The impact of site conditions and seed dispersal on restoration success in alluvial meadows

Abstract. We studied the restoration success of flood plain meadows in the northern Upper Rhine valley, where between 1988 and 1992, 35 ha of arable land was converted into grassland and subsequently managed for nature conservation. Remnant populations of typical alluvial meadow species were found in old meadows and along drainage ditches that dissect the whole area. We analysed the site conditions and phytosociological relevés in old and new meadows. Small differences in site parameters between old and new meadows contrasted with a clear floristic differentiation between the two meadow types. The vegetation of old meadows was much more differentiated along prevailing environmental gradients than the vegetation of new meadows. Despite the favourable site conditions for the re‐establishment of species‐rich meadows on the former arable land, restoration success was limited to the vicinity of remnant stands. In contrast to old meadows, indicator species of new grassland were still typical species of regularly disturbed ruderal and arable habitats, often capable of building up a persistent seed bank. The precise mapping of 23 target species revealed that even wind dispersal predominantly leads to re‐establishment in the close circumference of parent plants. We found no indication that regular flooding, hay‐making and autumnal grazing had an impact on recolonization of newly created grassland. Even under favourable conditions for the re‐establishment of target species, restoration success in alluvial meadows proved to be strongly dispersal limited. We discuss the implications of our findings for future restoration management in grasslands.     

Spatial Distribution of Optical Near-Fields in Plasmonic Gold Sphere Segment Voids

We present a comprehensive experimental and computational study on the electromagnetic field distribution in sphere segment void arrays. Surface plasmon polaritons can be excited in these void arrays, resulting in greatly enhanced electromagnetic fields. With the scanning near-field optical microscope (SNOM) we are able to measure the electromagnetic field distribution at the sample surface. For this purpose, an array of relatively large voids with a sphere diameter of 900 nm was fabricated, allowing for an easy access of the scanning glass-fibre tip and yielding very detailed scans. Complementary, finite-difference time-domain (FDTD) calculations on a complete void array have been performed and compared with the SNOM intensity maps and experimental reflectivity data. We show in a direct way both the existence of extended and localised modes in the Au void array for three different void depths. We also show and discuss the changes that the modes undergo for the different void depths and excitation wavelengths. Moreover, since the simulations were performed for two different void geometries, one containing perfectly spherical void surfaces and another more realistic one, which considers the presence of interstitial wall holes and other imperfections, as observed in scanning electron micrographs, we were able to determine by comparison with the experiment under which conditions an array of idealised sphere segment voids is a meaningful model. This demonstrates that both SNOM and FDTD simulations are powerful tools for understanding the plasmonic response of metallic nanostructures, thus enabling, for instance, a design for applications in ultra-sensitive optical detection.     

Improved Diagnosis in Children with Partial Epilepsy Using a Multivariable Prediction Model Based on EEG Network Characteristics

Background

Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics.

Methodology/Principal Findings

Routinely performed interictal EEG recordings at first presentation of 35 children diagnosed with partial epilepsies, and of 35 children in whom the diagnosis epilepsy was excluded (control group), were used to develop the prediction model. Children with partial epilepsy were individually matched on age and gender with children from the control group. Periods of resting-state EEG, free of abnormal slowing or epileptiform activity, were selected to construct functional networks of correlated activity. We calculated multiple network characteristics previously used in functional network epilepsy studies and used these measures to build a robust, decision tree based, prediction model. Based on epileptiform EEG activity only, EEG results supported the diagnosis of with a sensitivity and specificity of 0.77 and 0.91 respectively. In contrast, the prediction model had a sensitivity of 0.96 [95% confidence interval: 0.78–1.00] and specificity of 0.95 [95% confidence interval: 0.76–1.00] in correctly differentiating patients from controls. The overall discriminative power, quantified as the area under the receiver operating characteristic curve, was 0.89, defined as an excellent model performance. The need of a multivariable network analysis to improve diagnostic accuracy was emphasized by the lack of discriminatory power using single network characteristics or EEG''s power spectral density.

