3D noise power spectrum applied on clinical mdct scanners: effects of reconstruction algorithms and reconstruction filters

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters.A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed.In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements.The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Optoelectronic properties of CuPc thin films deposited at different substrate temperatures

Structural and optical characterization of copper phthalocyanine thin film thermally deposited at different substrate temperatures was the aim of this work. The morphology of the films shows strong dependence on temperature, as can be observed by atomic force microscopy and x-ray diffraction spectroscopy, specifically in the grain size and features of the grains. The increase in the crystal phase with substrate temperature is shown by x-ray diffractometry. Optical absorption coefficient measured by photothermal deflection spectroscopy and optical transmittance reveal a weak dependence on the substrate temperature. Besides, the electro-optical response measured by the external quantum efficiency of Schottky ITO/CuPc/Al diodes shows an optimized response for samples deposited at a substrate temperature of 60 °C, in correspondence to the I-V diode characteristics.

Clinical spectrum of tuberculous optic neuropathy.

PURPOSE: Tuberculous optic neuropathy may follow infection with Mycobacterium tuberculosis or administration of the bacille Calmette-Guerin. However, this condition is not well described in the ophthalmic literature. METHODS: Ophthalmologists, identified through professional electronic networks or previous publications, collected standardized clinical data relating to 62 eyes of 49 patients who they had managed with tuberculous optic neuropathy. RESULTS: Tuberculous optic neuropathy was most commonly manifested as papillitis (51.6 %), neuroretinitis (14.5 %), and optic nerve tubercle (11.3 %). Uveitis was an additional ocular morbidity in 88.7 % of eyes. In 36.7 % of patients, extraocular tuberculosis was present. The majority of patients (69.4 %) had resided in and/or traveled to an endemic area. Although initial visual acuity was 20/50 or worse in 62.9 % of 62 eyes, 76.7 % of 60 eyes followed for a median of 12 months achieved visual acuities of 20/40 or better. Visual field defects were reported for 46.8 % of eyes, but these defects recovered in 63.2 % of 19 eyes with follow-up. CONCLUSION: Visual recovery from tuberculous optic neuropathy is common, if the diagnosis is recognized and appropriate treatment is instituted. A tuberculous etiology should be considered when evaluating optic neuropathy in persons from endemic areas.

Low-mass diffraction dissociation at the LHC ; role of the background

A dual model with a nonlinear proton Regge trajectory in the missing mass (M_X^2) channel is constructed. A background based on a direct-channel exotic trajectory, developed and applied earlier for the inclusive electron-proton cross section description in the nucleon resonance region, is used. The parameters of the model are determined from the extrapolations to earlier experiments. Predictions for the low-mass (2 < M_X^2 < 8GeV^2) diffraction dissociation cross sections at the LHC energies are given.

Phosphorus acquisition and internal utilization efficiency in tropical maize genotypes

The objective of this work was to determine the relative importance of phosphorus acquisition efficiency (PAE - plant P uptake per soil available P), and phosphorus internal utilization efficiency (PUTIL - grain yield per P uptake) in the P use efficiency (PUE - grain yield per soil available P), on 28 tropical maize genotypes evaluated at three low P and two high P environments. PAE was almost two times more important than PUTIL to explain the variability observed in PUE, at low P environments, and three times more important at high P environments. These results indicate that maize breeding programs, to increase PUE in these environments, should use selection index with higher weights for PAE than for PUTIL. The correlation between these two traits showed no significance at low or at high P environments, which indicates that selection in one of these traits would not affect the other. The main component of PUTIL was P quotient of utilization (grain yield per grain P) and not the P harvest index (grain P per P uptake). Selection to reduce grain P concentration should increase the quotient of utilization and consequently increase PUTIL.

Ultraweak photon emission in assessing bone growth factor efficiency using fibroblastic differentiation.

Photons participate in many atomic and molecular interactions and changes. Recent biophysical research has shown the induction of ultraweak photons in biological tissue. It is now established that plants, animal and human cells emit a very weak radiation which can be readily detected with an appropriate photomultiplier system. Although the emission is extremely low in mammalian cells, it can be efficiently induced by ultraviolet light. In our studies, we used the differentiation system of human skin fibroblasts from a patient with Xeroderma Pigmentosum of complementation group A in order to test the growth stimulation efficiency of various bone growth factors at concentrations as low as 5 ng/ml of cell culture medium. In additional experiments, the cells were irradiated with a moderate fluence of ultraviolet A. The different batches of growth factors showed various proliferation of skin fibroblasts in culture which could be correlated with the ultraweak photon emission. The growth factors reduced the acceleration of the fibroblast differentiation induced by mitomycin C by a factor of 10-30%. In view that fibroblasts play an essential role in skin aging and wound healing, the fibroblast differentiation system is a very useful tool in order to elucidate the efficacy of growth factors.

Sugarcane crop efficiency in two growing seasons in São Paulo State, Brazil

A conceptual framework for crop production efficiency was derived using thermodynamic efficiency concept, in order to generate a tool for performance evaluation of agricultural systems and to quantify the interference of determining factors on this performance. In Thermodynamics, efficiency is the ratio between the output and input of energy. To establish this relationship in agricultural systems, it was assumed that the input energy is represented by the attainable crop yield, as predicted through simulation models based on environmental variables. The method of FAO's agroecological zones was applied to the assessment of the attainable sugarcane yield, while Instituto Brasileiro de Geografia e Estatística (IBGE) data were used as observed yield. Sugarcane efficiency production in São Paulo state was evaluated in two growing seasons, and its correlation with some physical factors that regulate production was calculated. A strong relationship was identified between crop production efficiency and soil aptitude. This allowed inferring the effect of agribusiness factors on crop production efficiency. The relationships between production efficiency and climatic variables were also quantified and indicated that solar radiation, annual rainfall, water deficiency, and maximum air temperature are the main factors affecting the sugarcane production efficiency.

