Analytical aspects in doping control: challenges and perspectives.

Since the first anti-doping tests in the 1960s, the analytical aspects of the testing remain challenging. The evolution of the analytical process in doping control is discussed in this paper with a particular emphasis on separation techniques, such as gas chromatography and liquid chromatography. These approaches are improving in parallel with the requirements of increasing sensitivity and selectivity for detecting prohibited substances in biological samples from athletes. Moreover, fast analyses are mandatory to deal with the growing number of doping control samples and the short response time required during particular sport events. Recent developments in mass spectrometry and the expansion of accurate mass determination has improved anti-doping strategies with the possibility of using elemental composition and isotope patterns for structural identification. These techniques must be able to distinguish equivocally between negative and suspicious samples with no false-negative or false-positive results. Therefore, high degree of reliability must be reached for the identification of major metabolites corresponding to suspected analytes. Along with current trends in pharmaceutical industry the analysis of proteins and peptides remains an important issue in doping control. Sophisticated analytical tools are still mandatory to improve their distinction from endogenous analogs. Finally, indirect approaches will be discussed in the context of anti-doping, in which recent advances are aimed to examine the biological response of a doping agent in a holistic way.

Tree and Brush Control For County Road Right-of-Way, Ocotober 2002

This manual summarizes the roadside tree and brush control methods used by all of Iowa's 99 counties. It is based on interviews conducted in Spring 2002 with county engineers, roadside managers and others. The target audience of this manual is the novice county engineer or roadside manager. Iowa law is nearly silent on roadside tree and brush control, so individual counties have been left to decide on the level of control they want to achieve and maintain. Different solutions have been developed but the goal of every county remains the same: to provide safe roads for the traveling public. Counties in eastern and southern Iowa appear to face the greatest brush control challenge. Most control efforts can be divided into two categories: mechanical and chemical. Mechanical control includes cutting tools and supporting equipment. A chain saw is the most widely used cutting tool. Tractor mounted boom mowers and brush cutters are used to prune miles of brush but have significant safety and aesthetic limitations and boom mowers are easily broken by inexperienced operators. The advent of tree shears and hydraulic thumbs offer unprecedented versatility. Bulldozers are often considered a method of last resort since they reduce large areas to bare ground. Any chipper that violently grabs brush should not be used. Chemical control is the application of herbicide to different parts of a plant: foliar spray is applied to leaves; basal bark spray is applied to the tree trunk; a cut stump treatment is applied to the cambium ring of a cut surface. There is reluctance by many to apply herbicide into the air due to drift concerns. One-third of Iowa counties do not use foliar spray. By contrast, several accepted control methods are directed toward the ground. Freshly cut stumps should be treated to prevent resprouting. Basal bark spray is highly effective in sensitive areas such as near houses. Interest in chemical control is slowly increasing as herbicides and application methods are refined. Fall burning, a third, distinctly separate technique is underused as a brush control method and can be effective if timed correctly. In all, control methods tend to reflect agricultural patterns in a county. The use of chain saws and foliar sprays tends to increase in counties where row crops predominate, and boom mowing tends to increase in counties where grassland predominates. For counties with light to moderate roadside brush, rotational maintenance is the key to effective control. The most comprehensive approach to control is to implement an integrated roadside vegetation management (IRVM) program. An IRVM program is usually directed by a Roadside Manager whose duties may be shared with another position. Funding for control programs comes from the Rural Services Basic portion of a county's budget. The average annual county brush control budget is about $76,000. That figure is thought not to include shared expenses such as fuel and buildings. Start up costs for an IRVM program are less if an existing control program is converted. In addition, IRVM budgets from three different northeastern Iowa counties are offered for comparison in this manual. The manual also includes a chapter on temporary traffic control in rural work zones, a summary of the Iowa Code as it relates to brush control, and rules on avoiding seasonal disturbance of the endangered Indiana bat. Appendices summarize survey and forest cover data, an equipment inventory, sample forms for record keeping, a sample brush control policy, a few legal opinions, a literature search, and a glossary.

