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.

Biomarkers of oxidative stress and its association with the urinary reducing capacity in bus maintenance workers.

BACKGROUND: Exposure to particles (PM) induces adverse health effects (cancer, cardiovascular and pulmonary diseases). A key-role in these adverse effects seems to be played by oxidative stress, which is an excess of reactive oxygen species relative to the amount of reducing species (including antioxidants), the first line of defense against reactive oxygen species. The aim of this study was to document the oxidative stress caused by exposure to respirable particles in vivo, and to test whether exposed workers presented changes in their urinary levels for reducing species.METHODS: Bus depot workers (n = 32) exposed to particles and pollutants (respirable PM4, organic and elemental carbon, particulate metal content, polycyclic aromatic hydrocarbons, NOx, O3) were surveyed over two consecutive days. We collected urine samples before and after each shift, and quantified an oxidative stress biomarker (8-hydroxy-2'-deoxyguanosine), the reducing capacity and a biomarker of PAH exposure (1-hydroxypyrene). We used a linear mixed model to test for associations between the oxidative stress status of the workers and their particle exposure as well as with their urinary level of reducing species.RESULTS: Workers were exposed to low levels of respirable PM4 (range 25-71 μg/m3). However, urinary levels of 8-hydroxy-2'-deoxyguanosine increased significantly within each shift and between both days for non-smokers. The between-day increase was significantly correlated (p < 0.001) with the concentrations of organic carbon, NOx, and the particulate copper content. The within-shift increase in 8OHdG was highly correlated to an increase of the urinary reducing capacity (Spearman ρ = 0.59, p < 0.0001).CONCLUSIONS: These findings confirm that exposure to components associated to respirable particulate matter causes a systemic oxidative stress, as measured with the urinary 8OHdG. The strong association observed between urinary 8OHdG with the reducing capacity is suggestive of protective or other mechanisms, including circadian effects. Additional investigations should be performed to understand these observations.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

The 2005 Pakistan earthquake revisited: Methods for integrated landslide assessment

Five years after the 2005 Pakistan earthquake that triggered multiple mass movements, landslides continue to pose a threat to the population of Azad Kashmir, especially during heavy monsoon rains. The thousands of landslides that were triggered by the 7.6 magnitude earthquake in 2005 were not just due to a natural phenomenon but largely induced by human activities, namely, road building, grazing, and deforestation. The damage caused by the landslides in the study area (381 km2) is estimated at 3.6 times the annual public works budget of Azad Kashmir for 2005 of US$ 1 million. In addition to human suffering, this cost constitutes a significant economic setback to the region that could have been reduced through improved land use and risk management. This article describes interdisciplinary research conducted 18 months after the earthquake to provide a more systemic approach to understanding risks posed by landslides, including the physical, environmental, and human contexts. The goal of this research is twofold: to present empirical data on the social, geological, and environmental contexts in which widespread landslides occurred following the 2005 earthquake; and, second, to describe straightforward methods that can be used for integrated landslide risk assessments in data-poor environments. The article analyzes limitations of the methodologies and challenges for conducting interdisciplinary research that integrates both social and physical data. This research concludes that reducing landslide risk is ultimately a management issue, based in land use decisions and governance.

In vitro activity of gallium maltolate against Staphylococci in logarithmic, stationary, and biofilm growth phases: comparison of conventional and calorimetric susceptibility testing methods.

Ga(3+) is a semimetal element that competes for the iron-binding sites of transporters and enzymes. We investigated the activity of gallium maltolate (GaM), an organic gallium salt with high solubility, against laboratory and clinical strains of methicillin-susceptible Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), methicillin-susceptible Staphylococcus epidermidis (MSSE), and methicillin-resistant S. epidermidis (MRSE) in logarithmic or stationary phase and in biofilms. The MICs of GaM were higher for S. aureus (375 to 2000 microg/ml) than S. epidermidis (94 to 200 microg/ml). Minimal biofilm inhibitory concentrations were 3,000 to >or=6,000 microg/ml (S. aureus) and 94 to 3,000 microg/ml (S. epidermidis). In time-kill studies, GaM exhibited a slow and dose-dependent killing, with maximal action at 24 h against S. aureus of 1.9 log(10) CFU/ml (MSSA) and 3.3 log(10) CFU/ml (MRSA) at 3x MIC and 2.9 log(10) CFU/ml (MSSE) and 4.0 log(10) CFU/ml (MRSE) against S. epidermidis at 10x MIC. In calorimetric studies, growth-related heat production was inhibited by GaM at subinhibitory concentrations; and the minimal heat inhibition concentrations were 188 to 4,500 microg/ml (MSSA), 94 to 1,500 microg/ml (MRSA), and 94 to 375 microg/ml (MSSE and MRSE), which correlated well with the MICs. Thus, calorimetry was a fast, accurate, and simple method useful for investigation of antimicrobial activity at subinhibitory concentrations. In conclusion, GaM exhibited activity against staphylococci in different growth phases, including in stationary phase and biofilms, but high concentrations were required. These data support the potential topical use of GaM, including its use for the treatment of wound infections, MRSA decolonization, and coating of implants.

