bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cluster-Based Collaborative Filtering for Sign Prediction in Social Networks with Positive and Negative Links
Authors: Javari Amin , Jalili Mahdi 
Published: April 2014

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network.
In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).
Description-Driven Community Detection
Authors: Pool Simon , Bonchi Francesco , Leeuwen Matthijs van 
Published: April 2014

Traditional approaches to community detection, as studied by physicists, sociologists, and more recently computer scientists, aim at simply partitioning the social network graph. However, with the advent of online social networking sites, richer data has become available: beyond the link information, each user in the network is annotated with additional information, for example, demographics, shopping behavior, or interests. In this context, it is therefore important to develop mining methods which can take advantage of all available information. In the case of community detection, this means finding
good communities
(a set of nodes cohesive in the social graph) which are associated with
good descriptions
in terms of user information (node attributes).

Having good descriptions associated to our models make them understandable by domain experts and thus more useful in real-world applications. Another requirement dictated by real-world applications, is to develop methods that can use, when available, any domain-specific background knowledge. In the case of community detection the background knowledge could be a vague description of the communities sought in a specific application, or some prototypical nodes (e.g., good customers in the past), that represent what the analyst is looking for (a community of similar users).
Towards this goal, in this article, we define and study the problem of finding a diverse set of cohesive communities with concise descriptions. We propose an effective algorithm that alternates between two phases: a hill-climbing phase producing (possibly overlapping) communities, and a description induction phase which uses techniques from supervised pattern set mining. Our framework has the nice feature of being able to build well-described cohesive communities starting from any given description or seed set of nodes, which makes it very flexible and easily applicable in real-world applications.

Our experimental evaluation confirms that the proposed method discovers cohesive communities with concise descriptions in realistic and large online social networks such as D
elicious
, F
lickr
, and L
ast
FM.
When Errors Become the Rule
Authors: Uneson Marcus 
Published: April 2014

Transformation-based learning (TBL) is a machine learning method for, in particular, sequential classification, invented by Eric Brill [Brill 1993b, 1995a]. It is widely used within computational linguistics and natural language processing, but surprisingly little in other areas.
TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies and seamlessly exploits information from heterogeneous discrete feature types. The learned representation—an ordered list of transformation rules—is compact and efficient, with clear semantics. Individual rules are interpretable and often meaningful to humans.
The present article offers a survey of the most important theoretical work on TBL, addressing a perceived gap in the literature. Because the method should be useful also outside the world of computational linguistics and natural language processing, a chief aim is to provide an informal but relatively comprehensive introduction, readable also by people coming from other specialities.
Statin therapy and thromboxane generation in patients with coronary artery disease treated with high-dose aspirin
Authors: Bliden Kevin P. , Singla Anand , Gesheff Martin G. , Toth Peter P. , Tabrizchi Ali , Ens Gordon , Guyer Kirk , Singh Mandeep , Franzese Christopher J. , Stapleton Dwight D. , Tantry Udaya S. , Gurbel Paul A. 
Published: April 2014

SummaryAspirin and statin therapy are mainstay treatments in patients with coronary artery disease (CAD). The relation between statin therapy, in vivo thromboxane (Tx) generation; a marker of inflammation, and blood thrombogenicity has never been explored. Urinary 11-dehydro (dh) TxB2 was determined in patients with suspected CAD on 325 mg daily aspirin therapy prior to undergoing cardiac catheterisation (n=281). Thrombogenicity was estimated by thrombelastographic measurement of thrombin-induced platelet-fibrin clot strength (TIP-FCS) and lipids/lipoproteins were determined by vertical density gradient ultracentrifugation/ELISA. The influence of statin therapy and dose was analysed by the atorvastatin equivalent dose (5–10 mg, 20–40 mg, or 80 mg daily). Statin therapy (n=186) was associated with a dose-dependent reduction in urinary 11-dh TxB2 (p=0.046) that was independent of LDL and apo B100 levels but was strongly related to TIP-FCS (p=0.006). By multivariate analysis, no statin therapy (n=95) and female gender were independently associated with high urinary 11-dh TxB2 [OR=2.95 (0.1.57–5.50, p=0.0007); OR=2.25 (1.24–4.05, p=0.007)], respectively. In aspirin-treated patients, statin therapy was independently and inversely associated with inflammation in a dose-dependent manner. Elevated 11-dh TxB2 was associated with a prothrombotic state indicated by high TIP-FCS. Our data suggest that measurement of urinary 11-dTxB2 may be a useful method to optimise statin dosing in order to reduce thrombotic risk.
Challenges of Decision Making Regarding Futility in a Randomized Trial
Authors: Yeatts Sharon D. , Martin Renee H. , Coffey Christopher S. , Lyden Patrick D. , Foster Lydia D. , Woolson Robert F. , Broderick Joseph P. , Di Tullio Marco R. , Jungreis Charles A. , Palesch Yuko Y. 
Published: April 2014

