University of Milano-Bicocca

About: University of Milano-Bicocca is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Blood pressure. The organization has 8972 authors who have published 22322 publications receiving 620484 citations. The organization is also known as: Università degli Studi di Milano-Bicocca & Universita degli Studi di Milano-Bicocca.

Pr3+-doped complex oxide single crystal scintillators

Abstract: We investigate and compare optical absorption, luminescence and scintillation properties of Pr-doped Y3Al5O12, Lu3Al5O12, Y2SiO5 and Lu2SiO5 single crystals. The processes determining the kinetics of the fast Pr3+ 5d–4f radiative transition are described. Among the studied host materials, only Lu3Al5O12 presents neither any 5d1–4f luminescence state nonradiative quenching nor Pr3+ ionization at room temperature. We evaluate the figure of merit of all materials for application as scintillators. The most promising system appears to be Lu3Al5O12 : Pr, since it combines an elevated density of 6.67 g cm−3, a fast scintillation response dominated by a 21 ns decay time and a spectrally corrected light yield about 160% with respect to that of Bi4Ge3O12 (BGO).

Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection

Abstract: Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining noise-to-image and image-to-image GANs for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain Magnetic Resonance (MR) images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for high-resolution MR image generation, first generates realistic/diverse 256×256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT) that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused GAN loss, further refines the texture/shape of the PGGAN-generated images similarly to the real ones. We thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks.

Beta Amyloid Aggregation Inhibitors: Small Molecules as Candidate Drugs for Therapy of Alzheimer's Disease

Abstract: The progressive production and subsequent accumulation of β-amyloid (Aβ), a proteolytic fragment of the membrane-associated amyloid precursor protein (APP), plays a central role in Alzheimer's Disease (AD). Aβ is released in a soluble form that may be responsible for cognitive dysfunction in the early stages of the disease, then progressively forms oligomeric, multimeric and fibrillar aggregates, triggering neurodegeneration. Eventually, the aggregation and accumulation of Aβ culminates with the formation of extracellular plaques, one of the morphological hallmarks of the disease, detectable post-mortem in AD brains. In this review we report the known structural features of amyloid peptides and fibrils, and we give an overview of all small molecules that have been found to interact with Aβ aggregation. Deeper knowledge of the mechanism leading to amyloid fibrils along with their molecular structure and the molecular interactions responsible for activity of small molecules could supply useful information for the design of new AD therapeutic agents.