bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of the Multigrid Method to the Two-Dimensional Doppler Radar Radial Velocity Data Assimilation
Authors: Li Wei , Xie Yuanfu , Deng Shiow-Ming , Wang Qi 
Published: September 2009

Abstract
In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.
An Isofactorial Change-of-Scale Model for the Wind Speed Probability Density Function
Authors: Morrissey Mark L. , Albers Angie , Greene J. Scott , Postawko Susan 
Published: September 2009

AbstractThe wind speed probability density function (PDF) is used in a variety of applications in meteorology, oceanography, and climatology usually as a dataset comparison tool of a function of a quantity such as momentum flux or wind power density. The wind speed PDF is also a function of measurement scale and sampling error. Thus, quantities derived from a function of the wind PDF estimated from measurements taken at different scales may yield vastly different results. This is particularly true in the assessment of wind power density and studies of model subgrid-scale processes related to surface energy fluxes. This paper presents a method of estimating the PDF of wind speed representing a specific scale, whether that is in time, space, or time–space. The concepts used have been developed in the field of nonlinear geostatistics but have rarely been applied to meteorological problems. The method uses an expansion of orthogonal polynomials that incorporates a scaling parameter whose values can be found from the variance of wind speed at the desired scale. Possible uses of this technique are for scale homogenization of model or satellite datasets used in comparison studies, investigations of subgrid-scale processes for development of parameterization schemes, or wind power density assessment.
Validation of Cloud-Resolving Model Background Data for Cloud Data Assimilation
Authors: Polkinghorne Rosanne , Vukicevic Tomislava , Evans K. Franklin 
Published: September 2009

Abstract
Results from a cloud-resolving model are systematically compared with a variety of observations, both ground based and satellite, in order to better understand the mean background errors and their correlations. This is a step in the direction of developing a background error covariance matrix for use in cloud data assimilation. Observation sources include the Geostationary Operational Environmental Satellite (GOES), the Atmospheric Emitted Radiance Interferometer (AERI), a microwave radiometer (MWR), radiosonde, and cloud radar. When exploring model biases in temperature, precipitable water vapor, and liquid water path, a warm and moist bias at night and a cool and dry bias during the day are observed. Values for the background decorrelation length of water variables are determined. In addition, a dynamic cloud mask is presented to give more control in the assimilation of cloudy satellite radiances, allowing different cloud types to be excluded from the assimilation as well as establishing values for the maximum residuals to be considered.
False Alarms, Tornado Warnings, and Tornado Casualties
Authors: Simmons Kevin M. , Sutter Daniel 
Published: September 2009

AbstractThis paper extends prior research on the societal value of tornado warnings to the impact of false alarms. Intuition and theory suggest that false alarms will reduce the response to warnings, yet little evidence of a “false alarm effect” has been unearthed. This paper exploits differences in the false-alarm ratio across the United States to test for a false-alarm effect in a regression model of tornado casualties from 1986 to 2004. A statistically significant and large false-alarm effect is found: tornadoes that occur in an area with a higher false-alarm ratio kill and injure more people, everything else being constant. The effect is consistent across false-alarm ratios defined over different geographies and time intervals. A one-standard-deviation increase in the false-alarm ratio increases expected fatalities by between 12% and 29% and increases expected injuries by between 14% and 32%. The reduction in the national tornado false-alarm ratio over the period reduced fatalities by 4%–11% and injuries by 4%–13%. The casualty effects of false alarms and warning lead times are approximately equal in magnitude, suggesting that the National Weather Service could not reduce casualties by trading off a higher probability of detection for a higher false-alarm ratio, or vice versa.
Making Science Useful to Decision Makers: Climate Forecasts, Water Management, and Knowledge Networks
Authors: Feldman David L. , Ingram Helen M. 
Published: September 2009

