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.

Testing the competitive exclusion principle using various niche parameters in a native (Natrix maura) and an introduced (N. tessellata) colubrid

Despite the increase of animal and plant introductions worldwide and the strong augmentation of the reptile trade, few invasive snake populations have been studied. Dice snakes (Natrix tessellata) were introduced to the shores of Lake Geneva (Switzerland) in the early 1920s, and are now well established. This region of introduction was previously inhabited by Viperine snakes (N. maura). Ever since these two species have been under monitoring (which began in 1996) the Viperine snake population has shown drastic decline. We examine here the possibility of trophic competition by analysing diet composition, prey size and trophic niche overlap. Spatial distribution is also assessed in order to address the question of spatial competitive exclusion. We found very similar diets, and thus a high trophic niche overlap, indicating no partitioning of the trophic resource. No arguments in favour of spatial competitive exclusion were found. Our study suggests that trophic competition may occur between the two natricines and that it may give an explanation for the drastic decline of the Viperine snake in this area. Other pathways potentially playing a role in the exclusion of the Viperine snake are discussed.

A statistical procedure for testing social reciprocity at group, dyadic and individual levels

This paper examines statistical analysis of social reciprocity, that is, the balance between addressing and receiving behaviour in social interactions. Specifically, it focuses on the measurement of social reciprocity by means of directionality and skew-symmetry statistics at different levels. Two statistics have been used as overall measures of social reciprocity at group level: the directional consistency and the skew-symmetry statistics. Furthermore, the skew-symmetry statistic allows social researchers to obtain complementary information at dyadic and individual levels. However, having computed these measures, social researchers may be interested in testing statistical hypotheses regarding social reciprocity. For this reason, it has been developed a statistical procedure, based on Monte Carlo sampling, in order to allow social researchers to describe groups and make statistical decisions.

Testing a large-scale forestry scenario model by means of successive inventories on a forest property

Abstract

Isothermal microcalorimetry for antifungal susceptibility testing of Mucorales, Fusarium spp., and Scedosporium spp.

We evaluated isothermal microcalorimetry for real-time susceptibility testing of non-Aspergillus molds. MIC and minimal effective concentration (MEC) values of Mucorales (n = 4), Fusarium spp. (n = 4), and Scedosporium spp. (n = 4) were determined by microbroth dilution according to the Clinical Laboratory Standard Institute M38-A2 guidelines. Heat production of molds was measured at 37 °C in Sabouraud dextrose broth inoculated with 2.5 × 10(4) spores/mL in the presence of amphotericin B, voriconazole, posaconazole, caspofungin, and anidulafungin. As determined by microcalorimetry, amphotericin B was the most active agent against Mucorales (MHIC 0.06-0.125 μg/mL) and Fusarium spp. (MHIC 1-4 μg/mL), whereas voriconazole was the most active agent against Scedosporium spp. (MHIC 0.25 to 8 μg/mL). The percentage of agreement (within one 2-fold dilution) between the MHIC and MIC (or MEC) was 67%, 92%, 75%, and 83% for amphotericin B, voriconazole, posaconazole, and caspofungin, respectively. Microcalorimetry provides additional information on timing of antifungal activity, enabling further investigation of drug-mold and drug-drug interaction, and optimization of antifungal treatment.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

An efficient algorithm to perform multiple testing in epistasis screening

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn’s disease. Results: In the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron(tm) Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn’s disease (CD) data. Conclusions: Our software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn’s disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations.

Assessment of Nondestructive Testing Technologies for Quality Control/Quality Assurance of Asphalt Mixtures, TR-653, 2015

Asphalt pavements suffer various failures due to insufficient quality within their design lives. The American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) has been proposed to improve pavement quality through quantitative performance prediction. Evaluation of the actual performance (quality) of pavements requires in situ nondestructive testing (NDT) techniques that can accurately measure the most critical, objective, and sensitive properties of pavement systems. The purpose of this study is to assess existing as well as promising new NDT technologies for quality control/quality assurance (QC/QA) of asphalt mixtures. Specifically, this study examined field measurements of density via the PaveTracker electromagnetic gage, shear-wave velocity via surface-wave testing methods, and dynamic stiffness via the Humboldt GeoGauge for five representative paving projects covering a range of mixes and traffic loads. The in situ tests were compared against laboratory measurements of core density and dynamic modulus. The in situ PaveTracker density had a low correlation with laboratory density and was not sensitive to variations in temperature or asphalt mix type. The in situ shear-wave velocity measured by surface-wave methods was most sensitive to variations in temperature and asphalt mix type. The in situ density and in situ shear-wave velocity were combined to calculate an in situ dynamic modulus, which is a performance-based quality measurement. The in situ GeoGauge stiffness measured on hot asphalt mixtures several hours after paving had a high correlation with the in situ dynamic modulus and the laboratory density, whereas the stiffness measurement of asphalt mixtures cooled with dry ice or at ambient temperature one or more days after paving had a very low correlation with the other measurements. To transform the in situ moduli from surface-wave testing into quantitative quality measurements, a QC/QA procedure was developed to first correct the in situ moduli measured at different field temperatures to the moduli at a common reference temperature based on master curves from laboratory dynamic modulus tests. The corrected in situ moduli can then be compared against the design moduli for an assessment of the actual pavement performance. A preliminary study of microelectromechanical systems- (MEMS)-based sensors for QC/QA and health monitoring of asphalt pavements was also performed.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Testing the effects of genetic crossing distance on embryo survival within a metapopulation of brown trout (Salmo trutta)

