Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

The many uses of regulatory impact assessment: A meta-analysis of EU and UK cases

Research on regulation has crossed paths with the literature on policy instruments, showing that regulatory policy instruments contain cognitive and normative beliefs about policy. Thus, their usage stacks the deck in favor of one type of actor or one type of regulatory solution. In this article, we challenge the assumption that there is a predetermined relationship between ideas, regulatory policy instruments, and outcomes. We argue that different combinations of conditions lead to different outcomes, depending on how actors use the instrument. Empirically, we analyze 31 EU and UK case studies of regulatory impact assessment (RIA) - a regulatory policy instrument that has been pivotal in the so-called better regulation movement. We distinguish four main usages of RIA, that is, political, instrumental, communicative, and perfunctory. We find that in our sample instrumental usage is not so rare and that the contrast between communicative and political usages is less stark than is commonly thought. In terms of policy recommendations, our analysis suggests that there may be different paths to desirable outcomes. Policymakers should therefore explore different combinations of conditions leading to the usages they deem desirable rather than arguing for a fixed menu of variables.

Cost-effectiveness analysis of a European primary-care physician training in smoking cessation counseling.

BACKGROUND: Physician training in smoking cessation counseling has been shown to be effective as a means to increase quit success. We assessed the cost-effectiveness ratio of a smoking cessation counseling training programme. Its effectiveness was previously demonstrated in a cluster randomized, control trial performed in two Swiss university outpatients clinics, in which residents were randomized to receive training in smoking interventions or a control educational intervention. DESIGN AND METHODS: We used a Markov simulation model for effectiveness analysis. This model incorporates the intervention efficacy, the natural quit rate, and the lifetime probability of relapse after 1-year abstinence. We used previously published results in addition to hospital service and outpatient clinic cost data. The time horizon was 1 year, and we opted for a third-party payer perspective. RESULTS: The incremental cost of the intervention amounted to US$2.58 per consultation by a smoker, translating into a cost per life-year saved of US$25.4 for men and 35.2 for women. One-way sensitivity analyses yielded a range of US$4.0-107.1 in men and US$9.7-148.6 in women. Variations in the quit rate of the control intervention, the length of training effectiveness, and the discount rate yielded moderately large effects on the outcome. Variations in the natural cessation rate, the lifetime probability of relapse, the cost of physician training, the counseling time, the cost per hour of physician time, and the cost of the booklets had little effect on the cost-effectiveness ratio. CONCLUSIONS: Training residents in smoking cessation counseling is a very cost-effective intervention and may be more efficient than currently accepted tobacco control interventions.

How Much Does It Cost? Optimization of Costs in Sequence Analysis of Social Science Data

One major methodological problem in analysis of sequence data is the determination of costs from which distances between sequences are derived. Although this problem is currently not optimally dealt with in the social sciences, it has some similarity with problems that have been solved in bioinformatics for three decades. In this article, the authors propose an optimization of substitution and deletion/insertion costs based on computational methods. The authors provide an empirical way of determining costs for cases, frequent in the social sciences, in which theory does not clearly promote one cost scheme over another. Using three distinct data sets, the authors tested the distances and cluster solutions produced by the new cost scheme in comparison with solutions based on cost schemes associated with other research strategies. The proposed method performs well compared with other cost-setting strategies, while it alleviates the justification problem of cost schemes.

Evolutionary history of the greater white-toothed shrew (Crocidura russula) inferred from analysis of mtDNA, Y, and X chromosome markers.

We investigate the evolutionary history of the greater white-toothed shrew across its distribution in northern Africa and mainland Europe using sex-specific (mtDNA and Y chromosome) and biparental (X chromosome) markers. All three loci confirm a large divergence between eastern (Tunisia and Sardinia) and western (Morocco and mainland Europe) lineages, and application of a molecular clock to mtDNA divergence estimates indicates a more ancient separation (2.25 M yr ago) than described by some previous studies, supporting claims for taxonomic revision. Moroccan ancestry for the mainland European population is inconclusive from phylogenetic trees, but is supported by greater nucleotide diversity and a more ancient population expansion in Morocco than in Europe. Signatures of rapid population expansion in mtDNA, combined with low X and Y chromosome diversity, suggest a single colonization of mainland Europe by a small number of Moroccan shrews >38 K yr ago. This study illustrates that multilocus genetic analyses can facilitate the interpretation of species' evolutionary history but that phylogeographic inference using X and Y chromosomes is restricted by low levels of observed polymorphism.

A Statistical Analysis of Iowa Premature Concrete Pavement Deterioration, Supplementary Study, HR-1063, 1997

This supplementary project has been undertaken as an effort to continue work previously completed in the Pooled Fund Study of Premature Concrete Pavement Deterioration. As such, it shares the objective of "Identifying the variables that are present in those pavements exhibiting premature deterioration," by collecting additional data and performing statistical analysis of those data. The approach and philosophy of this work are identical to that followed in the above project, and the Pooled Fund Study Final Report provides a detailed description of this process. This project has involved the collection of data for additional sites in the state of Iowa. These sites have then been added to sites collected in the original study, and statistical analysis has been performed on the entire set. It is hoped that this will have two major effects. First, using data from only one state allows for the analysis of a larger set of independent variables with a greater degree of commonality than was possible in the multi-state study, since the data are not limited by state to state differences in data collection and retention. Second, more data on additional sites will increase the degrees of freedom in the model and hopefully add confidence to the results.

Crystallization and preliminary X-ray analysis of monalysin, a novel β-pore-forming toxin from the entomopathogen Pseudomonas entomophila.

Monalysin was recently described as a novel pore-forming toxin (PFT) secreted by the Drosophila pathogen Pseudomonas entomophila. Recombinant monalysin is multimeric in solution, whereas PFTs are supposed to be monomeric until target membrane association. Monalysin crystals were obtained by the hanging-drop vapour-diffusion method using PEG 8000 as precipitant. Preliminary X-ray diffraction analysis revealed that monalysin crystals belonged to the monoclinic space group C2, with unit-cell parameters a = 162.4, b = 146.2, c = 144.4 Å, β = 122.8°, and diffracted to 2.85 Å resolution using synchrotron radiation. Patterson self-rotation analysis and Matthews coefficient calculation indicate that the asymmetric unit contains nine copies of monalysin. Heavy-atom derivative data were collected and a Ta6Br14 cluster derivative data set confirmed the presence of ninefold noncrystallographic symmetry.

Statistical Analysis of Highway Needs Condition Data: Manual vs. Automated, TR-494

This project examines similarities and differences between the automated condition data collected on and off county paved roads and the manual condition data collected by Iowa Department of Transportation (DOT) staff in 2000 and 2001. Also, the researchers will provide staff support to the advisory committee in exploring other options to the highway need process. The results show that the automated condition data can be used in a converted highway needs process with no major differences between the two methods. Even though the foundation rating difference was significant, the foundation rating weighting factor in HWYNEEDS is minimal and should not have a major impact. In terms of RUTF formula based distribution, the results clearly show the superiority of the condition-based analysis compared to the non-condition based. That correlation can be further enhanced by adding more distress variables to the analysis.

Analysis of variance of primary data on plant growth analysis

Plant growth analysis presents difficulties related to statistical comparison of growth rates, and the analysis of variance of primary data could guide the interpretation of results. The objective of this work was to evaluate the analysis of variance of data from distinct harvests of an experiment, focusing especially on the homogeneity of variances and the choice of an adequate ANOVA model. Data from five experiments covering different crops and growth conditions were used. From the total number of variables, 19% were originally homoscedastic, 60% became homoscedastic after logarithmic transformation, and 21% remained heteroscedastic after transformation. Data transformation did not affect the F test in one experiment, whereas in the other experiments transformation modified the F test usually reducing the number of significant effects. Even when transformation has not altered the F test, mean comparisons led to divergent interpretations. The mixed ANOVA model, considering harvest as a random effect, reduced the number of significant effects of every factor which had the F test modified by this model. Examples illustrated that analysis of variance of primary variables provides a tool for identifying significant differences in growth rates. The analysis of variance imposes restrictions to experimental design thereby eliminating some advantages of the functional growth analysis.

