Correlations between negative-induced cerebral dynamics and idiopathic intellectual disability: an EEG study

Intellectual disability has long been associated with deficits in socio-emotional processing. However, studies investigating brain dynamics of maladaptive socio-emotional skills associated with intellectual disability are scarce. Here, we compared differences in brain activity between low intelligence quotient (I.Q.<75, N=13) and normal controls (N=15) while evaluating their subjective emotions. Positive (P) and negative (N) valenced pictures were presented one at a time to participants of both groups, at a rate of ¾. The task required that each participant evaluate their subjective emotion and press a predefined push-button when done, alternatively P and N. Electroencephalographic (EEG) signals were continuously recorded, and the 1000ms time window following each picture was analyzed offline for power in frequency domain. Alpha low (8-10Hz) and upper (10-13Hz) frequency bands were then compared for both groups and for both P and N emotions in 12 distributed scalp electrodes. The qualitative evaluation of emotions was similar between both groups, with constant longer reaction times for the low IQ participants. The EEG signal comparison shows marked power decrease in upper alpha frequency range for N emotions in low intelligence group. Otherwise no significant difference was noticed between low and normal IQ. Main findings of the present study are (1) results do not support the hypothesis that impairment in developmental intelligence roots in maladaptive emotional processing; (2) the strong alpha power suppression during negative-induced emotions suggests the involvement of an extended neural network and more effortful inhibition processes than positive ones. We call for further studies with a larger sample.

Electrical stimulation induces propagated colonic contractions in an experimental model.

BACKGROUND: Direct colonic electrical stimulation may prove to be a treatment option for specific motility disorders such as chronic constipation. The aim of this study was to provoke colonic contractions using electrical stimulation delivered from a battery-operated device. METHODS: Electrodes were inserted into the caecal seromuscular layer of eight anaesthetized pigs. Contractions were induced by a neurostimulator (Medtronic 3625). Caecal motility was measured simultaneously by video image analysis, manometry and a technique assessing colonic transit. RESULTS: Caecal contractions were generated using 8-10 V amplitude, 1000 micros pulse width, 120 Hz frequency for 10-30 s, with an intensity of 7-15 mA. The maximal contraction strength was observed after 20-25 s. Electrical stimulation was followed by a relaxation phase of 1.5-2 min during which contractions propagated orally and aborally over at least 10 cm. Spontaneous and stimulated caecal motility values were significantly different for both intraluminal pressure (mean(s.d.) 332(124) and 463(187) mmHg respectively; P < 0.001, 42 experiments) and movement of contents (1.6(0.9) and 3.9(2.8) mm; P < 0.001, 40 experiments). CONCLUSION: Electrical stimulation modulated caecal motility, and provoked localized and propagated colonic contractions.

Differential effects of GABAB receptor subtypes, {gamma}-hydroxybutyric Acid, and Baclofen on EEG activity and sleep regulation.

The role of GABA(B) receptors in sleep is still poorly understood. GHB (γ-hydroxybutyric acid) targets these receptors and is the only drug approved to treat the sleep disorder narcolepsy. GABA(B) receptors are obligate dimers comprised of the GABA(B2) subunit and either one of the two GABA(B1) subunit isoforms, GABA(B1a) and GABA(B1b). To better understand the role of GABA(B) receptors in sleep regulation, we performed electroencephalogram (EEG) recordings in mice devoid of functional GABA(B) receptors (1(-/-) and 2(-/-)) or lacking one of the subunit 1 isoforms (1a(-/-) and 1b(-/-)). The distribution of sleep over the day was profoundly altered in 1(-/-) and 2(-/-) mice, suggesting a role for GABA(B) receptors in the circadian organization of sleep. Several other sleep and EEG phenotypes pointed to a more prominent role for GABA(B1a) compared with the GABA(B1b) isoform. Moreover, we found that GABA(B1a) protects against the spontaneous seizure activity observed in 1(-/-) and 2(-/-) mice. We also evaluated the effects of the GHB-prodrug GBL (γ-butyrolactone) and of baclofen (BAC), a high-affinity GABA(B) receptor agonist. Both drugs induced a state distinct from physiological sleep that was not observed in 1(-/-) and 2(-/-) mice. Subsequent sleep was not affected by GBL whereas BAC was followed by a delayed hypersomnia even in 1(-/-) and 2(-/-) mice. The differential effects of GBL and BAC might be attributed to differences in GABA(B)-receptor affinity. These results also indicate that all GBL effects are mediated through GABA(B) receptors, although these receptors do not seem to be involved in mediating the BAC-induced hypersomnia.

Subgroup And Quality Of Life Analyses Of The Phase Iii Clinical Trial Of Novottf-100a Versus Best Standard Chemotherapy For Recurrent Glioblastoma

BACKGROUND: NovoTTF is a portable device delivering low-intensity, intermediate-frequency, electric fields using noninvasive, disposable scalp electrodes. These fields physically interfere with cell division. Preliminary studies in recurrent and newly diagnosed glioblastoma (GBM) have shown promising results. A phase III study in recurrent GBM has recently been concluded. METHODS: Adults (KPS ≥ 70%) with recurrent GBM (any recurrence) were randomized (stratified by surgery and center) to either NovoTTF administered continuously (20-24 hours/day, 7 days/week) or the best available chemotherapy (best physician choice [BPC]). Primary endpoint was overall survival (OS); 6-month progression-free survival (PFS6), 1-year survival, and QOL were secondary endpoints. RESULTS: Two hundred thirty-seven patients were randomized (28 centers in the United States and Europe) to either NovoTTF alone (120 patients) or BPC (117 patients). Patient characteristics were balanced, median age was 54 years (range, 23-80 years), median KPS was 80% (range, 50-100). One quarter had surgery for recurrence, and over half were at their second or more recurrence. A survival advantage for the device group was seen in patients treated according to protocol (median OS, 7.8 months vs. 6.1 months; n = 185; p = 0.01). Moreover, subgroup analysis in patients with better prognostic baseline characteristics (KPS ≥ 80%; age ≤ 60; 1st-3rd recurrence) demonstrated a robust survival benefit for NovoTTF patients compared to matched BPC patients (median OS, 8.8 months vs. 6.6 months; n = 110; p < 0.01). In this group, 1-year survival was 35% vs. 20% and PFS6 was 25.6% vs. 7.7%. Interestingly, in patients who failed bevacizumab prior to the trial, OS was also significantly extended by NovoTTF (4.4 months vs. 3.1 months; n = 23 vs. n = 21; p < 0.02). Quality of life was equivalent or superior in NovoTTF patients. CONCLUSIONS: NovoTTF, a noninvasive, novel cancer treatment modality shows significant therapeutic efficacy with improved quality of life. The impact of NovoTTF was more pronounced when patients with better baseline prognostic factors were treated. A large scale phase III clinical trial in newly diagnosed GBM is currently being conducted.

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.

Cross-frequency coupling in EEG revealed by adaptive oscillation tracking.

Introduction: Neuronal oscillations have been the focus of increasing interest in the neuroscientific community, in part because they have been considered as a possible integrating mechanism through which internal states can influence stimulus processing in a top-down way (Engel et al., 2001). Moreover, increasing evidence indicates that oscillations in different frequency bands interact with one other through coupling mechanisms (Jensen and Colgin, 2007). The existence and the importance of these cross-frequency couplings during various tasks have been verified by recent studies (Canolty et al., 2006; Lakatos et al., 2007). In this study, we measure the strength and directionality of two types of couplings - phase-amplitude couplings and phase-phase couplings - between various bands in EEG data recorded during an illusory contour experiment that were identified using a recently-proposed adaptive frequency tracking algorithm (Van Zaen et al., 2010). Methods: The data used in this study have been taken from a previously published study examining the spatiotemporal mechanisms of illusory contour processing (Murray et al., 2002). The EEG in the present study were from a subset of nine subjects. Each stimulus was composed of 'pac-man' inducers presented in two orientations: IC, when an illusory contour was present, and NC, when no contour could be detected. The signals recorded by the electrodes P2, P4, P6, PO4 and PO6 were averaged, and filtered into the following bands: 4-8Hz, 8-12Hz, 15-25Hz, 35-45Hz, 45-55Hz, 55-65Hz and 65-75Hz. An adaptive frequency tracking algorithm (Van Zaen et al., 2010) was then applied in each band in order to extract the main oscillation and estimate its frequency. This additional step ensures that clean phase information is obtained when taking the Hilbert transform. The frequency estimated by the tracker was averaged over sliding windows and then used to compare the two conditions. Two types of cross-frequency couplings were considered: phase-amplitude couplings and phase-phase couplings. Both types were measured with the phase locking value (PLV, Lachaux et al., 1999) over sliding windows. The phase-amplitude couplings were computed with the phase of the low frequency oscillation and the phase of the amplitude of the high frequency one. Different coupling coefficients were used when measuring phase-phase couplings in order to estimate different m:n synchronizations (4:3, 3:2, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1 and 9:1) and to take into account the frequency differences across bands. Moreover, the direction of coupling was estimated with a directionality index (Bahraminasab et al., 2008). Finally, the two conditions IC and NC were compared with ANOVAs with 'subject' as a random effect and 'condition' as a fixed effect. Before computing the statistical tests, the PLV values were transformed into approximately normal variables (Penny et al., 2008). Results: When comparing the mean estimated frequency across conditions, a significant difference was found only in the 4-8Hz band, such that the frequency within this band was significantly higher for IC than NC stimuli starting at ~250ms post-stimulus onset (Fig. 1; solid line shows IC and dashed line NC). Significant differences in phase-amplitude couplings were obtained only when the 4-8 Hz band was taken as the low frequency band. Moreover, in all significant situations, the coupling strength is higher for the NC than IC condition. An example of significant difference between conditions is shown in Fig. 2 for the phase-amplitude coupling between the 4-8Hz and 55-65Hz bands (p-value in top panel and mean PLV values in the bottom panel). A decrease in coupling strength was observed shortly after stimulus onset for both conditions and was greater for the condition IC. This phenomenon was observed with all other frequency bands. The results obtained for the phase-phase couplings were more complex. As for the phase-amplitude couplings, all significant differences were obtained when the 4-8Hz band was considered as the low frequency band. The stimulus condition exhibiting the higher coupling strength depended on the ratio of the coupling coefficients. When this ratio was small, the IC condition exhibited the higher phase-phase coupling strength. When this ratio was large, the NC condition exhibited the higher coupling strength. Fig. 3 shows the phase-phase couplings between the 4-8Hz and 35-45Hz bands for the coupling coefficient 6:1, and the coupling strength was significantly higher for the IC than NC condition. By contrast, for the coupling coefficient 9:1 the NC condition gave the higher coupling strength (Fig. 4). Control analyses verified that it is not a consequence of the frequency difference between the two conditions in the 4-8Hz band. The directionality measures indicated a transfer of information from the low frequency components towards the high frequency ones. Conclusions: Adaptive tracking is a feasible method for EEG analyses, revealing information both about stimulus-related differences and coupling patterns across frequencies. Theta oscillations play a central role in illusory shape processing and more generally in visual processing. The presence vs. absence of illusory shapes was paralleled by faster theta oscillations. Phase-amplitude couplings were decreased more for IC than NC and might be due to a resetting mechanism. The complex patterns in phase-phase coupling between theta and beta/gamma suggest that the contribution of these oscillations to visual binding and stimulus processing are not as straightforward as conventionally held. Causality analyses further suggest that theta oscillations drive beta/gamma oscillations (see also Schroeder and Lakatos, 2009). The present findings highlight the need for applying more sophisticated signal analyses in order to establish a fuller understanding of the functional role of neural oscillations.

Suprachoroidal electrotransfer: a nonviral gene delivery method to transfect the choroid and the retina without detaching the retina.

Photoreceptors and retinal pigment epithelial cells (RPE) targeting remains challenging in ocular gene therapy. Viral gene transfer, the only method having reached clinical evaluation, still raises safety concerns when administered via subretinal injections. We have developed a novel transfection method in the adult rat, called suprachoroidal electrotransfer (ET), combining the administration of nonviral plasmid DNA into the suprachoroidal space with the application of an electrical field. Optimization of injection, electrical parameters and external electrodes geometry using a reporter plasmid, resulted in a large area of transfected tissues. Not only choroidal cells but also RPE, and potentially photoreceptors, were efficiently transduced for at least a month when using a cytomegalovirus (CMV) promoter. No ocular complications were recorded by angiographic, electroretinographic, and histological analyses, demonstrating that under selected conditions the procedure is devoid of side effects on the retina or the vasculature integrity. Moreover, a significant inhibition of laser induced-choroidal neovascularization (CNV) was achieved 15 days after transfection of a soluble vascular endothelial growth factor receptor-1 (sFlt-1)-encoding plasmid. This is the first nonviral gene transfer technique that is efficient for RPE targeting without inducing retinal detachment. This novel minimally invasive nonviral gene therapy method may open new prospects for human retinal therapies.

Reference values of fat-free and fat masses by bioelectrical impedance analysis in 3393 healthy subjects.

Determination of fat-free mass (FFM) and fat mass (FM) is of considerable interest in the evaluation of nutritional status. In recent years, bioelectrical impedance analysis (BIA) has emerged as a simple, reproducible method used for the evaluation of FFM and FM, but the lack of reference values reduces its utility to evaluate nutritional status. The aim of this study was to determine reference values for FFM, FM, and %FM by BIA in a white population of healthy subjects, to observe the changes in these values with age, and to develop percentile distributions for these parameters. Whole-body resistance of 1838 healthy white men and 1555 women, aged 15-64 y, was determined by using four skin electrodes on the right hand and foot. FFM and FM were calculated according to formulas validated for the subject groups and analyzed for age decades. This is the first study to present BIA-determined age- and sex-specific percentiles for FFM, FM, and %FM for healthy subjects, aged 15-64 y. Mean FM and %FM increased progressively in men and after age 45 y in women. The results suggest that any weight gain noted with age is due to a gain in FM. In conclusion, the data presented as percentiles can serve as reference to evaluate the normality of body composition of healthy and ill subject groups at a given age.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

3D crosshole ERT for aquifer characterization and monitoring of infiltrating river water

The hydrogeological properties and responses of a productive aquifer in northeastern Switzerland are investigated. For this purpose, 3D crosshole electrical resistivity tomography (ERT) is used to define the main lithological structures within the aquifer (through static inversion) and to monitor the water infiltration from an adjacent river. During precipitation events and subsequent river flooding, the river water resistivity increases. As a consequence, the electrical characteristics of the infiltrating water can be used as a natural tracer to delineate preferential flow paths and flow velocities. The focus is primarily on the experiment installation, data collection strategy, and the structural characterization of the site and a brief overview of the ERT monitoring results. The monitoring system comprises 18 boreholes each equipped with 10 electrodes straddling the entire thickness of the gravel aquifer. A multi-channel resistivity system programmed to cycle through various four-point electrode configurations of the 180 electrodes in a rolling sequence allows for the measurement of approximately 15,500 apparent resistivity values every 7 h on a continuous basis. The 3D static ERT inversion of data acquired under stable hydrological conditions provides a base model for future time-lapse inversion studies and the means to investigate the resolving capability of our acquisition scheme. In particular, it enables definition of the main lithological structures within the aquifer. The final ERT static model delineates a relatively high-resistivity, low-porosity, intermediate-depth layer throughout the investigated aquifer volume that is consistent with results from well logging and seismic and radar tomography models. The next step will be to define and implement an appropriate time-lapse ERT inversion scheme using the river water as a natural tracer. The main challenge will be to separate the superposed time-varying effects of water table height, temperature, and salinity variations associated with the infiltrating water.

Continuous assessment of electrical epileptic activity in acute stroke.