Predicting progeny performance from parental genetic divergence can potentially enhance the efficiency of supportive breeding programmes and facilitate risk assessment. Yet, experimental testing of the effects of breeding distance on offspring performance remains rare, especially in wild populations of vertebrates. Recent studies have demonstrated that embryos of salmonid fish are sensitive indicators of additive genetic variance for viability traits. We therefore used gametes of wild brown trout (Salmo trutta) from five genetically distinct populations of a river catchment in Switzerland, and used a full factorial design to produce over 2,000 embryos in 100 different crosses with varying genetic distances (FST range 0.005-0.035). Customized egg capsules allowed recording the survival of individual embryos until hatching under natural field conditions. Our breeding design enabled us to evaluate the role of the environment, of genetic and nongenetic parental contributions, and of interactions between these factors, on embryo viability. We found that embryo survival was strongly affected by maternal environmental (i.e. non-genetic) effects and by the microenvironment, i.e. by the location within the gravel. However, embryo survival was not predicted by population divergence, parental allelic dissimilarity, or heterozygosity, neither in the field nor under laboratory conditions. Our findings suggest that the genetic effects of inter-population hybridization within a genetically differentiated meta-population can be minor in comparison to environmental effects.

Alkali Content of Fly Ash – Measuring and Testing Strategies for Compliance, TR-628, 2015

Sodium and potassium are the common alkalis present in fly ash. Excessive amounts of fly ash alkalis can cause efflorescence problems in concrete products and raise concern about the effectiveness of the fly ash to mitigate alkali-silica reaction (ASR). The available alkali test, which is commonly used to measure fly ash alkali, takes approximately 35 days for execution and reporting. Hence, in many instances the fly ash has already been incorporated into concrete before the test results are available. This complicates the job of the fly ash marketing agencies and it leads to disputes with fly ash users who often are concerned with accepting projects that contain materials that fail to meet specification limits. The research project consisted of a lab study and a field study. The lab study focused on the available alkali test and how fly ash alkali content impacts common performance tests (mortar-bar expansion tests). Twenty-one fly ash samples were evaluated during the testing. The field study focused on the inspection and testing of selected, well documented pavement sites that contained moderately reactive fine aggregate and high-alkali fly ash. A total of nine pavement sites were evaluated. Two of the sites were control sites that did not contain fly ash. The results of the lab study indicated that the available alkali test is prone to experimental errors that cause poor agreement between testing labs. A strong (linear) relationship was observed between available alkali content and total alkali content of Class C fly ash. This relationship can be used to provide a quicker, more precise method of estimating the available alkali content. The results of the field study failed to link the use of high-alkali fly ash with the occurrence of ASR in the various concrete sites. Petrographic examination of the pavement cores indicated that Wayland sand is an ASR-sensitive aggregate. This was in good agreement with Iowa DOT field service records. It was recommended that preventative measures should be used when this source of sand is used in concrete mixtures.

Relating Creep Testing to Rutting of Asphalt Concrete Mixes, HR-311, Part 2, 1991

The Iowa Department of Transportation began creep and resilient modulus testing of asphalt concrete mixtures in 1989. Part 1 of this research reported in January 1990 was a laboratory study of hot mix asphalt (HMA) mixtures made with O, 30, 60, 85 and 100% crushed gravel, crushed limestone and crushed quartzite combined with uncrushed sand and gravel. Creep test results from Marshall specimens related well to the percent of crushed particles and the perceived resistance to rutting. The objective of this research, part 2, was to determine if there was a meaningful correlation between pavement rut depth and the resilient modulus or the creep resistance factor. Four and six inch diameter cores were drilled from rutted primary and interstate pavements and interstate pavements with design changes intended to resist rutting. The top 2 1/2 inches of each core, most of which was surface course, was used for creep and resilient modulus testing. There is a good correlation between the resilient modulus of four and six inch diameter cores. Creep resistance factors of four and six inch diameter cores also correlated well. There is a poor correlation between resilient modulus and the creep resistance factor. The rut depth per million 18,000 pound equivalent single axle loadings (ESAL) for these pavements did not correlate well with either the resilient modulus or the creep resistance factor.