Istituto Italiano di Tecnologia

The development of continuum robots with embedded sensing systems has been a research focus over the last decade. Specifically, for the robotic laparo-endoscopic single-site surgical (R-LESS) system, an economical solution that allows the integration of an electromagnetic-compatible shape-proprioception system is needed. In this article, we propose a modular sensing system named X-Sketch for the R-LESS system. The system comprises four fiber Bragg grating channels integrated with 3D-printed elastic joints that enable the detection of curvature variations. The modularity and customizability are achieved by the stacking design of the flexible joints and sensing units. The skeleton of the arm is constructed by an analytical kinematics process of the curvature information, during which a neural network is employed to perform the curvature regression. For the experiment, an R-LESS arm prototype integrated with the X-Sketch system is developed. Calibration and experimental evaluation are then conducted to assess the performance of the proposed sensing system. The results demonstrate the performance of the proposed system, achieving an average tip position error of 2.23±0.64mm, corresponding to <5% of the system’s length, suggesting the potential for X-Sketch application in R-LESS surgical operations.

This paper presents XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end Imitation Learning (IL) system for whole-body autonomous humanoid robots used in real-world Human-Robot Interaction (HRI) scenarios. The main contribution is an architecture for learning HRI behaviours using a data-driven approach. A diverse dataset is collected via teleoperation, covering multiple HRI scenarios, such as handshaking, handwaving, payload reception, walking, and walking with a payload. After synchronizing, filtering, and transforming the data, we show how to train the presented Deep Neural Networks (DNN), integrating exteroceptive and proprioceptive information to help the robot understand both its environment and its actions. The robot takes in sequences of images (RGB and depth) and joints state information to react accordingly. By fusing multimodal signals over time, the model enables autonomous capabilities in a robotic platform. The models are evaluated based on the success rates in the mentioned HRI scenarios and they are deployed on the ergoCub humanoid robot. XBG achieves success rates between 60% and 100% even when tested in unseen environments.

Edible electronics present a blossoming path to a greener and eco-friendly future for electronics, whilst being biocompatible with living beings. With this characteristic, edible electronics has been recently proposed for the design and fabrication of edible and digestible sensors. More precisely, it has become a strong and sustainable candidate for continuous and in vivo monitoring and diagnosis of patients. Yet, the field is in constant search for new functional materials satisfying the stringent and contrasting requirements of safe edibility and performing electronics. With this in mind, a novel edible substrate, based entirely on cookie dough is presented in this letter. An extensive mechanical and electrical characterization of the edible substrate is provided, aside from a clear step-by-step guide for its fabrication. Additionally, to prove the use of the cookie-dough substrate for food-based electronics, we demonstrate a voltage divider and a resonant circuit fabricated on it. Tests have been conducted in dry and wet conditions, simulating intraoral environment. Sensing capabilities have been also investigated, with variations of temperature and pH. These findings push the boundaries of edible electronics, enabling a growing community of researchers to utilize the proposed substrate and circuits in a broad range of sensor technologies and applications.

This work describes the scalability process of a continuous‐injection protocol employed to produce tin‐doped indium oxide nanocrystal dispersions. Different levels of manipulation starting from the synthesis and processing also related to the tuning of the optical response (considering the peculiar combination of UV and NIR absorption with visible transparency) make these materials incredibly versatile. But one of the most attractive features concern the modulation of their charge carrier density through chemical or post‐synthetic doping, as for the case of core‐shell materials, expanding the properties of the core composition. In addition, the colloidal nature of such materials allows for easy solution processing which enables an extensive use in different applications within current thin films base technologies.

The regenerative capacity of the central nervous system (CNS) is limited. Understanding and enhancing the mechanisms that induce neural differentiation of neural stem cells (NSCs) is crucial for advancing regenerative medicine; one significant challenge in this effort is the remote delivery of pro‐differentiation cues. In this framework, a nanotechnology‐based solution able to remotely trigger the differentiation of human NSCs (hNSCs) into neurons is proposed. The approach involves organic piezoelectric nanotransducers, which can be remotely activated by low‐intensity ultrasound (US) for local and noninvasive electrical stimulation. Highly biocompatible piezoelectric polymeric nanoparticles, when activated by US, demonstrate the ability to induce calcium influx, exit from the cell cycle, and neuronal differentiation in hNSCs, as evidenced by calcium imaging experiments and the expression analysis of the NeuN post‐mitotic neural marker; additionally, an increased outgrowth of the developing axons is observed. Gene expression analysis moreover suggests that the neural differentiation mechanism induced by piezoelectric stimulation acts by upregulating the calcium signaling‐sensitive NeuroD1 neural inducer and the Lamb1 marker, independently of the c‐Jun/c‐Fos pathway. Considering the high biocompatibility and the good piezoelectricity of the polymeric nanotransducers used in this work, it is believed that this “wireless” stimulation approach holds high potential in CNS regenerative medicine.

