Supernova neutrino physics with xenon dark matter detectors: A timely perspective

Dark matter detectors that utilize liquid xenon have now achieved tonne-scale targets, giving them sensitivity to all flavours of supernova neutrinos via coherent elastic neutrino-nucleus scattering. Considering for the first time a realistic detector model, we simulate the expected supernova neutrino signal for different progenitor masses and nuclear equations of state in existing and upcoming dual-phase liquid xenon experiments. We show that the proportional scintillation signal (S2) of a dual-phase detector allows for a clear observation of the neutrino signal and guarantees a particularly low energy threshold, while the backgrounds are rendered negligible during the supernova burst. XENON1T (XENONnT and LZ; DARWIN) experiments will be sensitive to a supernova burst up to 25 (35; 65) kpc from Earth at a significance of more than 5 sigma, observing approximately 35 (123; 704) events from a 27 Msun supernova progenitor at 10 kpc. Moreover, it will be possible to measure the average neutrino energy of all flavours, to constrain the total explosion energy, and to reconstruct the supernova neutrino light curve. Our results suggest that a large xenon detector such as DARWIN will be competitive with dedicated neutrino telescopes, while providing complementary information that is not otherwise accessible.Comment: 19 pages, 9 figures. Minor revisions compared to original version. Matches version published in Phys. Rev.

Fast-time Variations of Supernova Neutrino Fluxes and Detection Perspectives

AbstractIn the delayed explosion scenario of a core-collapse supernova, the accretion phase shows pronounced convective over-turns and a low-multipole hydrodynamic instability, the so-called standing accretion shock instability (SASI). Neutrino signal variations from the first full-scale three-dimensional core-collapse supernova simulations with sophisticated neutrino transport are presented as well as their detection perspectives in IceCube and Hyper-Kamiokande

Synergistic interaction of fatty acids and oxysterols impairs mitochondrial function and limits liver adaptation during nafld progression

The complete mechanism accounting for the progression from simple steatosis to steatohepatitis in nonalcoholic fatty liver disease (NAFLD) has not been elucidated. Lipotoxicity refers to cellular injury caused by hepatic free fatty acids (FFAs) and cholesterol accumulation. Excess cholesterol autoxidizes to oxysterols during oxidative stress conditions. We hypothesize that interaction of FAs and cholesterol derivatives may primarily impair mitochondrial function and affect biogenesis adaptation during NAFLD progression. We demonstrated that the accumulation of specific non-enzymatic oxysterols in the liver of animals fed high-fat+high-cholesterol diet induces mitochondrial damage and depletion of proteins of the respiratory chain complexes. When tested in vitro, 5α-cholestane-3β,5,6β-triol (triol) combined to FFAs was able to reduce respiration in isolated liver mitochondria, induced apoptosis in primary hepatocytes, and down-regulated transcription factors involved in mitochondrial biogenesis. Finally, a lower protein content in the mitochondrial respiratory chain complexes was observed in human non-alcoholic steatohepatitis. In conclusion, hepatic accumulation of FFAs and non-enzymatic oxysterols synergistically facilitates development and progression of NAFLD by impairing mitochondrial function, energy balance and biogenesis adaptation to chronic injury

Spontaneous emission control of colloidal nanocrystals using nanoimprinted photonic crystals

The authors report on the fabrication and optical characterizations of two-dimensional photonic crystals fabricated by nanoimprint lithography in a nanocomposite polymer incorporating highly luminescent and red emitting (CdSe)ZnS core-shell colloidal nanocrystals. Photonic crystal structures enhance the light emitted from the quantum sized nanoparticles in the composite layer by slowing the propagation speed of the photons, thus increasing the coupling to the out-of-plane radiative modes. A 200% enhancement of the light collection is achieved compared to an unpatterned sample. (c) 2007 American Institute of Physics. (DOI:10.1063/1.2430625

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

Crucial Physical Dependencies of the Core-Collapse Supernova Mechanism

We explore with self-consistent 2D F{\sc{ornax}} simulations the dependence of the outcome of collapse on many-body corrections to neutrino-nucleon cross sections, the nucleon-nucleon bremsstrahlung rate, electron capture on heavy nuclei, pre-collapse seed perturbations, and inelastic neutrino-electron and neutrino-nucleon scattering. Importantly, proximity to criticality amplifies the role of even small changes in the neutrino-matter couplings, and such changes can together add to produce outsized effects. When close to the critical condition the cumulative result of a few small effects (including seeds) that individually have only modest consequence can convert an anemic into a robust explosion, or even a dud into a blast. Such sensitivity is not seen in one dimension and may explain the apparent heterogeneity in the outcomes of detailed simulations performed internationally. A natural conclusion is that the different groups collectively are closer to a realistic understanding of the mechanism of core-collapse supernovae than might have seemed apparent.Comment: 25 pages; 10 figure