Debunking the Myth of Job Fit in Higher Education and Student Affairs (Book Review)

Book review of Debunking the Myth of Job Fit in Higher Education and Student Affairs by Jamie L. Workman, Daniel W. Calhoun, and Steven Tolman

In-Situ Monitoring of Suspended Sediments: Development of a Densimetric Monitoring Instrument

Proceedings of the 1999 Georgia Water Resources Conference, March 30 and 31, Athens, Georgia.We present a new technique for continuously monitoring total suspended solids concentrations. Our approach is to use a differential pressure sensor to measure the fluid density within the water column. The fluid density is directly affected by the concentration of suspended solids, thus allowing us to infer the sediment concentration. Ten demonstration sites on the Broad River in northeast Georgia have been selected for comparing the densimetric technique with turbidity, grab samples, and integrated samples. Variations in sediment concentrations with stage, velocity, and temperature will be evaluated.Sponsored and Organized by: U.S. Geological Survey, Georgia Department of Natural Resources, The University of Georgia, Georgia State University, Georgia Institute of TechnologyThis book was published by the Institute of Ecology, The University of Georgia, Athens, Georgia 30602-2202 with partial funding provided by the U.S. Department of Interior, geological Survey, through the Georgia Water Research Insttitute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-397). The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of the University of Georgia or the U.S. Geological Survey or the conference sponsors

A Robust Classifier to Distinguish Noise from fMRI Independent Components

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks–a novel and burgeoning research field–is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an agematched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method [1]. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function

A Robust Classifier to Distinguish Noise from fMRI Independent Components

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks–a novel and burgeoning research field–is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an agematched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method [1]. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function

Multidimensional Frequency Domain Analysis of Full-Volume fMRI Reveals Significant Effects of Age, Gender, and Mental Illness on the Spatiotemporal Organization of Resting-State Brain Activity

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function

Planning for Sustainability in Small Municipalities: The Influence of Interest Groups, Growth Patterns, and Institutional Characteristics

How and why small municipalities promote sustainability through planning efforts is poorly understood. We analyzed ordinances in 451 Maine municipalities and tested theories of policy adoption using regression analysis.We found that smaller communities do adopt programs that contribute to sustainability relevant to their scale and context. In line with the political market theory, we found that municipalities with strong environmental interests, higher growth, and more formal governments were more likely to adopt these policies. Consideration of context and capacity in planning for sustainability will help planners better identify and benefit from collaboration, training, and outreach opportunities

Effects of Timber Harvest on Amphibian Populations: Understanding Mechanisms from Forest Experiments

Accompanying appendix may be accessed at: http://hdl.handle.net/10355/1365Harvesting timber is a common form of land use that has the potential to cause declines in amphibian populations. It is essential to understand the behavior and fate of individuals and the resulting consequences for vital rates (birth, death, immigration, emigration) under different forest management conditions.We report on experimental studies conducted in three regions of the United States to identify mechanisms of responses by pond-breeding amphibians to timber harvest treatments. Our studies demonstrate that life stages related to oviposition and larval performance in the aquatic stage are sometimes affected positively by clearcutting, whereas effects on juvenile and adult terrestrial stages are mostly negative

The Risk of West Nile Virus Infection Is Associated with Combined Sewer Overflow Streams in Urban Atlanta, Georgia, USA

BACKGROUND: At present, the factors favoring transmission and amplification of West Nile Virus (WNV) within urban environments are poorly understood. In urban Atlanta, Georgia, the highly polluted waters of streams affected by combined sewer overflow (CSO) represent significant habitats for the WNV mosquito vector Culex quinquefasciatus. However, their contribution to the risk of WNV infection in humans and birds remains unclear.\ud \ud OBJECTIVES: Our goals were to describe and quantify the spatial distribution of WNV infection in mosquitoes, humans, and corvids, such as blue jays and American crows that are particularly susceptible to WNV infection, and to assess the relationship between WNV infection and proximity to CSO-affected streams in the city of Atlanta, Georgia.\ud \ud MATERIALS AND METHODS: We applied spatial statistics to human, corvid, and mosquito WNV surveillance data from 2001 through 2007. Multimodel analysis was used to estimate associations of WNV infection in Cx. quinquefasciatus, humans, and dead corvids with selected risk factors including distance to CSO streams and catch basins, land cover, median household income, and housing characteristics.\ud \ud RESULTS: We found that WNV infection in mosquitoes, corvids, and humans was spatially clustered and statistically associated with CSO-affected streams. WNV infection in Cx. quinquefasciatus was significantly higher in CSO compared with non-CSO streams, and WNV infection rates among humans and corvids were significantly associated with proximity to CSO-affected streams, the extent of tree cover, and median household income.\ud \ud CONCLUSIONS: Our study strongly suggests that CSO-affected streams are significant sources of Cx. quinquefasciatus mosquitoes that may facilitate WNV transmission to humans within urban environments. Our findings may have direct implications for the surveillance and control of WNV in other urban centers that continue to use CSO systems as a waste management practice

Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations.

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma

Relating Intrinsic Low-Frequency BOLD Cortical Oscillations to Cognition in Schizophrenia

The amplitude of low-frequency fluctuations (ALFF) in the blood oxygenation level-dependent (BOLD) signal during resting-state fMRI reflects the magnitude of local low-frequency BOLD oscillations, rather than interregional connectivity. ALFF is of interest to studies of cognition because fluctuations in spontaneous intrinsic brain activity relate to, and possibly even constrain, task-evoked brain responses in healthy people. Lower ALFF has been reported in schizophrenia, but the cognitive correlates of these reductions remain unknown. Here, we assess relationships between ALFF and attention and working memory in order to establish the functional relevance of intrinsic BOLD oscillatory power alterations with respect to specific cognitive impairments in schizophrenia. As part of the multisite FBIRN study, resting-state fMRI data were collected from schizophrenia subjects (SZ; n=168) and healthy controls (HC; n=166). Voxelwise fractional ALFF (fALFF), a normalized ALFF measure, was regressed on neuropsychological measures of sustained attention and working memory in SZ and HC to identify regions showing either common slopes across groups or slope differences between groups (all findings p<0.01 height, p<0.05 family-wise error cluster corrected). Poorer sustained attention was associated with smaller fALFF in the left superior frontal cortex and bilateral temporoparietal junction in both groups, with additional relationships in bilateral posterior parietal, posterior cingulate, dorsal anterior cingulate (ACC), and right dorsolateral prefrontal cortex (DLPFC) evident only in SZ. Poorer working memory was associated with smaller fALFF in bilateral ACC/mPFC, DLPFC, and posterior parietal cortex in both groups. Our findings indicate that smaller amplitudes of low-frequency BOLD oscillations during rest, measured by fALFF, were significantly associated with poorer cognitive performance, sometimes similarly in both groups and sometimes only in SZ, in regions known to subserve sustained attention and working memory. Taken together, these data suggest that the magnitude of resting-state BOLD oscillations shows promise as a biomarker of cognitive function in health and disease