Magnetic resonance imaging correlates of first-episode psychosis in young adult male patients: combined analysis of grey and white matter.

Background: Several patterns of grey and white matter changes have been separately described in young adults with first-episode psychosis. Concomitant investigation of grey and white matter densities in patients with first-episode psychosis without other psychiatric comorbidities that include all relevant imaging markers could provide clues to the neurodevelopmental hypothesis in schizophrenia. Methods: We recruited patients with first-episode psychosis diagnosed according to the DSM-IV-TR and matched controls. All participants underwent magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) analysis and mean diffusivity voxel-based analysis (VBA) were used for grey matter data. Fractional anisotropy and axial, radial and mean diffusivity were analyzed using tract-based spatial statistics (TBSS) for white matter data. Results: We included 15 patients and 16 controls. The mean diffusivity VBA showed significantly greater mean diffusivity in the first-episode psychosis than in the control group in the lingual gyrus bilaterally, the occipital fusiform gyrus bilaterally, the right lateral occipital gyrus and the right inferior temporal gyrus. Moreover, the TBSS analysis revealed a lower fractional anisotropy in the first-episode psychosis than in the control group in the genu of the corpus callosum, minor forceps, corticospinal tract, right superior longitudinal fasciculus, left middle cerebellar peduncle, left inferior longitudinal fasciculus and the posterior part of the fronto-occipital fasciculus. This analysis also revealed greater radial diffusivity in the first-episode psychosis than in the control group in the right corticospinal tract, right superior longitudinal fasciculus and left middle cerebellar peduncle. Limitations: The modest sample size and the absence of women in our series could limit the impact of our results. Conclusion: Our results highlight the structural vulnerability of grey matter in posterior areas of the brain among young adult male patients with first-episode psychosis. Moreover, the concomitant greater radial diffusivity within several regions already revealed by the fractional anisotropy analysis supports the idea of a late myelination in patients with first-episode psychosis.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Pavement Wear and Studded Tire Use in Iowa, HR-148, 1979

This report presents the results of surveys to determine studded tire usage in Iowa. Also reported are the results of measurements of transverse pavement profiles at selected locations where the pavement is subjected to a high volume of traffic. The surveys were made in January of each of the years 1969 through 1978 and in each of 27 areas into which the state was divided. Estimates of studded tire usage were also made at various locations on Interstate highways in Iowa. The lowest percentage of studded tires was observed in the initial count during the winter of 1968-69. Two years later the percentage had increased to the maximum (22.6%) and then began a gradual decline. The latest count in January of 1978 indicated 8.5% of the cars had studded tires. The decline in the use of studded tires is attributed to the efforts of the Iowa DOT and others to obtain a ban on studded tires and a continual increase in the use of radial tires with claims of improved traction. The wear measurements were recorded by camera. It was found that studded tires have worn ruts in Iowa pavements as deep as 5/16 inch. The ruts lead to water on the pavement and this causes hydroplaning, as well as splash and spray. The conclusion of the study was that studded tires should be banned in Iowa.

A circuit-based gatekeeper for adult neural stem cell proliferation: Parvalbumin-expressing interneurons of the dentate gyrus control the activation and proliferation of quiescent adult neural stem cells.

Newborn neurons are generated in the adult hippocampus from a pool of self-renewing stem cells located in the subgranular zone (SGZ) of the dentate gyrus. Their activation, proliferation, and maturation depend on a host of environmental and cellular factors but, until recently, the contribution of local neuronal circuitry to this process was relatively unknown. In their recent publication, Song and colleagues have uncovered a novel circuit-based mechanism by which release of the neurotransmitter, γ-aminobutyric acid (GABA), from parvalbumin-expressing (PV) interneurons, can hold radial glia-like (RGL) stem cells of the adult SGZ in a quiescent state. This tonic GABAergic signal, dependent upon the activation of γ(2) subunit-containing GABA(A) receptors of RGL stem cells, can thus prevent their proliferation and subsequent maturation or return them to quiescence if previously activated. PV interneurons are thus capable of suppressing neurogenesis during periods of high network activity and facilitating neurogenesis when network activity is low.

Alterations of the 5'untranslated region of SLC16A12 lead to age-related cataract.

