Dose-Effects Models for Space Radiobiology: An Overview on Dose-Effect Relationships

Space radiobiology is an interdisciplinary science that examines the biological effects of ionizing radiation on humans involved in aerospace missions. The dose-effect models are one of the relevant topics of space radiobiology. Their knowledge is crucial for optimizing radioprotection strategies (e.g., spaceship and lunar space station-shielding and lunar/Mars village design), the risk assessment of the health hazard related to human space exploration, and reducing damages induced to astronauts from galactic cosmic radiation. Dose-effect relationships describe the observed damages to normal tissues or cancer induction during and after space flights. They are developed for the various dose ranges and radiation qualities characterizing the actual and the forecast space missions [International Space Station (ISS) and solar system exploration]. Based on a Pubmed search including 53 papers reporting the collected dose-effect relationships after space missions or in ground simulations, 7 significant dose-effect relationships (e.g., eye flashes, cataract, central nervous systems, cardiovascular disease, cancer, chromosomal aberrations, and biomarkers) have been identified. For each considered effect, the absorbed dose thresholds and the uncertainties/limitations of the developed relationships are summarized and discussed. The current knowledge on this topic can benefit from further in vitro and in vivo radiobiological studies, an accurate characterization of the quality of space radiation, and the numerous experimental dose-effects data derived from the experience in the clinical use of ionizing radiation for diagnostic or treatments with doses similar to those foreseen for the future space missions. The growing number of pooled studies could improve the prediction ability of dose-effect relationships for space exposure and reduce their uncertainty level. Novel research in the field is of paramount importance to reduce damages to astronauts from cosmic radiation before Beyond Low Earth Orbit exploration in the next future. The study aims at providing an overview of the published dose-effect relationships and illustrates novel perspectives to inspire future research

Sensitivity on Earth Core and Mantle densities using Atmospheric Neutrinos

Neutrino radiography may provide an alternative tool to study the very deep structures of the Earth. Though these measurements are unable to resolve the fine density layer features, nevertheless the information which can be obtained are independent and complementary to the more conventional seismic studies. The aim of this paper is to assess how well the core and mantle averaged densities can be reconstructed through atmospheric neutrino radiography. We find that about a 2% sensitivity for the mantle and 5% for the core could be achieved for a ten year data taking at an underwater km^3 Neutrino Telescope. This result does not take into account systematics related to the details of the experimental apparatus.Comment: 11 pages, 11 figures, accepted for publication in JCA

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case

Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network

We have studied the performance of a new algorithm for electron/pion separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The software for separation consists of two parts: a shower reconstruction algorithm and a Neural Network that assigns to each reconstructed shower the probability to be an electron or a pion. The performance has been studied for the ECC of the OPERA experiment [1]. The $e/\pi$ separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data taken at CERN (pion beams) and at DESY (electron beams). The algorithm allows to achieve a 90% electron identification efficiency with a pion misidentification smaller than 1% for energies higher than 2 GeV

Determination of the muon charge sign with the dipolar spectrometers of the OPERA experiment

The OPERA long-baseline neutrino-oscillation experiment has observed the direct appearance of $\nu_\tau$ in the CNGS $\nu_\mu$ beam. Two large muon magnetic spectrometers are used to identify muons produced in the $\tau$ leptonic decay and in $\nu_\mu^{CC}$ interactions by measuring their charge and momentum. Besides the kinematic analysis of the $\tau$ decays, background resulting from the decay of charmed particles produced in $\nu_\mu^{CC}$ interactions is reduced by efficiently identifying the muon track. A new method for the charge sign determination has been applied, via a weighted angular matching of the straight track-segments reconstructed in the different parts of the dipole magnets. Results obtained for Monte Carlo and real data are presented. Comparison with a method where no matching is used shows a significant reduction of up to 40\% of the fraction of wrongly determined charges.Comment: 10 pages. Improvements in the tex

Procedure for short-lived particle detection in the OPERA experiment and its application to charm decays

The OPERA experiment, designed to perform the first observation of $\nu_\mu \rightarrow \nu_\tau$ oscillations in appearance mode through the detection of the $\tau$ leptons produced in $\nu_\tau$ charged current interactions, has collected data from 2008 to 2012. In the present paper, the procedure developed to detect $\tau$ particle decays, occurring over distances of the order of 1 mm from the neutrino interaction point, is described in detail. The results of its application to the search for charmed hadrons are then presented as a validation of the methods for $\nu_\tau$ appearance detection

Limits on muon-neutrino to tau-neutrino oscillations induced by a sterile neutrino state obtained by OPERA at the CNGS beam

The OPERA experiment, exposed to the CERN to Gran Sasso $\nu_\mu$ beam, collected data from 2008 to 2012. Four oscillated $\nu_\tau$ Charged Current interaction candidates have been detected in appearance mode, which are consistent with $\nu_\mu \to \nu_\tau$ oscillations at the atmospheric $\Delta m^2$ within the "standard" three-neutrino framework. In this paper, the OPERA $\nu_\tau$ appearance results are used to derive limits on the mixing parameters of a massive sterile neutrino.Comment: 11 pages, 4 figures; reference to Planck result updated in the Introduction. Submitted to JHE