Publications

The ASI cubesat LICIACube has been part of the first planetary defense mission DART, having among its scopes to complement the DRACO images to better constrain the Dimorphos shape. LICIACube had two different cameras, LEIA and LUKE, and to accomplish its goal, it exploited the unique possibility of acquiring images of the Dimorphos hemisphere not seen by DART from a vantage point of view, in both time and space. This work is indeed aimed at constraining the tridimensional shape of Dimorphos, starting from both LUKE images of the nonimpacted hemisphere of Dimorphos and the results obtained by DART looking at the impacted hemisphere. To this aim, we developed a semiautomatic Computer Vision algorithm, named VADER, able to identify objects of interest on the basis of physical characteristics, subsequently used as input to retrieve the shape of the ellipse projected in the LUKE images analyzed. Thanks to this shape, we then extracted information about the Dimorphos ellipsoid by applying a series of quantitative geometric considerations. Although the solution space coming from this analysis includes the triaxial ellipsoid found by using DART images, we cannot discard the possibility that Dimorphos has a more elongated shape, more similar to what is expected from previous theories and observations. The result of our work seems therefore to emphasize the unique value of the LICIACube mission and its images, making even clearer the need of having different points of view to accurately define the shape of an asteroid.

Type-2 diabetes mellitus is a frequent comorbidity of cirrhosis independently associated with cirrhosis-related complications and mortality. This post hoc analysis of the ANSWER trial database assessed the effects of long-term human albumin (HA) administration on top of the standard medical treatment (SMT) on the clinical outcomes of a subgroup of 85 outpatients with liver cirrhosis, uncomplicated ascites and insulin-treated diabetes mellitus type 2 (ITDM). Compared to patients in the SMT arm, the SMT + HA group showed a better overall survival (86% vs. 57%, p =.016) and lower incidence rates of paracenteses, overt hepatic encephalopathy, bacterial infections, renal dysfunction and electrolyte disorders. Hospital admissions did not differ between the two arms, but the number of days spent in hospital was lower in the SMT + HA group. In conclusion, in a subgroup of ITDM outpatients with decompensated cirrhosis and ascites, long-term HA administration was associated with better survival and a lower incidence of cirrhosis-related complications.

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.

Background: The last years have seen unprecedented improvement in breast cancer (BC) survival rates. However, this entirely apply to female BC patients, since gender minorities (male, transgender/gender-diverse) are neglected in BC phase III registration clinical trials. Methods: We conducted a scoping review of phase III clinical trials of agents with a current positioning within the therapeutic algorithms of BC. Results: We selected 51 phase III trials. Men enrollment was allowed in 35.3% of trials. In none of the trial inclusion/exclusion criteria referred to transgender/gender-diverse people. A numerical higher rate of enrolled men was observed in the contemporary as compared to historical group. We found a statistically significant association between the drug class and the possibility of including men: 100%, 80%, 50%, 33.3%, 25%, 10% and 9.1% of trials testing ICI/PARP-i, ADCs, PI3K/AKT/mTOR-i, anti-HER2 therapy, CDK4/6-i, ET alone, and CT alone. Overall, 77409 patients were enrolled, including 112 men (0.2%). None of the trial reported transgender/gender-diverse people proportion. Studies investigating PARP-i were significantly associated with the highest rate of enrolled men (1.42%), while the lowest rates were observed for trials of CT (0.13%), ET alone (0.10%), and CDK 4/6-I (0.08%), p < 0.001. Conclusions: We confirmed that gender minorities are severely underrepresented among BC registration trials. We observed a lower rate of men in trials envisaging endocrine manipulation or in less contemporary trials. This work sought to urge the scientific community to increase the awareness level towards the issue of gender minorities and to endorse more inclusive criteria in clinical trials.

