Characterization of soil chemical properties of strawberry fields using principal component analysis

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

A genomewide linkage study on suicidality in major depressive disorder confirms evidence for linkage to 2p12.

Suicidal behavior is commonly associated with depression. Twin studies indicate that both suicidality and major depressive disorder (MDD) are heritable. However, epidemiological evidence suggests that the inheritance of suicidality is likely to be independent of the underlying psychiatric disorder, implying a distinct genetic contribution to suicidality. We conducted a genomewide linkage search aiming to detect genomic loci that may harbor susceptibility genes contributing to risk for suicidality in recurrent MDD. Affected sibling pair (ASP) variance components analysis was performed using the Depression Network cohort of 971 ASPs. The quantitative trait measuring suicidality as a broad phenotype, encompassing ideation and suicide attempts, was established from Schedules for Clinical Assessment in Neuropsychiatry interview items. We examined 1,060 genotyped microsatellite markers with an average spacing of 3.3 cM. Empirical thresholds for linkage evidence were set by whole-genome simulations (LOD = 2.71 for genomewide significance, 1.71 for suggestive linkage). No genomewide significant findings were found. Marker D3S1234 on 3p14 achieved suggestive linkage and yielded a maximum LOD of 1.853 (P = 0.0017), loci 9p24.3 and 18q22-q23 achieved LOD scores >1.5. We found some support for linkage to 2p12 (LOD = 1.2, P = 0.0087) which was previously implicated in linkage studies of suicidality. Our follow-up meta-analysis of five studies showed strong linkage to this region (P = 2 × 10(-6) ). In conclusion, this study analyzed suicidality as a continuous trait in MDD. We found modest evidence for linkage on 3p14. Our meta-analysis supports previous evidence of linkage to suicidality on 2p12. Some candidate genes in these regions may plausibly be implicated in suicidality.

Were Spanish migrants attracted by industrial agglomerations?: an analysis for the interwar years in the light of the new economic geography

In this paper we examine whether access to markets had a significant influence onmigration choices of Spanish internal migrants in the inter-war years. We perform astructural contrast of a New Economic Geography model that focus on the forwardlinkage that links workers location choice with the geography of industrial production,one of the centripetal forces that drive agglomeration in the NEG models. The resultshighlight the presence of this forward linkage in the Spanish economy of the inter-warperiod. That is, we prove the existence of a direct relation between workers¿ localizationdecisions and the market potential of the host regions. In addition, the direct estimationof the values associated with key parameters in the NEG model allows us to simulatethe migratory flows derived from different scenarios of the relative size of regions andthe distances between them. We show that in Spain the power of attraction of theagglomerations grew as they increased in size, but the high elasticity estimated for themigration costs reduced the intensity of the migratory flows. This could help to explainthe apparently low intensity of internal migrations in Spain until its upsurge during the1920s. This also explains the geography of migrations in Spain during this period,which hardly affected the regions furthest from the large industrial agglomerations (i.e.,regions such as Andalusia, Estremadura and Castile-La Mancha) but had an intenseeffect on the provinces nearest to the principal centres of industrial development.

Were Spanish migrants attracted by industrial agglomerations?: an analysis for the interwar years in the light of the new economic geography

In this paper we examine whether access to markets had a significant influence onmigration choices of Spanish internal migrants in the inter-war years. We perform astructural contrast of a New Economic Geography model that focus on the forwardlinkage that links workers location choice with the geography of industrial production,one of the centripetal forces that drive agglomeration in the NEG models. The resultshighlight the presence of this forward linkage in the Spanish economy of the inter-warperiod. That is, we prove the existence of a direct relation between workers¿ localizationdecisions and the market potential of the host regions. In addition, the direct estimationof the values associated with key parameters in the NEG model allows us to simulatethe migratory flows derived from different scenarios of the relative size of regions andthe distances between them. We show that in Spain the power of attraction of theagglomerations grew as they increased in size, but the high elasticity estimated for themigration costs reduced the intensity of the migratory flows. This could help to explainthe apparently low intensity of internal migrations in Spain until its upsurge during the1920s. This also explains the geography of migrations in Spain during this period,which hardly affected the regions furthest from the large industrial agglomerations (i.e.,regions such as Andalusia, Estremadura and Castile-La Mancha) but had an intenseeffect on the provinces nearest to the principal centres of industrial development.

