Generalised collisional-radiative model for light elements. A: Data for the Li isonuclear sequence

A first stage collision database is assembled which contains electron-impact effective collision strengths, and ionization and recombination rate coefficients for Li, Li+, and Li2+. The first stage database is constructed using the R-matrix with pseudo-states, time-dependent close-coupling, converged close-coupling, and perturbative distorted-wave methods. A second stage collision database is then assembled which contains generalized collisional-radiative and radiated power loss coefficients. The second stage database is constructed by solution of collisional-radiative equations in the quasi-static equilibrium approximation using the first stage database. Both collision database stages reside in electronic form at the ORNL Controlled Fusion Atomic Data Center and in the ADAS database, and are easily accessed over the worldwide internet. ?? 2006 Elsevier Inc. All rights reserved.

Country-wise differences in perception of health-related messages in cereal-based food products

The objective of the present study was to explore the impact of health-related messages on the perceived overall healthiness and consumers' likelihood to buy cereal-based products or non-cereal products containing beneficial compounds from grains, across four European countries. The data were collected from a sample of 2392 members of the public in Finland, Germany, Italy and the UK. The results from a conjoint task with a main effects additive model were reported. In general, the presence of a verbal health claim on foods had positive influence on respondents perception of healthiness and on likelihood to buy the products, whereas the pictorial health claims were found to have a weak influence on the two dependent variables. However, the findings showed that health-related information on food labels differently influenced the healthiness perception and the likelihood to buy the product across the four countries, suggesting that different cultures, traditions, and eating habits have to be taken into account before positioning cereal-based products containing beneficial compounds from grains on the market. (C) 2009 Elsevier Ltd. All rights reserved.

Systematic review and meta-analysis of the association between complement component 3 and age-related macular degeneration: A HUGE review and meta-analysis

We performed a meta-analysis to estimate the magnitude of C3 gene polymorphism effects, and their possible mode of action, on age-related macular degeneration (AMD). The meta-analysis included 16 studies for rs2230199 and 7 studies for rs1047286. Data extraction and risk of bias assessments were performed in duplicate, and heterogeneity and publication bias were explored. There was moderate evidence for association between both polymorphisms and AMD in individuals of European descent. For rs2230199, patients with CG and GG genotypes were 1.44 (95% CI: 1.33 – 1.56) and 1.88 (95% CI: 1.59 – 2.23) times more likely to have AMD than patients with CC genotype. For rs1047286, those with GA and AA genotypes had 1.27 (95% CI: 1.15 – 1.41) and 1.70 (95% CI: 1.27 – 2.11) times higher risk of AMD than those with GG genotypes. These gene effects suggested an additive model. The population attributable risks for the GG/GC and AA/GA genotypes are approximately 5-10%. Stratification of studies on the basis of ethnicity indicates that these variants are very infrequent in Asian populations and the significance of the effect observed is based largely on the high frequency of these variants within individuals of European descent. This meta-analysis supports the association between C3 and AMD and provides a robust estimate of the genetic risk.

Behavioural context of multi-scale species distribution models assessed by radio-tracking

Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the 'central place' and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in . Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats. A Generalised Linear Model (GLM) of roost occurrence using land cover variables with mixed spatial scales indicated roost occurrence was positively associated with woodland on a fine scale and pasture on a broad scale. Habitat selection of radio-tracked bats mirrored the SDM with bats selecting for woodland in the immediate vicinity of individual roosts but avoiding this habitat in foraging areas, whilst pasture was significantly positively selected for in foraging areas. Using habitat selection derived from radio-tracking enables a multi-scale SDM to be interpreted in a behavioural context. We suggest that the multi-scale SDM of . M. nattereri describes a trade-off between the central place and foraging habitat. Multi-scale methods provide a greater understanding of the ecological processes which determine where species occur and allow integration of behavioural processes into SDMs. The findings have implications when assessing the resource use of a species at a single point in time. Doing so could lead to misinterpretation of habitat requirements as these can change within a short time period depending on specific behaviour, particularly if detectability changes depending on behaviour. © 2011 Gesellschaft für ökologie.

