Actin cables and the exocyst form two independent morphogenesis pathways in the fission yeast.

Cell morphogenesis depends on polarized exocytosis. One widely held model posits that long-range transport and exocyst-dependent tethering of exocytic vesicles at the plasma membrane sequentially drive this process. Here, we describe that disruption of either actin-based long-range transport and microtubules or the exocyst did not abolish polarized growth in rod-shaped fission yeast cells. However, disruption of both actin cables and exocyst led to isotropic growth. Exocytic vesicles localized to cell tips in single mutants but were dispersed in double mutants. In contrast, a marker for active Cdc42, a major polarity landmark, localized to discreet cortical sites even in double mutants. Localization and photobleaching studies show that the exocyst subunits Sec6 and Sec8 localize to cell tips largely independently of the actin cytoskeleton, but in a cdc42 and phospholipid phosphatidylinositol 4,5-bisphosphate (PIP₂)-dependent manner. Thus in fission yeast long-range cytoskeletal transport and PIP₂-dependent exocyst represent parallel morphogenetic modules downstream of Cdc42, raising the possibility of similar mechanisms in other cell types.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Independent relations of left ventricular structure with the 24-h urinary excretion of sodium and aldosterone

Objective: Previous studies reported on the association of left ventricular mass index (LVMI) with urinary sodium or with circulating or urinary aldosterone.We investigated the independent associations of LVMI with the urinary excretion of both sodium and aldosterone. Design and method: We randomly recruited 317 untreated subjects from a White population (45.1%women; mean age 48.2 years).Measurements included echocardiographic left ventricular (LV) properties, the 24 h urinary excretion of sodium and aldosterone, plasma renin activity (PRA), and proximal (RNaprox) and distal (RNadist) renal sodium reabsorption, assessed fromthe endogenous lithium clearance. Inmultivariable-adjusted models,we expressed changes in LVMI per 1 SD increase in the explanatory variables, while accounting for sex, age, systolic blood pressure and the waist-to-hip ratio. Results: LVMI increased independentlywith the urinary excretion of both sodium (+2.48 g/m2; P=0.005) and aldosterone (+2.63 g/m2; P=0.004). Higher sodium excretion was associated with increased mean wall thickness (MWT: +0.126 mm, P=0.054), but with no change in LV end-diastolic diameter (LVID: +0.12mm, P=0.64). In contrast, higher aldosterone excretion was associated with higher LVID (+0.54 mm; P=0.017), but with no change in MWT (+0.070mm; P=0.28).Higher RNadistwas associatedwith lower relativewall thickness (−0.81×10−2, P=0.017), because of opposite trends in LVID(+0.33 mm; P=0.13) and MWT (−0.130mm; P=0.040). LVMI was not associated with PRA or RNaprox. Conclusions: LVMI independently increased with both urinary sodium and aldosterone excretion. IncreasedMWT explained the association of LVMI with urinary sodium and increased LVID the association of LVMI with urinary aldosterone.

El multilingüisme en una Catalunya independent: representacions d'un hipotètic futur

Aquest Treball Final de Carrera (TFC) analitza les diferents propostes dels partits polítics catalanistes i dels seus militants juntament amb la dels sociolingüistes (o experts) sobre el multilingüisme en una suposada Catalunya independent

Delaware County Solid Waste Disposal Commission, Independent Auditor's Reports, Financial Statement and Required Supplementary Information, Schedule of Findings, June 30, 2006

Other Audit Reports - 28E Organizations

Delaware County Solid Waste Disposal Commission, Independent Auditor's Reports, Financial Statement and Required Supplementary Information, Schedule of Findings, June 30, 2004

Other Audit Reports - 28E Organizations

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.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

The T309G Murine Double Minute 2 Gene Polymorphism Is an Independent Prognostic Factor for Patients with Renal Cell Carcinoma.

The purpose of this study was to evaluate the association of the T309G MDM2 gene polymorphism with renal cell carcinoma (RCC) risk, pathology, and cancer-specific survival (CSS). T309G MDM2 was genotyped in 449 Caucasians, including 240 with RCC and 209 cancer-free controls. The T309G MDM2 genotype was TT in 174 (38.8%), GT in 214 (47.7%), and GG in 61 (13.6%) subjects, without any significant differences between cases and controls on both univariable (p=0.58) and multivariable logistic regression (each p>0.25). Furthermore, T309G MDM2 was not linked with T stage (p=0.75), N stage (p=0.37), M stage (p=0.94), grade (p=0.21), and subtype (p=0.55). There was, however, a statistically significant association of T309G MDM2 with CSS (p=0.022): patients with TT had significantly worse survival than GG/GT (p=0.009), while those with GT and GG had similar outcomes (p=0.92). The 5-year survival rate for patients with TT, GT, and GG was 69.5%, 84.5%, and 89.7%, respectively. On the multivariable analysis, T309G was identified as an independent prognostic factor. The T309G MDM2 polymorphism is an independent prognostic factor for patients with RCC, with the TT genotype being associated with worse prognosis. In this study, there were no significant associations with RCC risk and pathology.

Blood pressure-independent cardiac hypertrophy induced by locally activated renin-angiotensin system.

Cardiac hypertrophy is frequent in chronic hypertension. The renin-angiotensin system, via its effector angiotensin II (Ang II), regulates blood pressure and participates in sustaining hypertension. In addition, a growing body of evidence indicates that Ang II acts also as a growth factor. However, it is still a matter of debate whether the trophic effect of Ang II can trigger cardiac hypertrophy in the absence of elevated blood pressure. To address this question, transgenic mice overexpressing the rat angiotensinogen gene, specifically in the heart, were generated to increase the local activity of the renin-angiotensin system and therefore Ang II production. These mice develop myocardial hypertrophy without signs of fibrosis independently from the presence of hypertension, demonstrating that local Ang II production is important in mediating the hypertrophic response in vivo.

Independent Living Services Report, December 2015

Independent Living foster care is a foster care type of placement in which youth served must be at least 16 years of age and have the capacity to function outside the structure of a foster family or group care setting. This is a annual report about these living arrangements and how the Department of Human Services contracts them.