SNP2GO: functional analysis of genome-wide association studies.

Genome-wide association studies (GWAS) are designed to identify the portion of single-nucleotide polymorphisms (SNPs) in genome sequences associated with a complex trait. Strategies based on the gene list enrichment concept are currently applied for the functional analysis of GWAS, according to which a significant overrepresentation of candidate genes associated with a biological pathway is used as a proxy to infer overrepresentation of candidate SNPs in the pathway. Here we show that such inference is not always valid and introduce the program SNP2GO, which implements a new method to properly test for the overrepresentation of candidate SNPs in biological pathways.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Safety and functional outcome of thrombolysis in dissection-related ischemic stroke: a meta-analysis of individual patient data.

Background and Purpose-The safety and efficacy of thrombolysis in cervical artery dissection (CAD) are controversial. The aim of this meta-analysis was to pool all individual patient data and provide a valid estimate of safety and outcome of thrombolysis in CAD.Methods-We performed a systematic literature search on intravenous and intra-arterial thrombolysis in CAD. We calculated the rates of pooled symptomatic intracranial hemorrhage and mortality and indirectly compared them with matched controls from the Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register. We applied multivariate regression models to identify predictors of excellent (modified Rankin Scale=0 to 1) and favorable (modified Rankin Scale=0 to 2) outcome.Results-We obtained individual patient data of 180 patients from 14 retrospective series and 22 case reports. Patients were predominantly female (68%), with a mean +/- SD age of 46 +/- 11 years. Most patients presented with severe stroke (median National Institutes of Health Stroke Scale score=16). Treatment was intravenous thrombolysis in 67% and intra-arterial thrombolysis in 33%. Median follow-up was 3 months. The pooled symptomatic intracranial hemorrhage rate was 3.1% (95% CI, 1.3 to 7.2). Overall mortality was 8.1% (95% CI, 4.9 to 13.2), and 41.0% (95% CI, 31.4 to 51.4) had an excellent outcome. Stroke severity was a strong predictor of outcome. Overlapping confidence intervals of end points indicated no relevant differences with matched controls from the Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register.Conclusions-Safety and outcome of thrombolysis in patients with CAD-related stroke appear similar to those for stroke from all causes. Based on our findings, thrombolysis should not be withheld in patients with CAD. (Stroke. 2011;42:2515-2520.)

Methadone treatments in a Swiss Region, 2001-2008: a registry-based analysis.

ABSTRACT: BACKGROUND: To determine, in a region of Switzerland, the duration of retention in opioid substitution treatments with methadone (OSTM), duration of treatment interruptions, probability of re-entry to treatment after a treatment interruption, and associated factors. METHODS: A secondary analysis of registry-based data was performed with patients (n = 2880) registered in the methadone treatment register database of the Public Health Service of the canton of Vaud between January 1, 2001 and June 30, 2008. Survival analysis and multivariate analysis was conducted. RESULTS: The probability of remaining on treatment was 69% at 1 year and 45% at 3 years (n =1666). One-third of patients remained on treatment beyond 5 years. The estimated hazard of leaving treatment was increased by a ratio of 1.31 in the case of a first treatment (P = 0.001), 1.83 for those without a fixed home (P < 0.001), and 1.29 for those younger than 30 years old (P < 0.001). The probability of having begun a new treatment after a first interruption was 21% at one year, 38% at 3 years, and 43% at 5 years (n = 1581). Factors at the interruption of treatment associated with a higher probability of re-entering were: interruption not due to methadone withdrawal, bad physical health, and higher methadone dose. CONCLUSIONS: OSTM are long-term (maintenance) treatments in Switzerland. Younger age, bad living conditions at entry, and first treatment are predictors of lower retention. Approximately one-half of patients who interrupt treatment will re-enter treatment within 5 years.

