An integrated approach project for the revaluation of a traditional sourdough bread production chain

The influence of organic and conventional farming systems on the performance of a panel of old and modern Italian bread wheat varieties has been evaluated, with the aim to individuate an agronomic protocol suitable for the production of a sourdough bread traditionally prepared in a hill zone of Emilia-Romagna. The agronomic and technological characterisation of the wheat samples obtained in organic and conventional farming conditions has been done and the sensorial qualities of the sourdough bread obtained have been evaluated

The analysis of transformations for profit-and-loss data

We analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on the profitability of 1405 firms, 407 of which report losses. The problem in both cases is to use regression to predict performance from sets of explanatory variables. In one case, it is clear from scatter plots of the data that the negative responses have a lower variance than the positive responses and a different relationship with the explanatory variables. Because the data include negative responses, the Box–Cox transformation cannot be used. We develop a robust version of an extension to the Yeo–Johnson transformation which allows different transformations for positive and negative responses. Tests and graphical methods from our robust analysis enable the detection of outliers, the assessment of values of the two transformation parameters and the building of simple regression models. Performance comparisons are made with non-parametric transformations

The box-cox transformation: review and extensions

The Box-Cox power transformation family for non-negative responses in linear models has a long and interesting history in both statistical practice and theory, which we summarize. The relationship between generalized linear models and log transformed data is illustrated. Extensions investigated include the transform both sides model and the Yeo-Johnson transformation for observations that can be positive or negative. The paper also describes an extended Yeo-Johnson transformation that allows positive and negative responses to have different power transformations. Analyses of data show this to be necessary. Robustness enters in the fan plot for which the forward search provides an ordering of the data. Plausible transformations are checked with an extended fan plot. These procedures are used to compare parametric power transformations with nonparametric transformations produced by smoothing

DIAPHRAGMATIC MOBILITY, LUNG HYPERINFLATION AND EFFECTS OF THE PULMONARY REHABILITATION

Rationale: The diaphragm pathophysiological changes occurring in chronic obstructive pulmonary disease (COPD) leads to functional inefficiency that strongly correlates to the loss of lung function. Muscle fiber shortening follows lung hyperinflation, resulting to a chronic mechanical disadvantage, which worsens in COPD exacerbations. The diaphragmatic mobility (DM) is mostly assessed with techniques that exposes the patient to risks. The ultrasonography on M-mode is easy to use, safe and measures directly the diaphragmatic dome displacement. Goals: to determine whether the COPD impairs the DM, and verify improvements after an inpatient pulmonary rehabilitation (PR). Methods: ultrasonography on M-mode assessed the rest breathing and the slow deep inspiration on 52 patients and 15 healthy controls. Lung functions test, arterial blood gas analyses, six minute walk test were also performed. Results: after initial screening, 36 COPD patients ended the PR. The DM was lower on the slow deep inspiration on COPD patients and correlated with the COPD severity (r=0.8, p<0.001). The DM on rest breathing was higher for COPD patients and also correlated to the lung disease severity (r=0.74, p<0.001). After the PR the DM on the slow deep inspiration increases from 4.58cm\ub11.83cm to 5.45cm\ub11.56cm (p<0.01). Conclusions: ultrasonography on M-mode showed the correlation between DM impairment and COPD severity. The PR improves diaphragmatic function

Robust regression with density power divergence: theory, comparisons, and data analysis

Minimum density power divergence estimation provides a general framework for robust statistics, depending on a parameter α , which determines the robustness properties of the method. The usual estimation method is numerical minimization of the power divergence. The paper considers the special case of linear regression. We developed an alternative estimation procedure using the methods of S-estimation. The rho function so obtained is proportional to one minus a suitably scaled normal density raised to the power α . We used the theory of S-estimation to determine the asymptotic efficiency and breakdown point for this new form of S-estimation. Two sets of comparisons were made. In one, S power divergence is compared with other S-estimators using four distinct rho functions. Plots of efficiency against breakdown point show that the properties of S power divergence are close to those of Tukey's biweight. The second set of comparisons is between S power divergence estimation and numerical minimization. Monitoring these two procedures in terms of breakdown point shows that the numerical minimization yields a procedure with larger robust residuals and a lower empirical breakdown point, thus providing an estimate of α leading to more efficient parameter estimates

Retroperitoneal pararenal isolated neurofibroma: report of a case and review of literature

The neurofibroma is a tumour of neural origin. This kind of neoplasm, though, is generally skin located. Rare cases in deep organs or in the peritoneal cavity are also reported in the literature. There are two types of neurofibromas, localized and diffuse; the latter is associated with von Recklinghausen disease and always occurs together with skin neurofibromas. Here we report the case of a 47-year-old man affected by retroperitoneal neurofibroma, but not associated with von Recklinghausen disease. A computed tomography (CT) scan described a retroperitoneal pararenal lesion with no clear involvement of adjacent viscera. We describe the diagnostic modality, treatment planning and the timing of treatment of this neoplasm, reviewing also the literature

Molecular prevalence of Coxiella burnetii in bulk-tank milk from bovine dairy herds :systematic review and meta-analysis

Coxiella burnetii is an obligate intracellular zoonotic bacterium that causes Q fever. Ruminants, including cattle, are broadly known to be reservoirs for this bacterium. Since 2006, many research groups have evaluated the herd-level prevalence of C. burnetii in cattle by molecular techniques on composite milk samples. This study explored the global C. burnetii herd-level prevalence from studies done on bovine bulk-tank milk (BTM) samples using PCR-based analysis. Also, moderators were investigated to identify sources of heterogeneity. Databases (CAB Abstracts, Medline via Ovid, PubMed, Web of Science and Google Scholar) were searched for index articles on C. burnetii prevalence in BTM samples by PCR published between January-1973 and November-2018. Numerous studies (1054) were initially identified, from which seventeen original publications were included in the meta-analysis based on the pre-defined selection criteria. These studies comprised 4031 BTM samples from twelve countries. A random-effects model was used because of considerable heterogeneity (I2 = 98%) to estimate the herd-level prevalence of C. burnetii as 37.0%(CI95%25.2–49.5%). The average herd size appeared to account for a high level of the heterogeneity. No other moderators (geographic location, gross national income or notification criteria for Q fever) seemed to be determinant. This systematic evaluation demonstrated a high molecular prevalence of C. burnetii in BTM samples both in European and non-European countries, evidencing a widespread herd-level circulation of this agent in bovine dairy farms around the world. Meta-regression showed herd size as the most relevant moderator with the odds of a BTM sample testing positive doubling with every unit increase

The power of monitoring: how to make the most of a contaminated multivariate sample

Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in the presence of contamination by outliers. However, a disadvantage of having a single, even if robust, summary of the data is that important choices concerning parameters of the robust method, such as breakdown point, have to be made prior to the analysis. The effect of such choices may be difficult to evaluate. We argue that an effective solution is to look at several pictures, and possibly to a whole movie, of the available data. This can be achieved by monitoring, over a range of parameter values, the results computed through the robust methodology of choice. We show the information gain that monitoring provides in the study of complex data structures through the analysis of multivariate datasets using different high-breakdown techniques. Our findings support the claim that the principle of monitoring is very flexible and that it can lead to robust estimators that are as efficient as possible. We also address through simulation some of the tricky inferential issues that arise from monitoring