20,628 research outputs found

    An analysis of MRI derived cortical complexity in premature-born adults : regional patterns, risk factors, and potential significance

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    Premature birth bears an increased risk for aberrant brain development concerning its structure and function. Cortical complexity (CC) expresses the fractal dimension of the brain surface and changes during neurodevelopment. We hypothesized that CC is altered after premature birth and associated with long-term cognitive development. One-hundred-and-one very premature-born adults (gestational age <32 weeks and/or birth weight <1500 ​g) and 111 term-born adults were assessed by structural MRI and cognitive testing at 26 years of age. CC was measured based on MRI by vertex-wise estimation of fractal dimension. Cognitive performance was measured based on Griffiths-Mental-Development-Scale (at 20 months) and Wechsler-Adult-Intelligence-Scales (at 26 years). In premature-born adults, CC was decreased bilaterally in large lateral temporal and medial parietal clusters. Decreased CC was associated with lower gestational age and birth weight. Furthermore, decreased CC in the medial parietal cortices was linked with reduced full-scale IQ of premature-born adults and mediated the association between cognitive development at 20 months and IQ in adulthood. Results demonstrate that CC is reduced in very premature-born adults in temporoparietal cortices, mediating the impact of prematurity on impaired cognitive development. These data indicate functionally relevant long-term alterations in the brain’s basic geometry of cortical organization in prematurity

    Glucose hypometabolism in the Auditory Pathway in Age Related Hearing Loss in the ADNI cohort

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    Purpose: Hearing loss (HL) is one of the most common age-related diseases. Here, we investigate the central auditory correlates of HL in people with normal cognition and mild cognitive impairment (MCI) and test their association with genetic markers with the aim of revealing pathogenic mechanisms. / Methods: Brain glucose metabolism based on FDG-PET, self-reported HL status, and genetic data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. FDG-PET data was analysed from 742 control subjects (non-HL with normal cognition or MCI) and 162 cases (HL with normal cognition or MCI) with age ranges of 72.2 ± 7.1 and 77.4 ± 6.4, respectively. Voxel-wise statistics of FDG uptake differences between cases and controls were computed using the generalised linear model in SPM12. An additional 1515 FDG-PET scans of 618 participants were analysed using linear mixed effect models to assess longitudinal HL effects. Furthermore, a quantitative trait genome-wide association study (GWAS) was conducted on the glucose uptake within regions of interest (ROIs), which were defined by the voxel-wise comparison, using genotyping data with 5,082,878 variants available for HL cases and HL controls (N = 817). / Results: The HL group exhibited hypometabolism in the bilateral Heschl’s gyrus (kleft = 323; kright = 151; Tleft = 4.55; Tright = 4.14; peak Puncorr < 0.001), the inferior colliculus (k = 219;T = 3.53; peak Puncorr < 0.001) and cochlear nucleus (k = 18;T = 3.55; peak Puncorr < 0.001) after age correction and using a cluster forming height threshold P < 0.005 (FWE-uncorrected). Moreover, in an age-matched subset, the cluster comprising the left Heschl’s gyrus survived the FWE-correction (kleft = 1903; Tleft = 4.39; cluster PFWE-corr = 0.001). The quantitative trait GWAS identified no genome-wide significant locus in the three HL ROIs. However, various loci were associated at the suggestive threshold (p < 1e-05). / Conclusion: Compared to the non-HL group, glucose metabolism in the HL group was lower in the auditory cortex, the inferior colliculus, and the cochlear nucleus although the effect sizes were small. The GWAS identified candidate genes that might influence FDG uptake in these regions. However, the specific biological pathway(s) underlying the role of these genes in FDG-hypometabolism in the auditory pathway requires further investigation

    Association between community noise and children's cognitive and behavioral development: a prospective cohort study