Iron oxide nanocubes (IONCs) are among the most promising materials in magnetic hyperthermia (MHT) for tumor therapy as they can efficiently convert magnetic energy into heat under alternating magnetic field (AMF). Conventional IONCs syntheses are based on thermal decomposition methods, limited by the long reaction time (hours) and milligram‐scale production; while, solvothermal methods produce gram‐scale amount of high quality IONCs, but, reaction times are of the orders of hours. In this work, a microwave‐assisted route to shape‐control IONCs in which the reaction time is reduced to minutes while achieving a high iron conversion yield up to 80% is reported. The size of the IONCs (range 13–30 nm) is coarse‐tuned by adjusting the amount of benzaldehyde ligand, while fine‐size tuning is achieved by changing temperature and minute‐reaction time. IONCs exhibit superparamagnetic behavior at 298 K with saturation magnetization over 80 emu gIONC⁻¹ and possess high specific absorption rate values (SAR) up to 400 W gFe⁻¹ at clinical AMF conditions. These results mark a milestone for rapid synthesis of IONCs at high yield conversion of well‐defined size and shape nanocubes with benchmark MHT heat performance while using a fast route, with limited energy consumption which makes this method greener and cheaper than previous ones.

Two-photon imaging is a powerful method to record the activity of neuronal ensembles in the intact animal brain. Here, we describe a protocol to run a recent analytical approach, CITE-On, specifically designed for online morphological cell detection, segmentation, and signal extraction in two-photon functional imaging recordings. A background introduction and the description of the main challenges CITE-On was conceived to overcome are presented, together with a detailed experimental procedure to install CITE-On and run it on two-photon calcium imaging time-series (t-series). By performing fast image segmentation, CITE-On will facilitate the establishment of efficient close-loop experimental approaches, including all-optical two-photon imaging and perturbation experiments.

The long non-coding RNA (lncRNA), HAR1A is emerging as a putative tumour suppressor. In non-neoplastic brain cells, REST suppresses HAR1A expression. In gliomas REST acts as an oncogene and is a potential therapeutic target. It is therefore conceivable that REST promotes glioma progression by down-regulating HAR1A. To test this hypothesis, glioma clinical databases were analysed to study: (I) HAR1A/REST correlation; (II) HAR1A and REST prognostic role; (III) molecular pathways associated with these genes. HAR1A expression and subcellular localization were studied in glioblastoma and paediatric glioma cells. REST function was also studied in these cells, by observing the effects of gene silencing on: (I) HAR1A expression; (II) cancer cell proliferation, apoptosis, migration; (III) expression of neural differentiation genes. The same phenotypes (and cell morphology) were studied in HAR1A overexpressing cells. Our results show that REST and HAR1A are negatively correlated in gliomas. Higher REST expression predicts worse prognosis in low-grade gliomas (the opposite is true for HAR1A). REST-silencing induces HAR1A upregulation. HAR1A is primarily detected in the nucleus. REST-silencing dramatically reduces cell proliferation and induces apoptosis, but HAR1A overexpression has no major effect on investigated cell phenotypes. We also show that REST regulates the expression of neural differentiation genes and that its oncogenic function is primarily HAR1A-independent.

Pathogenic variants in KCNQ2 encoding Kv7.2 voltage-gated potassium channel subunits cause developmental encephalopathies ( KCNQ2 -encephalopathies), both with and without epilepsy. We herein describe the clinical, in vitro, and in silico features of two encephalopathy-causing variants (A317T, L318V) in Kv7.2 affecting two consecutive residues in the S 6 activation gate that undergoes large structural rearrangements during pore opening; the disease-causing A356T variant in KCNQ3 , paralogous to the A317T variant in KCNQ2 , was also investigated. Currents through KCNQ2 mutant channels displayed increased density, hyperpolarizing shifts in activation gating, faster activation and slower deactivation kinetics, and resistance to changes in the cellular concentrations of phosphatidylinositol 4,5-bisphosphate (PIP 2 ), a critical regulator of Kv7 channel function; all these features are consistent with a strong gain-of-function effect. An increase in the probability of single-channel opening, with no change in membrane abundance or single-channel conductance, was responsible for the observed gain-of-function effects. All-atom molecular dynamics simulations revealed that the mutations widened the inner pore gate and stabilized a constitutively open channel configuration in the closed state, with minimal effects on the open conformation. Thus, mutation-induced stabilization of the inner pore gate open configuration is a molecular pathogenetic mechanism for KCNQ2 -related encephalopathies.