Background: Foot drop syndrome (FDS), characterized by severe weakness and atrophy of the dorsiflexion muscles of the feet, is commonly found in patients with severe acquired brain injury (ABI). If the syndrome is unilateral, the cause is often a peroneal neuropathy (PN), due to compression of the nervous trunk on the neck of the fibula at the knee level; less frequently, the cause is a previous or concomitant lumbar radiculopathy. Bilateral syndromes are caused by polyneuropathies and myopathies. Central causes, due to brain or spinal injury, mimic this syndrome but are usually accompanied by other symptoms, such as spasticity. Critical illness polyneuropathy (CIP) and myopathy (CIM), isolated or in combination (critical illness polyneuromyopathy, CIPNM), have been shown to constitute an important cause of FDS in patients with ABI. Assessing the causes of FDS in the intensive rehabilitation unit (IRU) has several limitations, which include the complexity of the electrophysiological tests, limited availability of neurophysiology consultants, and the severe disturbance in consciousness and lack of cooperation from patients. Objectives: We sought to propose a simplified electrophysiological screening that identifies FDS causes, particularly PN and CIPNM, to help clinicians to recognize the significant clinical predictors of poor outcomes in severe ABI at admission to IRU. Methods: This prospective, single-center study included 20 severe ABI patients with FDS (11 females/9 males, mean age 55.10 + 16.26; CRS-R= 11.90 + 6.32; LCF: 3.30 + 1.30; DRS: 21.45 + 3.33), with prolonged rehabilitation treatment (≥2 months). We applied direct tibialis anterior muscle stimulation (DMS) associated with peroneal nerve motor conduction evaluation, across the fibular head (NCS), to identify CIP and/or CIM and to exclude demyelinating or compressive unilateral PN. Results: At admission to IRU, simplified electrophysiological screening reported four unilateral PN, four CIP and six CIM with a CIPNM overall prevalence estimate of about 50%. After 2 months, the CIPNM group showed significantly poorer outcomes compared to other ABI patients without CIPNM, as demonstrated by the lower probability of achieving endotracheal-tube weaning (20% versus 90%) and lower CRS-R and DRS scores. Due to the subacute rehabilitation setting of our study, it was not possible to evaluate the motor results of recovery of the standing position, functional walking and balance, impaired by the presence of unilateral PN. Conclusions: The implementation of the proposed simplified electrophysiological screening may enable the early identification of unilateral PN or CIPNM in severe ABI patients, thereby contributing to better functional prognosis in rehabilitative settings.

The ability to discern human intentions from brain signals has opened the possibility of leveraging Brain-Machine Interfaces (BMIs) for the control of robotic devices, especially benefiting individuals with severe motor disabilities. In this work, we present a novel approach for navigating a semiautonomous wheelchair towards targets generated by a BMI, all while ensuring collision avoidance. Our approach employs Nonlinear Model Predictive Control (NMPC) for real-time trajectory generation in unknown and dynamic environments. The empirical results obtained from real-world experiments clearly demonstrate the advancements of our solution over current state-of-the-art techniques. Our implementation is proven to outperform well-established methods in terms of both smoothness and alignment with the user’s intended behavior.

Recent studies in the domain of invasive brain-computer interfaces (BCIs) have revealed that neural activity recorded during the observation of robotic movements in a reach-and-grasp task carries information that can be utilized to improve the active online decoding of motor intention. In the non-invasive domain, the spectral characteristics of human brain activity during the observation of robotic movements has been widely investigated. However, focusing only on the frequency components of electroencephalography (EEG) for motor control decoding is a poorly suitable strategy due to its scarce temporal resolution. Following a different approach, we explored temporal features of EEG filtered in the delta band (Low-Frequency EEG, or LF-EEG) for the continuous decoding of control-oriented kinematic trajectories. We designed an experimental paradigm aimed at investigating how the observation of center-out target-oriented reaching movements executed by a robotic arm in the 2D plane is encoded in low-frequency EEG signals. By employing machine learning algorithms and novel approaches, we were able to continuously decode the LF-EEG into movement trajectories, achieving performance significantly above chance-level. This confirms that low-frequency neural activity measured non-invasively during a movement observation task contains adequate amounts of movement-related information for BCI applications.

This work presents and evaluates a method for reducing the number of hyper-parameters in the continuous control system used by a 2-class motor imagery (MI) brain-machine interface (BMI). The work focuses on two parameters (ω and ψ) used within a dynamical control system that considers the nature and temporal evolution of the BMI decoder output and that it has been already validated in the past.To identify the optimal values for the parameters, we analysed a dataset of 12 subjects performing 2-class MI tasks. For each subject, we defined a new metric to investigate the existence of a relationship between the hyper-parameters. The study reveals a quadratic relationship with coefficient of determination (R2) of 81.67%.Finally, the established relationship was evaluated through an closed-loop experiment involving three healthy subjects. Results demonstrated the potential use of the discovered quadratic relationship to reduce the number of parameters for the dynamical control system and, thus, to simplify the BMI operations.

Deploying service robots in our daily life, whether in restaurants, warehouses or hospitals, calls for the need to reason on the interactions happening in dense and dynamic scenes. In this paper, we present and benchmark three new approaches to model and predict multi-agent interactions in dense scenes, including the use of an intuitive qualitative representation. The proposed solutions take into account static and dynamic context to predict individual interactions. They exploit an input- and a temporal-attention mechanism, and are tested on medium and long-term time horizons. The first two approaches integrate different relations from the so-called Qualitative Trajectory Calculus (QTC) within a stateof-the-art deep neural network to create a symbol-driven neural architecture for predicting spatial interactions. The third approach implements a purely data-driven network for motion prediction, the output of which is post-processed to predict QTC spatial interactions. Experimental results on a popular robot dataset of challenging crowded scenarios show that the purely data-driven prediction approach generally outperforms the other two. The three approaches were further evaluated on a different but related human scenarios to assess their generalisation capability.

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning (CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models.