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Abstract BackgroundSpinal cord injuries (SCIs) cause debilitating secondary conditions such as severe muscle deterioration, cardiovascular, and metabolic dysfunctions, significantly impacting patients’ quality of life. Functional electrical stimulation (FES) combined with cycling exercise (FES-cycling) has shown promise in improving muscle function and health in individuals with SCI. ObjectiveThis pilot study aimed to investigate the potential role of multiparametric magnetic resonance imaging (MRI) to assess muscle health during and after an FES-cycling rehabilitation program. MethodsFour male participants with chronic SCI underwent a 6-month FES-cycling training program, consisting of two 30-minute sessions per week. MRI scans were performed at baseline (T012312 ResultsMuscle hypertrophy was observed, with an average increase in muscle volume of 22.3% at T120232 ConclusionsAlthough preliminary, this study provides evidence that 6 months of low-intensity FES-bike training can increase muscle volume and decrease fat infiltration in individuals with SCI. The study demonstrates that the use of a multiparametric MRI provides comprehensive insights into both macroscopic and microscopic changes within muscle tissues, supporting its integration into clinical practice for assessing the efficacy of rehabilitation interventions.

BackgroundIt has been widely demonstrated that siblings of children with autism spectrum disorder (ASD) have an increased risk of abnormal developmental trajectories. In response to this, early recognition protocols have been developed worldwide, aiming to promote early interventions that can positively impact the neurodevelopment of this population. This paper presents the protocol of a controlled trial: ERI-SIBS (Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders) is an innovative and ecological early recognition and intervention program designed specifically for siblings of children with ASD.MethodsWe aim to recruit siblings at low risk and high risk of neurodevelopmental disorders. Based on clinical evaluation at T0, we will allocate the infants into three groups: Group 1, infants at low risk without any signs of neurodevelopmental disorders; Group 2, infants at high risk without any signs of neurodevelopmental disorders; Group 3: infants at low or high risk with signs suggestive of neurodevelopmental disorders. Children of Group 2 will undergo Active Monitoring (one 90 min session once a month for 6 months), while children of Group 3 will undergo Early Intervention (one 90 min session once a week for 6 months). In both cases, the ERI-SIBS contents are based on a multidimensional and naturalistic approach and always involve caregivers. All recruited children will be evaluated at three different time points (T0 within the 8 months of life of the child, T1 after 6 months and T2 after 12 months) using behavioural, technological, and biological techniques to assess infants’ neurodevelopmental functions, parent-infant interaction, and early ASD markers.DiscussionThe ERI-SIBS study will expand knowledge regarding the impact of early intervention on families of infants at risk of neurodevelopmental disorders for the presence of a child with a diagnosis of ASD. The study will have the potential to significantly contribute to future research and the scientific and clinical debate on the best way to implement early intervention in at-risk populations. Clinical Trial RegistrationClinicaltrials.gov identifier (NCT06512649).

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication, social interaction, and restricted behaviors. The importance of early intervention has been widely demonstrated, and developmental trajectories in ASD emphasize the importance of nonverbal communication, such as intransitive gesture production, as a possible positive prognostic factor for language development. The use of technological tools in the therapy of individuals with ASD has also become increasingly important due to their higher engagement and responsiveness to technological objects, such as robots.Materials and methodsWe developed a training protocol using the humanoid robot NAO, called IOGIOCO (Interactive mirroring Games wIth sOCial rObot), based on the use of intransitive gestures embedded in naturalistic dialogues, stimulating a triadic interaction between child, robot and therapist. The training was divided into six levels; the first 2 levels were called “familiarization levels,” and the other 4 were “training levels”. The technological setup includes different complexity levels, from mirroring tasks to building spontaneous interactions. We tested the protocol on 10 preschool children with ASD (aged 2–6 years) for 14 weeks. We assessed them at recruitment (T0), at the end of training (T1), and after 6 months (T2).ResultsWe demonstrated the tolerability of the protocol. We found that one group (n=4, males and 2 females) reached the training level, while another and group (n=6 males) remained at a familiarization level (mirroring), we analyzed the results for the two groups. In the group that reached the training levels, we found promising results, such as an improvement in the Social Adaptive Domain of the ABAS-II questionnaire between T0 and T2.ConclusionWhile current results will need a Randomized Controlled Trial to be confirmed, the present work sets an important milestone in using social robots for ASD treatment, aimed at impacting social and communication skills in everyday life.

Children with Autism Spectrum Disorder (ASD) show severe attention deficits, hindering their capacity to acquire new skills. The automatic assessment of their attention response would provide the therapists with an important biomarker to better quantify their behaviour and monitor their progress during therapy. This work aims to develop a quantitative model, to evaluate the attention response of children with ASD, during robotic-assistive therapeutic sessions. Previous attempts to quantify the attention response of autistic subjects during human-robot interaction tasks were limited to restrained child movements. Instead, we developed an accurate quantitative system to assess the attention of ASD children in unconstrained scenarios. Our approach combines gaze extraction (Gaze360 model) with the definition of angular Areas-of-Interest, to characterise periods of attention towards elements of interest in the therapy environment during the session. The methodology was tested with 12 ASD children, achieving a mean test accuracy of 79.5 %. Finally, the proposed attention index was consistent with the therapists’ evaluation of patients, allowing a meaningful interpretation of the automatic evaluation. This encourages the future clinical use of the proposed system.

In the context of neurorehabilitation, there have been rapid and continuous improvements in sensors-based clinical tools to quantify limb performance. As a result of the increasing integration of technologies in the assessment procedure, the need to integrate evidence-based medicine with benchmarking has emerged in the scientific community. In this work, we present the experimental validation of our previously proposed benchmarking scheme for upper limb capabilities in terms of repeatability, reproducibility, and clinical meaningfulness. We performed a prospective multicenter study on neurologically intact young and elderly subjects and post-stroke patients while recording kinematics and electromyography. 60 subjects (30 young healthy, 15 elderly healthy, and 15 post-stroke) completed the benchmarking protocol. The framework was repeatable among different assessors and instrumentation. Age did not significantly impact the performance indicators of the scheme for healthy subjects. In post-stroke subjects, the movements presented decreased smoothness and speed, the movement amplitude was reduced, and the muscular activation showed lower power and lower intra-limb coordination. We revised the original framework reducing it to three motor skills, and we extracted 14 significant performance indicators with a good correlation with the ARAT clinical scale. The applicability of the scheme is wide, and it may be considered a valuable tool for upper limb functional evaluation in the clinical routine.

In modern applied research concerning human-machine interaction, the possibility of increasing the degree of intelligence and dexterity of the controlled plant by imitating human control dynamics has been a primary objective. To reach this goal, we propose a novel nonlinear modeling technique able to predict human force generated during a cooperative task with a controlled robot. The proposed Narmax model was constructed using an artificial neural network as a nonlinear functional approximator and was firstly trained offline with data acquired from ten subjects, performing a manipulation task on a small collaborative robot. Then, given the complexity of the system and the characteristics of human response, the exploitation of Peak to Peak Dynamics allowed the development of a reduced-order model that could reliably forecast the peak of human response. Ultimately, our human model was tested online on an industrial high-payload robot, showing its general applicability and how it can be used to let the robot anticipate human intention during collaborative manipulation.

This work proposes a recurrent neural network-based sim-to-real method to learn mobile robot localization using lidar data in dynamic environments. The main aim of the algorithm is to estimate a Cartesian position error relative to a saved position by means of stored lidar readings in a two-dimensional environment, using lidar data as input. To achieve this, we propose a method that first trains a model on synthetic and augmented LiDAR data to embed rigid transformations into the deep learning model and then fine-tunes the model on real positions using real-world data and external camera measures to produce training labels. This pre-training and fine-tuning approach considerably reduces the time, the computation power, and the amount of real-world data needed to have an accurate model, allowing running the fine-positioning model on the edge of autonomous mobile robots(AMRs). After optimizing the model architecture and hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of AMRs with that of a classical iterative closest point and advanced Monte Carlo localization.

Abstract Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user’s eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors’ knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.

Abstract Introduction To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision‐making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision‐making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. Methods In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer‐based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. Results The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision‐making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. Conclusion Future review efforts could utilize alternative search strategies to explore other lung cancer‐specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision‐making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

In many real-world applications (e.g., human-robot collaboration), the environment changes rapidly, and the intended path may become invalid due to moving obstacles. In these situations, the robot should quickly find a new path to reach the goal, possibly without stopping. Planning from scratch or repairing the current graph can be too expensive and time-consuming. This paper proposes MARS, a sampling-based Multi-pAth Replanning Strategy that enables a robot to move in dynamic environments with unpredictable obstacles. The novelty of the method is the exploitation of a set of precomputed paths to compute a new solution in a few hundred milliseconds when an obstacle obstructs the robot’s path. The algorithm enhances the search speed by using informed sampling, builds a directed graph to reuse results from previous replanning iterations, and improves the current solution in an anytime fashion to make the robot responsive to environmental changes. In addition, the paper presents a multithread architecture, applicable to several replanning algorithms, to handle the execution of the robot’s trajectory with continuous replanning and the collision checking of the traversed path. The paper compares state-of-the-art sampling-based path-replanning algorithms in complex and high-dimensional scenarios, showing that MARS is superior in terms of success rate and quality of solutions found. An open-source ROS-compatible implementation of the algorithm is also provided.

