Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

LMS tables for waist circumference and waist-to-height ratio among Colombian children and adolescents: The FUPRECOL study

Background: Indices predictive of central obesity include waist circumference (WC) and waist-to-height ratio (WHtR). The aims of this study were 1) to establish a Colombian youth smoothed centile charts and LMS tables for WC and WHtR and 2) to evaluate the utility of these parameters as predictors of overweight and obesity. Method: A cross-sectional study whose sample population comprised 7954 healthy Colombian schoolchildren [boys n=3460 and girls n=4494, mean (standard deviation) age 12.8 (2.3) years old]. Weight, height, body mass index (BMI), WC and WHtR and its percentiles were calculated. Appropriate cut-offs point of WC and WHtR for overweight and obesity, as defined by the International Obesity Task Force (IOTF) definitions, were selected using receiver operating characteristic (ROC) analysis. The discriminating power of WC and WHtR was expressed as area under the curve (AUC). Results: Reference values for WC and WHtR are presented. Mean WC increased and WHtR decreased with age for both genders. We found a moderate positive correlation between WC and BMI (r= 0.756, P < 0.01) and WHtR and BMI (r= 0.604, P < 0.01). The ROC analysis showed a high discrimination power in the identification of overweight and obesity for both measures in our sample population. Overall, WHtR was slightly a better predictor for overweight/obesity (AUC 95% CI 0.868-0.916) than the WC (AUC 95% CI 0.862-0.904). Conclusion: This paper presents the first sex- and age-specific WC and WHtR percentiles for both measures among Colombian children and adolescents aged 9–17.9 years. By providing LMS tables for Latin-American people based on Colombian reference data, we hope to provide quantitative tools for the study of obesity and its comorbidities.

Índice acumulativo de riesgo (nice) como prueba diagnóstica con base en tomografía corneal por elevación en la prevención de ectasia corneal post-lasik

Antecedentes La ectasia corneal post-lasik (ECPL) es una complicación infrecuente, pero devastadora en la cirugía lasik (queratomileusis asistida con éxcimer láser) para el tratamiento de la miopía con o sin astigmatismo. Con base en la tomografía corneal por elevación por imágenes de Scheimpflug (Sistema Pentacam HR, Oculus Wetzlar, Alemania), se propone un novedoso índice acumulativo de riesgo para ser utilizado como prueba diagnóstica de tamizaje y así prevenir esta complicación. Metodología Se realizó un estudio observacional analítico, de corte transversal tipo pruebas diagnósticas, con el fin de evaluar las características operativas del índice NICE teniendo como estándar de referencia el módulo de Belin-Ambrosio (Pentacam HR) utilizando un modelo de regresión logística binaria, tablas de contingencia y estimando el área bajo la curva ROC. Resultados Se evaluaron 361 ojos de los cuales el 59,3% provenían de pacientes de sexo femenino, la edad media global fue de 30 años (RIC 11,0). El modelo logístico binario aplicado se construyó con base en cuatro variables independientes cuantitativas (K2, PAQUI, EP e I-S) y una cualitativa (SEXO), y se determinó su relación con la variable dependiente, NICE (puntaje final). Las variables predictoras fueron estadísticamente significativas clasificando adecuadamente el 92,9% de los ojos evaluados según presencia o ausencia de riesgo. El coeficiente de Nagelkerke fue de 74,4%. Conclusiones El índice acumulativo de riesgo NICE es una herramienta diagnóstica novedosa en la evaluación de candidatos a cirugía refractiva lasik para prevenir la ectasia secundaria.

Factores de riesgo preoperatorios asociados a conversión a técnica abierta en colecistectomía laparoscópica de urgencia

Introducción: la colecistectomía laparoscópica es la técnica de elección en pacientes con indicación de extracción quirúrgica de la vesícula; sin embargo, en promedio 20% de éstos requieren conversión a técnica abierta. En este estudio se evaluaron los factores de riesgo preoperatorios para conversión en colecistectomía laparoscópica de urgencia. Metodología: se realizó un estudio de casos y controles no pareado. Se obtuvo información sociodemográfica y de variables de interés de los registros de historias clínicas de pacientes operados entre el 2013 y 2016. Se identificaron los motivos de conversión de técnica quirúrgica. Se caracterizó la población de estudio y se estimaron asociaciones según la naturaleza de las variables. Mediante un análisis de regresión logística se ajustaron posibles variables de confusión. Resultados: se analizaron los datos de 444 pacientes (111 casos y 333 controles). La causa de conversión más frecuente fue la dificultad técnica (50,5%). Se encontró que la mayor edad, el sexo masculino, el antecedente de cirugía abierta en hemiabdomen superior, el signo de Murphy clínico positivo, la dilatación de la vía biliar, la leucocitosis y la mayor experiencia del cirujano, fueron factores de riesgo para conversión. Se encontró un área bajo la curva ROC= 0,743 (IC95% 0,692–0,794, p= <0,001). Discusión: existen unos factores que se asocian a mayor riesgo de conversión en colecistectomía laparoscópica. La mayoría se relacionan con un proceso inflamatorio más severo, por lo que se debe evitar la prolongación del tiempo de espera entre el inicio de los síntomas y la extirpación quirúrgica de la vesícula.

The Drivers of Wage Inequality Across Europe: A Recentered Influence Function Regression Approach

This study analyzes the impact of individual characteristics as well as occupation and industry on male wage inequality in nine European countries. Unlike previous studies, we consider regression models for five inequality measures and employ the recentered influence function regression method proposed by Firpo et al. (2009) to test directly the influence of covariates on inequality. We conclude that there is heterogeneity in the effects of covariates on inequality across countries and throughout wage distribution. Heterogeneity among countries is more evident in education and experience whereas occupation and industry characteristics as well as holding a supervisory position reveal more similar effects. Our results are compatible with the skill biased technological change, rapid rise in the integration of trade and financial markets as well as explanations related to the increase of the remunerative package of top executives.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

Prediction Nomogram for 68Ga-PSMA-11 PET/CT in different clinical settings of PSA failure after radical treatment for Prostate Cancer

Objective The objective of this study was to develop a clinical nomogram to predict gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (68Ga-PSMA-11-PET/CT) positivity in different clinical settings of PSA failure. Materials and methods Seven hundred three (n = 703) prostate cancer (PCa) patients with confirmed PSA failure after radical therapy were enrolled. Patients were stratified according to different clinical settings (first-time biochemical recurrence [BCR]: group 1; BCR after salvage therapy: group 2; biochemical persistence after radical prostatectomy [BCP]: group 3; advanced stage PCa before second-line systemic therapies: group 4). First, we assessed 68Ga-PSMA-11-PET/CT positivity rate. Second, multivariable logistic regression analyses were used to determine predictors of positive scan. Third, regression-based coefficients were used to develop a nomogram predicting positive 68Ga-PSMA-11-PET/CT result and 200 bootstrap resamples were used for internal validation. Fourth, receiver operating characteristic (ROC) analysis was used to identify the most informative nomogram’s derived cut-off. Decision curve analysis (DCA) was implemented to quantify nomogram’s clinical benefit. Results 68Ga-PSMA-11-PET/CT overall positivity rate was 51.2%, while it was 40.3% in group 1, 54% in group 2, 60.5% in group 3, and 86.9% in group 4 (p < 0.001). At multivariable analyses, ISUP grade, PSA, PSA doubling time, and clinical setting were independent predictors of a positive scan (all p ≤ 0.04). A nomogram based on covariates included in the multivariate model demonstrated a bootstrap-corrected accuracy of 82%. The nomogram-derived best cut-off value was 40%. In DCA, the nomogram revealed clinical net benefit of > 10%. Conclusions This novel nomogram proved its good accuracy in predicting a positive scan, with values ≥ 40% providing the most informative cut-off in counselling patients to 68Ga-PSMA-11-PET/CT. This tool might be important as a guide to clinicians in the best use of PSMA-based PET imaging.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.

Bayesian Linear Regression for estimation of the Fractal Dimension of the Cerebral Cortex

The cerebral cortex presents self-similarity in a proper interval of spatial scales, a property typical of natural objects exhibiting fractal geometry. Its complexity therefore can be characterized by the value of its fractal dimension (FD). In the computation of this metric, it has usually been employed a frequentist approach to probability, with point estimator methods yielding only the optimal values of the FD. In our study, we aimed at retrieving a more complete evaluation of the FD by utilizing a Bayesian model for the linear regression analysis of the box-counting algorithm. We used T1-weighted MRI data of 86 healthy subjects (age 44.2 ± 17.1 years, mean ± standard deviation, 48% males) in order to gain insights into the confidence of our measure and investigate the relationship between mean Bayesian FD and age. Our approach yielded a stronger and significant (P < .001) correlation between mean Bayesian FD and age as compared to the previous implementation. Thus, our results make us suppose that the Bayesian FD is a more truthful estimation for the fractal dimension of the cerebral cortex compared to the frequentist FD.

Valutazione della risposta agli sversamenti di olio in mare tramite il software ROC - Response Options Calculator

L’aumento crescente della domanda di idrocarburi a livello mondiale ha determinato un progressivo aumento dell’estrazione “offshore” e del trasporto via mare di petrolio. In questo contesto non sono da escludersi incidenti e conseguenti sversamenti di olio in mare. Sversato sulla superficie del mare, l’olio, subisce una serie di trasformazioni chimico-fisiche che prendono il nome di “processi di weathering”. In base alla quantità di prodotto rilasciato, i rilasci di olio possono portare ad ingenti danni ambientali, minando la sopravvivenza degli ecosistemi contaminati dall’olio. La condizione di rilevante criticità ambientale prodotta da uno sversamento accidentale di olio richiede la preparazione di precisi piani di intervento. Tipicamente le azioni intraprese hanno lo scopo di attuare la riduzione della massa di olio galleggiante e di evitare lo spiaggiamento dell’olio. Il presente lavoro di tesi si colloca nell’ambito della modellazione matematica degli sversamenti superficiali di olio in mare, al fine di quantificare l’influenza delle diverse azioni di emergenza sull’olio sversato. Lo scopo della tesi è quello di testare un software denominato ROC - Response Options Calculator, per simulare i fenomeni di weathering degli sversamenti di olio in presenza di azioni di emergenza di diversa tipologia. Il lavoro di tesi è suddiviso come segue: il capitolo 1 ha un ruolo introduttivo; nel capitolo 2 è descritto in dettaglio il software ROC; nel capitolo 3 è presentata l’applicazione del codice ad un caso di studio rappresentato da una piattaforma “offshore”, per la quale sono considerati 3 differenti scenari di rilascio accidentale sulla superficie del mare, a ciascuno dei quali corrisponde una diversa risposta all’emergenza ed un diverso insieme di risorse; nello stesso capitolo è inoltre presentata l’applicazione di un’equazione per la progettazione della risposta meccanica ad uno dei rilasci del caso di studio; il capitolo 4 riporta alcune considerazioni conclusive.

Adapting The Sniffin' Sticks Olfactory Test To Diagnose Parkinson's Disease In Estonia.

The aim of the study was to develop a culturally adapted translation of the 12-item smell identification test from Sniffin' Sticks (SS-12) for the Estonian population in order to help diagnose Parkinson's disease (PD). A standard translation of the SS-12 was created and 150 healthy Estonians were questioned about the smells used as response options in the test. Unfamiliar smells were replaced by culturally familiar options. The adapted SS-12 was applied to 70 controls in all age groups, and thereafter to 50 PD patients and 50 age- and sex-matched controls. 14 response options from 48 used in the SS-12 were replaced with familiar smells in an adapted version, in which the mean rate of correct response was 87% (range 73-99) compared to 83% with the literal translation (range 50-98). In PD patients, the average adapted SS-12 score (5.4/12) was significantly lower than in controls (average score 8.9/12), p < 0.0001. A multiple linear regression using the score in the SS-12 as the outcome measure showed that diagnosis and age independently influenced the result of the SS-12. A logistic regression using the SS-12 and age as covariates showed that the SS-12 (but not age) correctly classified 79.0% of subjects into the PD and control category, using a cut-off of <7 gave a sensitivity of 76% and specificity of 86% for the diagnosis of PD. The developed SS-12 cultural adaption is appropriate for testing olfaction in Estonia for the purpose of PD diagnosis.

Lung Age Is Related To Carotid Structural Alterations In Hypertensive Subjects.

Hypertensive patients exhibit higher cardiovascular risk and reduced lung function compared with the general population. Whether this association stems from the coexistence of two highly prevalent diseases or from direct or indirect links of pathophysiological mechanisms is presently unclear. This study investigated the association between lung function and carotid features in non-smoking hypertensive subjects with supposed normal lung function. Hypertensive patients (n = 67) were cross-sectionally evaluated by clinical, hemodynamic, laboratory, and carotid ultrasound analysis. Forced vital capacity, forced expired volume in 1 second and in 6 seconds, and lung age were estimated by spirometry. Subjects with ventilatory abnormalities according to current guidelines were excluded. Regression analysis adjusted for age and prior smoking history showed that lung age and the percentage of predicted spirometric parameters associated with common carotid intima-media thickness, diameter, and stiffness. Further analyses, adjusted for additional potential confounders, revealed that lung age was the spirometric parameter exhibiting the most significant regression coefficients with carotid features. Conversely, plasma C-reactive protein and matrix-metalloproteinases-2/9 levels did not influence this relationship. The present findings point toward lung age as a potential marker of vascular remodeling and indicate that lung and vascular remodeling might share common pathophysiological mechanisms in hypertensive subjects.

Changes In The Bacterial Community Of Soil From A Neutral Mine Drainage Channel.

Mine drainage is an important environmental disturbance that affects the chemical and biological components in natural resources. However, little is known about the effects of neutral mine drainage on the soil bacteria community. Here, a high-throughput 16S rDNA pyrosequencing approach was used to evaluate differences in composition, structure, and diversity of bacteria communities in samples from a neutral drainage channel, and soil next to the channel, at the Sossego copper mine in Brazil. Advanced statistical analyses were used to explore the relationships between the biological and chemical data. The results showed that the neutral mine drainage caused changes in the composition and structure of the microbial community, but not in its diversity. The Deinococcus/Thermus phylum, especially the Meiothermus genus, was in large part responsible for the differences between the communities, and was positively associated with the presence of copper and other heavy metals in the environmental samples. Other important parameters that influenced the bacterial diversity and composition were the elements potassium, sodium, nickel, and zinc, as well as pH. The findings contribute to the understanding of bacterial diversity in soils impacted by neutral mine drainage, and demonstrate that heavy metals play an important role in shaping the microbial population in mine environments.

Prognostic Value Of Dna And Mrna E6/e7 Of Human Papillomavirus In The Evolution Of Cervical Intraepithelial Neoplasia Grade 2.

This study aimed at evaluating whether human papillomavirus (HPV) groups and E6/E7 mRNA of HPV 16, 18, 31, 33, and 45 are prognostic of cervical intraepithelial neoplasia (CIN) 2 outcome in women with a cervical smear showing a low-grade squamous intraepithelial lesion (LSIL). This cohort study included women with biopsy-confirmed CIN 2 who were followed up for 12 months, with cervical smear and colposcopy performed every three months. Women with a negative or low-risk HPV status showed 100% CIN 2 regression. The CIN 2 regression rates at the 12-month follow-up were 69.4% for women with alpha-9 HPV versus 91.7% for other HPV species or HPV-negative status (P < 0.05). For women with HPV 16, the CIN 2 regression rate at the 12-month follow-up was 61.4% versus 89.5% for other HPV types or HPV-negative status (P < 0.05). The CIN 2 regression rate was 68.3% for women who tested positive for HPV E6/E7 mRNA versus 82.0% for the negative results, but this difference was not statistically significant. The expectant management for women with biopsy-confirmed CIN 2 and previous cytological tests showing LSIL exhibited a very high rate of spontaneous regression. HPV 16 is associated with a higher CIN 2 progression rate than other HPV infections. HPV E6/E7 mRNA is not a prognostic marker of the CIN 2 clinical outcome, although this analysis cannot be considered conclusive. Given the small sample size, this study could be considered a pilot for future larger studies on the role of predictive markers of CIN 2 evolution.