Publications

With stress echo (SE) 2020 study, a new standard of practice in stress imaging was developed and disseminated: The ABCDE protocol for functional testing within and beyond CAD. ABCDE protocol was the fruit of SE 2020, and is the seed of SE 2030, which is articulated in 12 projects: 1-SE in coronary artery disease (SECAD); 2-SE in diastolic heart failure (SEDIA); 3-SE in hypertrophic cardiomyopathy (SEHCA); 4-SE post-chest radiotherapy and chemotherapy (SERA); 5-Artificial intelligence SE evaluation (AI-SEE); 6-Environmental stress echocardiography and air pollution (ESTER); 7-SE in repaired Tetralogy of Fallot (SETOF); 8-SE in post-COVID-19 (SECOV); 9: Recovery by stress echo of conventionally unfit donor good hearts (RESURGE); 10-SE for mitral ischemic regurgitation (SEMIR); 11-SE in valvular heart disease (SEVA); 12-SE for coronary vasospasm (SESPASM). The study aims to recruit in the next 5 years (2021-2025)≥10,000 patients followed for≥5 years (up to 2030) from≥20 quality-controlled laboratories from≥10 countries. In this COVID-19 era of sustainable health care delivery, SE2030 will provide the evidence to finally recommend SE as the optimal and versatile imaging modality for functional testing anywhere, any time, and in any patient.

The efficacy of non-invasive ventilation (NIV) in acute respiratory failure secondary to SARS-CoV-2 infection remains controversial. Current literature mainly examined efficacy, safety and potential predictors of NIV failure provided out of the intensive care unit (ICU). On the contrary, the outcomes of ICU patients, intubated after NIV failure, remain to be explored. The aims of the present study are: (1) investigating in-hospital mortality in coronavirus disease 2019 (COVID-19) ICU patients receiving endotracheal intubation after NIV failure and (2) assessing whether the length of NIV application affects patient survival. This observational multicenter study included all consecutive COVID-19 adult patients, admitted into the twenty-five ICUs of the COVID-19 VENETO ICU network (February–April 2020), who underwent endotracheal intubation after NIV failure. Among the 704 patients admitted to ICU during the study period, 280 (40%) presented the inclusion criteria and were enrolled. The median age was 69 [60–76] years; 219 patients (78%) were male. In-hospital mortality was 43%. Only the length of NIV application before ICU admission (OR 2.03 (95% CI 1.06–4.98), p = 0.03) and age (OR 1.18 (95% CI 1.04–1.33), p < 0.01) were identified as independent risk factors of in-hospital mortality; whilst the length of NIV after ICU admission did not affect patient outcome. In-hospital mortality of ICU patients intubated after NIV failure was 43%. Days on NIV before ICU admission and age were assessed to be potential risk factors of greater in-hospital mortality.

An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent methods make use of state-of-the-art machine learning techniques to learn a valid model for the target task. However, those techniques need a large amount of labeled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their informative content. In this work, we propose an alternative solution with respect to the common data augmentation methods, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images. Quantitative experiments, carried out on three publicly available datasets, show that (i) our model is capable of generating realistic multi-spectral images of plants and (ii) the usage of such synthetic images in the training process improves the segmentation performance of state-of-the-art semantic segmentation convolutional networks.

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource-use efficiency, reduction of chemical treatments, and optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multipurpose unmanned ground vehicles (UGVs). This article presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multispectral-perception algorithms and aerial and ground-based systems developed to monitor crop density, weed pressure, and crop nitrogen (N)-nutrition status and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment as well as the modules for collaborative mapping. We finally present the ground-intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and perform selective spraying without human intervention.

In dynamic driving simulators, the experience of operating a vehicle is reproduced by combining visual stimuli generated by graphical rendering with inertial stimuli generated by platform motion. Due to inherent limitations of the platform workspace, inertial stimulation is subject to shortcomings in the form of missing cues, false cues, and/or scaling errors, which negatively affect simulation fidelity. In the present study, we aim at quantifying the relative contribution of an active somatosensory stimulation to the perceived intensity of self-motion, relative to other sensory systems. Participants judged the intensity of longitudinal and lateral driving maneuvers in a dynamic driving simulator in passive driving conditions, with and without additional active somatosensory stimulation, as provided by an Active Seat (AS) and Active Belts (AB) integrated system (ASB). The results show that ASB enhances the perceived intensity of sustained decelerations, and increases the precision of acceleration perception overall. Our findings are consistent with models of perception, and indicate that active somatosensory stimulation can indeed be used to improve simulation fidelity.

While there are currently various approaches that define and adapt the conditions in which the user experiences content or service for several music and audio-related applications including entertainment, communication, audio documents preservation/restoration, we are missing worldwide accepted standards that enable data exchange and interoperability based on common interfaces for such applications. The Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) is an international non-profit organization whose mission is to develop such standards. Relying on Artificial Intelligence (AI), MPAI creates a workflow of AI Modules (AIM) that are interchangeable and upgradable without necessarily changing the logic of the application. A specific area of work, MPAI Context-based Audio Enhancement (MPAI-CAE), is showing tremendous possibilities for the Sound and Music Computing (SMC) community. MPAI-CAE applies context information to the input content to deliver the audio output via the most appropriate protocol. Three MPAI-CAE case studies particularly relevant for the SMC community will be presented in this paper: Audio recording preservation (ARP), a use case that covers the whole “philologically informed” archival process of an audio document, from the active sound documents preservation to the access to digitized files; Audio-on-the-go (AOG), which aims to improve safety and listening quality for situations in which the users are in motion in different environments; and Emotion-enhanced speech (EES), a use case that implements a user-friendly system control interface that generates speech with various levels of emotions.