In Human–Machine interaction, the possibility of increasing the intelligence and adaptability of the controlled plant by imitating human control behavior has been an objective of many research efforts in the last decades. From classical control-theory human control models to modern machine learning, neural networks, and reinforcement learning paradigms, the common denominator is the effort to model complex nonlinear dynamics typical of human activity. However, these approaches are very different, and finding a guiding line is challenging. This review investigates state-of-the-art techniques from the perspective of human control modeling, considering the different physiological districts involved as the starting point. The focus is mainly directed toward nonlinear dynamical system modeling, which constitutes the main challenge in this field. In the end, transport systems are presented as a technological scenario in which the discussed techniques are mainly applied.

Objective: The aim of the present study is to explore whether a single session of transcutaneous Vagus Nerve Stimulation (tVNS) can enhance the ipsilesional, and contralesional upper limb motor functions as well as cognitive functions in stroke patients. The effects of the stimulation were evaluated through two different tasks: the box and blocks test (BB), indexing manual dexterity, and the Go/No-go task, a visuomotor paradigm used to assess both motor readiness and response inhibition. Tests were administered without tVNS, during tVNS and during sham tVNS. Results: The BB showed a statistical difference for both contralesional side (p = 0.05) between Basal-Real condition (p = 0.042) and ipsilesional side (p = 0.001) between Basal-Real (p = 0.008) and for Real-Sham (p = 0.005). Any statistical difference was found for the mean latencies in the three conditions of the Go/No-go test. Conclusion: A single session of tVNS seems to improve upper limb motor functions but not cognitive functions in post-stroke patients, despite a positive trend was detected.

Abstract Background In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. Methods This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. Results We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. Conclusions This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Background Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1)

Abstract Introduction Soft robotic wearable devices, referred to as exosuits, can be a valid alternative to rigid exoskeletons when it comes to daily upper limb support. Indeed, their inherent flexibility improves comfort, usability, and portability while not constraining the user’s natural degrees of freedom. This review is meant to guide the reader in understanding the current approaches across all design and production steps that might be exploited when developing an upper limb robotic exosuit. Methods The literature research regarding such devices was conducted in PubMed, Scopus, and Web of Science. The investigated features are the intended scenario, type of actuation, supported degrees of freedom, low-level control, high-level control with a focus on intention detection, technology readiness level, and type of experiments conducted to evaluate the device. Results A total of 105 articles were collected, describing 69 different devices. Devices were grouped according to their actuation type. More than 80% of devices are meant either for rehabilitation, assistance, or both. The most exploited actuation types are pneumatic (52%) and DC motors with cable transmission (29%). Most devices actuate 1 (56%) or 2 (28%) degrees of freedom, and the most targeted joints are the elbow and the shoulder. Intention detection strategies are implemented in 33% of the suits and include the use of switches and buttons, IMUs, stretch and bending sensors, EMG and EEG measurements. Most devices (75%) score a technology readiness level of 4 or 5. Conclusion Although few devices can be considered ready to reach the market, exosuits show very high potential for the assistance of daily activities. Clinical trials exploiting shared evaluation metrics are needed to assess the effectiveness of upper limb exosuits on target users.

Abstract Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable objects making use of a compliant, under-actuated, sensorless robotic hand. The main aim of the paper is, therefore, finding the best position and joint configuration for the mentioned robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. Due to the complex grasping dynamics, learning-from-simulations approaches (e.g., Reinforcement Learning) are not effective in the faced context. Thus, trial-and-error-based methodologies have to be exploited. In order to save resources, a samples-efficient approach has to be employed. Indeed, a Bayesian approach to address the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired knowledge to grasp (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of-freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., deformable) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%.

Despite more than thirty-five years of research on wearable technologies to assist the upper-limb and a multitude of promising preliminary results, the goal of restoring pre-impairment quality of life of people with physical disabilities has not been fully reached yet. Whether it is for rehabilitation or for assistance, nowadays robotics is still only used in a few high-tech clinics and hospitals, limiting the access to a small amount of people. This work provides a description of the three major “revolutions” occurred in the field (end-effector robots, rigid exoskeletons, and soft exosuits), reviewing forty-eight systems for the upper-limb (excluding hand-only devices) used in eighty-nine studies enrolling a clinical population before June 2022. The review critically discusses the state of the art, analyzes the different technologies, and compares the clinical outcomes, with the goal of determine new potential directions to follow.

