Explaining consequences of employment insecurity: The dynamics of scarring in the United Kingdom, Poland and Norway

This deliverable presents three country studies on scarring effects of early employment insecurity in the United Kingdom, Poland and Norway. Traditional analysis of scarring effects has favoured the analysis of the impact of the experience of unemployment on the experience of subsequent unemployment (state dependence) and the monetary costs of previous unemployment in terms of lower subsequent wages (see e.g. Arulampalam, Booth and Taylor 2000; Arulampalam, Gregg and Gregory 2001). The three present country studies go beyond the traditional analysis of scarring effects in order to better understand the trade-offs experienced by young female and male workers when faced with an insecure labour market integration. With national longitudinal data, original methodological designs and research focus, each study contributes in an original way to the research literature. All three studies pay special attention to gender and education as potential moderating variables of scarring effects

Recent developments in the study of rapid human movements with the kinematic theory: Applications to handwriting and signature synthesis

International audienceHuman movement modeling can be of great interest for the design of pattern recognition systems relying on the understanding of the fine motor control (such as on-line handwriting recognition or signature verification) as well as for the development of intelligent systems involving in a way or another the processing of human movements. In this paper, we briefly list the different models that have been proposed in order to characterize the handwriting process and focus on a representation involving a vectorial summation of lognormal functions: the Sigma-lognormal model. Then, from a practical perspective, we describe a new stroke extraction algorithm suitable for the reverse engineering of handwriting signals. In the following section it is shown how the resulting representation can be used to study the writer and signer variability. We then report on two joint projects dealing with the automatic generation of synthetic specimens for the creation of large databases. The first application concerns the automatic generation of totally synthetic signature specimens for the training and evaluation of verification performances of automatic signature recognition systems. The second application deals with the synthesis of handwritten gestures for speeding up the learning process in customizable on-line recognition systems to be integrated in electronic pen pads

Mechanisms of grain refinement by intensive shearing of AZ91 alloy melt

The official published version of the article can be accessed at the link below.It has been demonstrated recently that intensive melt shearing can be an effective approach to the grain refinement of both shape casting and continuous casting of Mg alloys. In the present study, the mechanisms of grain refinement by intensive melt shearing were investigated through a combination of both modelling and experimental approaches. The measurement of the cooling curves during solidification, quantification of grain size of the solidified samples, and image analysis of the MgO particle size and size distribution in the pressurized filtration samples were performed for the AZ91 alloy with and without intensive melt shearing. The experimental results were then used as input parameters for the free growth model to investigate the mechanisms of grain refinement by intensive melt shearing. The experimental results showed that, although intensive melt shearing does not change the nucleation starting temperature, it increases the nucleation finishing temperature, giving rise to a reduced nucleation undercooling. The theoretical modelling using the free growth model revealed quantitatively that intensive melt shearing can effectively disperse MgO particles densely populated in the oxide films into more individual particles in the alloy melt, resulting in an increase in the MgO particle density by three orders of magnitude and the density of active nucleating MgO particles by a factor of 20 compared with those of the non-sheared melt. Therefore, the grain refining effect of intensive melt shearing can be confidently attributed to the significantly increased refining efficiency of the naturally occurring MgO particles in the alloy melt as potent nucleation sites.Financial support under Grant EP/H026177/1 from the EPSRC

The Effect of Processing and Seasonallity on the Iodine and Selenium Concentration of Cow's Milk Produced in Northern Ireland (NI): Implications for Population Dietary Intake

Cow’s milk is the most important dietary source of iodine in the UK and Ireland, and also contributes to dietary selenium intakes. The aim of this study was to investigate the effect of season, milk fat class (whole; semi-skimmed; skimmed) and pasteurisation on iodine and selenium concentrations in Northern Ireland (NI) milk, and to estimate the contribution of this milk to consumer iodine and selenium intakes. Milk samples (unpasteurised, whole, semi-skimmed and skimmed) were collected weekly from two large NI creameries between May 2013 and April 2014 and were analysed by inductively coupled plasma-mass spectrometry (ICP-MS). Using milk consumption data from the National Diet and Nutrition Survey (NDNS) Rolling Programme, the contribution of milk (at iodine and selenium concentrations measured in the present study) to UK dietary intakes was estimated. The mean ± standard deviation (SD) iodine concentration of milk was 475.9 ± 63.5 µg/kg and the mean selenium concentration of milk was 17.8 ± 2.7 µg/kg. Season had an important determining effect on the iodine, but not the selenium, content of cow’s milk, where iodine concentrations were highest in milk produced in spring compared to autumn months (534.3 ± 53.7 vs. 433.6 ± 57.8 µg/kg, respectively; p = 0.001). The measured iodine and selenium concentrations of NI milk were higher than those listed in current UK Food Composition Databases (Food Standards Agency (FSA) (2002); FSA (2015)). The dietary modelling analysis confirmed that milk makes an important contribution to iodine and selenium intakes. This contribution may be higher than previously estimated if iodine and selenium (+25.0 and +1.1 µg/day respectively) concentrations measured in the present study were replicable across the UK at the current level of milk consumption. Iodine intakes were theoretically shown to vary by season concurrent with the seasonal variation in NI milk iodine concentrations. Routine monitoring of milk iodine concentrations is required and efforts should be made to understand reasons for fluctuations in milk iodine concentrations, in order to realise the nutritional impact to consumers

European Mixed Forests: definition and research perspectives

peer-reviewedAim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) briefly review the research perspectives in mixed forests. Area of study: The definition is developed in Europe but can be tested worldwide. Material and methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests. Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any developmental stage, sharing common resources (light, water, and/or soil nutrients). The presence of each of the component species is normally quantified as a proportion of the number of stems or of basal area, although volume, biomass or canopy cover as well as proportions by occupied stand area may be used for specific objectives. A variety of structures and patterns of mixtures can occur, and the interactions between the component species and their relative proportions may change over time. The research perspectives identified are (i) species interactions and responses to hazards, (ii) the concept of maximum density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests. Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields of research indicate that gradient studies, experimental design approaches, and model simulations are key topics providing new research opportunities.The networking in this study has been supported by COST Action FP1206 EuMIXFOR

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments