Generating Isolated Elliptically Polarized Attosecond Pulses Using Bichromatic Counterrotating Circularly Polarized Laser Fields

We theoretically demonstrate the possibility to generate both trains and isolated attosecond pulses with high ellipticity in a practical experimental setup. The scheme uses circularly polarized, counterrotating two-color driving pulses carried at the fundamental and its second harmonic. Using a model Ne atom, we numerically show that highly elliptic attosecond pulses are generated already at the single-atom level. Isolated pulses are produced by using few-cycle drivers with controlled time delay between them.

Generating analysis topology using virtual topology operators

Virtual topology operations have been utilized to generate an analysis topology definition suitable for downstream mesh generation. Detailed descriptions are provided for virtual topology merge and split operations for all topological entities, where virtual decompositions are robustly linked to the underlying geometry. Current virtual topology technology is extended to allow the virtual partitioning of volume cells. A valid description of the topology, including relative orientations, is maintained which enables downstream interrogations to be performed on the analysis topology description, such as determining if a specific meshing strategy can be applied to the virtual volume cells. As the virtual representation is a true non-manifold description of the sub-divided domain the interfaces between cells are recorded automatically. Therefore, the advantages of non-manifold modelling are exploited within the manifold modelling environment of a major commercial CAD system without any adaptation of the underlying CAD model. A hierarchical virtual structure is maintained where virtual entities are merged or partitioned. This has a major benefit over existing solutions as the virtual dependencies here are stored in an open and accessible manner, providing the analyst with the freedom to create, modify and edit the analysis topology in any preferred sequence.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Individual factors and perceived community characteristics in relation to mental health and mental well-being

Background
It has been argued that though correlated with mental health, mental well-being is a distinct entity. Despite the wealth of literature on mental health, less is known about mental well-being. Mental health is something experienced by individuals, whereas mental well-being can be assessed at the population level. Accordingly it is important to differentiate the individual and population level factors (environmental and social) that could be associated with mental health and well-being, and as people living in deprived areas have a higher prevalence of poor mental health, these relationships should be compared across different levels of neighbourhood deprivation.

Methods
A cross-sectional representative random sample of 1,209 adults from 62 Super Output Areas (SOAs) in Belfast, Northern Ireland (Feb 2010 – Jan 2011) were recruited in the PARC Study. Interview-administered questionnaires recorded data on socio-demographic characteristics, health-related behaviours, individual social capital, self-rated health, mental health (SF-8) and mental well-being (WEMWBS). Multi-variable linear regression analyses, with inclusion of clustering by SOAs, were used to explore the associations between individual and perceived community characteristics and mental health and mental well-being, and to investigate how these associations differed by the level of neighbourhood deprivation.

Results
Thirty-eight and 30 % of variability in the measures of mental well-being and mental health, respectively, could be explained by individual factors and the perceived community characteristics. In the total sample and stratified by neighbourhood deprivation, age, marital status and self-rated health were associated with both mental health and well-being, with the ‘social connections’ and local area satisfaction elements of social capital also emerging as explanatory variables. An increase of +1 in EQ-5D-3 L was associated with +1SD of the population mean in both mental health and well-being. Similarly, a change from ‘very dissatisfied’ to ‘very satisfied’ for local area satisfaction would result in +8.75 for mental well-being, but only in the more affluent of areas.

Conclusions
Self-rated health was associated with both mental health and mental well-being. Of the individual social capital explanatory variables, ‘social connections’ was more important for mental well-being. Although similarities in the explanatory variables of mental health and mental well-being exist, socio-ecological interventions designed to improve them may not have equivalent impacts in rich and poor neighbourhoods.

Generating a Word-Emotion Lexicon from #Emotional Tweets

Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classi- fication performance.

Pulpal Neuropeptide Y levels in relation to pain experience

Neuropeptide Y is a 36 amino acid peptide that belongs to the pancreatic polypeptide family. It co-localises with adrenaline in sympathetic nerves and is released upon sympathetic activation resulting in vasoconstriction. In addition to its vascular effects NPY is also thought to have a role in pain modulation, angiogenesis and immunomodulation. Objectives: The aim of this study was to quantify the levels of NPY in human dental pulp tissue from intact and grossly carious teeth and to relate these results to pain experience. Methods: A total of 48 permanent teeth [mean age 32.1(+/- 11.2 years)] were included in the study, of these 22 were intact and 26 were grossly carious. In the grossly carious group, 17 teeth were reported painful prior to extraction and the remainder were reported non-painful. NPY was measured using a sensitive and specific radioimmunoassay which has been previously described. Pain was scored as either present or absent in all the teeth studied. Results: Of particular interest in this study was the finding that NPY levels were significantly higher in dental pulp tissue from non-painful grossly carious teeth (p= 0.006) compared with painful grossly carious teeth. Conclusions: The increased levels of NPY reported in non-painful grossly carious teeth may suggest a role for NPY in pain modulation in human dental pulp.

Selective Multi-Carrier Index Keying OFDM: Error Propagation Rate with Moment Generating Function

We propose a new selective multi-carrier index keying in orthogonal frequency division multiplexing (OFDM) systems that opportunistically modulate both a small subset of sub-carriers and their indices. Particularly, we investigate the performance enhancement in two cases of error propagation sensitive and compromised deviceto-device (D2D) communications. For the performance evaluation, we focus on analyzing the error propagation probability (EPP) introducing the exact and upper bound expressions on the detection error probability, in the presence of both imperfect and perfect detection of active multi-carrier indices. The average EPP results in closedform are generalized for various fading distribution using the moment generating function, and our numerical results clearly show that the proposed approach is desirable for reliable and energy-efficient D2D applications.

Biomarker responses to folic acid intervention in healthy adults: a meta-analysis of randomized controlled trials

BACKGROUND: The task of revising dietary folate recommendations for optimal health is complicated by a lack of data quantifying the biomarker response that reliably reflects a given folate intake.

OBJECTIVE: We conducted a dose-response meta-analysis in healthy adults to quantify the typical response of recognized folate biomarkers to a change in folic acid intake.

DESIGN: Electronic and bibliographic searches identified 19 randomized controlled trials that supplemented with folic acid and measured folate biomarkers before and after the intervention in apparently healthy adults aged ≥18 y. For each biomarker response, the regression coefficient (β) for individual studies and the overall pooled β were calculated by using random-effects meta-analysis.

RESULTS: Folate biomarkers (serum/plasma and red blood cell folate) increased in response to folic acid in a dose-response manner only up to an intake of 400 μg/d. Calculation of the overall pooled β for studies in the range of 50 to 400 μg/d indicated that a doubling of folic acid intake resulted in an increase in serum/plasma folate by 63% (71% for microbiological assay; 61% for nonmicrobiological assay) and red blood cell folate by 31% (irrespective of whether microbiological or other assay was used). Studies that used the microbiological assay indicated lower heterogeneity compared with studies using nonmicrobiological assays for determining serum/plasma (I(2) = 13.5% compared with I(2) = 77.2%) and red blood cell (I(2) = 45.9% compared with I(2) = 70.2%) folate.

CONCLUSIONS: Studies administering >400 μg folic acid/d show no dose-response relation and thus will not yield meaningful results for consideration when generating dietary folate recommendations. The calculated folate biomarker response to a given folic acid intake may be more robust with the use of a microbiological assay rather than alternative methods for blood folate measurement.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.