Spectrum of some trianguled categories

In this paper, we compute the triangular spectrum (as de fined by P. Balmer) of two classes of tensor triangulated categories which are quite common in algebraic geometry. One of them is the derived category of G-equivariant sheaves on a smooth scheme X, for a fi nite group G. The other class is the derived category of split superschemes.

Genetic heterogeneity of the Coppock-like cataract: a mutation in CRYBB2 on chromosome 22q11.2.

PURPOSE: To identify the genetic defect for the Coppock-like cataract (CCL) affecting a Swiss family, which defect was unlinked to the chromosome 2q33-35 CCL locus. METHODS: A large family was characterized for linkage analysis by slit lamp examination or by the review of drawings made before cataract extraction. The affection status was attributed before genotyping, and the genotyping was masked to the affection status. Two-point and multipoint linkage analyses were performed using the MLINK and the LINKMAP components of the LINKAGE program package (ver. 5.1), respectively. Mutational analysis of candidate genes was performed by a combination of direct cycle sequencing and an amplification refractory mutation system assay. RESULTS: Ten individuals were affected with the CCL phenotype. The disease was autosomal dominant and appeared to be fully penetrant. A new CCL locus was identified on chromosome 22q11.2 within a 11.67-cM interval (maximum lod score [Zmax] = 4.14; theta = 0). Mutational analysis of the CRYBB2 candidate gene identified a disease-causing mutation in exon 6. This sequence change was identical with that previously described to be associated with the cerulean cataract, a clinically distinct entity. CONCLUSIONS: The CCL phenotype is genetically heterogeneous with a second gene on chromosome 22q11.2, CRYBB2. The CCL and the cerulean cataract are two distinct clinical entities associated with the same genetic defect. This work provides evidence for a modifier factor that influences cataract formation and that remains to be identified.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

The effectiveness of a standardised preoperative preparation in reducing child and parent anxiety: a single-blind randomised controlled trial.

AIMS: To evaluate the effect of a structured preoperative preparation on child and parent state anxiety, child behavioural change and parent satisfaction. BACKGROUND: It is estimated that around 50-70% of hospitalised children experience severe anxiety and distress prior to surgery. Children who are highly anxious and distressed preoperatively are likely to be distressed on awakening and have negative postoperative behaviour. Although education before surgery has been found to be useful mostly in North America, the effectiveness of preoperative preparation programme adapted to the Australian context remains to be tested. DESIGN: This single-blind randomised controlled study was conducted at a tertiary referral hospital for children in Western Australia. METHODS: Following ethics approval and parental consent, 73 children and one of their carers (usually a parent) were randomly assigned into two groups. The control group had standard practice with no specific preoperative education and the experimental group received a preoperative preparation, including a photo file, demonstration of equipment using a role-modelling approach and a tour. RESULTS: The preoperative preparation reduced parent state anxiety significantly (-2·32, CI -4·06 to -0·56, p = 0·009), but not child anxiety (-0·59, CI -1·23 to 0·06, p = 0·07). There was no significant difference in child postoperative behaviour or parent satisfaction between the groups. There was a significant two-point pain score reduction in the preoperative preparation group, when compared with the control group median 2 (IQR 5) and 4 (IQR 4), respectively (p = 0·001).¦CONCLUSIONS: Preoperative preparation was more efficient on parent than child. Although the preoperative preparation had limited effect on child anxiety, it permitted to decrease pain experience in the postoperative period.¦RELEVANCE TO CLINICAL PRACTICE: Parents should be actively involved in their child preoperative preparation.

Correlations derived from modern nucleon-nucleon potentials

Various modern nucleon-nucleon (NN) potentials yield a very accurate fit to the nucleon-nucleon scattering phase shifts. The differences between these interactions in describing properties of nuclear matter are investigated. Various contributions to the total energy are evaluated employing the Hellmann-Feynman theorem. Special attention is paid to the two-nucleon correlation functions derived from these interactions. Differences in the predictions of the various interactions can be traced back to the inclusion of nonlocal terms.

Quantum metric spaces as a model for pregeometry

A new arena for the dynamics of spacetime is proposed, in which the basic quantum variable is the two-point distance on a metric space. The scaling dimension (that is, the Kolmogorov capacity) in the neighborhood of each point then defines in a natural way a local concept of dimension. We study our model in the region of parameter space in which the resulting spacetime is not too different from a smooth manifold.

Topologies for poin distributions

The concepts of void and cluster for an arbitrary point distribution in a domain D are defined and characterized by some parameters such as volume, density, number of points belonging to them, shape, etc. After assigning a weight to each void and clusterwhich is a function of its characteristicsthe concept of distance between two point configurations S1 and S2 in D is introduced, both with and without the help of a lattice in the domain D. This defines a topology for the point distributions in D, which is different for the different characterizations of the voids and clusters.

Boson-boson mixtures and triplet correlations

The hypernetted-chain formalism for boson-boson mixtures described by an extended Jastrow correlated wave function is derived, taking into account elementary diagrams and triplet correlations. The energy of an ideal boson 3He-4He mixture is computed for low values of the 3He concentration. The zero-3He-concentration limit provides a 3He chemical potential in good agreement with the experimental value, when a McMillan two-body correlation factor and the Lennard-Jones potential are adopted. If the Euler equations for the two-body correlation factors are solved in presence of triplet correlations, the agreement is again improved. At the experimental 4He equilibrium density, the 3He chemical potential turns out to be -2.58 K, to be compared with the experimental value, -2.79 K.