Integrative analysis of the Regulatory Region of the FGFR3 Oncogene

The study of transcriptional regulation often needs the integration of diverse yet independent data. In the present work, sequence conservation, predic-tion of transcription factor binding sites (TFBS) and gene expression analysis have been applied to the detection of putative transcription factor (TF) modules in the regulatory region of the FGFR3 oncogene. Several TFs with conserved binding sites in the FGFR3 regulatory region have shown high positive or negative corre-lation with FGFR3 expression both in urothelial carcinoma and in benign nevi. By means of conserved TF cluster analysis, two different TF modules have been iden-tified in the promoter and first intron of FGFR3 gene. These modules contain acti-vating AP2, E2F, E47 and SP1 binding sites plus motifs for EGR with possible repressor function.

Cluster glasses of ultrasoft particles

We present molecular dynamics (MD) simulations results for dense fluids of ultrasoft, fully penetrable particles. These are a binary mixture and a polydisperse system of particles interacting via the generalized exponential model, which is known to yield cluster crystal phases for the corresponding monodisperse systems. Because of the dispersity in the particle size, the systems investigated in this work do not crystallize and form disordered cluster phases. The clusteringtransition appears as a smooth crossover to a regime in which particles are mostly located in clusters, isolated particles being infrequent. The analysis of the internal cluster structure reveals microsegregation of the big and small particles, with a strong homo-coordination in the binary mixture. Upon further lowering the temperature below the clusteringtransition, the motion of the clusters" centers-of-mass slows down dramatically, giving way to a cluster glass transition. In the cluster glass, the diffusivities remain finite and display an activated temperature dependence, indicating that relaxation in the cluster glass occurs via particle hopping in a nearly arrested matrix of clusters. Finally we discuss the influence of the microscopic dynamics on the transport properties by comparing the MD results with Monte Carlo simulations.

Where and how family members spend time at home? A quantitative analysis of the observational tracking on the everyday lives of Italian families

This paper examines a dataset that derives from an observational tracking, in order to analyze where and how middle-class working families spend time at home. We use an ethnographic approach to study the everyday lives of Italian dual-income middle-class families, with the aim to analyze quantitatively the use of home spaces and the types of activities of family members on weekday afternoons and evenings. The different analyses (multiple correspondence analysis, agglomerative hierarchical cluster, discriminant analysis) show how particular spaces and activities in these spaces are dominated by certain family members. We suggest a combination of qualitative and quantitative methodologies as useful tools to explore in detail the everyday lives of families, and to understand how family members use the domestic spaces. In particular, we consider relevant the use of quantitative analyses to examine ethnographic data, especially in connection with the methodological reflexivity among researchers

Confirmatory factor analysis of an integrated model of psycopathology assessed with MMPI-2-RF and the MCMI-III

We presented an integrated hierarchical model of psychopathology that more accurately captures empirical patterns of comorbidity between clinical syndromes and personality disorders.In order to verify the structural validity of the model proposed, this study aimed to analyze the convergence between the Restructured Clinical (RC) scales and Personality scales (PSY-5) of the MMPI-2-RF and the Clinical Syndrome and Personality Disorder scales of the MCMI-III.The MMPI-2-RF and MCMI-III were administered to a clinical sample of 377 outpatients (167 men and 210 women).The structural hypothesiswas assessed by using a Confirmatory Factor Analytic design with four common superordinate factors. An independent-cluster-basis solution was proposed based on maximum likelihood estimation and the application of several fit indices.The fit of the proposed model can be considered as good and more so if we take into account its complexity.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.