI] 0.88, 1.09) or typical antipsychotic agents (relative risk 0.99, 95% CI 0.96, 1.03).CONCLUSION Clinical and methodological heterogeneity between the studies in this review led to an inconclusive answer to the question whether the use of antipsychotics is associated with the incidence of myocardial infarction in adults. Whilst results conflicted, the largest study did not find an association between the use of antipsychotic agents and an increased risk of myocardial infarction.
Development and effectiveness of a school programme on improving body image among elementary school students in Taiwan
Authors: Yeh Mei‐Chiang , Liou Yiing‐Mei , Chien Li‐Yin 
Published: June 2011

yeh m.‐c., liou y.‐m. & chien l.‐y. (2012) Development and effectiveness of a school programme on improving body image among elementary school students in Taiwan.Journal of Advanced Nursing 68(2), 434–443.AbstractAim. The objectives of this study were to develop a programme to improve body image among elementary school students and to evaluate the effectiveness of the programme.Background. The idea that a slim body is beautiful has become popular in recent decades, and has negatively impacted body image among school children. There is a need for further development of prevention programmes.Methods. This study applied a quasi‐experimental design. The study participants were 314 5th and 6th graders in two elementary schools in Taipei City, with 112 in the intervention group and 202 in the control group. The intervention group received an 8‐week educational programme. The study participants filled out structured questionnaires at preintervention, postintervention and 3 months after intervention between October 2007 and April 2008. The indicators of effectiveness included perceptional (estimation of body size), attitudinal (body satisfaction) and behavioural (diet and vomiting to lose weight) aspects of body image.Findings. Intervention students showed a greater increase in body satisfaction from pretest to posttest relative to controls. At 3 months after intervention, the intervention students still had significantly higher body satisfaction than controls. There were no statistically significant differences in the perceptional and behavioural aspects of body image.Conclusion. This study demonstrated that a school programme improved body satisfaction among elementary school students. The findings of this study could serve as a basis for further development of body image programmes for children to prevent a drastically deteriorating body image when they enter puberty.
Open‐source learning management systems: a predictive model for higher education
Authors: Williams van Rooij S. 
Published: June 2011

AbstractThe present study investigated the role of pedagogical, technical, and institutional profile factors in an institution of higher education's decision to select an open‐source learning management system (LMS). Drawing on the results of previous research that measured patterns of deployment of open‐source software (OSS) in US higher education and the relative importance of specific selection criteria when considering an OSS LMS, a Web survey was conducted among 285 Chief Information Officers and Chief Academic Officers from a variety of US institutions to determine patterns of OSS LMS deployment, as well as the influence of specific selection criteria. Binary logistic regression analysis revealed Carnegie Classification, institutional governance, previous experience with OSS, focus on student learning, and the commitment to organizational self‐reliance to be key influencers in the OSS LMS selection decision. The implications of these findings for future research and for institutional decision‐making are discussed.
An overview of hybrid marine and lacustrine seas and saline lakes of the world
Authors: Plotnikov Igor S. , Aladin Nikolai V. 
Published: June 2011

AbstractWater salinity is one of the major environmental factors influencing hydrobionts. Based on the concept of relativity and plurality of barrier salinity zones, barrier salinities are related to the development of the osmoregulatory capacities of hydrobionts and to the chemical composition of the water they inhabit. These zones are not of equal importance. The entire hydrosphere might be divided into freshwater, brackish water, marine and hyperhaline salinity zones and transitional zones between them. Approximate boundaries and corresponding barrier salinities are defined for all these zones. Revealing barrier salinity zones requires studying the osmoregulatory capacities of hydrobionts and the distribution of their salinity boundaries. We distinguish between four barrier salinities or horohalinicums for marine and continental waters: α (5–8‰), β (22–26‰), γ (45–50‰) and ™ (0.5–2‰). In metamorphized continental waters, barrier salinities are shifted to higher concentrations. Salinity barrier values can change the subsequent evolution of salinity adaptations and osmoregulation capacities. Maracaibo Lake is characterized by freshwater and freshwater–brackish water zones. Deepening of the lake shipping lane increased the salt flux, with the appearance of a brackish‐water zone. This study proposes to distinguish hybrid lentic waterbodies that include the Baltic Sea, Black Sea, Sea of Azov and Maracaibo Lake, which have lost their lacustrine characteristics and are now characterized by intermixing marine (saline) and continental (fresh) waters.