Phase-field model for Hele-Shaw flows with arabitrary contrast II: numerical approach

We present a phase-field model for the dynamics of the interface between two inmiscible fluids with arbitrary viscosity contrast in a rectangular Hele-Shaw cell. With asymptotic matching techniques we check the model to yield the right Hele-Shaw equations in the sharp-interface limit, and compute the corrections to these equations to first order in the interface thickness. We also compute the effect of such corrections on the linear dispersion relation of the planar interface. We discuss in detail the conditions on the interface thickness to control the accuracy and convergence of the phase-field model to the limiting Hele-Shaw dynamics. In particular, the convergence appears to be slower for high viscosity contrasts.

A steroidomic approach for biomarkers discovery in doping control.

Anti-doping authorities have high expectations of the athlete steroidal passport (ASP) for anabolic-androgenic steroids misuse detection. However, it is still limited to the monitoring of known well-established compounds and might greatly benefit from the discovery of new relevant biomarkers candidates. In this context, steroidomics opens the way to the untargeted simultaneous evaluation of a high number of compounds. Analytical platforms associating the performance of ultra-high pressure liquid chromatography (UHPLC) and the high mass-resolving power of quadrupole time-of-flight (QTOF) mass spectrometers are particularly adapted for such purpose. An untargeted steroidomic approach was proposed to analyse urine samples from a clinical trial for the discovery of relevant biomarkers of testosterone undecanoate oral intake. Automatic peak detection was performed and a filter of reference steroid metabolites mass-to-charge ratio (m/z) values was applied to the raw data to ensure the selection of a subset of steroid-related features. Chemometric tools were applied for the filtering and the analysis of UHPLC-QTOF-MS(E) data. Time kinetics could be assessed with N-way projections to latent structures discriminant analysis (N-PLS-DA) and a detection window was confirmed. Orthogonal projections to latent structures discriminant analysis (O-PLS-DA) classification models were evaluated in a second step to assess the predictive power of both known metabolites and unknown compounds. A shared and unique structure plot (SUS-plot) analysis was performed to select the most promising unknown candidates and receiver operating characteristic (ROC) curves were computed to assess specificity criteria applied in routine doping control. This approach underlined the pertinence to monitor both glucuronide and sulphate steroid conjugates and include them in the athletes passport, while promising biomarkers were also highlighted.

Accurate and efficient simulation of multiphase flow in a heterogeneous reservoir by using error estimate and control in the multiscale finite-volume framework

The multiscale finite-volume (MSFV) method is designed to reduce the computational cost of elliptic and parabolic problems with highly heterogeneous anisotropic coefficients. The reduction is achieved by splitting the original global problem into a set of local problems (with approximate local boundary conditions) coupled by a coarse global problem. It has been shown recently that the numerical errors in MSFV results can be reduced systematically with an iterative procedure that provides a conservative velocity field after any iteration step. The iterative MSFV (i-MSFV) method can be obtained with an improved (smoothed) multiscale solution to enhance the localization conditions, with a Krylov subspace method [e.g., the generalized-minimal-residual (GMRES) algorithm] preconditioned by the MSFV system, or with a combination of both. In a multiphase-flow system, a balance between accuracy and computational efficiency should be achieved by finding a minimum number of i-MSFV iterations (on pressure), which is necessary to achieve the desired accuracy in the saturation solution. In this work, we extend the i-MSFV method to sequential implicit simulation of time-dependent problems. To control the error of the coupled saturation/pressure system, we analyze the transport error caused by an approximate velocity field. We then propose an error-control strategy on the basis of the residual of the pressure equation. At the beginning of simulation, the pressure solution is iterated until a specified accuracy is achieved. To minimize the number of iterations in a multiphase-flow problem, the solution at the previous timestep is used to improve the localization assumption at the current timestep. Additional iterations are used only when the residual becomes larger than a specified threshold value. Numerical results show that only a few iterations on average are necessary to improve the MSFV results significantly, even for very challenging problems. Therefore, the proposed adaptive strategy yields efficient and accurate simulation of multiphase flow in heterogeneous porous media.

Quantitative antibiogram as a typing method for the prospective epidemiological surveillance and control of MRSA: comparison with molecular typing.

