Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Sublimation of ice–tholins mixtures: A morphological and spectro-photometric study

Sublimation, the direct transition from solid to gas phase, is a process responsible for shaping and changing the reflectance properties of many Solar System surfaces. In this study, we have characterized the evolution of the structure/texture and of the visible and near-infrared (VIS–NIR) spectral reflectance of surfaces made of water ice mixed with analogues of complex extraterrestrial organic matter, named tholins, under low temperature (<-70° C) and pressure (10-⁵mbar) conditions. The experiments were carried out in the SCITEAS simulation setup recently built as part of the Laboratory for Outflow Studies of Sublimating Materials (LOSSy) at the University of Bern (Pommerol, A. et al. [2015a]. Planet. Space Sci. 109–110, 106–122). As the water ice sublimated, we observed in situ the formation of a sublimation lag deposit made of a water-free porous (>90% porosity) network of organic filaments on top of the ice. The temporal evolution of the tholins and water ice spectral features (reflectance at the absorption bands wavelengths, red slope, from 0.40 to 1.90lm) are analyzed throughout the sublimation of the samples. We studied how different mixtures of tholins with water (0.1 wt.% tholins as coating or inclusions within the water particles), and different ice particle sizes (4.5 ± 2.5 or 67 ± 31lm) influence the morphological and spectral evolutions of the samples. The sublimation of the ice below the mantle produces a gas flow responsible for the ejection of mm to cm-sized fragments of the deposit in outbursts-like events. The results show remarkable differences between these samples in term of mantle structure, speed of mantle building, rates and surface area of mantle ejections. These data provide useful references for interpreting remote-sensing observations of icy Solar System surfaces, in particular the activity of comet nuclei where sublimation of organic-rich ices and deposition of organic-dust particles likely play a major role. Consequently, the data presented here could be of high interest for the interpretation of Rosetta, and also New Horizons, observations.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Binary recombinase systems for high-resolution conditional mutagenesis.

Conditional mutagenesis using Cre recombinase expressed from tissue specific promoters facilitates analyses of gene function and cell lineage tracing. Here, we describe two novel dual-promoter-driven conditional mutagenesis systems designed for greater accuracy and optimal efficiency of recombination. Co-Driver employs a recombinase cascade of Dre and Dre-respondent Cre, which processes loxP-flanked alleles only when both recombinases are expressed in a predetermined temporal sequence. This unique property makes Co-Driver ideal for sequential lineage tracing studies aimed at unraveling the relationships between cellular precursors and mature cell types. Co-InCre was designed for highly efficient intersectional conditional transgenesis. It relies on highly active trans-splicing inteins and promoters with simultaneous transcriptional activity to reconstitute Cre recombinase from two inactive precursor fragments. By generating native Cre, Co-InCre attains recombination rates that exceed all other binary SSR systems evaluated in this study. Both Co-Driver and Co-InCre significantly extend the utility of existing Cre-responsive alleles.

Future water quality monitoring--adapting tools to deal with mixtures of pollutants in water resource management.

Environmental quality monitoring of water resources is challenged with providing the basis for safeguarding the environment against adverse biological effects of anthropogenic chemical contamination from diffuse and point sources. While current regulatory efforts focus on monitoring and assessing a few legacy chemicals, many more anthropogenic chemicals can be detected simultaneously in our aquatic resources. However, exposure to chemical mixtures does not necessarily translate into adverse biological effects nor clearly shows whether mitigation measures are needed. Thus, the question which mixtures are present and which have associated combined effects becomes central for defining adequate monitoring and assessment strategies. Here we describe the vision of the international, EU-funded project SOLUTIONS, where three routes are explored to link the occurrence of chemical mixtures at specific sites to the assessment of adverse biological combination effects. First of all, multi-residue target and non-target screening techniques covering a broader range of anticipated chemicals co-occurring in the environment are being developed. By improving sensitivity and detection limits for known bioactive compounds of concern, new analytical chemistry data for multiple components can be obtained and used to characterise priority mixtures. This information on chemical occurrence will be used to predict mixture toxicity and to derive combined effect estimates suitable for advancing environmental quality standards. Secondly, bioanalytical tools will be explored to provide aggregate bioactivity measures integrating all components that produce common (adverse) outcomes even for mixtures of varying compositions. The ambition is to provide comprehensive arrays of effect-based tools and trait-based field observations that link multiple chemical exposures to various environmental protection goals more directly and to provide improved in situ observations for impact assessment of mixtures. Thirdly, effect-directed analysis (EDA) will be applied to identify major drivers of mixture toxicity. Refinements of EDA include the use of statistical approaches with monitoring information for guidance of experimental EDA studies. These three approaches will be explored using case studies at the Danube and Rhine river basins as well as rivers of the Iberian Peninsula. The synthesis of findings will be organised to provide guidance for future solution-oriented environmental monitoring and explore more systematic ways to assess mixture exposures and combination effects in future water quality monitoring.