Quantum metric spaces as a model for pregeometry

A new arena for the dynamics of spacetime is proposed, in which the basic quantum variable is the two-point distance on a metric space. The scaling dimension (that is, the Kolmogorov capacity) in the neighborhood of each point then defines in a natural way a local concept of dimension. We study our model in the region of parameter space in which the resulting spacetime is not too different from a smooth manifold.

Generalized percolation in random directed networks

We develop a general theory for percolation in directed random networks with arbitrary two-point correlations and bidirectional edgesthat is, edges pointing in both directions simultaneously. These two ingredients alter the previously known scenario and open new views and perspectives on percolation phenomena. Equations for the percolation threshold and the sizes of the giant components are derived in the most general case. We also present simulation results for a particular example of uncorrelated network with bidirectional edges confirming the theoretical predictions.

A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study.

A family history of coronary artery disease (CAD), especially when the disease occurs at a young age, is a potent risk factor for CAD. DNA collection in families in which two or more siblings are affected at an early age allows identification of genetic factors for CAD by linkage analysis. We performed a genomewide scan in 1,168 individuals from 438 families, including 493 affected sibling pairs with documented onset of CAD before 51 years of age in men and before 56 years of age in women. We prospectively defined three phenotypic subsets of families: (1) acute coronary syndrome in two or more siblings; (2) absence of type 2 diabetes in all affected siblings; and (3) atherogenic dyslipidemia in any one sibling. Genotypes were analyzed for 395 microsatellite markers. Regions were defined as providing evidence for linkage if they provided parametric two-point LOD scores >1.5, together with nonparametric multipoint LOD scores >1.0. Regions on chromosomes 3q13 (multipoint LOD = 3.3; empirical P value <.001) and 5q31 (multipoint LOD = 1.4; empirical P value <.081) met these criteria in the entire data set, and regions on chromosomes 1q25, 3q13, 7p14, and 19p13 met these criteria in one or more of the subsets. Two regions, 3q13 and 1q25, met the criteria for genomewide significance. We have identified a region on chromosome 3q13 that is linked to early-onset CAD, as well as additional regions of interest that will require further analysis. These data provide initial areas of the human genome where further investigation may reveal susceptibility genes for early-onset CAD.

Etude clinique et analyse de liaison au locus 3q28 de deux familles suisses avec atrophie optique dominante de Kjer (OPA1) [Clinical study and genetic 3q28 locus linkage in 2 Swiss families with Kjer dominant optic atrophy (OPA1)]

METHODS: We examined 20 patients from 2 unrelated Swiss families to describe their clinical phenotype. In addition, a linkage analysis was performed in an attempt to confirm the reported genetic homogeneity of this condition as well as to refine its genomic localization. RESULTS: Two point analysis provided a cumulative LOD-score of 3.03 with marker D3S 2305. The absence of recombination precluded further refinement of the disease interval. CONCLUSIONS: Our data confirm the genetic homogeneity and the extreme variability of expression, occasionally mimicking low tension glaucoma.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

1017-011 Tuttle Lake Watershed Final Report

The Tuttle Lake Watershed is approximately 125,000 acres and Tuttle Lake itself is 2,270 acres; 5,609 acres of the watershed lies in Iowa territory within Emmet County. It is a sub-watershed of the larger East Fork Des Moines River Watershed, also referred to as Hydrologic Unit Code 07100003. For the purpose of this document, grant money is only being applied for the project implementation in the Iowa portion of the Tuttle Lake Watershed. Tuttle Lake was placed on the 2002 EPA 303(d) Impaired Waters List due to a “very large population of suspended algae and very high levels of inorganic turbidity.” In 2004, the Iowa Department of Natural Resources (IDNR) completed a Total Maximum Daily Load (TMDL) study on Tuttle Lake and found excess sediment and phosphorus levels being the primary pollutants causing the algae and turbidity impairment. Although two point sources were located in Minnesota, IDNR determined that the influx of nutrients is likely from agricultural runoff and re-suspension of lake sediment. The condition of Tuttle Lake is such that the reduction of sediment, nutrients [phosphorus and nitrogen] and pathogens is the primary objective. To achieve that objective, wetlands will be constructed in this first phase to reduce the delivery of nitrogen, phosphorus, and sediment to Tuttle Lake.

Holographic collisions in confining theories

We study the gravitational dual of a high-energy collision in a confining gauge theory. We consider a linearized approach in which two point particles traveling in an AdS-soliton background suddenly collide to form an object at rest (presumably a black hole for large enough center-of-mass energies). The resulting radiation exhibits the features expected in a theory with a mass gap: late-time power law tails of the form t −3/2, the failure of Huygens" principle and distortion of the wave pattern as it propagates. The energy spectrum is exponentially suppressed for frequencies smaller than the gauge theory mass gap. Consequently, we observe no memory effect in the gravitational waveforms. At larger frequencies the spectrum has an upward-stairway structure, which corresponds to the excitation of the tower of massive states in the confining gauge theory. We discuss the importance of phenomenological cutoffs to regularize the divergent spectrum, and the aspects of the full non-linear collision that are expected to be captured by our approach.