Conclusions/Significance

Diagnostic accuracy in children with partial epilepsy is substantially improved with a model combining functional network characteristics derived from multi-channel electroencephalogram recordings. Early and accurate diagnosis is important to start necessary treatment as soon as possible and inform patients and parents on possible risks and psychosocial aspects in relation to the diagnosis.     

Functional and Structural Neural Network Characterization of Serotonin Transporter Knockout Rats

Brain serotonin homeostasis is crucially maintained by the serotonin transporter (5-HTT), and its down-regulation has been linked to increased vulnerability for anxiety- and depression-related behavior. Studies in 5-HTT knockout (5-HTT^-/-) rodents have associated inherited reduced functional expression of 5-HTT with increased sensitivity to adverse as well as rewarding environmental stimuli, and in particular cocaine hyperresponsivity. 5-HTT down-regulation may affect normal neuronal wiring of implicated corticolimbic cerebral structures. To further our understanding of its contribution to potential alterations in basal functional and structural properties of neural network configurations, we applied resting-state functional MRI (fMRI), pharmacological MRI of cocaine-induced activation, and diffusion tensor imaging (DTI) in 5-HTT^-/- rats and wild-type controls (5-HTT^+/+). We found that baseline functional connectivity values and cocaine-induced neural activity within the corticolimbic network was not significantly altered in 5-HTT^-/- versus 5-HTT^+/+ rats. Similarly, DTI revealed mostly intact white matter structural integrity, except for a reduced fractional anisotropy in the genu of the corpus callosum of 5-HTT^-/- rats. At the macroscopic level, analyses of complex graphs constructed from either functional connectivity values or structural DTI-based tractography results revealed that key properties of brain network organization were essentially similar between 5-HTT^+/+ and 5-HTT^-/- rats. The individual tests for differences between 5-HTT^+/+ and 5-HTT^-/- rats were capable of detecting significant effects ranging from 5.8% (fractional anisotropy) to 26.1% (pharmacological MRI) and 29.3% (functional connectivity). Tentatively, lower fractional anisotropy in the genu of the corpus callosum could indicate a reduced capacity for information integration across hemispheres in 5-HTT^-/- rats. Overall, the comparison of 5-HTT^-/- and wild-type rats suggests mostly limited effects of 5-HTT genotype on MRI-based measures of brain morphology and function.     

Pregnant Mothers with Resolved Anxiety Disorders and Their Offspring Have Reduced Heart Rate Variability: Implications for the Health of Children

Objective

Active anxiety disorders have lasting detrimental effects on pregnant mothers and their offspring but it is unknown if historical, non-active, maternal anxiety disorders have similar effects. Anxiety-related conditions, such as reduced autonomic cardiac control, indicated by reduced heart rate variability (HRV) could persist despite disorder resolution, with long-term health implications for mothers and children. The objective in this study is to test the hypotheses that pregnant mothers with a history of, but not current anxiety and their children have low HRV, predicting anxiety-like offspring temperaments.

Methods

The participants in this case-control study consist of 56 women during their first trimester and their offspring (15 male, 29 female). Women had a history of an anxiety disorder (n=22) or no psychopathology (n=34) determined using the Mini-International Neuropsychiatric Interview. The main outcome measures were indices of autonomic cardiac control including root mean square of successive differences (RMSSD) and high frequency (HF) variability. Children’s fearfulness was also assessed using the Laboratory Temperament Assessment Battery (Lab-TAB)-Locomotor Version.

Results

HRV was lower in women and children in the past anxiety group compared to controls. HRV measures for mothers and children were positively correlated in the anxiety group only. In all children, low HRV measures at 2-4 months were associated with a higher chance of fearful behavior at 9-10 months.

Conclusions

Pregnant women with previous but not current anxiety and their children have low HRV. Children with low HRV tend to show more fearfulness. These findings have implications for identifying children at risk of anxiety disorders and point to possible underlying mechanisms of child psychopathology.