PURPOSE. Knowledge of genetic factors predisposing to age-related cataract is very limited. The aim of this study was to identify DNA sequences that either lead to or predispose for this disease. METHODS. The candidate gene SLC16A12, which encodes a solute carrier of the monocarboxylate transporter family, was sequenced in 484 patients with cataract (134 with juvenile cataract, 350 with age-related cataract) and 190 control subjects. Expression studies included luciferase reporter assay and RT-PCR experiments. RESULTS. One patient with age-related cataract showed a novel heterozygous mutation (c.-17A>G) in the 5'untranslated region (5'UTR). This mutation is in cis with the minor G-allele of the single nucleotide polymorphism (SNP) rs3740030 (c.-42T/G), also within the 5'UTR. Using a luciferase reporter assay system, a construct with the patient's haplotype caused a significant upregulation of luciferase activity. In comparison, the SNP G-allele alone promoted less activity, but that amount was still significantly higher than the amount of the common T-allele. Analysis of SLC16A12 transcripts in surrogate tissue demonstrated striking allele-specific differences causing 5'UTR heterogeneity with respect to sequence and quantity. These differences in gene expression were mirrored in an allele-specific predisposition to age-related cataract, as determined in a Swiss population (odds ratio approximately 2.2; confidence intervals, 1.23-4.3). CONCLUSIONS. The monocarboxylate transporter SLC16A12 may contribute to age-related cataract. Sequences within the 5'UTR modulate translational efficiency with pathogenic consequences.

Conduit artery compliance and distensibility are not necessarily reduced in hypertension.

The goal of this study was to investigate whether the elastic behavior of conduit arteries of humans or rats is altered as a result of concomitant hypertension. Forearm arterial cross-sectional compliance-pressure curves were determined noninvasively by means of a high precision ultrasonic echo-tracking device coupled to a photoplethysmograph (Finapres system) allowing simultaneous arterial diameter and finger blood pressure monitoring. Seventeen newly diagnosed hypertensive patients with a humeral blood pressure of 163/103 +/- 4.4/2.2 mm Hg (mean +/- SEM) and 17 age- and sex-matched normotensive controls with a humeral blood pressure of 121/77 +/- 3.2/1.9 mm Hg were included in the study. Compliance-pressure curves were also established at the carotid artery of 16-week-old anesthetized spontaneously hypertensive rats (n = 14) as well as Wistar-Kyoto normotensive animals (n = 15) using the same echo-tracking device. In these animals, intra-arterial pressure was monitored in the contralateral carotid artery. Mean blood pressures averaged 197 +/- 4 and 140 +/- 3 mm Hg in the hypertensive and normotensive rats, respectively. Despite the considerable differences in blood pressure, the diameter-pressure and cross-sectional compliance-pressure and distensibility-pressure curves were not different when hypertensive patients or animals were compared with their respective controls. These results suggest that the elastic behavior of a medium size muscular artery (radial) in humans and of an elastic artery (carotid) in rats is not necessarily altered by an increase in blood pressure.

Cardiac rotation and relaxation in patients with aortic valve stenosis.

BACKGROUND: Diastolic dysfunction with delayed relaxation and abnormal passive elastic properties has been described in patients with severe pressure overload hypertrophy. The purpose of this study was to evaluate the time course of rotational motion of the left ventricle in patients with aortic valve stenosis using myocardial tagging. METHODS: Myocardial tagging is a non-invasive method based on magnetic resonance which makes it possible to label ('tag') specific myocardial regions. From the motion of the tag's cardiac rotation, radial displacement and translational motion can be determined. In 12 controls and 13 patients with severe aortic valve stenosis systolic and diastolic wall motion was assessed in an apical and basal short axis plane. RESULTS: The normal left ventricle performs a systolic wringing motion around the ventricular long axis with clockwise rotation at the base (-4.4+/-1.6 degrees) and counter-clockwise rotation at the apex (+6.8+/-2.5 degrees) when viewed from the apex. During early diastole an untwisting motion can be observed which precedes diastolic filling. In patients with aortic valve stenosis systolic rotation is reduced at the base (-2.4+/-2.0 degrees; P<0.01) but increased at the apex (+12.0+/-6.0 degrees; P<0.05). Diastolic untwisting is delayed and prolonged with a decrease in normalized rotation velocity (-6.9+/-1.1 s(-1)) when compared to controls (-10.7+/-2.2 s(-1); P<0.001). Maximal systolic torsion is 8.0+/-2.1 degrees in controls and 14.1+/-6.4 degrees (P<0.01) in patients with aortic valve stenosis. CONCLUSIONS: Left ventricular pressure overload hypertrophy is associated with a reduction in basal and an increase in apical rotation resulting in increased torsion of the ventricle. Diastolic untwisting is delayed and prolonged. This may explain the occurrence of diastolic dysfunction in patients with severe pressure overload hypertrophy.

Distribuição de raízes de laranja "Pêra" sob sequeiro e irrigação por microaspersão em solo arenoso

O trabalho teve por objetivo avaliar a distribuição de raízes de laranja "Pêra" sob condições não-irrigadas e irrigadas por microaspersão em solo arenoso de tabuleiro costeiro. As raízes foram extraídas em trincheiras, a partir do tronco, nas direções longitudinal e ortogonal à fileira de plantas, pelo método do monolito. Uma vez separadas, foram digitalizadas com uso de computador e scanner, para obter, com o uso do software Rootedge, os comprimentos e diâmetros dos segmentos de raízes de todas as amostras, que foram mapeados nos perfis amostrados. O sistema radicular sob irrigação por microaspersão apresentou maior expansão, tanto em profundidade como em distância radial do tronco, do que o sistema radicular sob condições não-irrigadas. Houve maior porcentagem de raízes finas nos perfis de solo sob microaspersão, em relação à condição não-irrigada, indicando a possibilidade de maior atividade do sistema radicular nesse sistema de irrigação. As posições mais adequadas para instalação de sensores de água do solo para a cultura da laranja sob microaspersão estão entre 0 e 2,5 m de distância radial a partir do tronco, em profundidades entre 0 e 1,0 m.

