Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

First consumption ever of multiple substances: applying an expert-based taxonomy to a Swiss national sample of adolescents.

The use of multiple legal and illegal substances by adolescents is a growing concern in all countries, but since no consensus about a taxonomy did emerge yet, it is difficult to understand the different patterns of consumption and to implement tailored prevention and treatment programs directed towards specific subgroups of the adolescent population. Using data from a Swiss survey on adolescent health, we analyzed the age at which ten legal and illegal substances were consumed for the first time ever by applying a method combining the strength of both automatic clustering and use of substance experts. Results were then compared to 30 socio-economic factors to establish the usefulness of and to validate our taxonomy. We also analyzed the succession of substance first use for each group. The final taxonomy consists of eight groups ranging from non-consumers to heavy drug addicts. All but four socio-economic factors were significantly associated with the taxonomy, the strongest associations being observed with health, behavior, and sexuality factors. Numerous factors influence adolescents in their decision to first try substances or to use them on a regular basis, and no factor alone can be considered as an absolute marker of problematic behavior regarding substance use. Different processes of experimentation with substances are associated with different behaviors, therefore focusing on only one substance or only one factor is not efficient. Prevention and treatment programs can then be tailored to address specific issues related to different youth subgroups.

Heterogeneity of t(4;14) in multiple myeloma. Long-term follow-up of 100 cases treated with tandem transplantation in IFM99 trials.

One hundred de novo multiple myeloma patients with t(4;14) treated with double intensive therapy according to IFM99 protocols were retrospectively analyzed. The median overall survival (OS) and event-free survival (EFS) were 41.4 and 21 months, respectively, as compared to 65 and 37 for patients included in the IFM99 trials without t(4;14) (P<10(-7)). We identified a subgroup of patients presenting at diagnosis with both low beta(2)-microglobulin <4 mg/l and high hemoglobin (Hb) >/=10 g/l (46% of the cases) with a median OS of 54.6 months and a median EFS of 26 months, respectively, which benefits from high-dose therapy (HDT); conversely patients with one or both adverse prognostic factor (high beta(2)-microglobulin and/or low Hb) had a poor outcome. The achievement of either complete response or very good partial response after HDT was also a powerful independent prognostic factor for both OS and EFS.

Multiple QTLs influencing triglyceride and HDL and total cholesterol levels identified in families with atherogenic dyslipidemia.

We conducted a genome-wide scan using variance components linkage analysis to localize quantitative-trait loci (QTLs) influencing triglyceride (TG), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol, and total cholesterol (TC) levels in 3,071 subjects from 459 families with atherogenic dyslipidemia. The most significant evidence for linkage to TG levels was found in a subset of Turkish families at 11q22 [logarithm of the odds ratio (LOD)=3.34] and at 17q12 (LOD=3.44). We performed sequential oligogenic linkage analysis to examine whether multiple QTLs jointly influence TG levels in the Turkish families. These analyses revealed loci at 20q13 that showed strong epistatic effects with 11q22 (conditional LOD=3.15) and at 7q36 that showed strong epistatic effects with 17q12 (conditional LOD=3.21). We also found linkage on the 8p21 region for TG in the entire group of families (LOD=3.08). For HDL-C levels, evidence of linkage was identified on chromosome 15 in the Turkish families (LOD=3.05) and on chromosome 5 in the entire group of families (LOD=2.83). Linkage to QTLs for TC was found at 8p23 in the entire group of families (LOD=4.05) and at 5q13 in a subset of Turkish and Mediterranean families (LOD=3.72). These QTLs provide important clues for the further investigation of genes responsible for these complex lipid phenotypes. These data also indicate that a large proportion of the variance of TG levels in the Turkish population is explained by the interaction of multiple genetic loci.