Automated segmentation of multiple red blood cells with digital holographic microscopy.

We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.

Do induced twin pregnancies influence the obstetric and neonatal results of multiple births born before 32 weeks? Comparison to spontaneous gestation

PURPOSE: To compare obstetric outcomes of induced preterm twin births (under 32 weeks gestation) with those spontaneously conceived. METHODS: Prospective study of twin pregnancies (25 induced and 157 spontaneously conceived) developed over a period of 16 years in a tertiary obstetric center. Demographic factors, obstetric complications, gestational age at delivery, mode of delivery, birth weight and immediate newborn outcome were compared. RESULTS: The analysis of obstetrical complications concerning urinary or other infections, hypertensive disorders of pregnancy, gestational diabetes, fetal malformations, intrauterine fetal death, intrauterine growth restriction and intrauterine discordant growth reveal no significant statistical differences between the two groups. First trimester bleeding was higher in the induced group (24 versus 8.3%, p=0.029). The cesarean delivery rate was 52.2% in spontaneous gestations and 64% in induced gestations. Gestational age at delivery, birth weight, Apgar scores at first and fifth minutes, admissions to Neonatal Intensive Care Unit and puerperal complications show no statistically significant differences between the two groups. These results were independent of chorionicity and induction method. CONCLUSION: The mode of conception did not influence obstetric and neonatal outcomes. Although induced pregnancies have higher risk of first trimester bleeding, significant differences were not observed regarding other obstetric and puerperal complications and neonatal results.

Magnetic Resocance Imaging Methods in the Follow-up of Multiple Sclerosis and in Farby Disease

Multiple sclerosis (MS) is a chronic immune-mediated inflammatory disorder of the central nervous system. MS is the most common disabling central nervous system (CNS) disease of young adults in the Western world. In Finland, the prevalence of MS ranges between 1/1000 and 2/1000 in different areas. Fabry disease (FD) is a rare hereditary metabolic disease due to mutation in a single gene coding α-galactosidase A (alpha-gal A) enzyme. It leads to multi-organ pathology, including cerebrovascular disease. Currently there are 44 patients with diagnosed FD in Finland. Magnetic resonance imaging (MRI) is commonly used in the diagnostics and follow-up of these diseases. The disease activity can be demonstrated by occurrence of new or Gadolinium (Gd)-enhancing lesions in routine studies. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced MR sequences which can reveal pathologies in brain regions which appear normal on conventional MR images in several CNS diseases. The main focus in this study was to reveal whether whole brain apparent diffusion coefficient (ADC) analysis can be used to demonstrate MS disease activity. MS patients were investigated before and after delivery and before and after initiation of diseasemodifying treatment (DMT). In FD, DTI was used to reveal possible microstructural alterations at early timepoints when excessive signs of cerebrovascular disease are not yet visible in conventional MR sequences. Our clinical and MRI findings at 1.5T indicated that post-partum activation of the disease is an early and common phenomenon amongst mothers with MS. MRI seems to be a more sensitive method for assessing MS disease activity than the recording of relapses. However, whole brain ADC histogram analysis is of limited value in the follow-up of inflammatory conditions in a pregnancy-related setting because the pregnancy-related physiological effects on ADC overwhelm the alterations in ADC associated with MS pathology in brain tissue areas which appear normal on conventional MRI sequences. DTI reveals signs of microstructural damage in brain white matter of FD patients before excessive white matter lesion load can be observed on conventional MR scans. DTI could offer a valuable tool for monitoring the possible effects of enzyme replacement therapy in FD.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Individual and social determinants of multiple chronic disease behavioural risk factors in Canadian children and adolescents