Aggregation under a forced convective flow

Morphological transitions are analyzed for a radial multiparticle diffusion-limited aggregation process grown under a convective drift. The introduction of a tangential flow changes the morphology of the diffusion-limited structure, into multiarm structures, inclined opposite to the flow, whose limit consists of single arms, when decreasing density. The case of shear flow is also considered. The anisotropy of the patterns is characterized in terms of a tangential correlation function based analysis. Comparison between the simulation results and preliminary experimental results has been done.

Résultats préliminaires de l'enclouage rétrograde de l'humérus [Preliminary results of retrograde nailing of the humerus]

A group of nine patients with a diaphyseal fracture of the humerus and treated with retrograde nailing were studied with a mean follow-up of 15.3 months. Six patients with a humeral fracture without neurological deficit showed a good shoulder and elbow mobility at the last visit. Three patients with neurological lesion preoperatively suffer from a diminished range of movement not related to the surgical procedure. During the operation and postoperatively we found no complication related to the implant and more precisely we could not find a iatrogenic fracture or nervous lesion except one intraoperative lesion of the radial nerve probably related to an important traction movement during reduction with complete remission. Consolidation has been achieved for all fractures but one. This patient suffers from a lesion of the brachial plexus with complete plegia of the arm and a vascular lesion. This patient had to be reoperated for an atrophic non-union by bone grafting and plate fixation. The retrograde nail is a good implant and must be considered in our treatment plans as much as conservative treatment or surgical treatment with plating, anterograde nailing or the use of an external fixator. Only then will we be able to give to the patient the most adapted treatment for his fracture.

Flow-targeted inversion-prepared b-TFE coronary MR angiography: initial results in patients.

PURPOSE: Visualization of coronary blood flow in the right and left coronary system in volunteers and patients by means of a modified inversion-prepared bright-blood coronary magnetic resonance angiography (cMRA) sequence. MATERIALS AND METHODS: cMRA was performed in 14 healthy volunteers and 19 patients on a 1.5 Tesla MR system using a free-breathing 3D balanced turbo field echo (b-TFE) sequence with radial k-space sampling. For magnetization preparation a slab selective and a 2D selective inversion pulse were used for the right and left coronary system, respectively. cMRA images were evaluated in terms of clinically relevant stenoses (< 50 %) and compared to conventional catheter angiography. Signal was measured in the coronary arteries (coro), the aorta (ao) and in the epicardial fat (fat) to determine SNR and CNR. In addition, maximal visible vessel length, and vessel border definition were analyzed. RESULTS: The use of a selective inversion pre-pulse allowed direct visualization of the coronary blood flow in the right and left coronary system. The measured SNR and CNR, vessel length, and vessel sharpness in volunteers (SNR coro: 28.3 +/- 5.0; SNR ao: 37.6 +/- 8.4; CNR coro-fat: 25.3 +/- 4.5; LAD: 128.0 cm +/- 8.8; RCA: 74.6 cm +/- 12.4; Sharpness: 66.6 % +/- 4.8) were slightly increased compared to those in patients (SNR coro: 24.1 +/- 3.8; SNR ao: 33.8 +/- 11.4; CNR coro-fat: 19.9 +/- 3.3; LAD: 112.5 cm +/- 13.8; RCA: 69.6 cm +/- 16.6; Sharpness: 58.9 % +/- 7.9; n.s.). In the patient study the assessment of 42 coronary segments lead to correct identification of 10 clinically relevant stenoses. CONCLUSION: The modification of a previously published inversion-prepared cMRA sequence allowed direct visualization of the coronary blood flow in the right as well as in the left coronary system. In addition, this sequence proved to be highly sensitive regarding the assessment of clinically relevant stenotic lesions.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

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.

Mechanical properties of viral capsids

Viruses are known to tolerate wide ranges of pH and salt conditions and to withstand internal pressures as high as 100 atmospheres. In this paper we investigate the mechanical properties of viral capsids, calling explicit attention to the inhomogeneity of the shells that is inherent to their discrete and polyhedral nature. We calculate the distribution of stress in these capsids and analyze their response to isotropic internal pressure (arising, for instance, from genome confinement and/or osmotic activity). We compare our results with appropriate generalizations of classical (i.e., continuum) elasticity theory. We also examine competing mechanisms for viral shell failure, e.g., in-plane crack formation vs radial bursting. The biological consequences of the special stabilities and stress distributions of viral capsids are also discussed.

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.