Background and Purpose—
Interventional Management of Stroke (IMS) III is a randomized, parallel arm trial comparing the approach of intravenous tissue-type plasminogen activator followed by endovascular treatment with intravenous tissue-type plasminogen activator alone in patients with acute ischemic stroke presenting <3 hours of symptom onset. The trial intended to enroll 900 subjects to ensure adequate statistical power to detect an absolute 10% difference in the percentage of subjects with good outcome, defined as modified Rankin Scale score of 0 to 2 at 3 months. In April 2012, after 656 subjects were randomized, further enrollment was terminated by the National Institute of Neurological Disorders and Stroke based on the prespecified criterion for futility using conditional power <20%.

Methods—
Conditional power was defined as the likelihood of finding statistical significance at the end of the study, given the accumulated data to date and with the assumption that a minimum hypothesized difference of 10% truly exists between the 2 groups. The evolution of study data leading to futility determination is described, including the interaction between the unblinded study statisticians and the Data and Safety Monitoring Board in the complex deliberation of analysis results.

Results—
The futility boundary was crossed at the trial’s fourth interim analysis. At this point, based on the conditional power criteria, the Data and Safety Monitoring Board recommended termination of the trial.

Conclusions—
Even in spite of prespecified interim analysis boundaries, interim looks at data pose challenges in interpretation and decision making, underscoring the importance of objective stopping criteria.

Clinical Trial Registration—

URL:
http://www.clinicaltrials.gov
. Unique identifier: NCT00359424.
Physical Mechanisms Related to Climate-Induced Drying of Two Semiarid Watersheds in the Southwestern United States
Authors: Rajagopal Seshadri , Dominguez Francina , Gupta Hoshin V. , Troch Peter A. , Castro Christopher L. 
Published: April 2014

Abstract
Water managers across the United States face the need to make informed policy decisions regarding long-term impacts of climate change on water resources. To provide a scientifically informed basis for this, the evolution of important components of the basin-scale water balance through the end of the twenty-first century is estimated. Bias-corrected and spatially downscaled climate projections, from phase 3 of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme, were used to drive a spatially distributed Variable Infiltration Capacity (VIC) model of hydrologic processes in the Salt–Verde basin in the southwestern United States. From the suite of CMIP3 models, the authors select a five-model subset, including three that best reproduce the historical climatology for the study region, plus two others to represent wetter and drier than model average conditions, so as to represent the range of GCM prediction uncertainty. For each GCM, data for three emission scenarios (A1B, A2, and B1) were used to drive the hydrologic model into the future. The projections of this model ensemble indicate a statistically significant 25% decrease in streamflow by the end of the twenty-first century. The primary cause for this change is due to projected decreases in winter precipitation accompanied by significant (temperature driven) reductions in storage of snow and increased winter evaporation. The results show that water management in central Arizona is highly likely to be impacted by changes in regional climate.
Nonlinear Equilibration of Baroclinic Instability: The Growth Rate Balance Model
Authors: Radko T. , Peixoto de Carvalho D. , Flanagan J. 
Published: April 2014

Abstract
A theoretical model is developed, which attempts to predict the lateral transport by mesoscale variability, generated and maintained by baroclinic instability of large-scale flows. The authors are particularly concerned by the role of secondary instabilities of primary baroclinically unstable modes in the saturation of their linear growth. Theory assumes that the fully developed equilibrium state is characterized by the comparable growth rates of primary and secondary instabilities. This assumption makes it possible to formulate an efficient algorithm for evaluating the equilibrium magnitude of mesoscale eddies as a function of the background parameters: vertical shear, stratification, beta effect, and bottom drag. The proposed technique is applied to two classical models of baroclinic instability—the Phillips two-layer model and the linearly stratified Eady model. Theory predicts that the eddy-driven lateral mixing rapidly intensifies with increasing shear and weakens when the beta effect is increased. The eddy transport is also sensitive to the stratification pattern, decreasing as the ratio of upper/lower layer depths in the Phillips model is decreased below unity. Theory is successfully tested by a series of direct numerical simulations that span a wide parameter range relevant for typical large-scale currents in the ocean. The spontaneous emergence of large-scale patterns induced by mesoscale variability, and their role in the cross-flow eddy transport, is examined using a suite of numerical simulations.
GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System
Authors: Wang Xuguang , Lei Ting 
Published: April 2014

