The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools

Cannabis cultivation in order to produce drugs is forbidden in Switzerland. Thus, law enforcement authorities regularly ask forensic laboratories to determinate cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. As required by the EU official analysis protocol the THC rate of cannabis is measured from the flowers at maturity. When laboratories are confronted to seedlings, they have to lead the plant to maturity, meaning a time consuming and costly procedure. This study investigated the discrimination of fibre type from drug type Cannabis seedlings by analysing the compounds found in their leaves and using chemometrics tools. 11 legal varieties allowed by the Swiss Federal Office for Agriculture and 13 illegal ones were greenhouse grown and analysed using a gas chromatograph interfaced with a mass spectrometer. Compounds that show high discrimination capabilities in the seedlings have been identified and a support vector machines (SVMs) analysis was used to classify the cannabis samples. The overall set of samples shows a classification rate above 99% with false positive rates less than 2%. This model allows then discrimination between fibre and drug type Cannabis at an early stage of growth. Therefore it is not necessary to wait plants' maturity to quantify their amount of THC in order to determine their chemotype. This procedure could be used for the control of legal (fibre type) and illegal (drug type) Cannabis production.

Monitoring of posture allocations and activities by a shoe-based wearable sensor.

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

Tip anchor flap in decubital surgery.

Anchoring a flap remains a key procedure in decubital surgery because a flap needs to be stable against shearing forces. This allows an early mobilization and undisturbed primary wound healing. This study evaluated a uniform group of eight paraplegic patients with sacral decubital ulcers and covered the lesions using gluteal rotation flaps with a deepithelialized tip to anchor the flap subcutaneously on the contralateral ischial tuber. Initial wound healing and recurrence after one year were evaluated. All but one flap showed uneventful wound healing, and all the flaps presented without any signs of recurrence or instability. The authors suggest that sufficient anchoring using a deepithelialized part of the flap helps to integrate and stabilize sacral rotation flaps.

Beliefs about and attitudes toward gambling in French-speaking Switzerland.

The present study aims to analyze attitudes and beliefs of the French-speaking general Swiss population (n = 2500; female n = 1280; mean age = 43 years) as regards gambling, which are to date almost exclusively studied in the North American and Australian contexts. Beliefs related to gambling include the perception of the effectiveness of preventive measures toward gambling, the comparative risk assessment of different addictive behaviors, the perceived risks of different types of gambling and attitudes are related to the gambler's personality. The general population perceived gambling rather negatively and was conscious of the potential risks of gambling; indeed, 59.0% of the sample identified gambling as an addictive practice. Slot machines were estimated to bear the highest risk. Compared with women and older people, men and young people indicated more positive beliefs about gambling; they perceived gambling as less addictive, supported structural preventive measures less often, and perceived gambling as a less serious problem for society. Gamblers were more likely to put their practices into perspective, perceiving gambling more positively than non-gamblers. General population surveys on such beliefs can deliver insights into preventive actions that should be targeted to young men who showed more favorable views of gambling, which have been shown to be associated with increased risk for problematic gambling.

Volatile transport during the crystallization of anatectic melts: Oxygen, boron and hydrogen stable isotope study on the metamorphic complex of Naxos, Greece

Crystallization of anatectic melts in high-temperature metamorphic terrains releases volatile-rich magmas that can be transported into adjacent lithologies. This study addresses the variations in the oxygen, boron and hydrogen isotopic composition of aplite-pegmatite dikes that formed during the crystallization of anatectic melts in regional high-temperature metamorphism on the island of Naxos, Greece, and propagated upward into the overlying sequences of metamorphic schist. The transport distance of these dikes was increased through a significant horizontal component of travel that was imposed by contemporaneous low-angle extensional shearing. Laser fluorination oxygen isotope analyses of quartz, tourmaline, garnet, and biotite mineral separates from the aplite-pegmatite dikes show a progressive rise in delta(18)O values with increasing distance from the core. Oxygen isotope fractionations among quartz, tourmaline, and garnet show temperature variations from > 700degreesC down to similar to400degreesC. This range is considered to reflect isotopic fractionation beginning with crystallization at high temperatures in water-undersaturated conditions and then evolving through lower temperature crystallization and retrograde sub-solidus exchange. Two processes are examined for the cause of the progressive increase in delta(18)O values: (1) heterogeneous delta(18)O sources and (2) fluid-rock exchange between the aplite/pegmatite magmas and their host rock. Although the former process cannot be ruled out, there is as yet no evidence in the exposed sequences on Naxos for the presence of a suitable high delta(18)O magma source. In contrast, a tendency for the delta(18)O of quartz in the aplite/pegmatite dikes to approach that of the quartz in the metamorphic rock suggests that fluid-rock exchange with the host rock may potentially be an important process. Advection of fluid into the magma is examined based on Darcian fluid flow into an initially water-undersaturated buoyantly propagating aplitic dike magma. It is shown that such advective flow could only account for part of the O-18-enrichment, unless it were amplified by repeated injection of magma pulses, fluid recycling, and deformation-assisted post-crystallization exchange. The mechanism is, however, adequate to account for hydrogen isotope equilibration between dike and host rock. In contrast, variations in the delta(11)B values of tourmalines suggest that B-11/B-10 fractionation during crystallization and/or magma degassing was the major control of boron geochemistry rather than fluid-rock interaction and that the boron isotopic system was decoupled from that of oxygen. Copyright (C) 2003 Elsevier Ltd.