Multivariate analyses of genotype x environment interaction of popcorn

The objectives of this work were to evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods. Nine popcorn cultivars were sown on four dates one month apart during each of the agricultural years 1998/1999 and 1999/2000. The experiments were carried out using randomized block designs, with four replicates. The cv. Zélia contributed the least to the GxE interaction. The cv. Viçosa performed similarly to cv. Rosa-claro. Optimization of GxE was obtained for cv. CMS 42 for a favorable mega-environment, and for cv. CMS 43 for an unfavorable environment. Multivariate analysis supported the results from the method of Eberhart & Russell. The graphic analysis of the Additive Main effects and Multiplicative Interaction (AMMI) model was simple, allowing conclusions to be made about stability, genotypic performance, genetic divergence between cultivars, and the environments that optimize cultivar performance. The graphic analysis of the Genotype main effects and Genotype x Environment interaction (GGE) method added to AMMI information on environmental stratification, defining mega-environments and the cultivars that optimized performance in those mega-environments. Both methods are adequate to explain the genotype x environment interactions.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Analysis of sensitivity with respect to a compositional parameter : A comment on 'Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling' by L. Loosvelt and co-authors

A comment about the article “Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling” writen by L. Loosvelt and co-authors. The present comment is centered in three specific points. The first one is related to the fact that the authors avoid the use of ilr-coordinates. The second one refers to some generalization of sensitivity analysis when input parameters are compositional. The third tries to show that the role of the Dirichlet distribution in the sensitivity analysis is irrelevant

Partial correlation : Network analysis

The final year project came to us as an opportunity to get involved in a topic which has appeared to be attractive during the learning process of majoring in economics: statistics and its application to the analysis of economic data, i.e. econometrics.Moreover, the combination of econometrics and computer science is a very hot topic nowadays, given the Information Technologies boom in the last decades and the consequent exponential increase in the amount of data collected and stored day by day. Data analysts able to deal with Big Data and to find useful results from it are verydemanded in these days and, according to our understanding, the work they do, although sometimes controversial in terms of ethics, is a clear source of value added both for private corporations and the public sector. For these reasons, the essence of this project is the study of a statistical instrument valid for the analysis of large datasets which is directly related to computer science: Partial Correlation Networks.The structure of the project has been determined by our objectives through the development of it. At first, the characteristics of the studied instrument are explained, from the basic ideas up to the features of the model behind it, with the final goal of presenting SPACE model as a tool for estimating interconnections in between elements in large data sets. Afterwards, an illustrated simulation is performed in order to show the power and efficiency of the model presented. And at last, the model is put into practice by analyzing a relatively large data set of real world data, with the objective of assessing whether the proposed statistical instrument is valid and useful when applied to a real multivariate time series. In short, our main goals are to present the model and evaluate if Partial Correlation Network Analysis is an effective, useful instrument and allows finding valuable results from Big Data.As a result, the findings all along this project suggest the Partial Correlation Estimation by Joint Sparse Regression Models approach presented by Peng et al. (2009) to work well under the assumption of sparsity of data. Moreover, partial correlation networks are shown to be a very valid tool to represent cross-sectional interconnections in between elements in large data sets.The scope of this project is however limited, as there are some sections in which deeper analysis would have been appropriate. Considering intertemporal connections in between elements, the choice of the tuning parameter lambda, or a deeper analysis of the results in the real data application are examples of aspects in which this project could be completed.To sum up, the analyzed statistical tool has been proved to be a very useful instrument to find relationships that connect the elements present in a large data set. And after all, partial correlation networks allow the owner of this set to observe and analyze the existing linkages that could have been omitted otherwise.

