Asteroids co-orbital motion classification based on Machine Learning

In this work, we explore how to classify asteroids in co-orbital motion with a given planet using Machine Learning. We consider four different kinds of motion in mean motion resonance with the planet, nominally Tadpole, Horseshoe and Quasi-satellite, building 3 datasets defined as Real (taking the ephemerides of real asteroids from the JPL Horizons system), Ideal and Perturbed (both simulated, obtained by propagating initial conditions considering two different dynamical systems) for training and testing the Machine Learning algorithms in different conditions. The time series of the variable theta (angle related to the resonance) are studied with a data analysis pipeline defined ad hoc for the problem and composed by: data creation and annotation, time series features extraction thanks to the tsfresh package (potentially followed by selection and standardization) and the application of Machine Learning algorithms for Dimensionality Reduction and Classification. Such approach, based on features extracted from the time series, allows to work with a smaller number of data with respect to Deep Learning algorithms, also allowing to define a ranking of the importance of the features. Physical Interpretability of the features is another key point of this approach. In addition, we introduce the SHapley Additive exPlanations for Explainability technique. Different training and test sets are used, in order to understand the power and the limits of our approach. The results show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance

Asteroids co-orbital motion classification based on Machine Learning

In this work, we explore how to classify asteroids in co-orbital motion with a given planet using Machine Learning. We consider four different kinds of motion in mean motion resonance with the planet, nominally Tadpole at L4 and L5, Horseshoe and Quasi-Satellite, building 3 data sets defined as Real (taking the ephemerides of real asteroids from the JPL Horizons system), Ideal and Perturbed (both simulated, obtained by propagating initial conditions considering two different dynamical systems) for training and testing the Machine Learning algorithms in different conditions. The time series of the variable θ (angle related to the resonance) are studied with a data analysis pipeline defined ad hoc for the problem and composed by: data creation and annotation, time series features extraction thanks to the tsfresh package (potentially followed by selection and standardization) and the application of Machine Learning algorithms for Dimensionality Reduction and Classification. Such approach, based on features extracted from the time series, allows to work with a smaller number of data with respect to Deep Learning algorithms, also allowing to define a ranking of the importance of the features. Physical Interpretability of the features is another key point of this approach. In addition, we introduce the SHapley Additive exPlanations for Explainability technique. Different training and test sets are used, in order to understand the power and the limits of our approach. The results show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance

Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we measured the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage

On the CFD Analysis of a Stratified Taylor-Couette System Dedicated to the Fabrication of Nanosensors

Since the pioneering work of Taylor, the analysis of flow regimes of incompressible, viscous fluids contained in circular Couette systems with independently rotating cylinders have charmed many researchers. The characteristics of such kind of flows have been considered for some industrial applications. Recently, Taylor-Couette flows found an innovative application in the production of optical fiber nanotips, to be used in molecular biology and medical diagnostic fields. Starting from the activity of Barucci et al., the present work concerns the numerical analysis of a Taylor-Couette system composed by two coaxial counter-rotating cylinders with low aspect ratio and radius ratio, filled with three stratified fluids. An accurate analysis of the flow regimes is performed, considering both the variation of inner and outer rotational speed and the reduction of fiber radius due to etching process. The large variety of individuated flow configurations provides useful information about the possible use of the Taylor-Couette system in a wide range of engineering applications. For the present case, the final objective is to provide accurate information to manufacturers of fiber nanotips about the expected flow regimes, thus helping them in the setup of the control process that will be used to generate high-quality products

The Story of an Egyptian Cat Mummy Through CT Examination

Much of the fascination surrounding Egyptian civilization is linked to the practice of mummification. In fact, to ensure the preservation of the body, the ancient Egyptians mummified both human and animal subjects. However, mummified animal remains are less well studied, although they represent a significant part of the material culture and history of ancient Egypt. The introduction of non-invasive imaging methods has allowed researchers to study the material hidden within the wrappings of mummies. In this article, the cat mummy currently exhibited at the Museo Etnologico Missionario di San Francesco di Fiesole (Florence, Italy), originating from Luxor and legally acquired during an expedition in the 20th century, was analyzed using computed tomography (CT). The CT enabled the identification of the casing content, showing the presence of an entire cat skeleton. The cat had several fractures, some of which were identified in the cervical region, possibly related to the cause of death. Furthermore, the zooarcheological analysis allowed the identification of the age at death of the cat, providing further information about the story of the mummy. This research provides a further contribution to the analysis of mummies, with a case study of a cat mummy that emphasizes the importance of CT scans in humanistic studies and museum environments

Ion-exchanged glass microrods for SERS detection of DNA

Different chemical or physical deposition processes have been previously proposed to equip surfaces with a layer of plasmonic NPs to produce effective SERS responses. Here, we present a SERS biosensor obtained by an ion-exchange process in soda-lime glass microrods for efficient DNA detection