Heterogeneous 40Ar* distributions in naturally deformed muscovite: in situ UV-laser ablation evidence for micro structurally controlled intragrain diffusion

Micas are commonly used in Ar-40/Ar-39 thermochronological studies of variably deformed rocks yet the physical basis by which deformation may affect radiogenic argon retention in mica is poorly constrained. This study examines the relationship between deformation and deformation-induced microstructures on radiogenic argon retention in muscovite, A combination of furnace step-heating and high-spatial resolution in situ UV-laser ablation Ar-40/Ar-39 analyses are reported for deformed muscovites sampled from a granitic pegmatite vein within the Siviez-Mischabel Nappe, western Swiss Alps (Penninic domain, Brianconnais unit). The pegmatite forms part of the Variscan (similar to 350 Ma) Alpine basement and exhibits a prominent Alpine S-C fabric including numerous mica `fish' that developed under greenschist facies metamorphic conditions, during the dominant Tertiary Alpine tectonic phase of nappe emplacement. Furnace step-heating of milligram quantities of separated muscovite grains yields an Ar-40/Ar-39 age spectrum with two distinct staircase segments but without any statistical plateau, consistent with a previous study from the same area. A single (3 X 5 mm) muscovite porphyroclast (fish) was investigated by in situ UV-laser ablation. A histogram plot of 170 individual Ar-40/Ar-39 UV-laser ablation ages exhibit a range from 115 to 387 Ma with modes at approximately 340 and 260 Ma. A variogram statistical treatment of the (40)Ad/Ar-39 results reveals ages correlated with two directions; a highly correlated direction at 310 degrees and a lesser correlation at 0 degrees relative to the sense of shearing. Using the highly correlated direction a statistically generated (Kriging method) age contour map of the Ar-40/Ar-39 data reveals a series of elongated contours subparallel to the C-surfaces which where formed during Tertiary nappe emplacement. Similar data distributions and slightly younger apparent ages are recognized in a smaller mica fish. The observed intragrain age variations are interpreted to reflect the partial loss of radiogenic argon during Alpine (similar to 35 Ma) greenschist facies metamorphism. One-dirnensional diffusion modelling results are consistent with the idea that the zones of youngest apparent age represent incipient shear band development within the mica porphyroclasts, thus providing a network of fast diffusion pathways. During Alpine greenschist facies metamorphism the incipient shear bands enhanced the intragrain loss of radiogenic argon. The structurally controlled intragrain age variations observed in this investigation imply that deformation has a direct control on the effective length scale for argon diffusion, which is consistent with the heterogeneous nature of deformation. (C) 2001 Elsevier Science B.V. All rights reserved.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Spatial analysis of corresponding fingerprint features from match and close non-match populations

The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework. In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.