OBJECTIVE: Evaluation of the quantitative antibiogram as an epidemiological tool for the prospective typing of methicillin-resistant Staphylococcus aureus (MRSA), and comparison with ribotyping. METHODS: The method is based on the multivariate analysis of inhibition zone diameters of antibiotics in disk diffusion tests. Five antibiotics were used (erythromycin, clindamycin, cotrimoxazole, gentamicin, and ciprofloxacin). Ribotyping was performed using seven restriction enzymes (EcoRV, HindIII, KpnI, PstI, EcoRI, SfuI, and BamHI). SETTING: 1,000-bed tertiary university medical center. RESULTS: During a 1-year period, 31 patients were found to be infected or colonized with MRSA. Cluster analysis of antibiogram data showed nine distinct antibiotypes. Four antibiotypes were isolated from multiple patients (2, 4, 7, and 13, respectively). Five additional antibiotypes were isolated from the remaining five patients. When analyzed with respect to the epidemiological data, the method was found to be equivalent to ribotyping. Among 206 staff members who were screened, six were carriers of MRSA. Both typing methods identified concordant of MRSA types in staff members and in the patients under their care. CONCLUSIONS: The quantitative antibiogram was found to be equivalent to ribotyping as an epidemiological tool for typing of MRSA in our setting. Thus, this simple, rapid, and readily available method appears to be suitable for the prospective surveillance and control of MRSA for hospitals that do not have molecular typing facilities and in which MRSA isolates are not uniformly resistant or susceptible to the antibiotics tested.

Sediment Control in Bridge Waterways, February 1990

The objective of this study was to develop guidelines for use of the Iowa Vanes technique for sediment control in bridge waterways. Iowa Vanes are small flow-training structures (foils) designed to modify the near-bed flow pattern and redistribute flow and sediment transport within the channel cross section. The structures are installed at an angleof attack of 15 - 25' with the flow, and their initial height is 0.2 - 0.5 times water depth at design stage. The vanes function by generating secondary circulation in the flow. The circulation alters magnitude and direction of the bed shear stress and causes a reduction in velocity and sediment transport in the vane controlled area. As a result, the river bed aggrades in the vane controlled area and degrades outside. This report summarizes the basic theory, describes results of laboratory and field tests, and presents the resulting design procedure. Design graphs have been developed based on the theory. The graphs are entered with basic flow variables and desired bed topography. The output is vane layout and design. The procedure is illustrated with two numerical examples prepared with data that are typical for many rivers in Iowa and the midwest. The report also discusses vane material. In most applications, the vane height will be between 30% and 50% of bankfull flow depth and the vane length will be two to three times vane height. The vanes will be placed in arrays along the bank of the river. Each array will contain two or more vanes. The vanes in an array will be spaced laterally a distance of two to three times vane height. The streamwise spacing between the arrays will be 15 to 30 times vane height, and the vane-to-bank distance will be three to four times vane height. The study also show that the first (most upstream) array in the vane system must be located a distance of at least three array spacings upstream from the bridge, and there must be at least three arrays in the system for it to be effective at and downstream from the third array.

Feedback control of unstable cellular solidification fronts

We present a feedback control scheme to stabilize unstable cellular patterns during the directional solidification of a binary alloy. The scheme is based on local heating of cell tips which protrude ahead of the mean position of all tips in the array. The feasibility of this scheme is demonstrated using phase-field simulations and, experimentally, using a real-time image processing algorithm, to track cell tips, coupled with a movable laser spot array device to heat the tips locally. We demonstrate, both numerically and experimentally, that spacings well below the threshold for a period-doubling instability can be stabilized. As predicted by the numerical calculations, cellular arrays become stable with uniform spacing through the feedback control which is maintained with minimal heating.

Nonlinear chemoconvection in the Methylene-blue-glucose system

Interfacial hydrodynamic instabilities arise in a range of chemical systems. One mechanism for instability is the occurrence of unstable density gradients due to the accumulation of reaction products. In this paper we conduct two-dimensional nonlinear numerical simulations for a member of this class of system: the methylene-blue¿glucose reaction. The result of these reactions is the oxidation of glucose to a relatively, but marginally, dense product, gluconic acid, that accumulates at oxygen permeable interfaces, such as the surface open to the atmosphere. The reaction is catalyzed by methylene-blue. We show that simulations help to disassemble the mechanisms responsible for the onset of instability and evolution of patterns, and we demonstrate that some of the results are remarkably consistent with experiments. We probe the impact of the upper oxygen boundary condition, for fixed flux, fixed concentration, or mixed boundary conditions, and find significant qualitative differences in solution behavior; structures either attract or repel one another depending on the boundary condition imposed. We suggest that measurement of the form of the boundary condition is possible via observation of oxygen penetration, and improved product yields may be obtained via proper control of boundary conditions in an engineering setting. We also investigate the dependence on parameters such as the Rayleigh number and depth. Finally, we find that pseudo-steady linear and weakly nonlinear techniques described elsewhere are useful tools for predicting the behavior of instabilities beyond their formal range of validity, as good agreement is obtained with the simulations.

