Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Recovery of soil physical properties by green manure, liming, gypsum and pasture and spontaneous native species¹

Inadequate usage can degrade natural resources, particularly soils. More attention has been paid to practices aiming at the recovery of degraded soils in the last years, e.g, the use of organic fertilizers, liming and introduction of species adapted to adverse conditions. The purpose of this study was therefore to investigate the recovery of physical properties of a Red Latosol (Oxisol) degraded by the construction of a hydroelectric power station. In the study area, a soil layer about 8m thick had been withdrawn by heavy machines leading not only to soil compaction, but resulting in high-degree degradation. The experiment was arranged in a completely randomized design with nine treatments and four replications. The treatments consisted of: 1- soil mobilization by tilling (to ensure the effect of mechanical mobilization in all treatments) without planting, but growth of spontaneous vegetation; 2- Black velvet bean (Stizolobium aterrimum Piper & Tracy); 3- Pigeonpea (Cajanus cajan (L.) DC); 4- Liming + black velvet bean; 5-Liming + pigeonpea until 1994, when replaced by jack bean (Canavalia ensiformis); 6- Liming + gypsum + black velvet bean; 7- Liming + gypsum + pigeonpea until 1994, when replaced by jack bean; and two controls as reference: 8- Native Cerrado vegetation and 9- bare soil (no tilling and no planting), left under natural conditions and in this situation, without spontaneous vegetation. In treatments 1 through 7, the soil was tilled. Treatments were installed in 1992 and left unmanaged for seven years, until brachiaria (Brachiaria decumbens) was planted in all plots in 1999. Seventeen years after implantation, the properties soil macroporosity, microporosity, total porosity, bulk density and aggregate stability were assessed in the previously described treatments in the soil layers 0.00-0.10; 0.10-0.20 and 0.20-0.40 m, and soil Penetration Resistance and soil moisture in 0.00-0.15 and 0.15-0.30 m. The plants were evaluated for: brachiaria dry matter and spontaneous growth of native tree species in the plots as of 2006. Results were analyzed by variance analysis and Tukey´s test at 5 % for mean comparison. In all treatments, except for the bare soil (no recovery measures), ongoing recovery of the degraded soil physical properties was observed. Macroporosity, soil bulk density and total porosity were good soil quality indicators. The occurrence of spontaneous native species indicated the soil recovery process. The best adapted species was Machaerium acutifolium Vogel, with the largest number of plants and most advanced development; the dry matter production of B. decumbens in recovering soil was similar to normal conditions, evidencing soil recovery.

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.

Travelling-stripe forcing generates hexagonal patterns

We study the response of Turing stripe patterns to a simple spatiotemporal forcing. This forcing has the form of a traveling wave and is spatially resonant with the characteristic Turing wavelength. Experiments conducted with the photosensitive chlorine dioxide-iodine-malonic acid reaction reveal a striking symmetry-breaking phenomenon of the intrinsic striped patterns giving rise to hexagonal lattices for intermediate values of the forcing velocity. The phenomenon is understood in the framework of the corresponding amplitude equations, which unveils a complex scenario of dynamical behaviors.

