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Impaired Prefrontal Hemodynamic Maturation in Autism and Unaffected Siblings

BACKGROUND: Dysfunctions of the prefrontal cortex have been previously reported in individuals with autism spectrum disorders (ASD). Previous studies reported that first-degree relatives of individuals with ASD show atypical brain activity during tasks associated with social function. However, developmental changes in prefrontal dysfunction in ASD and genetic influences on the phenomena remain unclear. In the present study, we investigated the change in hemoglobin concentration in the prefrontal cortex as measured with near-infrared spectroscopy, in children and adults with ASD during the letter fluency test. Moreover, to clarify the genetic influences on developmental changes in the prefrontal dysfunction in ASD, unaffected siblings of the ASD participants were also assessed. METHODOLOGY/PRINCIPAL FINDINGS: Study participants included 27 individuals with high-functioning ASD, age- and IQ-matched 24 healthy non-affected siblings, and 27 unrelated healthy controls aged 5 to 39 years. The relative concentration of hemoglobin ([Hb]) in the prefrontal cortex was measured during the letter fluency task. For children, neither the [oxy-Hb] change during the task nor task performances differed significantly among three groups. For adults, the [oxy-Hb] increases during the task were significantly smaller in the bilateral prefrontal cortex in ASD than those in control subjects, although task performances were similar. In the adult siblings the [oxy-Hb] change was intermediate between those in controls and ASDs. CONCLUSION/SIGNIFICANCE: Although indirectly due to a cross-sectional design, the results of this study indicate altered age-related change of prefrontal activity during executive processing in ASD. This is a first near-infrared spectroscopy study that implies alteration in the age-related changes of prefrontal activity in ASD and genetic influences on the phenomena

A Cell-Free Microtiter Plate Screen for Improved [FeFe] Hydrogenases

, a potential renewable fuel. Attempts to exploit these catalysts in engineered systems have been hindered by the biotechnologically inconvenient properties of the natural enzymes, including their extreme oxygen sensitivity. Directed evolution has been used to improve the characteristics of a range of natural catalysts, but has been largely unsuccessful for [FeFe] hydrogenases because of a lack of convenient screening platforms. [FeFe] hydrogenase HydA1 with a specific activity ∼4 times that of the wild-type enzyme. cell extracts, which allows unhindered access to the protein maturation and assay environment

Facial Identity Recognition in the Broader Autism Phenotype

Peer reviewedPublisher PD

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

<p>Abstract</p> <p>Background</p> <p>The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.</p> <p>Methods</p> <p>Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.</p> <p>Results</p> <p>Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (<it>P </it>< 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).</p> <p>Conclusions</p> <p>Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.</p

Combining farmers' decision rules and landscape stochastic regularities for landscape modelling

International audienceLandscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.L'organisation du paysage influe sur les problèmes environnementaux. Modéliser les paysages pour les décrire à l'aide de formes significatives est une étage clé. Les paysages agricoles sont principalement construits par les agriculteurs dont les décision d'assolement peuvent être génériques et déterminer des régularités dans l'organisation du paysage. Cet article contribue à l'agronomie des paysage qui est une discipline émergente. Nous cherchons à articuler les échelles du paysage et de l'exploitation agricole en développant deux méthodes : l'une consiste à identifier les décisions des agriculteurs par le bais d'enquêtes, l'autre consiste à retrouver des régularités stochastiques dans le paysage par le bais de fouille de données. Nous avons appliqué cette approche au paysage de la plaine de Niort en France. Les résultats montrent que les décisions des agriculteurs en matière de tournesol et maïs sont génériques et ont des effets sur le paysages que des méthodes de fouille de données révèlent et quantifient