AbstractMoving from climate science to adaptive action is an immense challenge, especially in highly institutionalized sectors such as water resources. Knowledge networks are valuable strategies to put climate information to use. They overcome barriers to information adoption such as stovepipes, pipelines, and restricted decision space, and they can be responsive to issues of salience and the hurdles of reliability, credibility, and trust. Collaboration and adaptive management efforts among resource managers and forecast producers with differing missions show that mutual learning informed by climate information can occur among scientists of different disciplinary backgrounds and between scientists and water managers. The authors show how, through construction of knowledge networks and their institutionalization through boundary organizations focused on salient problems, climate information can positively affect water resources decision making.
Weather-Index Drought Insurance in Burkina-Faso: Assessment of Its Potential Interest to Farmers
Authors: Berg Alexis , Quirion Philippe , Sultan Benjamin 
Published: September 2009

Abstract
By using a detailed agricultural and climate dataset over Burkina-Faso and simple assumptions regarding the form of an insurance contract, the authors investigate the potential economic efficiency for farmers of a weather-index insurance system in this country. To do so, the results of more than 3000 simulated contracts applied to 30 districts, 21 yr (1984–2004), and five crops (cotton, millet, sorghum, maize, and groundnut) are explored. It is found that such an insurance system, even based on a simple weather index like cumulative rainfall during the rainy season, can present a significant economic efficiency for some crops and districts. The determinants of the efficiency of such contracts are analyzed in terms of yield/index correlations and yield variability. As a consequence of these two main determinants, the farmer’s gain from an insurance contract is higher in the driest part of the country. In the same way, maize and groundnuts are the most suitable to implement an insurance system since their respective yields show a large variance and a generally high correlation with the weather index. However, the implementation of a real weather-index insurance system in West Africa raises a number of key practical issues related to cultural, economic, and institutional aspects.
Hydrologic Interdependencies and Human Cooperation: The Process of Adapting to Droughts
Authors: Endter-Wada Joanna , Selfa Theresa , Welsh Lisa W. 
Published: September 2009

AbstractThe Bear River Basin, which includes portions of Idaho, Utah, and Wyoming in the United States, has a dynamic history of human hydrologic adaptations in relation to a highly variable water supply. These adaptations are embedded in a geographical setting highly influenced by the legal, policy, and institutional contexts that govern allocation of water in this generally arid region. In response to several years of drought and a historically low water year in 2004, water users in the Bear River Basin tested the efficacy of the “law of the river” and innovative agreements that they had negotiated in recent years to help mitigate impacts related to water shortages. Three innovations were identified as being key to a successful response to the 2004 drought: 1) a precedent-setting voluntary settlement agreement, 2) technical work in river modeling and instrumentation, and 3) extraordinary communication strategies employed throughout the drought. Based on case study research and utilizing a “ways of knowing” theoretical framework, the authors report on an unfolding contemporary history of how people in the Bear River Basin have learned to deal with uncertainties and risks associated with both droughts and floods. Their story has important implications for the understanding of conflict and cooperation in water systems, management of transboundary waters, and the promotion of sustainable water resource governance.
Changes in Ice Storm Impacts over Time: 1886–2000
Authors: Call David A. 
Published: September 2009

Abstract
Ice storms have a variety of negative effects on society. Through an analysis of newspaper accounts of nine exceptional ice storms, the most widespread and longest lasting impact is the loss of electrical power. Power outages also cause secondary effects, such as carbon monoxide poisoning and fire, and they can force people to leave their homes because of a lack of heat. Other impacts of ice storms are transportation disruptions, school and business closings, and economic losses to agriculture and some business sectors. However, some businesses, such as those associated with the hospitality sector, actually benefit from ice storms.
Modern power outages have a longer duration than those associated with earlier storms. Rural areas are most likely to suffer from long power outages because utilities prioritize areas with greater numbers of customers and because fallen trees may limit accessibility. Several suggestions for reducing electrical disruption, such as aggressive tree-trimming programs and burial of lines, are analyzed. While these may help, less reliance on electricity for lighting and heating systems could also provide a benefit.
Formulation of novel lipid-coated magnetic nanoparticles as the probe for in vivo imaging
Authors: Huang Huey-Chung , Chang Po-Yuan , Chang Karen , Chen Chao-Yu , Lin Chung-Wu , Chen Jyh-Horng , Mou Chung-Yuan , Chang Zee-Fen , Chang Fu-Hsiung 
Published: September 2009