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

The physical boundaries of our body do not define what we perceive as self. This malleable representation arises from the neural integration of sensory information coming from the environment. Manipulating the visual and haptic cues produces changes in body perception, inducing the Full Body Illusion (FBI), a vastly used approach to exploring humans’ perception. After pioneering FBI demonstrations, issues arose regarding its setup, using experimenter-based touch and pre-recorded videos. Moreover, its outcome measures are based mainly on subjective reports, leading to biased results, or on heterogeneous objective ones giving poor consensus on their validity. To address these limitations, we developed and tested a multisensory platform allowing highly controlled experimental conditions, thanks to the leveraged use of innovative technologies: Virtual Reality (VR) and Transcutaneous Electrical Nerve Stimulation (TENS). This enabled a high spatial and temporal precision of the visual and haptic cues, efficiently eliciting FBI. While it matched the classic approach in subjective measures, our setup resulted also in significant results for all objective measurements. Importantly, FBI was elicited when all 4 limbs were multimodally stimulated but also in a single limb condition. Our results behoove the adoption of a comprehensive set of measures, introducing a new neuroscientific platform to investigate body representations.

Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with computers and machines. Recently, deep learning algorithms such as Convolutional Neural Networks (CNNs) have gained interest for decoding the hand movement intention from EMG signals. However, deep networks require a large dataset to train appropriately. Creating such a database for a single subject could be very time-consuming. In this study, we addressed this issue from two perspectives: (i) we proposed a subject-transfer framework to use the knowledge learned from other subjects to compensate for a target subject’s limited data; (ii) we proposed a task-transfer framework in which the knowledge learned from a set of basic hand movements is used to classify more complex movements, which include a combination of mentioned basic movements. We introduced two CNN-based architectures for hand movement intention detection and a subject-transfer learning approach. Classifiers are tested on the Nearlab dataset, a sEMG hand/wrist movement dataset including 8 movements and 11 subjects, along with their combination, and on open-source hand sEMG dataset “NinaPro DataBase 2 (DB2).” For the Nearlab database, the subject-transfer learning approach improved the average classification accuracy of the proposed deep classifier from 92.60 to 93.30% when classifier was utilizing 10 other subjects’ data via our proposed framework. For Ninapro DB2 exercise B (17 hand movement classes), this improvement was from 81.43 to 82.87%. Moreover, three stages of analysis in task-transfer approach proved that it is possible to classify combination hand movements using the knowledge learned from a set of basic hand movements with zero, few samples and few seconds of data from the target movement classes. First stage takes advantage of shared muscle synergies to classify combined movements, while second and third stages take advantage of novel algorithms using few-shot learning and fine-tuning to use samples from target domain to further train the classifier trained on the source database. The use of information learned from basic hand movements improved classification accuracy of combined hand movements by 10%.

C-band technology is emerging as an exciting innovative approach to the creation of compact new accelerators. Besides the possibility to sustain higher gradients and higher repetition rate operation at normal conducting temperature, it also allows one to increase the machine performance in terms of beam brightness. We propose the design study of a normal conducting, high gradient C-band injector aiming at the production of high brightness electron beams—up to 2.6×10^{3} TA/m^{2}—at high repetition rate—up to 1 KHz—as desired to enhance the capabilities of modern radiation sources. This paper reports on beam dynamics studies that guided the injector design looking for a good compromise between the machine compactness and performances. For this purpose, a new standing wave C-band gun consisting of 2.6 cells has been designed to enhance the final beam brightness together with its own mode launcher to ensure the needed peak field at the cathode—up to 180 MV/m. Several working points in terms of peak current, transverse emittance and brightness are explored, showing the flexibility allowed by the proposed photoinjector. The integration of the gun mode launcher, that relies on a four-port configuration, in the injector design and its effect on the beam quality are also reported.

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG–cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms. [ABSTRACT FROM AUTHOR]

We reproduced a decision-making network model using the neural simulator software neural simulation tool (NEST), and we embedded the spiking neural network in a virtual robotic agent performing a simulated behavioral task. The present work builds upon the concept of replicability in neuroscience, preserving most of the computational properties in the initial model although employing a different software tool. The proposed implementation successfully obtains equivalent results from the original study, reproducing the salient features of the neural processes underlying a binary decision. Furthermore, the resulting network is able to control a robot performing an in silico visual discrimination task, the implementation of which is openly available on the EBRAINS infrastructure through the neuro robotics platform (NRP).