Dorsal fractures of the Triquetrum : MRI findings with an emphasis on dorsal carpal ligament injuries

Contexte et but de l'étude: Les fractures du triquetrum sont les deuxièmes fractures des os du carpe en fréquence, après celles du scaphoïde. Elles représentent environ 3.5% de toutes les lésions traumatiques du poignet, et résultent le plus souvent d'une chute de sa hauteur avec réception sur le poignet en hyper-extension. Leur mécanisme physiopathologique reste débattu. La première théorie fut celle de l'avulsion ligamentaire d'un fragment osseux dorsal. Puis, Levy et coll. ainsi que Garcia-Elias ont successivement suggéré que ces fractures résultaient plutôt d'une impaction ulno-carpienne. De nombreux ligaments (intrinsèques et extrinsèques du carpe) s'insèrent sur les versants palmaires et dorsaux du triquetrum. Ces ligaments jouent un rôle essentiel dans le maintien de la stabilité du carpe. Bien que l'arthro-IRM du poignet soit l'examen de référence pour évaluer ces ligaments, Shahabpour et coll. ont récemment démontré leur visibilité en IRM tridimensionnelle (volumique) après injection iv. de produit de contraste (Gadolinium). L'atteinte ligamentaire associée aux fractures dorsales du triquetrum n'a jusqu'à présent jamais été évalué. Ces lésions pourraient avoir un impact sur l'évolution et la prise en charge de ces fractures. Les objectifs de l'étude étaient donc les suivants: premièrement, déterminer l'ensemble des caractéristiques des fractures dorsales du triquetrum en IRM, en mettant l'accent sur les lésions ligamentaires extrinsèques associées; secondairement, discuter les différents mécanismes physiopathologiques (i.e. avulsion ligamentaire ou impaction ulno-carpienne) de ces fractures d'après nos résultats en IRM. Patients et méthodes: Ceci est une étude rétrospective multicentrique (CHUV, Lausanne; Hôpital Cochin, AP-HP, Paris) d'examens IRM et radiographies conventionnelles du poignet. A partir de janvier 2008, nous avons recherché dans les bases de données institutionnelles les patients présentant une fracture du triquetrum et ayant bénéficié d'une IRM volumique du poignet dans un délai de six semaines entre le traumatisme et l'IRM. Les examens IRM ont été effectués sur deux machines à haut champ magnétique (3 Tesla) avec une antenne dédiée et un protocole d'acquisition incluant une séquence tridimensionnelle isotropique (« 3D VIBE ») après injection iv. de produit de contraste (Gadolinium). Ces examens ont été analysés par deux radiologues ostéo-articulaires expérimentés. Les mesures ont été effectuées par un troisième radiologue ostéo-articulaire. En ce qui concerne l'analyse qualitative, le type de fracture du triquetrum (selon la classification de Garcia-Elias), la distribution de l'oedème osseux post- traumatique, ainsi que le nombre et la distribution des lésions ligamentaires extrinsèques associées ont été évalués. Pour l'analyse quantitative, l'index du processus de la styloïde ulnaire (selon la formule de Garcia-Elias), le volume du fragment osseux détaché du triquetrum, et la distance séparant ce fragment osseux du triquetrum ont été mesurés.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Synorogenic extension: Quantitative constraints on the age and displacement of the Zanskar shear zone (northwest Himalaya)

subsequent extension-induced exhumation. Geochronological dating of various Structural, thermobarometric, and geochronological data place limits on the age and tectonic displacement along the Zanskar shear zone, a major north-dipping synorogenic extensional structure separating the high-grade metamorphic sequence of the High Himalayan Crystalline Sequence from the overlying low-grade sedimentary rocks of the Tethyan Himalaya, A complete Barrovian metamorphic succession, from kyanite to biotite zone mineral assemblages, occurs within the I-km-thick Zanskar shear zone. Thermobarometric data indicate a difference In equilibration depths of 12 +/- 3 km between the lower kyanite zone and the garnet zone, which is Interpreted as a minimum estimate for the finite vertical displacement accommodated by the Zanskar shear zone. For the present-day dip of the structure (20 degrees), a simple geometrical model shows that a net slip of 35 +/- 9 km is required to regroup these samples to the same structural level. Because the kyanite to garnet zone rocks represent only part of the Zanskar shear zone, and because its original dip may have been less than the present-day dip, these estimates fur the finite displacement represent minimum values. Field relations and petrographic data suggest that migmatization and associated leucogranite intrusion in the footwall of the Zanskar shear zone occurred as a continuous profess starting at the Barrovian metamorphic peak and lasting throughout the subsequent extension-induced exhumation. Geochronological dataing of various leucogranitic plutons and dikes in the Zanskar shear zone footwall indicates that the main ductile shearing along the structure ended by 19.8 Ma and that extension most likely initiated shortly before 22.2 Ma.