Background The use of light to control the activity of living cells is a promising approach in cardiac research due to its unparalleled spatio-temporal selectivity and minimal invasiveness. Ziapin2, a newly synthesized azobenzene compound, has recently been reported as an efficient tool for light-driven modulation of excitation-contraction coupling (ECC) in human-induced pluripotent stem cells–derived cardiomyocytes. However, the exact biophysical mechanism of this process remains incompletely understood. Methods To address this, we performed a detailed electrophysiological characterization in a more mature cardiac model, specifically adult mouse ventricular myocytes (AMVMs). Results Our in vitro results demonstrate that Ziapin2 can photomodulate cardiac ECC in mature AMVMs without affecting the main transporters and receptors located within the sarcolemma. We established a connection between Ziapin2-induced membrane thickness modulation and light-generated action potentials by showcasing the pivotal role of stretch-activated channels (SACs). Notably, our experimental findings, through pharmacological blockade, suggest that non-selective SACs might serve as the biological culprit responsible for the effect. Conclusions Taken together, these findings elucidate the intricacies of Ziapin2-mediated photostimulation mechanism and open new perspectives for its application in cardiac research.

Electrochemical liquid phase transmission electron microscopy (EC‐LPTEM) is an invaluable tool for investigating the structural and morphological properties of functional materials in electrochemical systems for energy transition. Despite its potential, standardized experimental protocols and a consensus on data interpretation are lacking, due to a variety of commercial and customized electrical and microfluidic configurations. Given the small size of a typical electrochemical cell used in these experiments, frequent electrolyte renewal is crucial to minimize local chemical alterations from reactions and radiolysis. This study explores the effects of modifying the flow configuration within the liquid cell under experimental conditions relevant for energy applications in aqueous‐based electrolytes, revealing how changes in mass transport dynamics drastically influence the electrochemical response of the cell. Two different cell designs are compared: convection‐ and diffusion–governed. Ex situ and in situ comparative flow experiments show that the diffusion cell mitigates gas bubbles formation and improves removal of gaseous products. The electrodeposition of Zn nanostructures and the characterization of a Cu‐based catalyst are presented as proof‐of‐concept experiments for energy storage and CO2 reduction reaction (CO2RR) applications, respectively. The reported findings demonstrate that controlling mass transport in the liquid cell setup is crucial to obtain reliable operando experimental electrochemical conditions.

Machine learning (ML) is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) of the enzyme:substrate complex where the substrate assumes a precise arrangement in the active site necessary to initiate a reaction. Traditional methods are hindered by the complexity of the multidimensional free energy landscape involved in the transition from nonreactive to reactive conformations. Here, we applied ML techniques to address this challenge, focusing on human pancreatic α-amylase, a crucial enzyme in type-II diabetes treatment. Using ML-based collective variables (CVs), we correlated the probability of being in a RC with the experimental catalytic activity of several malto-oligosaccharide substrates. Our findings demonstrate a remarkable transferability of these CVs across various compounds, significantly streamlining the modeling process and reducing both computational demand and manual intervention in setting up simulations for new substrates. This approach not only advances our understanding of enzymatic processes but also holds substantial potential for accelerating drug discovery by enabling rapid and accurate evaluation of drug efficacy across different generations of inhibitors.

Organic Electronic platforms for biosensing are being demonstrated at a fast pace, especially in healthcare applications where the use of organic (semi‐)conductive materials leads to devices that efficiently interface living matter. Nevertheless, interesting properties of organic (semi‐)conductors are usually neglected in the development of (bio‐)sensors. Among these, the non‐linear response when operated under dynamic biasing conditions (i.e., with pulsed driving voltages), thus mimicking synaptic plasticity phenomena, offers promising and largely unexplored possibilities for bio‐sensing. The artificial synaptic response's figures of merit reflect the composition of the surrounding environment and, ultimately, the ion concentration and dynamics at the organic (semi‐)conductor/electrolyte interface. Therefore, new sensing strategies that rely on the effect of target analytes on the short‐term plasticity response of Organic Neuromorphic Devices are being demonstrated. This work presents the development of a label‐free Single Electrode Neuromorphic Device (SEND) specifically designed for in vivo real‐time mapping of dopamine concentration. The device response is investigated as a function of the driving frequency, resulting in the determination of the optimal operational configuration for minimally invasive neuromorphic devices. It exhibits stable multi‐parametric response in complex fluids, in brain's mechanical models and in vivo, enabling monitoring of local variations of dopamine concentration in the rat brain.

