Harmonia axyridis: an environmental risk assessment for Northwest Europe

In this paper, we summarize the international situation with respect to environmental risk assessment for biological control agents. Next, we apply a recently designed, comprehensive risk evaluation method consisting of a stepwise procedure to evaluate the environmental risks of Harmonia axyridis in Northwest Europe. This resulted in the very clear conclusion that H. axyridis is a potentially risky species for Northwest Europe, because it is able to establish, it has a very wide host range including species from other insect orders and even beyond the class of Insecta, it may feed on plant materials, it can cover large distances (>50 km per year), it does move into non-target areas, it may attack many non-target species including beneficial insects and insects of conservation concern, its activities have resulted in the reduction of populations of native predators in North America, it is known as a nuisance in North America and recently also in Northwest Europe, and it may develop as a pest of fruit in North America. Considering the H. axyridis case, current knowledge would lead to the conclusion that, although the predator is capable to effectively control several pest species, its risks are manifold and it should, thus, not have been released in Northwest Europe. At the time of the first releases in Nortwest Europe in 1995, the available scientific literature made clear that H. axyridis is a large sized polyphagous predator and has a great reproductive capacity in comparison with other ladybird beetles, and that there was a need to study non-target effects because of its polyphagous behaviour. In retrospect, this information should have been sufficient to reject import and release of this species, but it was apparently ignored by those who considered release of this predator in Northwest Europe. The case of Harmonia releases in Northwest Europe underlines that there is an urgent need for harmonized, world-wide regulation of biological control agents, including an information system on risky natural enemy species

The effect of operations on the ground noise footprints associated with a large multibladed, nonbanging helicopter

In order to expand the data base of helicopter external noise characteristics, a flyover noise measurement program was conducted utilizing the NASA Civil Helicopter Research Aircraft. The remotely operated multiple array acoustics range (ROMAAR) and a 2560-m linear microphone array were utilized for the purpose of documenting the noise characteristics of the test helicopter during flyby and landing operations. By utilizing both ROMAAR concept and the linear array, the data necessary to plot the ground noise footprints and noise radiation patterns were obtained. Examples of the measured noise signature of the test helicopter, the ground noise footprint or contours, and the directivity patterns measured during level flyby and landing operations of a large, multibladed, nonbanging helicopter, the CH-53, are presented

Growth rates and relative change in non-structural carbohydrates of dipterocarp seedlings in response to light acclimation

Background: Acclimation to light is a driver of tropical forest dynamics and key to understanding the coexistence of dipterocarps, and how their demographic rates and traits trade-off. Aims: We examined light niche divergence in six dipterocarp species and hypothesised that seedlings can be functionally grouped, and allocate resources to either growth or storage in response to light changes. Methods: A pot experiment was performed to measure size-specific growth rate, wood density and total non-structural carbohydrate (NSC) concentrations of dipterocarp seedlings exposed to a simulated gap opening. Results: Light-demanding species responded to a gap opening with increased growth and decreased wood density, whereas shade-tolerant species showed a greater relative increase in NSC concentration. Iditol – an alditol – was identified, and Dryobalanops lanceolata responded to a gap opening with a significantly smaller increase in alditol concentration compared to other species. Conclusions: We group light-demanding and shade-tolerant species based on their acclimation to light and show that a generalist species is unique based on its response of NSC concentration to a gap opening. Our findings emphasise that the ecology of these species needs to be further studied in the context of their physiology to support their effective use in large-scale forest restoration efforts

Rotwein unter Hochspannung: Mehrjährige Qualitäts- Untersuchung mit Gas-Discharge-Visualisation (GDV)

We investigated whether we can detect any differences in red wines produced either by bio-dynamic or by standard organic agriculture. We used standard methods to investigate the quality of the wines and Gas-Discharge-Visualisation (GDV) method to investigate a holistic quality of the wines. With the GDV-method, samples are exposed to high voltage. The halo-like gas discharge caused by a burst of electron emission of the sample is captured by a digital camera underneath a transparent, dielectric surface. The wine samples measured originate from an On-Farm experiment in South of France with two separately managed but neighbouring blocks (same soil and climate conditions): bio-organic and bio-dynamic. Apart from the use of bio-dynamic preparations, plant protection and fertilization was the same in both blocks. The vinification of the sampled grapes was made in two replicates which were analysed separately. During the three years of examination, the bio-dynamic samples did not reveal significant differences when assessed with standard methods (sensory triangle test, polyphenol analysis etc.). However, with GDV measurements the values for the imageparameter “mean intensity” were mostly higher for the wines from bio-dynamically produced grapes. In a „mixed effect model“ (GDV-parameter „mean intensity“ as dependent variable, replication und cultivation-system as fix and year as random effect) the difference was statistically significant. We conclude that the GDV-method has an interesting potential to detect very sensitively differences in food attributes. However, in order to interpret the results in terms of consumer-relevant quality further research is needed

Abnormal lateralization of functional connectivity between language and default mode regions in autism

Background: Lateralization of brain structure and function occurs in typical development, and abnormal lateralization is present in various neuropsychiatric disorders. Autism is characterized by a lack of left lateralization in structure and function of regions involved in language, such as Broca and Wernicke areas. Methods: Using functional connectivity magnetic resonance imaging from a large publicly available sample (n = 964), we tested whether abnormal functional lateralization in autism exists preferentially in language regions or in a more diffuse pattern across networks of lateralized brain regions. Results: The autism group exhibited significantly reduced left lateralization in a few connections involving language regions and regions from the default mode network, but results were not significant throughout left- and right-lateralized networks. There is a trend that suggests the lack of left lateralization in a connection involving Wernicke area and the posterior cingulate cortex associates with more severe autism. Conclusions: Abnormal language lateralization in autism may be due to abnormal language development rather than to a deficit in hemispheric specialization of the entire brain

Multisite functional connectivity MRI classification of autism: ABIDE results

Background:: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites. Methods:: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million “connections”) after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier. Results:: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times. Conclusions:: Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders

scMRI Reveals Large-Scale Brain Network Abnormalities in Autism

Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a ‘posteriorization’ of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI