Georgia State University

About: Georgia State University is a education organization based out in Atlanta, Georgia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13988 authors who have published 35895 publications receiving 1164332 citations. The organization is also known as: GSU & Georgia State.

Stellar diameters and temperatures. iii. main-sequence a, f, g, and k stars: additional high-precision measurements and empirical relations

Abstract: Based on CHARA Array measurements, we present the angular diameters of 23 nearby, main-sequence stars, ranging from spectral types A7 to K0, 5 of which are exoplanet host stars. We derive linear radii, effective temperatures, and absolute luminosities of the stars using Hipparcos parallaxes and measured bolometric fluxes. The new data are combined with previously published values to create an Angular Diameter Anthology of measured angular diameters to main-sequence stars (luminosity classes V and IV). This compilation consists of 125 stars with diameter uncertainties of less than 5%, ranging in spectral types from A to M. The large quantity of empirical data is used to derive color-temperature relations to an assortment of color indices in the Johnson (BVR_(J)I_(J)JHK), Cousins (R_(C)I_(C)), Kron (R_(K)I_(K)), Sloan (griz), and WISE (W_(3)W_(4)) photometric systems. These relations have an average standard deviation of ~3% and are valid for stars with spectral types A0-M4. To derive even more accurate relations for Sun-like stars, we also determined these temperature relations omitting early-type stars (T_eff > 6750 K) that may have biased luminosity estimates because of rapid rotation; for this subset the dispersion is only ~2.5%. We find effective temperatures in agreement within a couple of percent for the interferometrically characterized sample of main-sequence stars compared to those derived via the infrared flux method and spectroscopic analysis.

Evolution of responses to (un)fairness

Abstract: The human sense of fairness is an evolutionary puzzle. To study this, we can look to other species, in which this can be translated empirically into responses to reward distribution. Passive and active protest against receiving less than a partner for the same task is widespread in species that cooperate outside kinship and mating bonds. There is less evidence that nonhuman species seek to equalize outcomes to their own detriment, yet the latter has been documented in our closest relatives, the apes. This reaction probably reflects an attempt to forestall partner dissatisfaction with obtained outcomes and its negative impact on future cooperation. We hypothesize that it is the evolution of this response that allowed the development of a complete sense of fairness in humans, which aims not at equality for its own sake but for the sake of continued cooperation.

Estimating Granger causality from fourier and wavelet transforms of time series data.

Abstract: Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.

Executive Functioning in High‐functioning Children with Autism

Abstract: Executive functioning was investigated in 34 children (24 boys and 10 girls) with developmental language disorder (DLD) and 21 children (18 boys and 3 girls) with high-functioning autistic disorder (HAD) matched on Full Scale IQ, Nonverbal IQ, age (mean age 9 year, 1 month), and SES. The DLD group had a Verbal IQ that was 10 points higher than the HAD group. These children were given the Wisconsin Card Sorting Test (WCST), the Mazes subtest from the WISC-R, the Underlining test, and the Rapid Automatized Naming test. In addition, these children were given the Vineland Scales of Adaptive Functioning and the Wing Diagnostic Symptom Checklist in order to assess severity of autistic symptomatology. Results indicated that the only significant difference between the two groups on the cognitive tasks was perseverative errors on the WCST; there was no significant difference on total number of categories achieved or total number of errors on the WCST or on the other executive function measures. There was also significant overlap in the scores between the two groups and the difference in perseverative errors was no longer significant when Verbal IQ was partialled out. Executive functioning was strongly related to all IQ variables in the DLD group and particularly related to Verbal IQ in the HAD group. Although there was a relationship in the HAD group between executive functioning and adaptive functioning, as well as between executive functioning and autistic symptomatology, these relationships were generally no longer significant in the HAD group after the variance due to Verbal IQ was accounted for. The results are interpreted to indicate that although impaired executive functioning is a commonly associated feature of autism, it is not universal in autism and is unlikely to cause autistic behaviors or deficits in adaptive function.

Identification of Essential Proteins Based on Edge Clustering Coefficient

Abstract: Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node's essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.