Formation of a spatial memory trace : a behavioral, cellular and molecular study

THESIS ABSTRACTThis thesis project was aimed at studying the molecular mechanisms underlying learning and memory formation, in particular as they relate to the metabolic coupling between astrocytes and neurons. For that, changes in the metabolic activity of different mice brain regions after 1 or 9 days of training in an eight-arm radial maze were assessed by (14C) 2-deoxyglucose (2DG) autoradiography. Significant differences in the areas engaged during the behavioral task at day 1 (when animals are confronted for the first time to the learning task) and at day 9 (when animals are highly performing) have been identified. These areas include the hippocampus, the fornix, the parietal cortex, the laterodorsal thalamic nucleus and the mammillary bodies at day 1 ; and the anterior cingulate, the retrosplenial cortex and the dorsal striatum at day 9. Two of these cerebral regions (those presenting the greatest changes at day 1 and day 9: the hippocampus and the retrosplenial cortex, respectively) were microdissected by laser capture microscopy and selected genes related to neuron-glia metabolic coupling, glucose metabolism and synaptic plasticity were analyzed by RT-PCR. 2DG and gene expression analysis were performed at three different times: 1) immediately after the end of the behavioral paradigm, 2) 45 minutes and 3) 6 hours after training. The main goal of this study was the identification of the metabolic adaptations following the learning task. Gene expression results demonstrate that the learning task profoundly modulates the pattern of gene expression in time, meaning that these two cerebral regions with high 2DG signal (hippocampus and retrosplenial cortex) have adapted their metabolic molecular machinery in consequence. Almost all studied genes show a higher expression in the hippocampus at day 1 compared to day 9, while an increased expression was found in the retrosplenial cortex at day 9. We can observe these molecular adaptations with a short delay of 45 minutes after the end of the task. However, 6 hours after training a high gene expression was found at day 9 (compared to day 1) in both regions, suggesting that only one day of training is not sufficient to detect transcriptional modifications several hours after the task. Thus, gene expression data match 2DG results indicating a transfer of information in time (from day 1 to day 9) and in space (from the hippocampus to the retrosplenial cortex), and this at a cellular and a molecular level. Moreover, learning seems to modify the neuron-glia metabolic coupling, since several genes involved in this coupling are induced. These results also suggest a role of glia in neuronal plasticity.RESUME DU TRAVAIL DE THESECe projet de thèse a eu pour but l'étude des mécanismes moléculaires qui sont impliqués dans l'apprentissage et la mémoire et, en particulier, à les mettre en rapport avec le couplage métabolique existant entre les astrocytes et les neurones. Pour cela, des changements de l'activité métabolique dans différentes régions du cerveau des souris après 1 ou 9 jours d'entraînement dans un labyrinthe radial à huit-bras ont été évalués par autoradiographie au 2-désoxyglucose (2DG). Des différences significatives dans les régions engagées pendant la tâche comportementale au jour 1 (quand les animaux sont confrontés pour la première fois à la tâche) et au jour 9 (quand les animaux ont déjà appris) ont été identifiés. Ces régions incluent, au jour 1, l'hippocampe, le fornix, le cortex pariétal, le noyau thalamic laterodorsal et les corps mamillaires; et, au jour 9, le cingulaire antérieur, le cortex retrosplenial et le striatum dorsal. Deux de ces régions cérébrales (celles présentant les plus grands changements à jour 1 et à jour 9: l'hippocampe et le cortex retrosplenial, respectivement) ont été découpées par microdissection au laser et quelques gènes liés au couplage métabolique neurone-glie, au métabolisme du glucose et à la plasticité synaptique ont été analysées par RT-PCR. L'étude 2DG et l'analyse de l'expression de gènes ont été exécutés à trois temps différents: 1) juste après entraînement, 2) 45 minutes et 3) 6 heures après la fin de la tâche. L'objectif principal de cette étude était l'identification des adaptations métaboliques suivant la tâche d'apprentissage. Les résultats de l'expression de gènes démontrent que la tâche d'apprentissage module profondément le profile d'expression des gènes dans le temps, signifiant que ces deux régions cérébrales avec un signal 2DG élevé (l'hippocampe et le cortex retrosplenial) ont adapté leurs « machines moléculaires » en conséquence. Presque tous les gènes étudiés montrent une expression plus élevée dans l'hippocampe au jour 1 comparé au jour 9, alors qu'une expression accrue a été trouvée dans le cortex retrosplenial au jour 9. Nous pouvons observer ces adaptations moléculaires avec un retard court de 45 minutes après la fin de la tâche. Cependant, 6 heures après l'entraînement, une expression de gènes élevée a été trouvée au jour 9 (comparé à jour 1) dans les deux régions, suggérant que seulement un jour d'entraînement ne suffit pas pour détecter des modifications transcriptionelles plusieurs heures après la tâche. Ainsi, les données d'expression de gènes corroborent les résultats 2DG indiquant un transfert d'information dans le temps (de jour 1 à jour 9) et dans l'espace (de l'hippocampe au cortex retrosplenial), et ceci à un niveau cellulaire et moléculaire. D'ailleurs, la tâche d'apprentissage semble modifier le couplage métabolique neurone-glie, puisque de nombreux gènes impliqués dans ce couplage sont induits. Ces observations suggèrent un rôle important de la glie dans les mécanismes de plasticité du système nerveux.

MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.

Density-based hierarchical clustering of pyro-sequences on a large scale--the case of fungal ITS1.

MOTIVATION: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. RESULTS: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. AVAILABILITY: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. CONTACT: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch.

Travelling-stripe forcing generates hexagonal patterns

We study the response of Turing stripe patterns to a simple spatiotemporal forcing. This forcing has the form of a traveling wave and is spatially resonant with the characteristic Turing wavelength. Experiments conducted with the photosensitive chlorine dioxide-iodine-malonic acid reaction reveal a striking symmetry-breaking phenomenon of the intrinsic striped patterns giving rise to hexagonal lattices for intermediate values of the forcing velocity. The phenomenon is understood in the framework of the corresponding amplitude equations, which unveils a complex scenario of dynamical behaviors.

Experimental evidence of localized oscillations in the photosensitive chlorine dioxide-iodine-malonic acid reaction

The interaction between Hopf and Turing modes has been the subject of active research in recent years. We present here experimental evidence of the existence of mixed Turing-Hopf modes in a two-dimensional system. Using the photosensitive chlorine dioxide-iodine-malonic acid reaction (CDIMA) and external constant background illumination as a control parameter, standing spots oscillating in amplitude and with hexagonal ordering were observed. Numerical simulations in the Lengyel-Epstein model for the CDIMA reaction confirmed the results.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

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 Texturing by Milling, HR-283, 1987

Experience has shown that milling machines with carbide tipped teeth have the capability of profiling most asphalt concrete (ac) and portland cement concrete (pcc) pavements. Most standard milling operations today leave a very coarse, generally objectionable surface texture. This research utilized a Cedarapids Wirtgen 1900C mill modified by adding additional teeth. There were 411 teeth at a 5 millimeter transverse spacing (standard spacing is 15 mm) on a 6 ft. 4 in. long drum. The mill was used to profile and texture the surface of one ac and two pcc pavements. One year after the milling operation there is still some noticeable change in tire noise but the general appearance is good. The milling operation with the additional teeth provides an acceptable surface texture with improved Friction Numbers when compared to a nonmilled surface.

Evaluation of the user-friendliness of seven new generation intensive care ventilators.

OBJECTIVE: To explore the user-friendliness and ergonomics of seven new generation intensive care ventilators. DESIGN: Prospective task-performing study. SETTING: Intensive care research laboratory, university hospital. METHODS: Ten physicians experienced in mechanical ventilation, but without prior knowledge of the ventilators, were asked to perform eight specific tasks [turning the ventilator on; recognizing mode and parameters; recognizing and setting alarms; mode change; finding and activating the pre-oxygenation function; pressure support setting; stand-by; finding and activating non-invasive ventilation (NIV) mode]. The time needed for each task was compared to a reference time (by trained physiotherapist familiar with the devices). A time >180 s was considered a task failure. RESULTS: For each of the tests on the ventilators, all physicians' times were significantly higher than the reference time (P < 0.001). A mean of 13 +/- 8 task failures (16%) was observed by the ventilator. The most frequently failed tasks were mode and parameter recognition, starting pressure support and finding the NIV mode. Least often failed tasks were turning on the pre-oxygenation function and alarm recognition and management. Overall, there was substantial heterogeneity between machines, some exhibiting better user-friendliness than others for certain tasks, but no ventilator was clearly better that the others on all points tested. CONCLUSIONS: The present study adds to the available literature outlining the ergonomic shortcomings of mechanical ventilators. These results suggest that closer ties between end-users and manufacturers should be promoted, at an early development phase of these machines, based on the scientific evaluation of the cognitive processes involved by users in the clinical setting.