A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability phenotype and high mutation frequency.

Microsatellite instability (MSI) occurs in 10-20% of colorectal tumours and is associated with good prognosis. Here we describe the development and validation of a genomic signature that identifies colorectal cancer patients with MSI caused by DNA mismatch repair deficiency with high accuracy. Microsatellite status for 276 stage II and III colorectal tumours has been determined. Full-genome expression data was used to identify genes that correlate with MSI status. A subset of these samples (n = 73) had sequencing data for 615 genes available. An MSI gene signature of 64 genes was developed and validated in two independent validation sets: the first consisting of frozen samples from 132 stage II patients; and the second consisting of FFPE samples from the PETACC-3 trial (n = 625). The 64-gene MSI signature identified MSI patients in the first validation set with a sensitivity of 90.3% and an overall accuracy of 84.8%, with an AUC of 0.942 (95% CI, 0.888-0.975). In the second validation, the signature also showed excellent performance, with a sensitivity 94.3% and an overall accuracy of 90.6%, with an AUC of 0.965 (95% CI, 0.943-0.988). Besides correct identification of MSI patients, the gene signature identified a group of MSI-like patients that were MSS by standard assessment but MSI by signature assessment. The MSI-signature could be linked to a deficient MMR phenotype, as both MSI and MSI-like patients showed a high mutation frequency (8.2% and 6.4% of 615 genes assayed, respectively) as compared to patients classified as MSS (1.6% mutation frequency). The MSI signature showed prognostic power in stage II patients (n = 215) with a hazard ratio of 0.252 (p = 0.0145). Patients with an MSI-like phenotype had also an improved survival when compared to MSS patients. The MSI signature was translated to a diagnostic microarray and technically and clinically validated in FFPE and frozen samples.

Weak-type estimates for Haar shift operators: Sharp power on the A(p) characteristic

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Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called “byproduct” of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.

Robust discrimination between EEG responses to categories of environmental sounds in early coma.

Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.

Inter-informant agreement and prevalence estimates for substance use disorders: direct interview versus family history method.

OBJECTIVES: Family studies typically use multiple sources of information on each individual including direct interviews and family history information. The aims of the present study were to: (1) assess agreement for diagnoses of specific substance use disorders between direct interviews and the family history method; (2) compare prevalence estimates according to the two methods; (3) test strategies to approximate prevalence estimates according to family history reports to those based on direct interviews; (4) determine covariates of inter-informant agreement; and (5) identify covariates that affect the likelihood of reporting disorders by informants. METHODS: Analyses were based on family study data which included 1621 distinct informant (first-degree relatives and spouses) - index subject pairs. RESULTS: Our main findings were: (1) inter-informant agreement was fair to good for all substance disorders, except for alcohol abuse; (2) the family history method underestimated the prevalence of drug but not alcohol use disorders; (3) lowering diagnostic thresholds for drug disorders and combining multiple family histories increased the accuracy of prevalence estimates for these disorders according to the family history method; (4) female sex of index subjects was associated with higher agreement for nearly all disorders; and (5) informants who themselves had a history of the same substance use disorder were more likely to report this disorder in their relatives, which entails the risk of overestimation of the size of familial aggregation. CONCLUSION: Our findings have important implications for the best-estimate procedure applied in family studies.

Quantifying uncertainty: physicians' estimates of infection in critically ill neonates and children.

To determine the diagnostic accuracy of physicians' prior probability estimates of serious infection in critically ill neonates and children, we conducted a prospective cohort study in 2 intensive care units. Using available clinical, laboratory, and radiographic information, 27 physicians provided 2567 probability estimates for 347 patients (follow-up rate, 92%). The median probability estimate of infection increased from 0% (i.e., no antibiotic treatment or diagnostic work-up for sepsis), to 2% on the day preceding initiation of antibiotic therapy, to 20% at initiation of antibiotic treatment (P<.001). At initiation of treatment, predictions discriminated well between episodes subsequently classified as proven infection and episodes ultimately judged unlikely to be infection (area under the curve, 0.88). Physicians also showed a good ability to predict blood culture-positive sepsis (area under the curve, 0.77). Treatment and testing thresholds were derived from the provided predictions and treatment rates. Physicians' prognoses regarding the presence of serious infection were remarkably precise. Studies investigating the value of new tests for diagnosis of sepsis should establish that they add incremental value to physicians' judgment.

Theoretical estimates of consumable food and probability of acquiring food in larvae of Chrysomya putoria (Diptera: Calliphoridae)

An indirect estimate of consumable food and probability of acquiring food in a blowfly species, Chrysomya putoria, is presented. This alternative procedure combines three distinct models to estimate consumable food in the context of the exploitative competition experienced by immature individuals in blowfly populations. The relevant parameters are derived from data for pupal weight and survival and estimates of density-independent larval mortality in twenty different larval densities. As part of this procedure, the probability of acquiring food per unit of time and the time taken to exhaust the food supply are also calculated. The procedure employed here may be valuable for estimations in insects whose immature stages develop inside the food substrate, where it is difficult to partial out confounding effects such as separation of faeces. This procedure also has the advantage of taking into account the population dynamics of immatures living under crowded conditions, which are particularly characteristic of blowflies and other insects as well.

Gaussian estimates for the density of the non-linear stochastic heat equation in any space dimension

In this paper, we establish lower and upper Gaussian bounds for the probability density of the mild solution to the stochastic heat equation with multiplicative noise and in any space dimension. The driving perturbation is a Gaussian noise which is white in time with some spatially homogeneous covariance. These estimates are obtained using tools of the Malliavin calculus. The most challenging part is the lower bound, which is obtained by adapting a general method developed by Kohatsu-Higa to the underlying spatially homogeneous Gaussian setting. Both lower and upper estimates have the same form: a Gaussian density with a variance which is equal to that of the mild solution of the corresponding linear equation with additive noise.

Regional value added in Italy over the long run (1891-2001): linking indirect estimates with official figures, and implications

This paper presents value added estimates for the Italian regions, in benchmark years from 1891 until 1951, which are linked to those from official figures available from 1971 in order to offer a long-term picture. Sources and methodology are documented and discussed, whilst regional activity rates and productivity are also presented and compared. Thus some questions are briefly reconsidered: the origins and extent of the north-south divide, the role of migration and regional policy in shaping the pattern of regional inequality, the importance of social capital, and the positioning of Italy in the international debate on regional convergence, where it stands out for the long run persistence of its disparities.

Parent-child agreement and prevalence estimates of diagnoses in childhood: direct interview versus family history method.

Diagnostic information on children is typically elicited from both children and their parents. The aims of the present paper were to: (1) compare prevalence estimates according to maternal reports, paternal reports and direct interviews of children [major depressive disorder (MDD), anxiety and attention-deficit and disruptive behavioural disorders]; (2) assess mother-child, father-child and inter-parental agreement for these disorders; (3) determine the association between several child, parent and familial characteristics and the degree of diagnostic agreement or the likelihood of parental reporting; (4) determine the predictive validity of diagnostic information provided by parents and children. Analyses were based on 235 mother-offspring, 189 father-offspring and 128 mother-father pairs. Diagnostic assessment included the Kiddie-schedule for Affective Disorders and Schizophrenia (K-SADS) (offspring) and the Diagnostic Interview for Genetic Studies (DIGS) (parents and offspring at follow-up) interviews. Parental reports were collected using the Family History - Research Diagnostic Criteria (FH-RDC). Analyses revealed: (1) prevalence estimates for internalizing disorders were generally lower according to parental information than according to the K-SADS; (2) mother-child and father-child agreement was poor and within similar ranges; (3) parents with a history of MDD or attention deficit hyperactivity disorder (ADHD) reported these disorders in their children more frequently; (4) in a sub-sample followed-up into adulthood, diagnoses of MDD, separation anxiety and conduct disorder at baseline concurred with the corresponding lifetime diagnosis at age 19 according to the child rather than according to the parents. In conclusion, our findings support large discrepancies of diagnostic information provided by parents and children with generally lower reporting of internalizing disorders by parents, and differential reporting of depression and ADHD by parental disease status. Follow-up data also supports the validity of information provided by adolescent offspring.

Preliminary estimates and forecasts of the population prevalence of self reported of hypertension, of ischaemic heart disease and stroke in the Republic of Ireland from 2005 - 2015

Ireland and Northern Ireland’s Population Health Observatory (INIsPHO) recently published estimates of the population prevalence of diabetes in 2005 and forecasts to 2010 and 2015 for the island of Ireland, at the national and sub-national levels. These estimates are based the PBS Model developed by York and Humber Public Health Observatory (YHPHO), Brent NHS Trust and the School of Health and Related Research (ScHARR).The Department of Health and Children (DoHC) has requested additional estimates and forecasts for hypertension.This paper outlines the results from preliminary work from the initial steps towards a more systematic approach to monitoring the prevalence of other chronic diseases on the island.

Comparison of IPH prevalence estimates with other survey-based estimates

The Institute of Public Health in Ireland publishes estimates and forecasts of the prevalence of chronic health conditions for national and subnational areas on the island of Ireland. The estimates and forecasts are based on statistical models of nationally representative health survey data that estimate the risk of having the condition. The risks of having the condition are then applied to population estimates and projections. The purpose of this document is to: 1. Compare IPH prevalence estimates with prevalence estimates from other health surveys on the island. 2. Highlight the methodological issues in comparing prevalence estimates from different surveys.

Beurling-Landau densities of weighted Fekete sets and correlation kernel estimates

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Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.