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.

Genetic control of grain yield and nitrogen use efficiency in tropical maize

The objectives of this work were to study the genetic control of grain yield (GY) and nitrogen (N) use efficiency (NUE, grain yield/N applied) and its primary components, N uptake efficiency (NUpE, N uptake/N applied) and N utilization efficiency (NUtE, grain yield/N uptake), in maize grown in environments with high and low N availability. Experiments with 31 maize genotypes (28 hybrid crosses and three controls) were carried out in soils with high and low N rates, in the southeast of the state of Minas Gerais, Brazil. There was a reduction of 23.2% in average GY for maize grown in soil with low N, in comparison to that obtained with high N. There were 26.5, 199 and 400% increases in NUtE, NUpE, and NUE, respectively, for maize grown with low N. The general combining ability (GCA) and specific combining ability (SCA) were significant for GY, NUE and NUpE for maize grown in high N soil. Only GCA was significant for NUpE for maize grown in low N soil. The GCA and SCA for NUtE were not significant in either environment. Additive and non-additive genetic effects are responsible for the genetic control of NUE and GY for maize grown in soils with high N availability, although additive effects are more important.

Mapping the human connectome at multiple scales with diffusion spectrum MRI.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Estimating water application efficiency for drip irrigation emitter patterns on banana

The objective of this work was to evaluate root and water distribution in irrigated banana (Musa sp.), in order to determine the water application efficiency for different drip irrigation emitter patterns. Three drip emitter patterns were studied: two 4-L h-1 emitters per plant (T1), four 4-L h-1 emitters per plant (T2), and five 4-L h-1 emitters per plant (T3). The emitters were placed in a lateral line. In the treatment T3, the emitters formed a continuous strip. The cultivated area used was planted with banana cultivar BRS Tropical, with a 3-m spacing between rows and a 2.5-m spacing between plants. Soil moisture and root length data were collected during the first production cycle at five radial distances and depths, in a 0.20x0.20 m vertical grid. The experiment was carried out in a sandy clay loam Xanthic Hapludox. Soil moisture data were collected every 10 min for a period of five days using TDR probes. Water application efficiency was of 83, 88 and 92% for the systems with two, four and five emitters per plant, respectively. It was verified that an increase in the number of emitters in the lateral line promoted better root distribution, higher water extraction, and less deep percolation losses.

Efficiency in inventories of ants in a forest reservein Central Amazonia

The objective of this work was to evaluate an inventory method efficiency for ants. We used subsamples collected in 24 transects of 100 m, distributed in 6 plots of 600 ha each in primary forest, as part of a long-term project. Ten litter subsamples were extracted per transect using Winkler extractors. Ants were identified to genus level, and Crematogaster, Gnamptogenys and Pachycondyla genera to species/morphospecies level. To evaluate the consequences of reduced sampling on the retention of ecological information, we estimated the lowest number of subsamples needed to detect the effects of environmental variables. Multidimensional scaling (MDS) was used to generate dissimilarity matrices, and Mantel correlations between each reduced-sampling effort and maximum effort were used as an index of how much information was maintained and could still be used in multivariate analyses. Lower p-values was observed on the effect of soil pH in the community of genera, and on the effect of the litter volume for the community of Crematogaster. The trend was still detectable in the analysis based on reduced-sampling. The number of subsamples can be reduced, and the cost-efficiency of the protocol can be improved with little loss of information.

Solar radiation use efficiency by soybean under field conditions in the Amazon region

The objective of this work was to evaluate the efficiency of soybean (Glycine max) in intercepting and using solar radiation under natural field conditions, in the Amazon region, Brazil. The meteorological data and the values of soybean growth and leaf area were obtained from an agrometeorological experiment carried out in Paragominas, Pará state, during 2007 and 2008. The radiation use efficiency (RUE) was obtained from the ratio between the above-ground biomass production and the intercepted photosynthetically active radiation (PAR) accumulated to 99 and 95 days after sowing, in 2007 and 2008, respectively. Climatic conditions during the experiment were very distinct, with reduction in rainfall in 2007, which began during the soybean mid-cycle, due to the El Niño phenomenon. An important reduction in the leaf area index and biomass production was observed during 2007. Under natural field conditions in the Amazon region, the values of RUE were 1.46 and 1.99 g MJ-1 PAR in the 2007 and 2008 experiments, respectively. The probable reason for the differences found between these years might be associated to the water restriction in 2007 coupled with the higher air temperature and vapor pressure deficit, and also to the increase in the fraction of diffuse radiation that reached the land surface in 2008.

Road Use Tax Fund Efficiency Report savings status, December 31, 2015

Senate File 2355, 85th General Assembly, states the Iowa Department of Transportation shall submit annual reports regarding the implementation of efficiency measures identified in the “Road Use Tax Fund Efficiency Report,” January 2012. This report shall provide details of activities undertaken in the previous year relating to one-time and long-term program efficiencies and partnership efficiencies. Issues to be covered in the reports shall include but are not limited to savings realized from the implementation of particular efficiency measures; updates concerning measures that have not been implemented; efforts involving cities, counties, other jurisdictions, or stakeholder interest groups; any new efficiency measures identified or undertaken; and identification of any legislative action that may be required to achieve efficiencies.