Near-UV OH Prompt Emission in the Innermost Coma of 103P/Hartley 2

The Deep Impact spacecraft fly-by of comet 103P/Hartley 2 occurred on 2010 November 4, one week after perihelion with a closest approach (CA) distance of about 700 km. We used narrowband images obtained by the Medium Resolution Imager (MRI) onboard the spacecraft to study the gas and dust in the innermost coma. We derived an overall dust reddening of 15\%/100 nm between 345 and 749 nm and identified a blue enhancement in the dust coma in the sunward direction within 5 km from the nucleus, which we interpret as a localized enrichment in water ice. OH column density maps show an anti-sunward enhancement throughout the encounter except for the highest resolution images, acquired at CA, where a radial jet becomes visible in the innermost coma, extending up to 12 km from the nucleus. The OH distribution in the inner coma is very different from that expected for a fragment species. Instead, it correlates well with the water vapor map derived by the HRI-IR instrument onboard Deep Impact \citep{AHearn2011}. Radial profiles of the OH column density and derived water production rates show an excess of OH emission during CA that cannot be explained with pure fluorescence. We attribute this excess to a prompt emission process where photodissociation of H$_2$O directly produces excited OH*($A^2\it{\Sigma}^+$) radicals. Our observations provide the first direct imaging of Near-UV prompt emission of OH. We therefore suggest the use of a dedicated filter centered at 318.8 nm to directly trace the water in the coma of comets.Comment: 21 page

Phobos as a D-type captured asteroid, spectral modeling from 0.25 to 4.0 μm

This paper describes the spectral modeling of the surface of Phobos in the wavelength range between 0.25 and 4.0 μm. We use complementary data to cover this spectral range: the OSIRIS (Optical, Spectroscopic, and Infrared Remote Imaging System on board the ESA Rosetta spacecraft) reflectance spectrum that Pajola et al. merged with the VSK-KRFM-ISM (Videospectrometric Camera (VSK)-Combined Radiometer and Photometer for Mars (KRFM)-Imaging Spectrometer for Mars (ISM) on board the USSR Phobos 2 spacecraft) spectra by Murchie & Erard and the IRTF (NASA Infrared Telescope Facility, Hawaii, USA) spectra published by Rivkin et al. The OSIRIS data allow the characterization of an area of Phobos covering from 86.°8 N to 90° S in latitude and from 126° W to 286° W in longitude. This corresponds chiefly to the trailing hemisphere, but with a small sampling of the leading hemisphere as well. We compared the OSIRIS results with the Trojan D-type asteroid 624 Hektor and show that the overall slope and curvature of the two bodies over the common wavelength range are very similar. This favors Phobos being a captured D-type asteroid as previously suggested. We modeled the OSIRIS data using two models, the first one with a composition that includes organic carbonaceous material, serpentine, olivine, and basalt glass, and the second one consisting of Tagish Lake meteorite and magnesium-rich pyroxene glass. The results of these models were extended to longer wavelengths to compare the VSK-KRFM-ISM and IRTF data. The overall shape of the second model spectrum between 0.25 and 4.0 μm shows curvature and an albedo level that match both the OSIRIS and Murchie & Erard data and the Rivkin et al. data much better than the first model. The large interval fit is encouraging and adds weight to this model, making it our most promising fit for Phobos. Since Tagish Lake is commonly used as a spectral analog for D-type asteroids, this provides additional support for compositional similarities between Phobos and D-type asteroids. © 2013. The American Astronomical Society. All rights reserved

A proposal of quantum-inspired machine learning for medical purposes: An application case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation

Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome

Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype

A roadmap towards breast cancer therapies supported by explainable artificial intelligence

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients

A Gradient-Based Approach for Breast DCE-MRI Analysis

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis

Prenatal tobacco smoke exposure increases hospitalizations for bronchiolitis in infants

