Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.

Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease

We undertook a two-stage genome-wide association study (GWAS) of Alzheimer's disease (AD) involving over 16,000 individuals, the most powerful AD GWAS to date. In stage 1 (3,941 cases and 7,848 controls), we replicated the established association with the apolipoprotein E (APOE) locus (most significant SNP, rs2075650, P = 1.8 x 10(-157)) and observed genome-wide significant association with SNPs at two loci not previously associated with the disease: at the CLU (also known as APOJ) gene (rs11136000, P = 1.4 x 10(-9)) and 5. to the PICALM gene (rs3851179, P = 1.9 x 10(-8)). These associations were replicated in stage 2 (2,023 cases and 2,340 controls), producing compelling evidence for association with Alzheimer's disease in the combined dataset (rs11136000, P = 8.5 x 10(-10), odds ratio = 0.86; rs3851179, P = 1.3 x 10(-9), odds ratio = 0.86).

Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions

The objective of this paper is to describe and evaluate the application of the Text Encoding Initiative (TEI) Guidelines to a corpus of oral French, this being the first corpus of oral French where the TEI has been used. The paper explains the purpose of the corpus, both in creating a specialist corpus of néo-contage that will broaden the range of oral corpora available, and, more importantly, in creating a dataset to explore a variety of oral French that has a particularly interesting status in terms of factors such as conception orale/écrite, réalisation médiale and comportement communicatif (Koch and Oesterreicher 2001). The linguistic phenomena to be encoded are both stylistic (speech and thought presentation) and syntactic (negation, detachment, inversion), and all represent areas where previous research has highlighted the significance of factors such as medium, register and discourse type, as well as a host of linguistic factors (syntactic, phonetic, lexical). After a discussion of how a tagset can be designed and applied within the TEI to encode speech and thought presentation, negation, detachment and inversion, the final section of the paper evaluates the benefits and possible drawbacks of the methodology offered by the TEI when applied to a syntactic and stylistic markup of an oral corpus.

Evidence for progressive Holocene aridification in southern Africa recorded in Namibian hyrax middens: Implications for African Monsoon dynamics and the

Presented here are stable nitrogen isotope data from a rock hyrax (Procavia capensis) middens from northwestern Namibia that record a series of rapid aridification events beginning at ca. 3800 cal yr BP, and which mark a progressive decrease in regional humidity across the Holocene. Strong correlations exist between this record and other terrestrial and marine archives from southern Africa, indicating that the observed pattern of climate change is regionally coherent. Combined, these data indicate hemispheric synchrony in tropical African climate change during the Holocene, with similar trends characterising the termination of the 'African Humid Period' (AHP) in both the northern and southern tropics. These findings run counter to the widely accepted model of direct low-latitude insolation forcing, which requires an antiphase relationship to exist between the hemispheres. The combined dataset highlights: 1) the importance of forcing mechanisms influencing the high northern latitudes in effecting low-latitude climate change in Africa, and 2) the potential importance of solar forcing and variations in the Earth's geomagnetic shield in determining both long-term and rapid centennial-scale climate changes, identifying a possible mechanism for the variations marking the AHP termination in both the southern and northern tropics. (C) 2010 University of Washington. Published by Elsevier Inc. All rights reserved.

Effect of signal intensity normalization on the multivariate analysis of spectral data in complex 'real-world' datasets

Spectral signal intensities, especially in 'real-world' applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential.

Incorporating Higher Moments into Value at Risk Forecasting

Value-at-risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time-varying higher-moments models.

Six ways to leave unemployment

This paper uses a unique Portuguese dataset to examine the effect of access to unemployment benefits (UBs) and their maximum potential duration on escape rates from unemployment. In examining the time profile of transitions out of unemployment, the principal contributions of the paper are twofold. First, it provides a detailed state space of potential outcomes: open-ended employment, fixed-term contracts, part-time work, government-provided jobs, self employment, and labour force withdrawal. Second, it is able to exploit major exogenous discontinuities in the maximum duration of unemployment benefits to identify disincentive effects. While confirming strong disincentive effects, it is shown that use of an aggregate hazard function regression model compounds very different and even contradictory effects of the determinants of unemployment.

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Background. The assembly of the tree of life has seen significant progress in recent years but algae and protists have been largely overlooked in this effort. Many groups of algae and protists have ancient roots and it is unclear how much data will be required to resolve their phylogenetic relationships for incorporation in the tree of life. The red algae, a group of primary photosynthetic eukaryotes of more than a billion years old, provide the earliest fossil evidence for eukaryotic multicellularity and sexual reproduction. Despite this evolutionary significance, their phylogenetic relationships are understudied. This study aims to infer a comprehensive red algal tree of life at the family level from a supermatrix containing data mined from GenBank. We aim to locate remaining regions of low support in the topology, evaluate their causes and estimate the amount of data required to resolve them. Results. Phylogenetic analysis of a supermatrix of 14 loci and 98 red algal families yielded the most complete red algal tree of life to date. Visualization of statistical support showed the presence of five poorly supported regions. Causes for low support were identified with statistics about the age of the region, data availability and node density, showing that poor support has different origins in different parts of the tree. Parametric simulation experiments yielded optimistic estimates of how much data will be needed to resolve the poorly supported regions (ca. 103 to ca. 104 nucleotides for the different regions). Nonparametric simulations gave a markedly more pessimistic image, some regions requiring more than 2.8 105 nucleotides or not achieving the desired level of support at all. The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches. Conclusions. Our study takes the red algae one step closer to meaningful inclusion in the tree of life. In addition to the recovery of stable relationships, the recognition of five regions in need of further study is a significant outcome of this work. Based on our analyses of current availability and future requirements of data, we make clear recommendations for forthcoming research.