Confocal reflectance microscopy for determination of microbubble resonator thickness

Optical Micro Bubble Resonators (OMBR) are emerging as new type of sensors characterized by high Q-factor and embedded micro-fluidic. Sensitivity is related to cavity field penetration and, therefore, to the resonator thickness. At the state of the art, methods for OMBR's wall thickness evaluation rely only on a theoretical approach. The purpose of this study is to create a non-destructive method for measuring the shell thickness of a microbubble using reflectance confocal microscopy. The method was validated through measurements on etched capillaries with different thickness and finally it was applied on microbubble resonators

A SERS affinity bioassay based on ion-exchanged glass microrods

14noThe well-known enhancement effect of surface-enhanced Raman spectroscopy (SERS) is associated with the presence of metallic nanostructures at the substrate surface. Different bottom-up and top-down processes have been proposed to impart the substrate with such a nanostructured layer. The former approaches are low cost but may suffer from reusability and stability. The latter strategies are expensive, time consuming and require special equipment that complicate the fabrication process. Here, we present the possibility to obtain stable and reusable SERS substrates by a low-cost silver-sodium ion-exchange process in soda-lime glass microrods. The microrods were obtained by cutting the tip of the ion-exchanged soda-lime fiber, resulting in disks of about few millimeters in length and one hundred microns in diameter. A thermal annealing post-process was applied to trigger the reduction of Ag+ ions into nanoparticles (AgNPs) within the ion-exchanged glass microrods. Afterwards, ion-exchange and thermal treatments were carefully tuned to assure the presence of silver NPs exposed on the surface of the microrods, without using any chemical etching. An AFM analysis confirmed the presence of AgNPs with size of tens of nm on the surface of the fiber probe. A SERS affinity bioassay was developed on the probe with the final aim of detecting microRNA fragments acting as biomarkers of different diseases. Specifically a DNA hybridization assay was built up by anchoring a molecular beacon containing a Raman tag on the Ag surface via thiol chemistry. Initial SERS experiments confirmed the presence of the beacon on the NPs embedded on the microrods surface, as monitored by detecting main spectral bands ascribed to the oligonucleotide chain. Finally, the ability of the platform to interact with the target microRNA sequence was assessed. The analysis was repeated on a number of miRNA sequences differing from the target to evaluate the specificity of the proposed assay.openopenBerneschi, Simone; D'Andrea, Cristiano; Giannetti, Ambra; De Angelis, Marella; Banchelli, Martina; Barucci, Andrea; Boetti, Nadia Giovanna; Pelli, Stefano; Baldini, Francesco; Pini, Roberto; Janner, Davide; Pugliese, Diego; Milanese, Daniel; Matteini, PaoloBerneschi, Simone; D'Andrea, Cristiano; Giannetti, Ambra; De Angelis, Marella; Banchelli, Martina; Barucci, Andrea; Boetti, Nadia Giovanna; Pelli, Stefano; Baldini, Francesco; Pini, Roberto; Janner, Davide; Pugliese, Diego; Milanese, Daniel; Matteini, Paol

Course and Lethality of SARS-CoV2 Epidemic in Nursing Homes after Vaccination in Florence, Italy

Evidence on the effectiveness of SARS-CoV-2 vaccines in nursing home (NHs) residents is limited. We examined the impact of the BNT162b2 mRNA SARS-CoV-2 vaccine on the course of the epidemic in NHs in the Florence Health District, Italy, before and after vaccination. Moreover, we assessed survival and hospitalization by vaccination status in SARS-CoV-2-positive cases occurring during the post-vaccination period. We calculated the weekly infection rates during the pre-vaccination (1 October–26 December 2020) and post-vaccination period (27 December 2020–31 March 2021). Cox analysis was used to analyze survival by vaccination status. The study involved 3730 residents (mean age 84, 69% female). Weekly infection rates fluctuated during the pre-vaccination period (1.8%–6.5%) and dropped to zero during the post-vaccination period. Nine unvaccinated (UN), 56 partially vaccinated (PV) and 35 fully vaccinated (FV) residents tested SARS-CoV-2+ during the post-vaccination period. FV showed significantly lower hospitalization and mortality rates than PV and UV (hospitalization: FV 3%, PV 14%, UV 33%; mortality: FV 6%, PV 18%, UV 56%). The death risk was 84% and 96% lower in PV (HR 0.157, 95%CI 0.049–0.491) and FV (HR 0.037, 95%CI 0.006–0.223) versus UV. SARS-CoV-2 vaccination was followed by a marked decline in infection rates and was associated with lower morbidity and mortality among infected NH residents