BACKGROUND: Tobacco smoke exposure (TSE) is a worldwide health problem and it is considered a risk factor for pregnant women's and children's health, particularly for respiratory morbidity during the first year of life. Few significant birth cohort studies on the effect of prenatal TSE via passive and active maternal smoking on the development of severe bronchiolitis in early childhood have been carried out worldwide. METHODS: From November 2009 to December 2012, newborns born at ≥ 33 weeks of gestational age (wGA) were recruited in a longitudinal multi-center cohort study in Italy to investigate the effects of prenatal and postnatal TSE, among other risk factors, on bronchiolitis hospitalization and/or death during the first year of life. RESULTS: Two thousand two hundred ten newborns enrolled at birth were followed-up during their first year of life. Of these, 120 (5.4%) were hospitalized for bronchiolitis. No enrolled infants died during the study period. Prenatal passive TSE and maternal active smoking of more than 15 cigarettes/daily are associated to a significant increase of the risk of offspring children hospitalization for bronchiolitis, with an adjHR of 3.5 (CI 1.5-8.1) and of 1.7 (CI 1.1-2.6) respectively. CONCLUSIONS: These results confirm the detrimental effects of passive TSE and active heavy smoke during pregnancy for infants' respiratory health, since the exposure significantly increases the risk of hospitalization for bronchiolitis in the first year of lif

Risk factors for bronchiolitis hospitalization during the first year of life in a multicenter Italian birth cohort

BACKGROUND: Respiratory Syncytial Virus (RSV) is one of the main causes of respiratory infections during the first year of life. Very premature infants may contract more severe diseases and 'late preterm infants' may also be more susceptible to the infection. The aim of this study is to evaluate the risk factors for hospitalization during the first year of life in children born at different gestational ages in Italy. METHODS: A cohort of 33-34 weeks gestational age (wGA) newborns matched by sex and age with two cohort of newborns born at 35-37 wGA and > 37 wGA were enrolled in this study for a three-year period (2009-2012). Hospitalization for bronchiolitis (ICD-9 code 466.1) during the first year of life was assessed through phone interview at the end of the RSV season (November-March) and at the completion of the first year of life. RESULTS: The study enrolled 2314 newborns, of which 2210 (95.5 %) had a one year follow-up and were included in the analysis; 120 (5.4 %) were hospitalized during the first year of life for bronchiolitis. Children born at 33-34 wGA had a higher hospitalization rate compared to the two other groups. The multivariate analysis carried out on the entire population associated the following factors with higher rates for bronchiolitis hospitalization: male gender; prenatal treatment with corticosteroids; prenatal exposure to maternal smoking; singleton delivery; respiratory diseases in neonatal period; surfactant therapy; lack of breastfeeding; siblings <10 years old; living in crowded conditions and/or in unhealthy households and early exposure to the epidemic RSV season. When analysis was restricted to preterms born at 33-34 wGA the following variables were associated to higher rates of bronchiolitis hospitalization: male gender, prenatal exposure to maternal smoking, neonatal surfactant therapy, having siblings <10 years old, living in crowded conditions and being exposed to epidemic season during the first three months of life. CONCLUSION: Our study identified some prenatal, perinatal and postnatal conditions proving to be relevant and independent risk factors for hospitalization for bronchiolitis during the first year of life. The combination of these factors may lead to consider palivizumab prophylaxis in Italy

Dancing With Parkinson's Disease: The SI-ROBOTICS Study Protocol

Introduction: Parkinson's disease (PD) is one of the most frequent causes of disability among older people, characterized by motor disorders, rigidity, and balance problems. Recently, dance has started to be considered an effective exercise for people with PD. In particular, Irish dancing, along with tango and different forms of modern dance, may be a valid strategy to motivate people with PD to perform physical activity. The present protocol aims to implement and evaluate a rehabilitation program based on a new system called “SI-ROBOTICS,” composed of multiple technological components, such as a social robotic platform embedded with an artificial vision setting, a dance-based game, environmental and wearable sensors, and an advanced AI reasoner module. Methods and Analysis: For this study, 20 patients with PD will be recruited. Sixteen therapy sessions of 50 min will be conducted (two training sessions per week, for 8 weeks), involving two patients at a time. Evaluation will be primarily focused on the acceptability of the SI-ROBOTICS system. Moreover, the analysis of the impact on the patients' functional status, gait, balance, fear of falling, cardio-respiratory performance, motor symptoms related to PD, and quality of life, will be considered as secondary outcomes. The trial will start in November 2021 and is expected to end by April 2022. Discussions: The study aims to propose and evaluate a new approach in PD rehabilitation, focused on the use of Irish dancing, together with a new technological system focused on helping the patient perform the dance steps and on collecting kinematic and performance parameters used both by the physiotherapist (for the evaluation and planning of the subsequent sessions) and by the system (to outline the levels of difficulty of the exercise). Ethics and Dissemination: The study was approved by the Ethics Committee of the IRCCS INRCA. It was recorded in ClinicalTrials.gov on the number NCT05005208. The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings