Impacts of Hurricanes Katrina and Rita on the microbial landscape of the New Orleans area

Author Posting. © The Author(s), 2007. This is the author's version of the work. It is posted here by permission of National Academy of Sciences of the USA for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences 104 (2007): 9029-9034, doi:10.1073/pnas.0610552104.Floodwaters in New Orleans from Hurricanes Katrina and Rita were observed to contain high levels of fecal indicator bacteria and microbial pathogens, generating concern about long-term impacts of these floodwaters on the sediment and water quality of the New Orleans area and Lake Pontchartrain. We show here that fecal indicator microbe concentrations in offshore waters from Lake Pontchartrain returned to prehurricane concentrations within 2 months of the flooding induced by these hurricanes. Vibrio and Legionella species within the lake were more abundant in samples collected shortly after the floodwaters had receded compared with samples taken within the subsequent 3 months; no evidence of a long-term hurricane-induced algal bloom was observed. Giardia and Cryptosporidium were detected in canal waters. Elevated levels of fecal indicator bacteria observed in sediment could not be solely attributed to impacts from floodwaters, as both flooded and nonflooded areas exhibited elevated levels of fecal indicator bacteria. Evidence from measurements of Bifidobacterium and bacterial diversity analysis suggest that the fecal indicator bacteria observed in the sediment were from human fecal sources. Epidemiologic studies are highly recommended to evaluate the human health effects of the sediments deposited by the floodwaters.This work was funded by NSF-NIEHS Oceans and Human Health Program (NSF OCE0432368, OCE0432479, OCE0430724 and NIEHS P50 ES12736, ES012740, ES012742), the NSF-SGER Program (OCE 0554402, OCE 0554674, OCE 0554850, OCE0600130), the NSF-REU Program, and by the Georgia Sea Grant College Program (NA04OAR170033)

Well being of obstetric patients on minimal blood transfusions (WOMB trial)

Background: Primary postpartum haemorrhage is an obstetrical emergency often causing acute anaemia that may require immediate red blood cell (RBC) transfusion. This anaemia results in symptoms such as fatigue, whic

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine