Statistics in archaeology: New directions

Connections between Statistics and Archaeology have always appeared veryfruitful. The objective of this paper is to offer an outlook of somestatistical techniques that are being developed in the most recentyears and that can be of interest for archaeologists in the short run.

Traitement des epilepsies réfractaires: rôle de la stimulation electrique [Treatment of pharmacoresistant epilepsy: stimulated refractoriness].

Antiepileptic drugs allow controlling seizures in 70% of patients. For the others, a presurgical work-up should be undertaken, especially if a focal seizure origin is suspected; however, only a fraction of pharmacoresistant patients will be offered resective (curative) surgery. In the last 15 years, several palliative therapies using extra- or intracranial electrical stimulations have been developed. This article presents the vagal nerve stimulation, the deep brain stimulation (targeting the mesiotemporal region or the thalamus), and the cortical stimulation "on demand". All show an overall long-term responder rate between 30-50%, but less than 5% of patients becoming seizure free. It is to hope that a better understanding of epileptogenic mechanisms and of the implicated neuronal networks will lead to an improvement of these proportions.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

Reconeixement de veu mitjançant xarxes neuronals

La interacció home-màquina per mitjà de la veu cobreix moltes àrees d’investigació. Es destaquen entre altres, el reconeixement de la parla, la síntesis i identificació de discurs, la verificació i identificació de locutor i l’activació per veu (ordres) de sistemes robòtics. Reconèixer la parla és natural i simple per a les persones, però és un treball complex per a les màquines, pel qual existeixen diverses metodologies i tècniques, entre elles les Xarxes Neuronals. L’objectiu d’aquest treball és desenvolupar una eina en Matlab per al reconeixement i identificació de paraules pronunciades per un locutor, entre un conjunt de paraules possibles, i amb una bona fiabilitat dins d’uns marges preestablerts. El sistema és independent del locutor que pronuncia la paraula, és a dir, aquest locutor no haurà intervingut en el procés d’entrenament del sistema. S’ha dissenyat una interfície que permet l’adquisició del senyal de veu i el seu processament mitjançant xarxes neuronals i altres tècniques. Adaptant una part de control al sistema, es podria utilitzar per donar ordres a un robot com l’Alfa6Uvic o qualsevol altre dispositiu.

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelationbetween variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy.

Remaining phosphorus estimated by pedotransfer function

Although the determination of remaining phosphorus (Prem) is simple, accurate values could also be estimated with a pedotransfer function (PTF) aiming at the additional use of soil analysis data and/or Prem replacement by an even simpler determination. The purpose of this paper was to develop a pedotransfer function to estimate Prem values of soils of the State of São Paulo based on properties with easier or routine laboratory determination. A pedotransfer function was developed by artificial neural networks (ANN) from a database of Prem values, pH values measured in 1 mol L-1 NaF solution (pH NaF) and soil chemical and physical properties of samples collected during soil classification activities carried out in the State of São Paulo by the Agronomic Institute of Campinas (IAC). Furthermore, a pedotransfer function was developed by regressing Prem values against the same predictor variables of the ANN-based PTF. Results showed that Prem values can be calculated more accurately with the ANN-based pedotransfer function with the input variables pH NaF values along with the sum of exchangeable bases (SB) and the exchangeable aluminum (Al3+) soil content. In addition, the accuracy of the Prem estimates by ANN-based PTF were more sensitive to increases in the experimental database size. Although the database used in this study was not comprehensive enough for the establishment of a definitive pedotrasnfer function for Prem estimation, results indicated the inclusion of Prem and pH NaF measurements among the soil testing evaluations as promising ind order to provide a greater database for the development of an ANN-based pedotransfer function for accurate Prem estimates from pH NaF, SB, and Al3+ values.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

Regulated exocytosis from astrocytes physiological and pathological related aspects.

Astrocytes have traditionally been considered ancillary, satellite cells of the nervous system. However, it is a very recent acquisition that glial cells generate signaling loops which are integral to the brain circuitry and participate, interactively with neuronal networks, in the processing of information. Such a conceptual breakthrough makes this field of investigation one of the hottest in neuroscience, as it calls for a revision of past theories of brain function as well as for new strategies of experimental exploration of brain function. Glial cells are electrically not excitable, and it was only the use of optical recording techniques together with calcium sensitive dyes, that allowed the chemical excitability of glial cells to become apparent. Studies using these new techniques have shown for the first time that glial cells are activated by surrounding synaptic activity and translate neuronal signals into their own calcium code. Intracellular calcium concentration([Ca2+]i) elevations in glial cells have then shown to underlie spatial transfer of information in the glial network, accompanied by release of chemical transmitters (gliotransmitters) such as glutamate and back-signaling to neurons. As a consequence, optical imaging techniques applied to cell cultures or intact tissue have become a state-of-the-art technology for studying glial cell signaling. The molecular mechanisms leading to release of "gliotransmitters," especially glutamate, from glia are under debate. Accumulating evidence clearly indicates that astrocytes secrete numerous transmitters by Ca(2+)-dependent exocytosis. This review will discuss the mechanisms underlying the release of chemical transmitters from astrocytes with a particular emphasis to the regulated exocytosis processes.

Sleep function: current questions and new approaches.

The mammalian brain oscillates through three distinct global activity states: wakefulness, non-rapid eye movement (NREM) sleep and REM sleep. The regulation and function of these 'vigilance' or 'behavioural' states can be investigated over a broad range of temporal and spatial scales and at different levels of functional organization, i.e. from gene expression to memory, in single neurons, cortical columns or the whole brain and organism. We summarize some basic questions that have arisen from recent approaches in the quest for the functions of sleep. Whereas traditionally sleep was viewed to be regulated through top-down control mechanisms, recent approaches have emphasized that sleep is emerging locally and regulated in a use-dependent (homeostatic) manner. Traditional markers of sleep homeostasis, such as the electroencephalogram slow-wave activity, have been linked to changes in connectivity and plasticity in local neuronal networks. Thus waking experience-induced local network changes may be sensed by the sleep homeostatic process and used to mediate sleep-dependent events, benefiting network stabilization and memory consolidation. Although many questions remain unanswered, the available data suggest that sleep function will best be understood by an analysis which integrates sleep's many functional levels with its local homeostatic regulation.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Consommation de cannabis : quels sont les risques ?

Abstract - Cannabis: what are the risks ? Cannabinoids from cannabis have a dual use and display often opposite pharmacological properties depending on the circumstances of use and the administered dose. Cannabinoids constitute mainly a recreative or addictive substance, but also a therapeutic drug. They can be either neurotoxic or neuroprotector, carcinogenic or an anti-cancer drug, hyperemetic or antiemetic, pro-inflammatory or anti-inflammatory... Improvement in in-door cultivation techniques and selection of high yield strains have resulted in a steadily increase of THC content. Cannabis is the most frequently prohibited drug used in Switzerland and Western countries. About half of teenagers have already experimented cannabis consumption. About 10% of cannabis users smoke it daily and can be considered as cannabis-dependant. About one third of these cannabis smokers are chronically intoxicated. THC, the main psychoactive drug interacts with the endocannnabinoid system which is made of cellular receptors, endogenous ligands and a complex intra-cellular biosynthetic, degradation and intra-cellular messengers machinery. The endocannabinoid system plays a major role in the fine tuning of the nervous system. It is thought to be important in memory, motor learning, and synaptic plasticity. At psychoactive dose, THC impairs psychomotor and neurocognitive performances. Learning and memory abilities are diminished. The risk to be responsible of a traffic car accident is slightly increased after administration of cannabis alone and strongly increased after combined use of alcohol and cannabis. With the exception of young children, cannabis intake does not lead to potentially fatal intoxication. However, cannabis exposure can act as trigger for cardiovascular accidents in rare vulnerable people. Young or vulnerable people are more at risk to develop a psychosis at adulthood and/or to become cannabis-dependant. Epidemiological studies have shown that the risk to develop a schizophrenia at adulthood is increased for cannabis smokers, especially for those who are early consumers. Likewise for the risk of depression and suicide attempt. Respiratory disease can be worsen after cannabis smoking. Pregnant and breast-feeding mothers should not take cannabis because THC gets into placenta and concentrates in breast milk. The most sensitive time-period to adverse side-effects of cannabis starts from foetus and extends to adolescence. The reason could be that the endocannabinoid system, the main target of THC, plays a major role in the setup of neuronal networks in the immature brain. The concomitant use of other psychoactive drugs such as alcohol, benzodiazepines or cocaine should be avoided because of possible mutual interactions. Furthermore, it has been demonstrated that a cross-sensitisation exists between most addictive drugs at the level of the brain reward system. Chronic use of cannabis leads to tolerance and withdrawals symptoms in case of cannabis intake interruption. Apart from the aforementioned unwanted side effects, cannabis displays useful and original medicinal properties which are currently under scientific evaluation. At the moment the benefit/risk ratio is not yet well assessed. Several minor phytocannabinoids or synthetic cannabinoids devoid of psychoactive properties could find their way in the modern pharmacopoeia (e.g. ajulemic acid). For therapeutic purposes, special cannabis varieties with unique cannabinoids composition (e.g. a high cannabidiol content) are preferred over those which are currently used for recreative smoking. The administration mode also differs in such a way that inhalation of carcinogenic pyrolytic compounds resulting from cannabis smoking is avoided. This can be achieved by inhaling cannabis vapors at low temperature with a vaporizer device. Résumé Les cannabinoïdes contenus dans la plante de cannabis ont un double usage et possèdent des propriétés opposées suivant les circonstances et les doses employées. Les cannabinoïdes, essentiellement drogue récréative ou d'abus pourraient, pour certains d'entre eux, devenir des médicaments. Selon les conditions d'utilisation, ils peuvent être neurotoxiques ou neuroprotecteurs, carcinogènes ou anticancéreux, hyper-émétiques ou antiémétiques, pro-inflammatoires ou anti-inflammatoires... Les techniques de culture sous serre indoor ainsi que la sélection de variétés de cannabis à fort potentiel de production ont conduit à un accroissement notable des taux de THC. Le cannabis est la drogue illégale la plus fréquemment consommée en Suisse et ailleurs dans le monde occidental. Environ la moitié des jeunes ont déjà expérimenté le cannabis. Environ 10 % des consommateurs le fument quotidiennement et en sont devenus dépendants. Un tiers de ces usagers peut être considéré comme chroniquement intoxiqué. Le THC, la principale substance psychoactive du cannabis, interagit avec le "système endocannabinoïde". Ce système est composé de récepteurs cellulaires, de ligands endogènes et d'un dispositif complexe de synthèse, de dégradation, de régulation et de messagers intra-cellulaires. Le système endocannabinoïde joue un rôle clé dans le réglage fin du système nerveux. Les endocannabinoïdes régulent la mémorisation, l'apprentissage moteur et la plasticité des liaisons nerveuses. À dose psychoactive, le THC réduit les performances psychomotrices et neurocognitives. Les facultés d'apprentissage et de mémorisation sont diminuées. Le risque d'être responsable d'un accident de circulation est augmenté après prise de cannabis, et ceci d'autant plus que de l'alcool aura été consommé parallèlement. À l'exception des jeunes enfants, la consommation de cannabis n'entraîne pas de risque potentiel d'intoxication mortelle. Toutefois, le cannabis pourrait agir comme facteur déclenchant d'accident cardiovasculaire chez de rares individus prédisposés. Les individus jeunes, et/ou vulnérables ont un risque significativement plus élevé de développer une psychose à l'âge adulte ou de devenir dépendant au cannabis. Des études épidémiologiques ont montré que le risque de développer une schizophrénie à l'âge adulte était augmenté pour les consommateurs de cannabis et ceci d'autant plus que l'âge de début de consommation était précoce. Il en va de même pour le risque de dépression. Les troubles respiratoires pourraient être exacerbés par la prise de cannabis. Les femmes enceintes et celles qui allaitent ne devraient pas consommer de cannabis car le THC traverse la barrière hémato-placentaire, en outre, il se concentre dans le lait maternel. La période de la vie la plus sensible aux effets néfastes du cannabis correspond à celle allant du foetus à l'adolescent. Le système endocannabinoïde sur lequel agit le THC serait en effet un acteur majeur orchestrant le développement des réseaux neuronaux dans le cerveau immature. La prise concomitante d'autres psychotropes comme l'alcool, les benzodiazépines ou la cocaïne conduit à des renforcements mutuels de leurs effets délétères. De plus, il a été montré l'existence d'une sensibilité croisée pour la majorité des psychotropes qui agissent sur le système de la récompense, le cannabis y compris, ce qui augmente ainsi le risque de pharmacodépendance. La prise régulière de doses élevées de cannabis entraîne l'apparition d'une tolérance et de symptômes de sevrage discrets à l'arrêt de la consommation. À part les effets négatifs mentionnés auparavant, le cannabis possède des propriétés médicales originales qui sont l'objet d'études attentives. Plusieurs cannabinoïdes mineurs naturels ou synthétiques, comme l'acide ajulémique, pourraient trouver un jour une place dans la pharmacopée. En usage thérapeutique, des variétés particulières de cannabis sont préférées, par exemple celles riches en cannabidiol non psychoactif. Le mode d'administration diffère de celui utilisé en mode récréatif. Par exemple, la vaporisation des cannabinoïdes à basse température est préférée à l'inhalation du "joint".

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.