Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

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.

Extensor carpi ulnaris accessory tendinous slips (ECUATS): MRI features at 3.0T and correlation with tendinosis : 49

Purpose: To assess the visibility and the features of ECUATS on 3.0-T MRI studies, and evaluate their correlation with tendinosis. Methods and materials: Our retrospective study was approved by IRB, with waiver of informed consent. Fifty wrist MRI and 48 MR arthrographies from 98 patients (55 males, 43 females, mean age 42.3 years) performed between January and November 2009 on 3.0-T units were reviewed. Images (transverse T1, T2, FS Gd T1 and VIBE) were independently analyzed by two radiologists, and a consensus reached with a third reader in case of disagreement. The visibility of ECUATS was assessed on each available transverse sequence. When present, ECUATS' origins, diameters and insertions were noted. ECU tendinosis was also evaluated. Inter-rater agreement was assessed using Cohen's Kappa coefficient. Results: ECUATS observed prevalence was 23.5% (23/98). ECUATS were more frequently noted on the VIBE sequence, with a good inter-rater agreement (Kappa = 0.72). Origins were noted in 95.7% of cases: 3 were at the level of, and 20 distal to ECU subsheath. Insertions were seen in 43.5%: 2 were on 5th metacarpal bone, 8 on extensor apparatus of 5th finger. ECUATS mean shortest and longest diameters were 0.54 and 0.85 mm respectively. ECU tendinosis was statistically more frequently noted in patients with ECUATS (p <0.05). Conclusion: ECUATS are readily visible on 3.0-T MRI studies, especially on transverse GRE VIBE images. ECU tendinosis is more frequently noted in patients bearing ECUATS.

High-growth firms and innovation: an empirical analysis for Spanish firms

This paper analyses the effect of R&D investment on firm growth. We use an extensive sample of Spanish manufacturing and service firms. The database comprises diverse waves of Spanish Community Innovation Survey and covers the period 2004–2008. First, a probit model corrected for sample selection analyses the role of innovation on the probability of being a high-growth firm (HGF). Second, a quantile regression technique is applied to explore the determinants of firm growth. Our database shows that a small number of firms experience fast growth rates in terms of sales or employees. Our results reveal that R&D investments positively affect the probability of becoming a HGF. However, differences appear between manufacturing and service firms. Finally, when we study the impact of R&D investment on firm growth, quantile estimations show that internal R&D presents a significant positive impact for the upper quantiles, while external R&D shows a significant positive impact up to the median. Keywords : High-growth firms, Firm growth, Innovation activity. JEL Classifications : L11, L25, L26, O30

Proteomic and transcriptomic profiling of Staphylococcus aureus surface LPXTG-proteins: correlation with agr genotypes and adherence phenotypes.

Staphylococcus aureus infections involve numerous adhesins and toxins, which expression depends on complex regulatory networks. Adhesins include a family of surface proteins covalently attached to the peptidoglycan via a conserved LPXTG motif. Here we determined the protein and mRNA expression of LPXTG-proteins of S. aureus Newman in time-course experiments, and their relation to fibrinogen adherence in vitro. Experiments were performed with mutants in the global accessory-gene regulator (agr), surface protein A (Spa), and fibrinogen-binding protein A (ClfA), as well as during growth in iron-rich or iron-poor media. Surface proteins were recovered by trypsin-shaving of live bacteria. Released peptides were analyzed by liquid chromatography coupled to tandem mass-spectrometry. To unambiguously identify peptides unique to LPXTG-proteins, the analytical conditions were refined using a reference library of S. aureus LPXTG-proteins heterogeneously expressed in surrogate Lactococcus lactis. Transcriptomes were determined by microarrays. Sixteen of the 18 LPXTG-proteins present in S. aureus Newman were detected by proteomics. Nine LPXTG-proteins showed a bell-shape agr-like expression that was abrogated in agr-negative mutants including Spa, fibronectin-binding protein A (FnBPA), ClfA, iron-binding IsdA, and IsdB, immunomodulator SasH, functionally uncharacterized SasD, biofilm-related SasG and methicillin resistance-related FmtB. However, only Spa and SasH modified their proteomic and mRNA profiles in parallel in the parent and its agr- mutant, whereas all other LPXTG-proteins modified their proteomic profiles independently of their mRNA. Moreover, ClfA became highly transcribed and active in fibrinogen-adherence tests during late growth (24 h), whereas it remained poorly detected by proteomics. On the other hand, iron-regulated IsdA-B-C increased their protein expression by >10-times in iron-poor conditions. Thus, proteomic, transcriptomic, and adherence-phenotype demonstrated differential profiles in S. aureus. Moreover, trypsin peptide signatures suggested differential protein domain exposures in various environments, which might be relevant for anti-adhesin vaccines. A comprehensive understanding of the S. aureus physiology should integrate all three approaches.

Correlation of Locally-Based Performance of Asphalts with Their Physicochemical Parameters, Progress Report 2, HR-298, 1989

This project was undertaken to study the relationships between the performance of locally available asphalts and their physicochemical properties under Iowa conditions with the ultimate objective of development of a locally and performance-based asphalt specification for durable pavements. Physical and physicochemical tests were performed on three sets of asphalt samples including: (a) twelve samples from local asphalt suppliers and their TFOT residues, (b) six core samples of known service records, and (c) a total of 79 asphalts from 10 pavement projects including original, lab aged and recovered asphalts from field mixes, as well as from lab aged mixes. Tests included standard rheological tests, HP-GPC and TMA. Some specific viscoelastic tests (at 5 deg C) were run on b samples and on some a samples. DSC and X-ray diffraction studies were performed on a and b samples. Furthermore, NMR techniques were applied to some a, b and c samples. Efforts were made to identify physicochemical properties which are correlated to physical properties known to affect field performance. The significant physicochemical parameters were used as a basis for an improved performance-based trial specification for Iowa to ensure more durable pavements.

Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of São Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of São Paulo.

Adult native septic arthritis: a review of 10 years of experience and lessons for empirical antibiotic therapy.

Objectives To review the epidemiology of native septic arthritis to establish local guidelines for empirical antibiotic therapy as part of an antibiotic stewardship programme. Methods We conducted a 10 year retrospective study based on positive synovial fluid cultures and discharge diagnosis of septic arthritis in adult patients. Microbiology results and medical records were reviewed. Results Between 1999 and 2008, we identified 233 episodes of septic arthritis. The predominant causative pathogens were methicillin-susceptible Staphylococcus aureus (MSSA) and streptococci (respectively, 44.6% and 14.2% of cases). Only 11 cases (4.7%) of methicillin-resistant S. aureus (MRSA) arthritis were diagnosed, among which 5 (45.5%) occurred in known carriers. For large-joint infections, amoxicillin/clavulanate or cefuroxime would have been appropriate in 84.5% of cases. MRSA and Mycobacterium tuberculosis would have been the most frequent pathogens that would not have been covered. In contrast, amoxicillin/clavulanate would have been appropriate for only 75.3% of small-joint infections (82.6% if diabetics are excluded). MRSA and Pseudomonas aeruginosa would have been the main pathogens not covered. Piperacillin/tazobactam would have been appropriate in 93.8% of cases (P < 0.01 versus amoxicillin/clavulanate). This statistically significant advantage is lost after exclusion of diabetics (P = 0.19). Conclusions Amoxicillin/clavulanate or cefuroxime would be adequate for empirical coverage of large-joint septic arthritis in our area. A broad-spectrum antibiotic would be significantly superior for small-joint infections in diabetics. Systematic coverage of MRSA is not justified, but should be considered for known carriers. These recommendations are applicable to our local setting. They might also apply to hospitals sharing the same epidemiology.

Correlation of physical and chemical attributes of soil with sugarcane yield

The objective of this work was to evaluate the correlation between sugarcane yield and some physical and chemical attributes of soil. For this, a 42‑ha test area in Araras, SP, Brazil, was used. Soil properties were determined from samples collected at the beginning of the 2003/2004 harvest season, using a regular 100x100 m grid. Yield assessment was done with a yield monitor (Simprocana). Correlation analyses were performed between sugarcane yield and the following soil properties: pH, pH CaCl2, N, C, cone index, clay content, soil organic matter, P, K, Ca, Mg, H+AL, cation exchange capacity, and base saturation. Correlation coefficients were respectively ‑0.05, ‑0.29, 0.33, 0.41, ‑0.27, 0.22, 0.44, ‑0.24, trace, ‑0.06, 0.01, 0.32, 0.14, and 0.04. Correlations of chemical and physical attributes of soil with sugarcane yield are weak, and, per se, they are not able to explain sugarcane yield variation, which suggests that other variables, besides soil attributes, should be analysed.

Impact of phenotype definition on genome-wide association signals: empirical evaluation in human immunodeficiency virus type 1 infection.

Discussion on improving the power of genome-wide association studies to identify candidate variants and genes is generally centered on issues of maximizing sample size; less attention is given to the role of phenotype definition and ascertainment. The authors used genome-wide data from patients infected with human immunodeficiency virus type 1 (HIV-1) to assess whether differences in type of population (622 seroconverters vs. 636 seroprevalent subjects) or the number of measurements available for defining the phenotype resulted in differences in the effect sizes of associations between single nucleotide polymorphisms and the phenotype, HIV-1 viral load at set point. The effect estimate for the top 100 single nucleotide polymorphisms was 0.092 (95% confidence interval: 0.074, 0.110) log(10) viral load (log(10) copies of HIV-1 per mL of blood) greater in seroconverters than in seroprevalent subjects. The difference was even larger when the authors focused on chromosome 6 variants (0.153 log(10) viral load) or on variants that achieved genome-wide significance (0.232 log(10) viral load). The estimates of the genetic effects tended to be slightly larger when more viral load measurements were available, particularly among seroconverters and for variants that achieved genome-wide significance. Differences in phenotype definition and ascertainment may affect the estimated magnitude of genetic effects and should be considered in optimizing power for discovering new associations.

Time in e-Learning Research: A Qualitative Review of the Empirical Consideration of Time in Research into e-Learning

Peer-reviewed

An Empirical Study on Faculty Perceptions and Teaching Practices of Wikipedia

Some faculty members from different universities around the world have begun to use Wikipedia as a teaching tool in recent years. These experiences show, in most cases, very satisfactory results and a substantial improvement in various basic skills, as well as a positive influence on the students' motivation. Nevertheless and despite the growing importance of e-learning methodologies based on the use of the Internet for higher education, the use of Wikipedia as a teaching resource remains scarce among university faculty.Our investigation tries to identify which are the main factors that determine acceptance or resistance to that use. We approach the decision to use Wikipedia as a teaching tool by analyzing both the individual attributes of faculty members and the characteristics of the environment where they develop their teaching activity. From a specific survey sent to all faculty of the Universitat Oberta de Catalunya (UOC), pioneer and leader in online education in Spain, we have tried to infer the influence of these internal and external elements. The questionnaire was designed to measure different constructs: perceived quality of Wikipedia, teaching practices involving Wikipedia, use experience, perceived usefulness and use of 2.0 tools. Control items were also included for gathering information on gender, age, teaching experience, academic rank, and area of expertise.Our results reveal that academic rank, teaching experience, age or gender, are not decisive factors in explaining the educational use of Wikipedia. Instead, the decision to use it is closely linked to the perception of Wikipedia's quality, the use of other collaborative learning tools, an active attitude towards web 2.0 applications, and connections with the professional non-academic world. Situational context is also very important, since the use is higher when faculty members have got reference models in their close environment and when they perceive it is positively valued by their colleagues. As far as these attitudes, practices and cultural norms diverge in different scientific disciplines, we have also detected clear differences in the use of Wikipedia among areas of academic expertise. As a consequence, a greater application of Wikipedia both as a teaching resource and as a driver for teaching innovation would require much more active institutional policies and some changes in the dominant academic culture among faculty members.