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    BACKGROUND: Noise exposure has been associated with adverse cognitive and behavioral outcomes in children, but evidence on longitudinal associations between community noise and child development in low- and middle-income countries is rare. We investigated associations between community noise and behavioral and cognitive development in preschool children in Sao Paulo. METHODS: We linked child development data from the Sao Paulo Western Region Birth Cohort with average (Lden) and night-time (Lnight) community noise exposure at children's home, estimated by means of a land use regression model using various predictors (roads, schools, greenness, residential and informal settlements). Outcomes were the Strengths and Difficulties Questionnaire (SDQ) and Regional Project on Child Development Indicators (PRIDI) at 3 years of age and the Child Behavior Checklist (CBCL) and International Development and Early Learning Assessment (IDELA) at 6 years of age. We investigated the relationship between noise exposure and development using cross-sectional and longitudinal regression models. RESULTS: Data from 3385 children at 3 years of age and 1546 children at 6 years of age were analysed. Mean Lden and Lnight levels were 70.3 dB and 61.2 dB, respectively. In cross-sectional analyses a 10 dB increase of Lden above 70 dB was associated with a 32% increase in the odds of borderline or abnormal SDQ total difficulties score (OR = 1.32, 95% CI: 1.04; 1.68) and 0.72 standard deviation (SD) increase in the CBCL total problems z-score (95% CI: 0.55; 0.88). No cross-sectional association was found for cognitive development. In longitudinal analyses, each 10 dB increase was associated with a 0.52 SD increase in behavioral problems (95% CI: 0.28; 0.77) and a 0.27 SD decrease in cognition (95%-CI: 0.55; 0.00). Results for Lnight above 60 dB were similar. DISCUSSION: Our findings suggest that community noise exposure above Lden of 70 dB and Lnight of 60 dB may impair behavioral and cognitive development of preschool children

    Novel system of pavement cracking detection algorithms using 1mm 3D surface data

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    Pavement cracking is one of the major concerns for pavement design and management. There have been rapid developments of automated pavement cracking detection in recent years. However, none of them has been widely accepted so far due to lack of capability of maintaining consistently high detection accuracy for various pavement surfaces. Using 1mm 3D data collected by WayLink Digital Highway Data Vehicle (DHDV), an entire system of algorithms, which consists of Fully Automated Cracking Detection Subsystem, Interactive Cracking Detection Subsystem and Noisy Pattern Detection Subsystem, is proposed in this study for improvements in adaptability, reliability and interactivity of pavement cracking detection.The Fully Automated Cracking Detection Subsystem utilizes 3D Shadow Simulation to find lower areas in local neighborhood, and then eliminates noises by subsequent noise suppressing procedures. The assumption behind 3D Shadow Simulation is that local lower areas will be shadowed under light with a certain projection angle. According to the Precision-Recall Analysis on two real pavement segments, the fully automated subsystem can achieve a high level of Precision and Recall on both pavement segments.The Interactive Cracking Detection Subsystem implements an interactive algorithm proposed in this study, which is capable of improving its detection accuracy by adjustments based on the operator's feedback, to provide a slower but more flexible as well as confident approach to pavement cracking detection. It is demonstrated in the case study that the interactive subsystem can retrieve almost 100 percent of cracks with nearly no noises.The Noisy Pattern Detection Subsystem is proposed to exclude pavement joints and grooves from cracking detection so that false-positive errors on rigid pavements can be reduced significantly. This subsystem applies Support Vector Machines (SVM) to train the classifiers for the recognition of transverse groove, transverse joint, longitudinal groove and longitudinal joint respectively. Based on the trained classifiers, pattern extraction procedures are developed to find the exact locations of pavement joints and grooves.Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of multi objective genetic algorithms, is employed in this study to optimize parameters of the fully automated subsystem for the pursuing of high Precision and high Recall simultaneously. In addition to NSGA-II, an Auxiliary Prediction Model (APM) is proposed in this study to assist NSGA-II for faster convergence and better diversity.Finally, CPU-based and GPU-based Parallel Computing Techniques, including MultiGPU, GPU streaming, Multi-Core and Multi-Threading are combined in this study to increase the processing speed for all computational tasks that can be synchronous

    Jets in Hadron-Hadron Collisions

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    In this article, we review some of the complexities of jet algorithms and of the resultant comparisons of data to theory. We review the extensive experience with jet measurements at the Tevatron, the extrapolation of this acquired wisdom to the LHC and the differences between the Tevatron and LHC environments. We also describe a framework (SpartyJet) for the convenient comparison of results using different jet algorithms.Comment: 68 pages, 54 figure
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