The last decade has seen a flowering of applications driven by brain-machine interfaces (BMIs), particularly brain-actuated robotic devices designed to restore the independence of people suffering from severe motor disabilities. This review provides an overview of the state of the art of noninvasive BMI-driven devices based on 86 studies published in the last 15 years, with an emphasis on the interactions among the user, the BMI system, and the robot. We found that BMIs are used mostly to drive devices for navigation (e.g., telepresence mobile robots), with BMI paradigms based mainly on exogenous stimulation, and the majority of brain-actuated robots adopt a discrete control strategy. Most critically, in only a few works have disabled people evaluated a brain-actuated robot. The review highlights the most urgent challenges in the field, from the integration between BMI and robotics to the need for a user-centered design to boost the translational impact of BMIs.

Genetic polymorphisms influencing muscle structure and metabolism may affect the phenotype of metabolic myopathies. We here analyze the possible influence of a wide panel of “exercise genes” on the severity and progression of respiratory dysfunction in late-onset Pompe disease (LOPD). We stratified patients with comparable age and disease duration according to the severity of their respiratory phenotype, assessed by both upright FVC% and postural drop in FVC%. We included 43 patients with LOPD (25 males, age 50.8 ± 13.6 yr) with a 2-yr follow-up since the beginning of enzyme replacement therapy (ERT). Twenty-two patients showed a postural drop >25% T0, seven other patients developed it during the follow-up. We analyzed the relationship between the progression of respiratory dysfunction and genetic polymorphisms affecting muscle function and structure [angiotensin converting enzyme (ACE), α-actinin 3 (ACTN3), peroxisome proliferator-activated receptor a (PPR-a), angiotensin (AGT)], glycogen metabolism [glycogen synthase (GYS), glycogen synthase kinase-3 isoform b (GSK3b)], and autophagy [sirtuin 1 (SIRT1), autophagy-related gene 7 (ATG7)]. Individuals carrying two copies of the ACE D-allele shared a 24-fold increase in the risk of severe respiratory dysfunction and progression during the 2-yr follow-up. ACTN3-XX polymorphism was also associated with worse respiratory outcome. The study of exercise genes is of particular interest in respiratory muscles, due to their peculiar features, that is, continuous, low-intensity contraction and prominent recruitment of type I fibers. In line with previous observations on skeletal muscles, ACE-DD and ACTN3-XX genotypes were associated with indirect evidence of more severe respiratory phenotypes. On the contrary, polymorphisms related to autophagy and glycogen metabolism did not seem to influence respiratory muscles.

Here we reviewed the last evidence on the application of electroencephalography (EEG) as a non-invasive and portable neuroimaging method useful to extract hallmarks of neuroplasticity induced by virtual reality (VR) rehabilitation approaches in stroke patients. In the neurorehabilitation context, VR training has been used extensively to hamper the effects of motor treatments on the stroke’s brain. The concept underlying VR therapy is to improve brain plasticity by engaging users in multisensory training. In this narrative review, we present the key concepts of VR protocols applied to the rehabilitation of stroke patients and critically discuss challenges of EEG signal when applied as endophenotype to extract neurophysiological markers. When VR technology was applied to magnify the effects of treatments on motor recovery, significant EEG-related neural improve-ments were detected in the primary motor circuit either in terms of power spectral density or as time-frequency domains.

Movement disorders are increasingly being recognized as a manifestation of childhood-onset mitochondrial diseases (MDs). However, the spectrum and characteristics of these conditions have not been studied in detail in the context of a well-defined cohort of patients. We retrospectively explored a cohort of individuals with childhood-onset MDs querying the Nationwide Italian Collaborative Network of Mitochondrial Diseases database. Using a customized online questionnaire, we attempted to collect data from the subgroup of patients with movement disorders. Complete information was available for 102 patients. Movement disorder was the presenting feature of MD in 45 individuals, with a mean age at onset of 11 years. Ataxia was the most common movement disorder at onset, followed by dystonia, tremor, hypokinetic disorders, chorea, and myoclonus. During the disease course, most patients (67.7%) encountered a worsening of their movement disorder. Basal ganglia involvement, cerebral white matter changes, and cerebellar atrophy were the most commonly associated neuroradiological patterns. Forty-one patients harbored point mutations in the mitochondrial DNA, 10 carried mitochondrial DNA rearrangements, and 41 cases presented mutations in nuclear-DNA-encoded genes, the latter being associated with an earlier onset and a higher impairment in activities of daily living. Among our patients, 32 individuals received pharmacological treatment; clonazepam and oral baclofen were the most commonly used drugs, whereas levodopa and intrathecal baclofen administration were the most effective. A better delineation of the movement disorders phenotypes starting in childhood may improve our diagnostic workup in MDs, fine tuning management, and treatment of affected patients.