VIS-NIR reflectance of water ice/regolith analogue mixtures and implications for the detectability of ice mixed within planetary regoliths

Permanently shadowed regions at the poles of the Moon and Mercury have been pointed out as candidates for hosting water ice at their surface. We have measured in the laboratory the visible and near infrared spectral range (VIS-NIR) bidirectional reflectance of intimate mixtures of water ice and the JSC-1AF lunar simulant for different ice concentrations, particle sizes, and measurement geometries. The nonlinearity between the measured reflectance and the amount of ice in the mixture can be reproduced to some extent by the mixing formulas of standard reflectance models, in particular, those of Hapke and Hiroi, which are tested here. Estimating ice concentrations from reflectance data without knowledge of the mixing coefficientsstrongly dependent on the size/shape of the grainscan result in large errors. According to our results, it is possible that considerable amounts of water ice might be intimately mixed in the regolith of the Moon and Mercury without producing noticeable photometric signatures.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^

Assessing high-priority mixtures: A review of methanol as influenced by ethanol

The intent of this research was to identify the level of risk methanol posed to a fetus during an ethanol co-exposure. This investigation was prompted by the known competitive inhibition properties of ethanol and the developmental toxicity of methanol. Integrated into this research was the practicality necessitated by regulatory processes, namely: does the risk justify the expense of additional research. To this end, the scope and nature of exposures were summarized to illustrate the ubiquity of these chemicals and the potential for dual exposure. Similarly, severity of outcome was evaluated by systematically reviewing the LOAELs, NOAELs, and statistical significance contained in methanol-induced developmental studies. Results. Blood methanol levels corresponding to developmental effects in laboratory studies were found to be substantially higher than the blood methanol levels predicted in high-risk methanol-ethanol exposure scenarios. This indicates that ethanol would not likely exacerbate methanol toxicity to the point of teratogenicity; however, it is important to note that the developmental toxicity of ethanol—an established human teratogen—was not included in the evaluation. Ethanol's contribution as a developmental toxicant rather than merely as an attenuator of methanol toxicity undermines the severity of effects possible from this chemical combination. Therefore further evaluation is needed to assess the developmental toxicities following dual exposures before rendering methanol and ethanol a high-priority mixture.^

Effects of various pollutant mixtures on immune responses of the blue mussel (Mytilus edulis) collected at a salinity gradient in Danish coastal waters, links to supplementary material

The Baltic Sea is a semi-enclosed sea with a steady salinity gradient (3 per mil-30 per mil). Organisms have adapted to such low salinities, but are suspected to be more susceptible to stress. Within the frame of the integrated environmental monitoring BONUS + project "BEAST" the applicability of immune responses of the blue mussel was investigated in Danish coastal waters. The sampling sites were characterised by a salinity range (11-19 per mil) and different mixtures of contaminants (metals, PAHs and POPs), according to chemical analysis of mussel tissues. Variation partitioning (redundancy analysis) was applied to decompose salinity and contamination effects. The results indicated that cellular immune responses (total and differential haemocyte count, phagocytic activity and apoptosis) were mainly influenced by contaminants, whereas humoral factors (haemolytic activity) were mainly impacted by salinity. Hence, cellular immune functions may be suitable as biomarkers in monitoring programmes for the Baltic Sea and other geographic regions with salinity variances of the studied range.