OBJECTIVE: To determine the incidence and risk factors of electrical seizures and other electrical epileptic activity using continuous EEG (cEEG) in patients with acute stroke. METHODS: One hundred consecutive patients with acute stroke admitted to our stroke unit underwent cEEG using 10 electrodes. In addition to electrical seizures, repetitive focal sharp waves (RSHWs), repetitive focal spikes (RSPs), and periodic lateralized epileptic discharges (PLEDs) were recorded. RESULTS: In the 100 patients, cEEG was recorded for a mean duration of 17 hours 34 minutes (range 1 hour 12 minutes to 37 hours 10 minutes). Epileptic activity occurred in 17 patients and consisted of RSHWs in seven, RSPs in seven, and PLEDs in three. Electrical seizures occurred in two patients. On univariate Cox regression analysis, predictors for electrical epileptic activity were stroke severity (high score on the National Institutes of Health Stroke Scale) (hazard ratio [HR] 1.12; p = 0.002), cortical involvement (HR 5.71; p = 0.021), and thrombolysis (HR 3.27; p = 0.040). Age, sex, stroke type, use of EEG-modifying medication, and cardiovascular risk factors were not predictors of electrical epileptic activity. On multivariate analysis, stroke severity was the only independent predictor (HR 1.09; p = 0.016). CONCLUSION: In patients with acute stroke, electrical epileptic activity occurs more frequently than previously suspected.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

The oxidative potential of differently charged silver and gold nanoparticles on three human lung epithelial cell types

Background: Nanoparticle (NPs) functionalization has been shown to affect their cellular toxicity. To study this, differently functionalized silver (Ag) and gold (Au) NPs were synthesised, characterised and tested using lung epithelial cell systems. Mehtods: Monodispersed Ag and Au NPs with a size range of 7 to 10 nm were coated with either sodium citrate or chitosan resulting in surface charges from ¿50 mV to +70 mV. NP-induced cytotoxicity and oxidative stress were determined using A549 cells, BEAS-2B cells and primary lung epithelial cells (NHBE cells). TEER measurements and immunofluorescence staining of tight junctions were performed to test the growth characteristics of the cells. Cytotoxicity was measured by means of the CellTiter-Blue ® and the lactate dehydrogenase assay and cellular and cell-free reactive oxygen species (ROS) production was measured using the DCFH-DA assay. Results: Different growth characteristics were shown in the three cell types used. A549 cells grew into a confluent mono-layer, BEAS-2B cells grew into a multilayer and NHBE cells did not form a confluent layer. A549 cells were least susceptible towards NPs, irrespective of the NP functionalization. Cytotoxicity in BEAS-2B cells increased when exposed to high positive charged (+65-75 mV) Au NPs. The greatest cytotoxicity was observed in NHBE cells, where both Ag and Au NPs with a charge above +40 mV induced cytotoxicity. ROS production was most prominent in A549 cells where Au NPs (+65-75 mV) induced the highest amount of ROS. In addition, cell-free ROS measurements showed a significant increase in ROS production with an increase in chitosan coating. Conclusions: Chitosan functionalization of NPs, with resultant high surface charges plays an important role in NP-toxicity. Au NPs, which have been shown to be inert and often non-cytotoxic, can become toxic upon coating with certain charged molecules. Notably, these effects are dependent on the core material of the particle, the cell type used for testing and the growth characteristics of these cell culture model systems.

Experimental and clinical radiofrequency ablation: proposal for standardized description of coagulation size and geometry.

BACKGROUND: Radiofrequency (RF) ablation is used to obtain local control of unresectable tumors in liver, kidney, prostate, and other organs. Accurate data on expected size and geometry of coagulation zones are essential for physicians to prevent collateral damage and local tumor recurrence. The aim of this study was to develop a standardized terminology to describe the size and geometry of these zones for experimental and clinical RF. METHODS: In a first step, the essential geometric parameters to accurately describe the coagulation zones and the spatial relationship between the coagulation zones and the electrodes were defined. In a second step, standard terms were assigned to each parameter. RESULTS: The proposed terms for single-electrode RF ablation include axial diameter, front margin, coagulation center, maximal and minimal radius, maximal and minimal transverse diameter, ellipticity index, and regularity index. In addition a subjective description of the general shape and regularity is recommended. CONCLUSIONS: Adoption of the proposed standardized description method may help to fill in the many gaps in our current knowledge of the size and geometry of RF coagulation zones.