Technological regimes, Schumpeterian patterns of innovation and firm-level productivity growth

The article investigates the relationships between technological regimes and firm-level productivity performance, and it explores how such a relationship differs in different Schumpeterian patterns of innovation. The analysis makes use of a rich dataset containing data on innovation and other economic characteristics of a large representative sample of Norwegian firms in manufacturing and service industries for the period 1998–2004. First, we decompose TFP growth into technical progress and efficiency changes by means of data envelopment analysis. We then estimate an empirical model that relates these two productivity components to the characteristics of technological regimes and a set of other firm-specific factors. The results indicate that: (i) TFP growth has mainly been achieved through technical progress, while technical efficiency has on average decreased; (ii) the characteristics of technological regimes are important determinants of firm-level productivity growth, but their impacts on technical progress are different from the effects on efficiency change; (iii) the estimated model works differently in the two Schumpeterian regimes. Technical progress has been more dynamic in Schumpeter Mark II industries, while efficiency change has been more important in Schumpeter Mark I markets.

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping.

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.

Contingency Fit, Institutional Fit, and Firm Performance: A Metafit Approach to Organization–Environment Relationships

In this paper, we attempt to reconcile contingency and institutional fit approaches concerning the organization-environment relationship. While prior scholarly research has examined both theories and compared their impacts on organizational fit and performance, we lay the groundwork for a meta-fit approach by investigating how contingency and institutional fit interact to influence firm performance. We test our theoretical framework using a dataset of 3,259 respondents from 1,904 companies regarding task environmental demands and institutional demands on organizational design across a broad range of industries and firm size classes. Our results show that contingency and institutional fit provide complementary and interdependent explanations of firm performance. Importantly, our findings indicate that for firms under conditions of “quasi-fit” rather than perfect contingency fit or optimal institutional fit, improvements in contingency and/or institutional fit will result in better performance. However, firms with high contingency fit are less vulnerable to deviation from institutional fit in the formation of firm performance, while firms with perfect institutional fit will slightly decrease their performance when they strive to achieve contingency fit.

Interaction between interleukin 3 and dystrobrevin-binding protein 1 in schizophrenia

Schizophrenia is a common psychotic mental disorder that is believed to result from the effects of multiple genetic and environmental factors. In this study, we explored gene-gene interactions and main effects in both case-control (657 cases and 411 controls) and family-based (273 families, 1350 subjects) datasets of English or Irish ancestry. Fifty three markers in 8 genes were genotyped in the family sample and 44 markers in 7 genes were genotyped in the case-control sample. The Multifactor Dimensionality Reduction Pedigree Disequilibrium Test (MDR-PDT) was used to examine epistasis in the family dataset and a 3-locus model was identified (permuted p=0.003). The 3-locus model involved the IL3 (rs2069803), RGS4 (rs2661319), and DTNBP1 (rs21319539) genes. We used MDR to analyze the case-control dataset containing the same markers typed in the RGS4, IL3 and DTNBP1 genes and found evidence of a joint effect between IL3 (rs31400) and DTNBP1 (rs760761) (cross-validation consistency 4/5, balanced prediction accuracy=56.84%, p=0.019). While this is not a direct replication, the results obtained from both the family and case-control samples collectively suggest that IL3 and DTNBP1 are likely to interact and jointly contribute to increase risk for schizophrenia. We also observed a significant main effect in DTNBP1, which survived correction for multiple comparisons, and numerous nominally significant effects in several genes. (C) 2008 Elsevier B.V. All rights reserved.

Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease

Background: Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology: We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.

Spatial Mobility Intentions, the Labour Market and Incapacity Benefit Claimants Evidence from Northern Ireland

The paper analyses the work-related spatial mobility intentions of incapacity benefit (IB) claimants in Northern Ireland using a new survey dataset. Greater understanding of the prospective mobility of benefit claimants contributes to debates about employability, inclusion in the labour market and arguments about spatial mismatch. The analysis finds that IB claimants are not markedly less mobile than other population groups; that mobility is shaped by age, level of qualification, illness type, ability to drive and motivation; and that geography is important in several ways. The paper concludes by arguing that limited spatial mobility is a good indicator of disadvantage and that spatial mobility should be placed nearer the centre of the design and delivery of labour market policy

Replication of EPHA1 and CD33 associations with late-onset Alzheimer’s disease: a multi-centre case-control study

A recently published genome-wide association study (GWAS) of late-onset Alzheimer's disease (LOAD) revealed genome-wide significant association of variants in or near MS4A4A, CD2AP, EPHA1 and CD33. Meta-analyses of this and a previously published GWAS revealed significant association at ABCA7 and MS4A, independent evidence for association of CD2AP, CD33 and EPHA1 and an opposing yet significant association of a variant near ARID5B. In this study, we genotyped five variants (in or near CD2AP, EPHA1, ARID5B, and CD33) in a large (2,634 LOAD, 4,201 controls), independent dataset comprising six case-control series from the USA and Europe. We performed meta-analyses of the association of these variants with LOAD and tested for association using logistic regression adjusted by age-at-diagnosis, gender, and APOE e4 dosage.