Inhibitory Effect of Phosphonium-Based Ionic Liquids on Coal Oxidation

To develop a chemical inhibitor that can efficiently suppress coal oxidation, nine tetraalkylphosphonium-based ionic liquids (ILs) and one imidazolium-based IL [1-allyl-3-methylimidazolium chloride ([AMIm]Cl)] were examined as additives. These ILs were used to treat and investigate the inhibitory effect on the oxidation activity and the structure of lignite coal. Characterization using thermogravimetric analysis showed that phosphonium-based ILs are able to inhibit coal oxidation up to 400 degrees C with the tributylethylphosphonium diethylphosphate ([P-4,P-4,P-4,P-2][DEP]) found to be the most effective. In contrast to the tetraalkylphosphonium-based ILs, inhibition using [AMIm]Cl was only found to be effective at temperatures below 250 degrees C, indicating that the tetraallcylphosphonium-based ILs may be more suitable for the future application of suppressing coal spontaneous combustion over a wide range of temperatures. Fourier transform infrared spectroscopic data showed that the various functional groups change in the coal following IL treatment, which are a decrease in the minerals and hydrogen bonds in all treated coals, while decreased aliphatic hydrocarbon and increased carbonyl bonds only appeared in some samples. During the oxidation of coal, the decomposition of aliphatic hydrocarbon groups is inhibited and the formation of carbonyl groups is delayed, so that the evolved gas concentration decreased, as shown by the temperature-programmed oxidation-mass spectrometry results. The deployment of the [P-4,P-4,P-4,P-2][ DEP] and tributylmethylphosphonium methylsulfate Its as additives also show good inhibitory effect on coal oxidation over the temperature range studied, and a relatively stronger interaction between [P-4,P-4,P-4,P-2] [DEP] and coal is demonstrated by the additive model.

Susceptibility to Invasive Meningococcal Disease: Polymorphism of Complement System Genes and Neisseria meningitidis Factor H Binding Protein

BACKGROUND: Neisseria meningitidis can cause severe infection in humans. Polymorphism of Complement Factor H (CFH) is associated with altered risk of invasive meningococcal disease (IMD). We aimed to find whether polymorphism of other complement genes altered risk and whether variation of N. meningitidis factor H binding protein (fHBP) affected the risk association.

METHODS: We undertook a case-control study with 309 European cases and 5,200 1958 Birth Cohort and National Blood Service cohort controls. We used additive model logistic regression, accepting P<0.05 as significant after correction for multiple testing. The effects of fHBP subfamily on the age at infection and severity of disease was tested using the independent samples median test and Student's T test. The effect of CFH polymorphism on the N. meningitidis fHBP subfamily was investigated by logistic regression and Chi squared test.

RESULTS: Rs12085435 A in C8B was associated with odds ratio (OR) of IMD (0.35 [95% CI 0.19-0.67]; P = 0.03 after correction). A CFH haplotype tagged by rs3753396 G was associated with IMD (OR 0.56 [95% CI 0.42-0.76], P = 1.6x10-4). There was no bacterial load (CtrA cycle threshold) difference associated with carriage of this haplotype. Host CFH haplotype and meningococcal fHBP subfamily were not associated. Individuals infected with meningococci expressing subfamily A fHBP were younger than those with subfamily B fHBP meningococci (median 1 vs 2 years; P = 0.025).

DISCUSSION: The protective CFH haplotype alters odds of IMD without affecting bacterial load for affected heterozygotes. CFH haplotype did not affect the likelihood of infecting meningococci having either fHBP subfamily. The association between C8B rs12085435 and IMD requires independent replication. The CFH association is of interest because it is independent of known functional polymorphisms in CFH. As fHBP-containing vaccines are now in use, relationships between CFH polymorphism and vaccine effectiveness and side-effects may become important.

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.

A novel shrinkage technique based on the normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm proposed by us. This algorithm can converge to feature basis vectors, which behave in the locality and orientation in spatial and frequency domain. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In the denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as the NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. This denoising method is evaluated by values of the normalized signal to noise rate (SNR). Experimental results show that the NNSC shrinkage approach is indeed efficient and effective in denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulation results show that our method outperforms the three kinds of denoising approaches mentioned above.

A Physically-Constrained Source Model For FDTD Acoustic Simulation

The Finite Difference Time Domain (FDTD) method is becoming increasingly popular for room acoustics simulation. Yet, the literature on grid excitation methods is relatively sparse, and source functions are traditionally implemented in a hard or additive form
using arbitrarily-shaped functions which do not necessarily obey the physical laws of sound generation. In this paper we formulate
a source function based on a small pulsating sphere model. A physically plausible method to inject a source signal into the grid
is derived from first principles, resulting in a source with a near-flat spectrum that does not scatter incoming waves. In the final
discrete-time formulation, the source signal is the result of passing a Gaussian pulse through a digital filter simulating the dynamics of the pulsating sphere, hence facilitating a physically correct means to design source functions that generate a prescribed sound field.

Illumination invariant facial recognition using a piecewise-constant lighting model

In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present AGWAN (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the AGWAN model to real-world graphs and for generating random graphs from the model. Using the Enron “who communicates with whom” social graph, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to the structure of real-world graphs.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.