Contexte: Les facteurs de risque comportementaux, notamment l’inactivité physique, le comportement sédentaire, le tabagisme, la consommation d’alcool et le surpoids sont les principales causes modifiables de maladies chroniques telles que le cancer, les maladies cardiovasculaires et le diabète. Ces facteurs de risque se manifestent également de façon concomitante chez l’individu et entraînent des risques accrus de morbidité et de mortalité. Bien que les facteurs de risque comportementaux aient été largement étudiés, la distribution, les patrons d’agrégation et les déterminants de multiples facteurs de risque comportementaux sont peu connus, surtout chez les enfants et les adolescents. Objectifs: Cette thèse vise 1) à décrire la prévalence et les patrons d’agrégation de multiples facteurs de risque comportementaux des maladies chroniques chez les enfants et adolescents canadiens; 2) à explorer les corrélats individuels, sociaux et scolaires de multiples facteurs de risque comportementaux chez les enfants et adolescents canadiens; et 3) à évaluer, selon le modèle conceptuel de l’étude, l’influence longitudinale d’un ensemble de variables distales (c’est-à-dire des variables situées à une distance intermédiaire des comportements à risque) de type individuel (estime de soi, sentiment de réussite), social (relations sociales, comportements des parents/pairs) et scolaire (engagement collectif à la réussite, compréhension des règles), ainsi que de variables ultimes (c’est-à-dire des variables situées à une distance éloignée des comportements à risque) de type individuel (traits de personnalité, caractéristiques démographiques), social (caractéristiques socio-économiques des parents) et scolaire (type d’école, environnement favorable, climat disciplinaire) sur le taux d’occurrence de multiples facteurs de risque comportementaux chez les enfants et adolescents canadiens. Méthodes: Des données transversales (n = 4724) à partir du cycle 4 (2000-2001) de l’Enquête longitudinale nationale sur les enfants et les jeunes (ELNEJ) ont été utilisées pour décrire la prévalence et les patrons d’agrégation de multiples facteurs de risque comportementaux chez les jeunes canadiens âgés de 10-17 ans. L’agrégation des facteurs de risque a été examinée en utilisant une méthode du ratio de cas observés sur les cas attendus. La régression logistique ordinale a été utilisée pour explorer les corrélats de multiples facteurs de risque comportementaux dans un échantillon transversal (n = 1747) de jeunes canadiens âgés de 10-15 ans du cycle 4 (2000-2001) de l’ELNEJ. Des données prospectives (n = 1135) à partir des cycle 4 (2000-2001), cycle 5 (2002-2003) et cycle 6 (2004-2005) de l’ELNEJ ont été utilisées pour évaluer l’influence longitudinale des variables distales et ultimes (tel que décrit ci-haut dans les objectifs) sur le taux d’occurrence de multiples facteurs de risque comportementaux chez les jeunes canadiens âgés de 10-15 ans; cette analyse a été effectuée à l’aide des modèles de Poisson longitudinaux. Résultats: Soixante-cinq pour cent des jeunes canadiens ont rapporté avoir deux ou plus de facteurs de risque comportementaux, comparativement à seulement 10% des jeunes avec aucun facteur de risque. Les facteurs de risque comportementaux se sont agrégés en de multiples combinaisons. Plus précisément, l’occurrence simultanée des cinq facteurs de risque était 120% plus élevée chez les garçons (ratio observé/attendu (O/E) = 2.20, intervalle de confiance (IC) 95%: 1.31-3.09) et 94% plus élevée chez les filles (ratio O/E = 1.94, IC 95%: 1.24-2.64) qu’attendu. L’âge (rapport de cotes (RC) = 1.95, IC 95%: 1.21-3.13), ayant un parent fumeur (RC = 1.49, IC 95%: 1.09-2.03), ayant rapporté que la majorité/tous de ses pairs consommaient du tabac (RC = 7.31, IC 95%: 4.00-13.35) ou buvaient de l’alcool (RC = 3.77, IC 95%: 2.18-6.53), et vivant dans une famille monoparentale (RC = 1.94, IC 95%: 1.31-2.88) ont été positivement associés aux multiples comportements à risque. Les jeunes ayant une forte estime de soi (RC = 0.92, IC 95%: 0.85-0.99) ainsi que les jeunes dont un des parents avait un niveau d’éducation postsecondaire (RC = 0.58, IC 95%: 0.41-0.82) étaient moins susceptibles d’avoir de multiples facteurs de risque comportementaux. Enfin, les variables de type social distal (tabagisme des parents et des pairs, consommation d’alcool par les pairs) (Log du rapport de vraisemblance (LLR) = 187.86, degrés de liberté = 8, P < 0,001) et individuel distal (estime de soi) (LLR = 76.94, degrés de liberté = 4, P < 0,001) ont significativement influencé le taux d’occurrence de multiples facteurs de risque comportementaux. Les variables de type individuel ultime (âge, sexe, anxiété) et social ultime (niveau d’éducation du parent, revenu du ménage, structure de la famille) ont eu une influence moins prononcée sur le taux de cooccurrence des facteurs de risque comportementaux chez les jeunes. Conclusion: Les résultats suggèrent que les interventions de santé publique devraient principalement cibler les déterminants de type individuel distal (tel que l’estime de soi) ainsi que social distal (tels que le tabagisme des parents et des pairs et la consommation d’alcool par les pairs) pour prévenir et/ou réduire l’occurrence de multiples facteurs de risque comportementaux chez les enfants et les adolescents. Cependant, puisque les variables de type distal (telles que les caractéristiques psychosociales des jeunes et comportements des parents/pairs) peuvent être influencées par des variables de type ultime (telles que les caractéristiques démographiques et socioéconomiques), les programmes et politiques de prévention devraient également viser à améliorer les conditions socioéconomiques des jeunes, particulièrement celles des enfants et des adolescents des familles les plus démunies.

Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.

Sparse Representations of Multiple Signals

We discuss the problem of finding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple approximation method to the problem and we show experimental results using artificially generated signals. Furthermore,we use our approximation method to find sparse representations of classes of real signals, specifically of images of pedestrians. We discuss the relation between our formulation of the sparsity problem and the problem of finding representations of objects that are compact and appropriate for detection and classification.

Modeling Habitat Associations for the Common Loon (Gavia immer) at Multiple Scales in Northeastern North America

Common Loon (Gavia immer) is considered an emblematic and ecologically important example of aquatic-dependent wildlife in North America. The northern breeding range of Common Loon has contracted over the last century as a result of habitat degradation from human disturbance and lakeshore development. We focused on the state of New Hampshire, USA, where a long-term monitoring program conducted by the Loon Preservation Committee has been collecting biological data on Common Loon since 1976. The Common Loon population in New Hampshire is distributed throughout the state across a wide range of lake-specific habitats, water quality conditions, and levels of human disturbance. We used a multiscale approach to evaluate the association of Common Loon and breeding habitat within three natural physiographic ecoregions of New Hampshire. These multiple scales reflect Common Loon-specific extents such as territories, home ranges, and lake-landscape influences. We developed ecoregional multiscale models and compared them to single-scale models to evaluate model performance in distinguishing Common Loon breeding habitat. Based on information-theoretic criteria, there is empirical support for both multiscale and single-scale models across all three ecoregions, warranting a model-averaging approach. Our results suggest that the Common Loon responds to both ecological and anthropogenic factors at multiple scales when selecting breeding sites. These multiscale models can be used to identify and prioritize the conservation of preferred nesting habitat for Common Loon populations.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres

This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.

Dimension and coverage of multiple-models structures using clustering techniques

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales

Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.

Joint compensation of multiple RF impairments in MIMO STBC systems

In this paper, we propose a compensation method for the joint effect of high-power amplifier (HPA) nonlinearity, in-phase/quadrature-phase (I/Q) imbalance and crosstalk in multiple-input multiple-output (MIMO) orthogonal space-time block coding (OSTBC) systems. The performance of the MIMO OSTBC equipped with the proposed compensation mechanism is evaluated in terms of average symbol error probability and system capacity, in Rayleigh fading channels. Numerical results are provided and show the effects on performance of several system parameters, namely, the HPA parameters, image-leakage ratio, crosstalk, numbers of antennas, and phase-shift keying modulation order.