How important are interview methods and questionnaire designs in research on self-reported juvenile delinquency? An experimental comparison of internet vs. paper-and-pencil questionnaires and different definitions of the reference period

There has been relatively little change over recent decades in the methods used in research on self-reported delinquency. Face-to-face interviews and selfadministered interviews in the classroom are still the predominant alternatives envisaged. New methods have been brought into the picture by recent computer technology, the Internet, and an increasing availability of computer equipment and Internet access in schools. In the autumn of 2004, a controlled experiment was conducted with 1,203 students in Lausanne (Switzerland), where "paper-and-pencil" questionnaires were compared with computer-assisted interviews through the Internet. The experiment included a test of two different definitions of the (same) reference period. After the introductory question ("Did you ever..."), students were asked how many times they had done it (or experienced it), if ever, "over the last 12 months" or "since the October 2003 vacation". Few significant differences were found between the results obtained by the two methods and for the two definitions of the reference period, in the answers concerning victimisation, self-reported delinquency, drug use, failure to respond (missing data). Students were found to be more motivated to respond through the Internet, take less time for filling out the questionnaire, and were apparently more confident of privacy, while the school principals were less reluctant to allow classes to be interviewed through the Internet. The Internet method also involves considerable cost reductions, which is a critical advantage if self-reported delinquency surveys are to become a routinely applied method of evaluation, particularly so in countries with limited resources. On balance, the Internet may be instrumental in making research on self-reported delinquency far more feasible in situations where limited resources so far have prevented its implementation.

Analyse critique des résultats de biopsies musculaires et de la méthodologie utilisée dans 889 cas d'affections neuromusculaires [Critical analysis of the results of muscle biopsies and of the methods used in 889 cases of neuromuscular disorders]

The contribution of muscle biopsies to the diagnosis of neuromuscular disorders and the indications of various methods of examination are investigated by analysis of 889 biopsies from patients suffering from myopathic and/or neurogenic disorders. Histo-enzymatic studies performed on frozen material as well as immunohistochemistry and electron microscopy allowed to provide specific diagnoses in all the neurogenic disorders (polyneuropathies and motor neuron diseases), whereas one third of myopathies remained uncertain. Confrontation of neuropathological data with the clinical indications for histological investigations shows that muscle biopsies reveal the diagnosis in 25% of the cases (mainly in congenital and metabolic myopathies) and confirm and/or complete the clinical diagnosis in 50%. In the remaining cases with non specific abnormalities neuropathological investigations may help the clinician by excluding well defined neuromuscular disorders. Analysis of performed studies and results of investigations show the contribution and specificity of each method for the diagnosis. Statistical evaluation of this series indicates that cryostat sectioning for histo- and immunochemical and electron microscopy increases the rate of diagnoses of neuromuscular diseases: full investigation was necessary for the diagnosis in 30% of the cases. The interpretation of the wide range of pathological reactions in muscles requires a close cooperation with the clinician.

Particle exposure scenarios for research and production activities involving nanomaterials

Nanomaterials with structures in the nanoscale (1 to 100 nm) often have chemical, physical and bioactive characteristics different from those of larger entities of the same material. This is interesting for industry but raises questions about the health of exposed people. However, little is known so far about the exposure of workers to inhalable airborne nanomaterials. We investigated several activities in research laboratories and industry to learn about relevant exposure scenarios. Work process analyses were combined with measurements of airborne particle mass concentrations and number−size distributions. Background levels in research settings were mostly low, while in industrial production, levels were sometimes elevated, especially in halls near busy roads or in the presence of diesel fork lifts without particle filters. Peak levels were found in an industrial setting dealing with powders (up to 80,000 particles/cm³ and up to 15 mg/m³). Mostly low concentrations were found for activities involving liquid applications. However, centrifugation and lyophilization of nanoparticle containing solutions resulted in very high particle number concentrations (up to 300,000 particles/cm³), whereas no increases were seen for the same activities conducted with nanoparticle−free liquids. No significant increases of particle concentrations were found for processes involving nanoparticles bound to surfaces. Also no increases were observed in laboratories that were visualizing properties and structures of small amounts of nanomaterials. Conclusion: When studying exposure scenarios for airborne nanomaterials, the focus should not only be on processes involving nano−powders, but also on processes involving intensively treated nanoparticle−containing liquids. Acknowledgement: We thank Chantal Imhof, MSc and Guillaume Ferraris, MSc for their contributions.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.