AbstractA four-dimensional (4D) ensemble–variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts.
A Study of Two Propagating Heavy-Rainfall Episodes near Taiwan during SoWMEX/TiMREX IOP-8 in June 2008. Part I: Synoptic Evolution, Episode Propagation, and Model Control Simulation
Authors: Wang Chung-Chieh , Chieh-Sheng Hsu Jason , Tai-Jen Chen George , Lee Dong-In 
Published: April 2014

Abstract
This paper is the first of a two-part study to investigate two rain-producing episodes in the longitude–time (Hovmöller) space upstream from Taiwan during the eighth intensive observing period (IOP-8, 12–17 June 2008) of the Southwest Monsoon Experiment/Terrain-influenced Monsoon Rainfall Experiment (SoWMEX/TiMREX), with a goal to better understand the mechanism and controlling factors for their organization and propagation. Both in a prefrontal environment, the first episode moved eastward and the second was a rare westward-moving event, and each caused heavy rainfall in Taiwan, on 14 and 16 June, respectively. In Part II, the roles played by synoptic conditions and terrain effects are further examined through sensitivity tests.
With the aid from a successful simulation with a grid spacing of 2.5 km, the structure and organization of convection embedded in the two episodes are shown to be different. With stronger low-level vertical wind shear in its environment, the first episode consisted of well-organized squall-line-type convective systems and propagated eastward mainly through cold-pool dynamics. However, the convection of the second episode was scattered and less organized with weaker vertical shear, and individual cells traveled with background flow toward the north-northeast. Throughout the 6-day case period, the southwesterly low-level jet (LLJ) is found to have much control over the general region of convection, and thus dictates the overall rainfall pattern in the Hovmöller space at the regional scale. The rapid development of the mei-yu front and LLJ over southeastern China during 16–17 June, to the west of the previous location of the jet, is found to result in the westward movement of the second episode.
Determining an Optimal Decay Factor for Bias-Correcting MOS Temperature and Dewpoint Forecasts
Authors: Glahn Bob 
Published: April 2014

Abstract
Model output statistics (MOS) forecast relationships for temperature and dewpoint developed with least squares regression and put into operation by the National Weather Service (NWS) are unbiased over the sample period of development. However, short-term biases within that period can exist, and application of the regression equations to new data may produce forecasts with short- or long-term biases. Because NWP models undergo changes over time, MOS forecasts can be biased because of these changes, and also possibly because of local environmental changes. These biases can be largely eliminated. In the decaying average method, a “decay factor” is used. This value affects not only the short- and long-term bias characteristics, but also other accuracy measures of the forecasts. This paper shows how different values of the decay factor affect MOS temperature and dewpoint forecasts, and the range of factors that would be appropriate for bias correcting those forecasts. Biases and other quality measures are shown for both cool and warm season samples before and after various values of the decay factor have been applied.
Pseudomonas aeruginosa clinical and environmental isolates constitute a single population with high phenotypic diversity
Authors: Grosso-Becerra María-Victoria , Santos-Medellín Christian , González-Valdez Abigail , Méndez José-Luis , Delgado Gabriela , Morales-Espinosa Rosario , Servín-González Luis , Alcaraz Luis-David , Soberón-Chávez Gloria 
Published: April 2014

Abstract

Background

Pseudomonas aeruginosa is an opportunistic pathogen with a high incidence of hospital infections that represents a threat to immune compromised patients. Genomic studies have shown that, in contrast to other pathogenic bacteria, clinical and environmental isolates do not show particular genomic differences. In addition, genetic variability of all the P. aeruginosa strains whose genomes have been sequenced is extremely low. This low genomic variability might be explained if clinical strains constitute a subpopulation of this bacterial species present in environments that are close to human populations, which preferentially produce virulence associated traits.

