Parenting difficulties and postnatal depression: implications for primary healthcare assessment and intervention

Postnatal depression (PND) is associated with impairments in the mother–child relationship, and these impairments are themselves associated with adverse child outcomes. Thus, compared to the children of non-depressed mothers, children of mothers with PND are more likely to be insecurely attached, and to have externalising behaviour problems and poor cognitive development. Each of these three child outcomes is predicted by a particular pattern of difficulty in parenting: insecure attachment is related to maternal insensitivity, particularly in relation to infant distress and emotional vulnerability; externalising problems are particularly common in the context of hostile parenting; and poor cognitive development is related to parental difficulties in noticing infant signs of interest and supporting their engagement with the environment. This article sets out procedures for how parenting could be assessed in ways that are sensitive to the domain-specific associations between parenting and child outcome, while remaining sensitive to the child's developmental stage. This set of assessments requires field testing.

The impact of two arable field margin management schemes on litter decomposition

A primary objective of agri-environment schemes is the conservation of biodiversity; in addition to increasing the value of farmland for wildlife, these schemes also aim to restore natural ecosystem functioning. The management of scheme options can influence their value for delivering ecosystem services by modifying the composition of floral and faunal communities. This study examines the impact of an agri-environment scheme prescription on ecosystem functioning by testing the hypothesis that vegetation management influences decomposition rates in grassy arable field margins. The effects of two vegetation management practices in arable field margins - cutting and soil disturbance (scarification) - on litter decomposition were compared using a litterbag experimental approach in early April 2006. Bags had either small mesh designed to restrict access to soil macrofauna, or large mesh that would allow macrofauna to enter. Bags were positioned on the soil surface or inserted into the soil in cut and scarified margins, retrieved after 44, 103 and 250 days and the amount of litter mass remaining was calculated. Litter loss from the litterbags with large mesh was greater than from the small mesh bags, providing evidence that soil macrofauna accelerate rates of litter decomposition. In the large mesh bags, the proportion of litter remaining in bags above and belowground in the cut plots was similar, while in the scarified plots, there was significantly more litter left in the aboveground bags than in the belowground bags. This loss of balance between decomposition rates above and belowground in scarified margins may have implications for the development and maintenance of grassy arable field margins by influencing nutrient availability for plant communities. (C) 2008 Elsevier B.V. All rights reserved.

A practical method for testing high-speed networking hardware architectures

This paper deals with the key issues encountered in testing during the development of high-speed networking hardware systems by documenting a practical method for "real-life like" testing. The proposed method is empowered by modern and commonly available Field Programmable Gate Array (FPGA) technology. Innovative application of standard FPGA blocks in combination with reconfigurability are used as a back-bone of the method. A detailed elaboration of the method is given so as to serve as a general reference. The method is fully characterised and compared to alternatives through a case study proving it to be the most efficient and effective one at a reasonable cost.

Testing isotherm models and recovering empirical relationships for adsorption in microporous carbons using virtual carbon models and grand canonical Monte Carlo simulations

Using the plausible model of activated carbon proposed by Harris and co-workers and grand canonical Monte Carlo simulations, we study the applicability of standard methods for describing adsorption data on microporous carbons widely used in adsorption science. Two carbon structures are studied, one with a small distribution of micropores in the range up to 1 nm, and the other with micropores covering a wide range of porosity. For both structures, adsorption isotherms of noble gases (from Ne to Xe), carbon tetrachloride and benzene are simulated. The data obtained are considered in terms of Dubinin-Radushkevich plots. Moreover, for benzene and carbon tetrachloride the temperature invariance of the characteristic curve is also studied. We show that using simulated data some empirical relationships obtained from experiment can be successfully recovered. Next we test the applicability of Dubinin's related models including the Dubinin-Izotova, Dubinin-Radushkevich-Stoeckli, and Jaroniec-Choma equations. The results obtained demonstrate the limits and applications of the models studied in the field of carbon porosity characterization.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.