George Mason University

About: George Mason University is a education organization based out in Fairfax, Virginia, United States. It is known for research contribution in the topics: Population & Politics. The organization has 12490 authors who have published 39989 publications receiving 1301688 citations. The organization is also known as: Mason & George Mason.

Determinants of corporate restructuring: The relative importance of corporate governance, takeover threat, and free cash flow

Abstract: This study seeks to estimate the relative importance of free cash flow, corporate governance, and takeover threat in determining financial and portfolio restructuring. The free cash flow hypothesis and agency theory prescriptions are used as the basis for developing a model of restructuring. A simple analysis of variance method is used to decompose restructuring transactions and outcomes into the three effects. The results support the hypothesis that financial and portfolio restructuring are motivated, in part, by agency costs. Decomposition of variances indicates that restructuring is equally explained by free cash flow and interaction of governance and takeover threat with free cash flow.

Spectral Properties of Bright Fermi-Detected Blazars in the Gamma-Ray Band

Abstract: The gamma-ray energy spectra of bright blazars of the LAT Bright AGN Sample (LBAS) are investigated using Fermi-LAT data. Spectral properties (hardness, curvature and variability) established using a data set accumulated over 6 months of operation are presented and discussed for different blazar classes and subclasses: Flat Spectrum Radio Quasars (FSRQs), Low-synchrotron peaked BLLacs (LSP-BLLacs), Intermediate-synchrotron peaked BLLacs (ISP-BLLacs) and High-synchrotron peaked BLLacs (HSP-BLLacs). The distribution of photon index (obtained from a power-law fit above 100 MeV) is found to correlate strongly with blazar subclass. The change in spectral index from that averaged over the six month observing period is < 0.2-0.3 when the flux varies by about an order of magnitude, with a tendency toward harder spectra when the flux is brighter for FSRQs and LSP-BLLacs. A strong departure from a single power-law spectrum appears to be a common feature for FSRQs. This feature is also present for some high-luminosity LSP-BLLacs, and a small number of ISP-BLLacs. It is absent in all LBAS HSP-BLLacs. For 3C 454.3 and AO 0235+164, the two brightest FSRQ source and LSP-BLLac source respectively, a broken power law gives the most acceptable of power law, broken power law, and curved forms. The consequences of these findings are discussed.

Phytoplankton and iron: validation of a global three-dimensional ocean biogeochemical model

Abstract: The JGOFS program and NASA ocean-color satellites have provided a wealth of data that can be used to test and validate models of ocean biogeochemistry. A coupled three-dimensional general circulation, biogeochemical, and radiative model of the global oceans was validated using these in situ data sources and satellite data sets. Biogeochemical processes in the model were determined from the influences of circulation and turbulence dynamics, irradiance availability, and the interactions among four phytoplankton functional groups (diatoms, chlorophytes, cyanobacteria, and coccolithophores) and four nutrients (nitrate, ammonium, silica, and dissolved iron). Annual mean log-transformed dissolved iron concentrations in the model were statistically positively correlated on basin scale with observations ðPo0:05Þ over the eight (out of 12) major oceanographic basins where data were available. The model tended to overestimate in situ observations, except in the Antarctic where a large underestimate occurred. Inadequate scavenging and excessive remineralization and/or regeneration were possible reasons for the overestimation. Basin scale model chlorophyll seasonal distributions were positively correlated with SeaWiFS chlorophyll in each of the 12 oceanographic basins ðPo0:05Þ: The global mean difference was 3.9% (model higher than SeaWiFS). The four phytoplankton groups were initialized as homogeneous and equal distributions throughout the model domain. After 26 years of simulation, they arrived at reasonable distributions throughout the global oceans: diatoms predominated high latitudes, coastal, and equatorial upwelling areas, cyanobacteria predominated the mid-ocean gyres, and chlorophytes and coccolithophores represented transitional assemblages. Seasonal patterns exhibited a range of relative responses: from a seasonal succession in the North Atlantic with coccolithophores replacing diatoms as the dominant group in mid-summer, to successional patterns with cyanobacteria replacing diatoms in mid-summer in the central North Pacific. Diatoms were associated with regions where nutrient availability was high. Cyanobacteria predominated in quiescent regions with low nutrients. While the overall patterns of phytoplankton functional group distributions exhibited broad qualitative agreement with in situ data, quantitative comparisons were mixed. Three of the four phytoplankton groups exhibited statistically significant correspondence across basins. Diatoms did not. Some basins exhibited excellent correspondence, while most showed moderate agreement, with two functional groups in agreement with data and the other two in disagreement.

Predicting protein functions using incomplete hierarchical labels

Abstract: Protein function prediction is to assign biological or biochemical functions to proteins, and it is a challenging computational problem characterized by several factors: (1) the number of function labels (annotations) is large; (2) a protein may be associated with multiple labels; (3) the function labels are structured in a hierarchy; and (4) the labels are incomplete. Current predictive models often assume that the labels of the labeled proteins are complete, i.e. no label is missing. But in real scenarios, we may be aware of only some hierarchical labels of a protein, and we may not know whether additional ones are actually present. The scenario of incomplete hierarchical labels, a challenging and practical problem, is seldom studied in protein function prediction. In this paper, we propose an algorithm to Predict protein functions using Incomplete hierarchical LabeLs (PILL in short). PILL takes into account the hierarchical and the flat taxonomy similarity between function labels, and defines a Combined Similarity (ComSim) to measure the correlation between labels. PILL estimates the missing labels for a protein based on ComSim and the known labels of the protein, and uses a regularization to exploit the interactions between proteins for function prediction. PILL is shown to outperform other related techniques in replenishing the missing labels and in predicting the functions of completely unlabeled proteins on publicly available PPI datasets annotated with MIPS Functional Catalogue and Gene Ontology labels. The empirical study shows that it is important to consider the incomplete annotation for protein function prediction. The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels. The Matlab code of PILL is available upon request.