AbstractBackgroundApplication of superparamagnetic iron oxide nanoparticles (SPIOs) as the contrast agent has improved the quality of magnetic resonance (MR) imaging. Low efficiency of loading the commercially available iron oxide nanoparticles into cells and the cytotoxicity of previously formulated complexes limit their usage as the image probe. Here, we formulated new cationic lipid nanoparticles containing SPIOs feasible forin vivoimaging.MethodsHydrophobic SPIOs were incorporated into cationic lipid 1,2-dioleoyl-3-(trimethylammonium) propane (DOTAP) and polyethylene-glycol-2000-1,2-distearyl-3-sn-phosphatidylethanolamine (PEG-DSPE) based micelles by self-assembly procedure to form lipid-coated SPIOs (L-SPIOs). Trace amount of Rhodamine-dioleoyl-phosphatidylethanolamine (Rhodamine-DOPE) was added as a fluorescent indicator. Particle size and zeta potential of L-SPIOs were determined by Dynamic Light Scattering (DLS) and Laser Doppler Velocimetry (LDV), respectively. HeLa, PC-3 and Neuro-2a cells were tested for loading efficiency and cytotoxicity of L-SPIOs using fluorescent microscopy, Prussian blue staining and flow cytometry. L-SPIO-loaded CT-26 cells were tested forin vivoMR imaging.ResultsThe novel formulation generates L-SPIOs particle with the average size of 46 nm. We showed efficient cellular uptake of these L-SPIOs with cationic surface charge into HeLa, PC-3 and Neuro-2a cells. The L-SPIO-loaded cells exhibited similar growth potential as compared to unloaded cells, and could be sorted by a magnet stand over ten-day duration. Furthermore, when SPIO-loaded CT-26 tumor cells were injected into Balb/c mice, the growth status of these tumor cells could be monitored using optical and MR images.ConclusionWe have developed a novel cationic lipid-based nanoparticle of SPIOs with high loading efficiency, low cytotoxicity and long-term imaging signals. The results suggested these newly formulated non-toxic lipid-coated magnetic nanoparticles as a versatile image probe for cell tracking.
Finding local communities in protein networks
Authors: Voevodski Konstantin , Teng Shang-Hua , Xia Yu 
Published: September 2009

Abstract

Background
Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks.

Results
We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent.

Conclusion
The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to investigate functional modules that a particular protein is a part of.
Feature selection for fMRI-based deception detection
Authors: Jin Bo , Strasburger Alvin , Laken Steven J , Kozel F Andrew , Johnson Kevin A , George Mark S , Lu Xinghua 
Published: September 2009

Abstract

Background
Functional magnetic resonance imaging (fMRI) is a technology used to detect brain activity. Patterns of brain activation have been utilized as biomarkers for various neuropsychiatric applications. Detecting deception based on the pattern of brain activation characterized with fMRI is getting attention – with machine learning algorithms being applied to this field in recent years. The high dimensionality of fMRI data makes it a difficult task to directly utilize the original data as input for classification algorithms in detecting deception. In this paper, we investigated the procedures of feature selection to enhance fMRI-based deception detection.

Results
We used the t-statistic map derived from the statistical parametric mapping analysis of fMRI signals to construct features that reflect brain activation patterns. We subsequently investigated various feature selection methods including an ensemble method to identify discriminative features to detect deception. Using 124 features selected from a set of 65,166 original features as inputs for a support vector machine classifier, our results indicate that feature selection significantly enhanced the classification accuracy of the support vector machine in comparison to the models trained using all features and dimension reduction based models. Furthermore, the selected features are shown to form anatomic clusters within brain regions, which supports the hypothesis that specific brain regions may play a role during deception processes.