Results
In this work, we sequenced the genomes and performed phenotypic descriptions for four non-human P. aeruginosa isolates collected from a plant, the ocean, a water-spring, and from dolphin stomach. We show that the four strains are phenotypically diverse and that this is not reflected in genomic variability, since their genomes are almost identical. Furthermore, we performed a detailed comparative genomic analysis of the four strains studied in this work with the thirteen previously reported P. aeruginosa genomes by means of describing their core and pan-genomes.

Conclusions
Contrary to what has been described for other bacteria we have found that the P. aeruginosa core genome is constituted by a high proportion of genes and that its pan-genome is thus relatively small. Considering the high degree of genomic conservation between isolates of P. aeruginosa from diverse environments, including human tissues, some implications for the treatment of infections are discussed. This work also represents a methodological contribution for the genomic study of P. aeruginosa, since we provide a database of the comparison of all the proteins encoded by the seventeen strains analyzed.
Delineation of immunodominant and cytadherence segment(s) of Mycoplasma pneumoniae P1 gene
Authors: Chourasia Bishwanath K , Chaudhry Rama , Malhotra Pawan 
Published: April 2014

Abstract

Background
Adhesion of Mycoplasma pneumoniae (M. pneumoniae) to host epithelial cells requires several adhesin proteins like P1, P30 and P116. Among these proteins, P1 protein has been inedited as one of the major adhesin and immunogenic protein present on the attachment organelle of M. pneumoniae. In the present study, we scanned the entire sequence of M. pneumoniae P1 protein to identify the immunodominant and cytadherence region(s). M. pneumoniae P1 gene was synthesized in four segments replacing all the UGA codons to UGG codons. Each of the four purified P1 protein fragment was analyzed for its immunogenicity with anti-M. pneumoniae M129 antibodies (Pab M129) and sera of M. pneumoniae infected patients by western blotting and ELISA. Antibodies were produced against all the P1 protein fragments and these antibodies were used for M. pneumoniae adhesion, M. pneumoniae adhesion inhibition and M. pneumoniae surface exposure assays using HEp-2 cells lines.

Results
Our results show that the immunodominant regions are distributed throughout the entire length of P1 protein, while only the N- and C- terminal region(s) of P1 protein are surface exposed and block cytadhesion to HEp-2 cells, while antibodies to two middle fragments failed to block cytadhesion.

Conclusions
These results have important implications in designing strategies to block the attachment of M. pneumoniae to epithelial cells, thus preventing the development of atypical pneumonia.
Clinical predictors of incident gallstone disease in a Chinese population in Taipei, Taiwan
Authors: Chen Jau-Yuan , Hsu Chung-Te , Liu Jorn-Hon , Tung Tao-Hsin 
Published: April 2014

Abstract

Background
Gallstone disease (GSD) is a common gastrointestinal disorder throughout the world. The authors explored the incidence of GSD in Taiwan and its condition-associated predictive factors.

Methods
The initial study cohort comprised 2386 healthy adult participants, who were voluntarily admitted to a teaching hospital for a physical check-up in 2002 in Taipei, Taiwan. After excluding 126 patients who exhibited prevalent GSD, 2260 non-GSD participants received annual follow-up screenings for GSD until 31 December, 2007. Of those, 1296 (57.3%) patients were re-examined to collect blood samples and conduct ultrasound sonography.

Results
Among the 1296 participants who exhibited no GSD at the first screening, 23 patients developed GSD during 3640 person-years of follow-up. The incidence was 0.632% per year (95% CI: 0.292%–2.009%). After conducting a Cox regression, increased age (50–59 years versus < 40 years, RR = 2.16 [95% CI: 1.09–5.97], 60+ years versus < 40 years, RR = 3.81 [95% CI: 2.77–8.63]), high body mass index (≥27 kg/m2 versus < 24 kg/m2, RR = 1.64 [95% CI: 1.07–2.98]), high fasting plasma glucose levels (≥126 mg/dL versus < 110 mg/dL, RR = 1.68, 95% CI: 1.10–3.87), and nonalcoholic fatty liver disease (yes versus no, RR = 1.44, 95% CI: 1.21–1.90) appeared to be significantly related to developing GSD.

Conclusion
Increased age is a well-established risk factor for developing GSD. The current findings indicated that high body mass index, elevated fasting plasma glucose levels, and nonalcoholic fatty liver disease were also associated with GSD.