Hematopoietic stem cell transplantation in Switzerland: a comprehensive quality control report on centre effect.

Interest groups advocate centre-specific outcome data as a useful tool for patients in choosing a hospital for their treatment and for decision-making by politicians and the insurance industry. Haematopoietic stem cell transplantation (HSCT) requires significant infrastructure and represents a cost-intensive procedure. It therefore qualifies as a prime target for such a policy. We made use of the comprehensive database of the Swiss Blood Stem Cells Transplant Group (SBST) to evaluate potential use of mortality rates. Nine institutions reported a total of 4717 HSCT - 1427 allogeneic (30.3%), 3290 autologous (69.7%) - in 3808 patients between the years 1997 and 2008. Data were analysed for survival- and transplantation-related mortality (TRM) at day 100 and at 5 years. The data showed marked and significant differences between centres in unadjusted analyses. These differences were absent or marginal when the results were adjusted for disease, year of transplant and the EBMT risk score (a score incorporating patient age, disease stage, time interval between diagnosis and transplantation, and, for allogeneic transplants, donor type and donor-recipient gender combination) in a multivariable analysis. These data indicate comparable quality among centres in Switzerland. They show that comparison of crude centre-specific outcome data without adjustment for the patient mix may be misleading. Mandatory data collection and systematic review of all cases within a comprehensive quality management system might, in contrast, serve as a model to ascertain the quality of other cost-intensive therapies in Switzerland.

Adoption of the Healthy Heart Kit by Alberta family physicians

OBJECTIVE: The Healthy Heart Kit (HHK) is a risk management and patient education kit for the prevention of cardiovascular disease (CVD) and the promotion of CV health. There are currently no published data examining predictors of HHK use by physicians. The main objective of this study was to examine the association between physicians' characteristics (socio-demographic, cognitive, and behavioural) and the use of the HHK. METHODS: All registered family physicians in Alberta (n=3068) were invited to participate in the "Healthy Heart Kit" Study. Consenting physicians (n=153) received the Kit and were requested to use it for two months. At the end of this period, a questionnaire collected data on the frequency of Kit use by physicians, as well as socio-demographic, cognitive, and behavioural variables pertaining to the physicians. RESULTS: The questionnaire was returned by 115 physicians (follow-up rate = 75%). On a scale ranging from 0 to 100, the mean score of Kit use was 61 [SD=26]. A multiple linear regression showed that "agreement with the Kit" and the degree of "confidence in using the Kit" was strongly associated with Kit use, explaining 46% of the variability for Kit use. Time since graduation was inversely associated with Kit use, and a trend was observed for smaller practices to be associated with lower use. CONCLUSION: Given these findings, future research and practice should explore innovative strategies to gain initial agreement among physicians to employ such clinical tools. Participation of older physicians and solo-practitioners in this process should be emphasized.

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.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

An approach to predicting bowing control parameter contours in violin performance

We present a machine learning approach to modeling bowing control parametercontours in violin performance. Using accurate sensing techniqueswe obtain relevant timbre-related bowing control parameters such as bowtransversal velocity, bow pressing force, and bow-bridge distance of eachperformed note. Each performed note is represented by a curve parametervector and a number of note classes are defined. The principal componentsof the data represented by the set of curve parameter vectors are obtainedfor each class. Once curve parameter vectors are expressed in the new spacedefined by the principal components, we train a model based on inductivelogic programming, able to predict curve parameter vectors used for renderingbowing controls. We evaluate the prediction results and show the potentialof the model by predicting bowing control parameter contours from anannotated input score.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools

PURPOSE: Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks.METHODS: The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential.RESULTS: The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment.CONCLUSIONS: A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/. Copyright © 2012 John Wiley & Sons, Ltd.