This paper proposes a novel Hierarchical Quadratic Programming (HQP)-based framework that enables multi-tasking control under multiple Human-Robot Interaction (HRI) scenarios. The proposed controllers’ formulations are inspired by real-world contact-rich scenarios, which currently constitute one of the main limitations in terms of widespread practical deployment. Indeed, HRI can occur through different modalities, based on human’s needs. The objective is to create a unique framework for various types of possible interactions, avoiding the necessity of switching between different control architectures, which requires dealing with discontinuities. To achieve this, we firstly propose a HQP-based hybrid Cartesian/joint space impedance control formulation. Based on the robot’s dynamics, this controller enables an adaptive compliance behaviour, while achieving hierarchical motion control. This is validated through a series of experiments that show the accuracy of trajectory tracking, which remains in the order of 10mm during fast motions thanks to the addition of the robot dynamics. Besides, the hybrid compliance behaviour allows to deviate from such accuracy when an interaction is present. We then consider the case in which the human needs to move the robot directly, by proposing a hybrid admittance/impedance controller, that is again based on a HQP formulation and provides inherent softening when conflicting tasks are present, or in close-to-limit and near-singular configurationsa. This is validated through several experiments in which the human easily moves the robot in the workspace via direct physical interaction. Next, we formulate an additional hierarchy that enables force control and allows to maintain a specific interaction force at the end effector. We then extend this to simultaneous force and trajectory tracking. Overall, we obtain a multi-purpose HQP-based control framework, that seamlessly switchwes between interaction modes, enabling multiple hierarchical behaviours, and covering a wide spectrum of interaction types, essential for synergistic HRI.

Alternative configuration of lithium cell exploits electrode and polymer electrolyte cast all‐in‐one to form a membrane electrode assembly (MEA), in analogy to fuel cell technology. The electrolyte is based on polyethylene oxide (PEO), lithium bis‐trifluoromethane sulfonyl imide (LiTFSI) conducting salt, LiNO3 sacrificial film‐forming agent to stabilize the lithium metal, and fumed silica (SiO2) to increase the polymer amorphous degree. The membrane has conductivity ranging from ~5×10⁻⁴ S cm⁻¹ at 90 °C to 1×10⁻⁴ S cm⁻¹ at 50 °C, lithium transference number of ~0.4, and relevant interphase stability. The MEA including LiFePO4 (LFP) cathode is cycled in polymer lithium cells operating at 3.4 V and 70 °C, with specific capacity of ~155 mAh g⁻¹ (1 C=170 mA gLFP⁻¹) for over 100 cycles, without signs of decay or dendrite formation. The cell exploiting the MEA shows enhanced electrochemical performance as compared with the one using simple polymeric membrane stacked between cathode and anode. Furthermore, the MEA reveals the key advantage of possible scalability and applicability in roll‐to‐roll systems for achieving high‐energy lithium metal battery, as demonstrated by pouch‐cell application. These data may trigger new interest on this challenging battery exploiting the polymer configuration for achieving environmentally/economically sustainable, and safe energy storage.

A dibenzo[hi,st]ovalene (DBOV) derivative bay-substituted with two piperazinylphenyl (PZP) groups (DBOV–PZP) was synthesized. Comprehensive investigations of its photophysical properties revealed acid-induced fluorescence enhancement through the protonation of PZP units, leading...

We present ab initio simulations of a large system of 2400 particles of molten NaCl to investigate the behavior of collective mode dispersion beyond the hydrodynamic regime. In particular, we aim to explain the unusually strong increase in the apparent speed of sound with wave number, which significantly exceeds the typical positive sound dispersion of 10%–25% observed in simple liquids. We compare dispersions of “bare” acoustic and optic modes in NaCl with ab initio simulations of other ionic melts such as CuCl and LiBr, metallic liquid alloys such as Pb44Bi56 and Li4Tl, and the regular Lennard-Jones KrAr liquid simulated by classical molecular dynamics. Analytical expressions for the “bare” acoustic and optic branches of collective excitations help us to identify the impact of the high-frequency optic branch on the emergence of “fast sound” in binary melts. Our findings show that in ionic melts, the high-frequency speed of sound is much larger than in the simple Lennard-Jones liquids and metallic melts, leading to an observed strong viscoelastic increase in the apparent speed of sound—more than double its adiabatic value.

The final chapter of this book summarises the lessons learned from the studies addressing the question of whether and when humans adopt the Intentional Stance towards robots. It highlights that both parties—the robot and the human, as well as interaction between them, play a role in whether the human will attribute intentional agency to the robot, or will rather treat it as a mechanical device. From the human side, individual differences, state of mind, as well as education and cultural background, are all contributing factors to whether, and to what extent, the Intentional Stance is adopted. From the robot side, its appearance, human-like behaviour, behavioural variability, and human-like motor repertoire will impact the degree to which Intentional Stance is adopted. Finally, in interaction, what matters is engagement, social bonding, and attunement. This chapter concludes with the final take-home message: adoption of the Intentional Stance is a gradient and can range from ‘just a manner of speaking’ to a full-blown attribution of mental states to the system. This take-home message is then followed with open questions for future research, and an invitation to the final part of the book, the interview with Prof. Daniel Dennett, the originator of the concept of the Intentional Stance.