On the relationship between empirical likelihood and empirical saddlepoint approximation for multivariate M-estimators

SUMMARY By comparing the expansions of the empirical log-likelihood ratio and the empirical cumulant generating function calculated at the saddlepoint, we investigate the relationship between empirical likelihood and empirical saddlepoint approximations. This leads to a nonparametric approximation of the density of a multivariate M-estimator based on the empirical likelihood and, on the other hand, it provides nonparametric confidence regions based on the empirical cumulant generating function. Some examples illustrate the use of the empirical likelihood in saddlepoint approximations and vice vers

Spin and Statistics and First Principles

It was shown in the early Seventies that, in Local Quantum Theory (that is the most general formulation of Quantum Field Theory, if we leave out only the unknown scenario of Quantum Gravity) the notion of Statistics can be grounded solely on the local observable quantities (without assuming neither the commutation relations nor even the existence of unobservable charged field operators); one finds that only the well known (para)statistics of Bose/Fermi type are allowed by the key principle of local commutativity of observables. In this frame it was possible to formulate and prove the Spin and Statistics Theorem purely on the basis of First Principles. In a subsequent stage it has been possible to prove the existence of a unique, canonical algebra of local field operators obeying ordinary Bose/Fermi commutation relations at spacelike separations. In this general guise the Spin - Statistics Theorem applies to Theories (on the four dimensional Minkowski space) where only massive particles with finite mass degeneracy can occur. Here we describe the underlying simple basic ideas, and briefly mention the subsequent generalisations; eventually we comment on the possible validity of the Spin - Statistics Theorem in presence of massless particles, or of violations of locality as expected in Quantum Gravity.Comment: Survey based on a talk given at the Meeting on "Theoretical and experimental aspects of the spin - statistics connection and related symmetries", Trieste, Italy - October 21-25, 200

Enhancing cardiovascular risk stratification: Radiomics of coronary plaque and perivascular adipose tissue – Current insights and future perspectives

Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation

A note on comonotonicity and positivity of the control components of decoupled quadratic FBSDE

In this small note we are concerned with the solution of Forward-Backward Stochastic Differential Equations (FBSDE) with drivers that grow quadratically in the control component (quadratic growth FBSDE or qgFBSDE). The main theorem is a comparison result that allows comparing componentwise the signs of the control processes of two different qgFBSDE. As a byproduct one obtains conditions that allow establishing the positivity of the control process.Comment: accepted for publicatio

Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification

Introduction: Coronary Artery Disease (CAD) is a leading cause of global mortality, accurate stenosis grading is crucial for treatment planning, it currently requires time-consuming manual assessment and suffers from interobserver variability. Few deep learning methods have been proposed for automated scoring, but none have explored combining radiomic and autoencoder (AE)-based features. This study develops a machine learning approach combining radiomic and AE-based features for stenosis grade evaluation from multiplanar reconstructed images (MPR) cardiac computed tomography (CCTA) images. Methods: The dataset comprised 2,548 CCTA-derived MPR images from 220 patients, classified as no-CAD, non-obstructive CAD or obstructive CAD. Sixty-four AE-based and 465 2D radiomic features, were processed separately or combined. The dataset was split into training (85%) and test (15%) sets. Relevant features were selected and input to a random forest classifier. A cascade pipeline stratified the three classes via two sub-tasks: (a) no CAD vs. CAD, and (b) nonobstructive vs. obstructive CAD. Results: The AE-based model identified 17 and 6 features as relevant for the sub-task (a) and (b), respectively, while 44 and 30 features were selected in the radiomic model. The two models reached an overall balanced accuracy of 0.68 and 0.82 on the test set, respectively. Fifteen and 35 features were indeed selected in the combined model which outperformed the single ones achieving on the test set an overall balanced accuracy, sensitivity and specificity of 0.91, 0.91, and 0.94, respectively. Conclusion: Integration of radiomics and deep learning shows promising results for stenosis assessment in CAD patients

An image-based kinematic model of the tibiotalar and subtalar joints and its application to gait analysis in children with Juvenile Idiopathic Arthritis

In vivo estimates of tibiotalar and the subtalar joint kinematics can unveil unique information about gait biomechanics, especially in the presence of musculoskeletal disorders affecting the foot and ankle complex. Previous literature investigated the ankle kinematics on ex vivo data sets, but little has been reported for natural walking, and even less for pathological and juvenile populations. This paper proposes an MRI-based morphological fitting methodology for the personalised definition of the tibiotalar and the subtalar joint axes during gait, and investigated its application to characterise the ankle kinematics in twenty patients affected by Juvenile Idiopathic Arthritis (JIA). The estimated joint axes were in line with in vivo and ex vivo literature data and joint kinematics variation subsequent to inter-operator variability was in the order of 1°. The model allowed to investigate, for the first time in patients with JIA, the functional response to joint impairment. The joint kinematics highlighted changes over time that were consistent with changes in the patient’s clinical pattern and notably varied from patient to patient. The heterogeneous and patient-specific nature of the effects of JIA was confirmed by the absence of a correlation between a semi-quantitative MRI-based impairment score and a variety of investigated joint kinematics indexes. In conclusion, this study showed the feasibility of using MRI and morphological fitting to identify the tibiotalar and subtalar joint axes in a non-invasive patient-specific manner. The proposed methodology represents an innovative and reliable approach to the analysis of the ankle joint kinematics in pathological juvenile populations