The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices regarding data handling, experimental design, and model evaluation is crucial. This work summarizes and strictly observes such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. We investigate the impact of different data augmentation techniques and model complexity on the overall performance. We consider MRI data from ADNI dataset to address a classification problem employing 3D Convolutional Neural Network (CNN). The experiments are designed to compensate for data scarcity and initial random parameters by utilizing cross-validation and multiple training trials. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures, each varying in the number of convolutional layers. Specifically, the augmentation strategies are based on affine transformations, such as zoom, shift, and rotation, applied concurrently or separately. The combined effect of data augmentation and model complexity leads to a variation in prediction performance up to 10% of accuracy. When affine transformation are applied separately, the model is more accurate, independently from the adopted architecture. For all strategies, the model accuracy followed a concave behavior at increasing number of convolutional layers, peaking at an intermediate value of layers. The best model (8 CL, (B)) is the most stable across cross-validation folds and training trials, reaching excellent performance both on the testing set and on an external test set

Energy and mechanical aspects on the thermal activation of diaphragm walls for heating and cooling

Underground geotechnical structures, such as deep and shallow foundations, diaphragm walls, tunnel linings and anchors are being increasingly employed as energy geostructures to exchange heat with the ground by installing absorber pipes into the structural elements. This paper focuses on the application of this technology to reinforced concrete diaphragm walls used for construction of underground car parks, basements and metro stations, with the purpose of heating and cooling the adjacent buildings. Preliminary numerical modelling allowed optimising the geothermal plant design of the diaphragm wall. Then its energy efficiency is investigated through finite element thermo-hydro coupled analyses together with the effects of the thermal activation on the surrounding soil. Finally, finite difference thermo-mechanical analyses are used to study the mechanical effects induced by the thermal activation

Multi-Output Learning via Spectral Filtering

In this paper we study a class of regularized kernel methods for vector-valued learning which are based on filtering the spectrum of the kernel matrix. The considered methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector-valued extensions of L2 boosting. Computational properties are discussed for various examples of kernels for vector-valued functions and the benefits of iterative techniques are illustrated. Generalizing previous results for the scalar case, we show finite sample bounds for the excess risk of the obtained estimator and, in turn, these results allow to prove consistency both for regression and multi-category classification. Finally, we present some promising results of the proposed algorithms on artificial and real data

Temporal prediction of multiple sclerosis evolution from patient-centered outcomes

Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Despite the high variability of its clinical presentation, relapsing and progressive multiple sclerosis are considered the two main disease types, with the former possibly evolving into the latter. Recently, the attention of the medical community toward the use of patient-centered outcomes in multiple sclerosis has significantly increased. Such patient-friendly measures are devoted to the assessment of the impact of the disease on several domains of the patient life. In this work, we investigate on use of patient-centered outcomes to predict the evolution of the disease and to assess its impact on patients\u201a\uc4\uf4 lives. To this aim, we build a novel temporal model based on gradient boosting classification and multiple-output elastic-net regression. The model provides clinically interpretable results along with accurate predictions of the disease course evolution

Surface symmetry-breaking and strain effects on orbital occupancy in transition metal perovskite epitaxial films

The electron occupancy of 3d-orbitals determines the properties of transition metal oxides. This can be achieved, for example, through thin-film heterostructure engineering of ABO(3) oxides, enabling emerging properties at interfaces. Interestingly, epitaxial strain may break the degeneracy of 3d-e(g) and t(2g) orbitals, thus favoring a particular orbital filling with consequences for functional properties. Here we disclose the effects of symmetry breaking at free surfaces of ABO(3) perovskite epitaxial films and show that it can be combined with substrate-induced epitaxial strain to tailor at will the electron occupancy of in-plane and out-of-plane surface electronic orbitals. We use X-ray linear dichroism to monitor the relative contributions of surface, strain and atomic terminations to the occupancy of 3z(2)-r(2) and x(2)-y(2) orbitals in La(2/3)Sr(1/3)MnO(3) films. These findings open the possibility of an active tuning of surface electronic and magnetic properties as well as chemical properties (catalytic reactivity, wettability and so on)

Parameter space exploration within dynamic simulations of signaling networks

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Fieldlike and antidamping spin-orbit torques in as-grown and annealed Ta/CoFeB/MgO layers

We present a comprehensive study of the current-induced spin-orbit torques in perpendicularly magnetized Ta/CoFeB/MgO layers. The samples were annealed in steps up to 300 degrees C and characterized using x-ray absorption spectroscopy, transmission electron microscopy, resistivity, and Hall effect measurements. By performing adiabatic harmonic Hall voltage measurements, we show that the transverse (field-like) and longitudinal (antidamping-like) spin-orbit torques are composed of constant and magnetization-dependent contributions, both of which vary strongly with annealing. Such variations correlate with changes of the saturation magnetization and magnetic anisotropy and are assigned to chemical and structural modifications of the layers. The relative variation of the constant and anisotropic torque terms as a function of annealing temperature is opposite for the field-like and antidamping torques. Measurements of the switching probability using sub-{\mu}s current pulses show that the critical current increases with the magnetic anisotropy of the layers, whereas the switching efficiency, measured as the ratio of magnetic anisotropy energy and pulse energy, decreases. The optimal annealing temperature to achieve maximum magnetic anisotropy, saturation magnetization, and switching efficiency is determined to be between 240 degrees and 270 degrees C

A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

Background Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome. Results We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo) taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification. Conclusions Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.Foundation KiKaChildren's Neuroblastoma Cancer FoundationSKK FoundationDutch Cancer Societ