Characterization and multivariate classification of grapes and wines of two Cabernet Sauvignon clones

The objective of this work was to assess and characterize two clones, 169 and 685, of Cabernet Sauvignon grapes and to evaluate the wine produced from these grapes. The experiment was carried out in São Joaquim, SC, Brazil, during the 2009 harvest season. During grape ripening, the evolution of physical-chemical properties, phenolic compounds, organic acids, and anthocyanins was evaluated. During grape harvest, yield components were determined for each clone. Individual and total phenolics, individual and total anthocyanins, and antioxidant activity were evaluated for wine. The clones were also assessed regarding the duration of their phenological cycle. During ripening, the evolution of phenolic compounds and of physical-chemical parameters was similar for both clones; however, during harvest, significant differences were observed regarding yield, number of bunches per plant and berries per bunch, leaf area, and organic acid, polyphenol, and anthocyanin content. The wines produced from these clones showed significant differences regarding chemical composition. The clones showed similar phenological cycle and responses to bioclimatic parameters. Principal component analysis shows that clone 685 is strongly correlated with color characteristics, mainly monomeric anthocyanins, while clone 169 is correlated with individual phenolic compounds.

Effects of bisoprolol fumarate on left ventricular size, function, and exercise capacity in patients with heart failure: analysis with magnetic resonance myocardial tagging.

BACKGROUND: Recent data suggest that beta-blockers can be beneficial in subgroups of patients with chronic heart failure (CHF). For metoprolol and carvedilol, an increase in ejection fraction has been shown and favorable effects on the myocardial remodeling process have been reported in some studies. We examined the effects of bisoprolol fumarate on exercise capacity and left ventricular volume with magnetic resonance imaging (MRI) and applied a novel high-resolution MRI tagging technique to determine myocardial rotation and relaxation velocity. METHODS: Twenty-eight patients (mean age, 57 +/- 11 years; mean ejection fraction, 26 +/- 6%) were randomized to bisoprolol fumarate (n = 13) or to placebo therapy (n = 15). The dosage of the drugs was titrated to match that of the the Cardiac Insufficiency Bisoprolol Study protocol. Hemodynamic and gas exchange responses to exercise, MRI measurements of left ventricular end-systolic and end-diastolic volumes and ejection fraction, and left ventricular rotation and relaxation velocities were measured before the administration of the drug and 6 and 12 months later. RESULTS: After 1 year, heart rate was reduced in the bisoprolol fumarate group both at rest (81 +/- 12 before therapy versus 61 +/- 11 after therapy; P <.01) and peak exercise (144 +/- 20 before therapy versus 127 +/- 17 after therapy; P <.01), which indicated a reduction in sympathetic drive. No differences were observed in heart rate responses in the placebo group. No differences were observed within or between groups in peak oxygen uptake, although work rate achieved was higher (117.9 +/- 36 watts versus 146.1 +/- 33 watts; P <.05) and exercise time tended to be higher (9.1 +/- 1.7 minutes versus 11.4 +/- 2.8 minutes; P =.06) in the bisoprolol fumarate group. A trend for a reduction in left ventricular end-diastolic volume (-54 mL) and left ventricular end-systolic volume (-62 mL) in the bisoprolol fumarate group occurred after 1 year. Ejection fraction was higher in the bisoprolol fumarate group (25.0 +/- 7 versus 36.2 +/- 9%; P <.05), and the placebo group remained unchanged. Most changes in volume and ejection fraction occurred during the latter 6 months of treatment. With myocardial tagging, insignificant reductions in left ventricular rotation velocity were observed in both groups, whereas relaxation velocity was reduced only after bisoprolol fumarate therapy (by 39%; P <.05). CONCLUSION: One year of bisoprolol fumarate therapy resulted in an improvement in exercise capacity, showed trends for reductions in end-diastolic and end-systolic volumes, increased ejection fraction, and significantly reduced relaxation velocity. Although these results generally confirm the beneficial effects of beta-blockade in patients with chronic heart failure, they show differential effects on systolic and diastolic function.

Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?

We propose a multivariate approach to the study of geographic species distribution which does not require absence data. Building on Hutchinson's concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in Situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.