Conclusion
Feature selection not only enhances classification accuracy in fMRI-based deception detection but also provides support for the biological hypothesis that brain activities in certain regions of the brain are important for discrimination of deception.
Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia
Authors: Yang Xinan , Huang Yong , Chen James L , Xie Jianming , Sun Xiao , Lussier Yves A 
Published: September 2009

Abstract

Background
Current outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.

Results
This proof-of-concept study, which focuses on epigenetic mechanisms, was conducted in a well-studied set of 132 acute lymphoblastic leukemia (ALL) microarrays annotated with nine distinct phenotypes and three measures of response to therapy. We used established parametric and non parametric statistics to derive the PGnet tripartite network that consisted of 10 phenotypes and 33 significant clusters of GEMs comprising 535 distinct genes. The significance of PGnet was estimated from empirical p-values, and a robust subnetwork derived from ALL outcome data was produced by repeated random sampling. The evaluation of derived robust network to predict outcome (relapse of ALL) was significant (p = 3%), using one hundred three-fold cross-validations and the shrunken centroids classifier.

Conclusion
To our knowledge, this is the first method predicting co-expression networks of genes associated with epigenetic mechanisms and to demonstrate its inherent capability to predict therapeutic outcome. This PGnet approach can be applied to any regulatory mechanisms including transcriptional or microRNA regulation in order to derive predictive molecular profiles that are mechanistically anchored. The implementation of PGnet in R is freely available at http://Lussierlab.org/publication/PGnet.
The potential for automated question answering in the context of genomic medicine: an assessment of existing resources and properties of answers
Authors: Overby Casey Lynnette , Tarczy-Hornoch Peter , Demner-Fushman Dina 
Published: September 2009

AbstractKnowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to therapy. These scientific discoveries need to be translated into practical applications to optimize patient care. Translating research into practice can be facilitated by supplying clinicians with research evidence. We assessed the role of existing tools in extracting answers to translational research questions in the area of genomic medicine. We: evaluate the coverage of translational research terms in the Unified Medical Language Systems (UMLS) Metathesaurus; determine where answers are most often found in full-text articles; and determine common answer patterns. Findings suggest that we will be able to leverage the UMLS in development of natural language processing algorithms for automated extraction of answers to translational research questions from biomedical text in the area of genomic medicine.
Dynamic localisation of Ran GTPase during the cell cycle
Authors: Hutchins James RA , Moore William J , Clarke Paul R 
Published: September 2009

Abstract

Background
Ran GTPase has multiple functions during the cell division cycle, including nucleocytoplasmic transport, mitotic spindle assembly and nuclear envelope formation. The activity of Ran is determined by both its guanine nucleotide-bound state and its subcellular localization.

Results
Here, we have characterised the localisation and mobility of Ran coupled to green fluorescent protein (GFP) during the cell cycle in live human cells. Ran-GFP is nuclear during interphase and is dispersed throughout the cell during mitosis. GFP-RanQ69L, a mutant locked in the GTP-bound state, is less highly concentrated in the nucleus and associates with nuclear pore complexes within the nuclear envelope. During mitosis, GFP-RanQ69L is excluded from chromosomes and localizes to the spindle. By contrast, GFP-RanT24N, a mutant with low affinity for nucleotides, interacts relatively stably with chromatin throughout the cell cycle and is highly concentrated on mitotic chromosomes.

Conclusion
These results show that Ran interacts dynamically with chromatin, nuclear pore complexes and the mitotic spindle during the cell cycle. These interactions are dependent on the nucleotide-bound state of the protein. Our data indicate that Ran-GTP generated at chromatin is highly mobile and interacts dynamically with distal structures that are involved in nuclear transport and mitotic spindle assembly.