Design, synthesis, docking, and biochemical characterization of non-nucleoside SARS-CoV-2 RdRp inhibitors.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide pandemic. The identification of effective antiviral drugs remains an urgent medical need. In this context, here we report 17 new 1,4-benzopyrone derivatives, which have been designed, synthesized, and characterized for their ability to block the RNA-dependent RNA polymerase (RdRp) enzyme, a promising target for antiviral drug discovery. This compound series represents a good starting point for developing non-nucleoside inhibitors of RdRp. Compounds 4, 5, and 8 were the most promising drug-like candidates with good potency in inhibiting RdRp, improved in vitro pharmacokinetics compared to the initial hits, and no cytotoxicity effects on normal cell (HEK-293). Compound 8 (ARN25592) stands out as the most promising inhibitor. Our results indicate that this new chemical class of 1,4-benzopyrone derivatives deserves further exploration towards novel and potent antiviral drugs for the treatment of SARS-CoV-2 and potentially other viruses.

Recruiting Mechanism and Functional Role of a Third Metal Ion in the Enzymatic Activity of 5' Structure-Specific Nucleases.

Enzymes of the 5' structure-specific nuclease family are crucial for DNA repair, replication, and recombination. One such enzyme is the human exonuclease 1 (hExo1) metalloenzyme, which cleaves DNA strands, acting primarily as a processive 5'-3' exonuclease and secondarily as a 5'-flap endonuclease. Recently, in crystallo reaction intermediates have elucidated how hExo1 exerts hydrolysis of DNA phosphodiester bonds. These hExo1 structures show a third metal ion intermittently bound close to the two-metal-ion active site, to which recessed ends or 5'-flap substrates bind. Evidence of this third ion has been observed in several nucleic-acid-processing metalloenzymes. However, there is still debate over what triggers the (un)binding of this transient third ion during catalysis and whether this ion has a catalytic function. Using extended molecular dynamics and enhanced sampling free-energy simulations, we observed that the carboxyl side chain of Glu89 (located along the arch motif in hExo1) flips frequently from the reactant state to the product state. The conformational flipping of Glu89 allows one metal ion to be recruited from the bulk and promptly positioned near the catalytic center. This is in line with the structural evidence. Additionally, our simulations show that the third metal ion assists the departure, through the mobile arch, of the nucleotide monophosphate product from the catalytic site. Structural comparisons of nuclease enzymes suggest that this Glu(Asp)-mediated mechanism for third ion recruitment and nucleic acid hydrolysis may be shared by other 5' structure-specific nucleases.

A review on animal-robot interaction: from bio-hybrid organisms to mixed societies.

Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal-robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication.

Measuring 3D-orthodontic actions to guide clinical treatments involving coil springs and miniscrews.

The understanding of the phenomena at the base of tooth movement, due to orthodontic therapy, is an ambitious topic especially with regard to the "optimal forces" able to move teeth without causing irreversible tissue damages. To this aim, a measuring platform for detecting 3D orthodontic actions has been developed. It consists of customized load cells and dedicated acquisition electronics. The force sensors are able to detect, simultaneously and independently of each other, the six orthodontic components which a tooth is affected by. They have been calibrated and then applied on a clinical case that required NiTi closed coil springs and miniscrews for the treatment of upper post-extraction spaces closure. The tests have been conducted on teeth stumps belonging to a plaster cast of the patient's mouth. The load cells characteristics (sensor linearity and repeatability) have been analyzed (0.97 < R 2 < 1; 6.3*10 -6 % < STD < 8.8 %) and, on the basis of calibration data, the actions exerted on teeth have been determined. The biomechanical behavior of the frontal group and clinical interpretation of the results are discussed.

Lateralized courtship in a parasitic wasp.

Lateralization (i.e. left-right asymmetries in the brain and behaviour) of courtship displays has been examined in a growing number vertebrate species, while evidence for invertebrates is limited. In this study, we investigated lateralization of courtship and mating displays in the parasitic wasp Leptomastidea abnormis. Results showed a population-level lateralization of male courtship displays. Male antennal tapping on the female's head was right-biased. However, right-biased male courtship acts were not characterized by higher male antennal tapping frequencies, nor success in mating although antennal tapping frequency was higher in males with mating success with respect to unsuccessful males. Overall, our results add basic knowledge to the behavioural ecology of insect parasitoids. To the best of our knowledge, this is the first report of behavioural lateralization in parasitic Hymenoptera.

Lateralisation of aggressive displays in a tephritid fly.

Lateralisation (i.e. different functional and/or structural specialisations of the left and right sides of the brain) of aggression has been examined in several vertebrate species, while evidence for invertebrates is scarce. In this study, we investigated lateralisation of aggressive displays (boxing with forelegs and wing strikes) in the Mediterranean fruit fly, Ceratitis capitata. We attempted to answer the following questions: (1) do medflies show lateralisation of aggressive displays at the population-level; (2) are there sex differences in lateralisation of aggressive displays; and (3) does lateralisation of aggression enhance fighting success? Results showed left-biased population-level lateralisation of aggressive displays, with no consistent differences among sexes. In both male-male and female-female conflicts, aggressive behaviours performed with left body parts led to greater fighting success than those performed with right body parts. As we found left-biased preferential use of body parts for both wing strikes and boxing, we predicted that the left foreleg/wing is quicker in exploring/striking than the right one. We characterised wing strike and boxing using high-speed videos, calculating mean velocity of aggressive displays. For both sexes, aggressive displays that led to success were faster than unsuccessful ones. However, left wing/legs were not faster than right ones while performing aggressive acts. Further research is needed on proximate causes allowing enhanced fighting success of lateralised aggressive behaviour. This is the first report supporting the adaptive role of lateralisation of aggressive displays in insects.

Novel universal system for 3-dimensional orthodontic force-moment measurements and its clinical use.

INTRODUCTION: Orthodontic treatment is an important part of dental health care in Europe: the percentages of the population undergoing therapy vary from 10% to 55%. Therefore, quantifying effective orthodontic loads is a challenging topic with regard to the predictability of tooth movements and the reduction of traumatic side effects. METHODS: A customized measuring platform was developed and used for detecting orthodontic forces in a range between 0.1 and 2 N. The system consists of 6 load cells, each equipped with 6 strain gauges. The tests were conducted on a 3-dimensional printed malocclused mouth model and on a plaster cast. Four types of superelastic ligation and 2 types of invisible aligners were tested to analyze, respectively, a malocclusion with a high maxillary canine, and the effects on the axial rotation of a maxillary central incisor with and without a divot in the invisible aligners. RESULTS: Optimal treatment forces are exerted by low-friction wires, especially if they are partially engaged. Moreover, by reducing the treatment force, there is less necessity of anchoring to surrounding teeth, thus decreasing the side effects. The efficacy of using invisible aligners with a divot was validated. CONCLUSIONS: This platform allowed measurement, at the radicular level, of the resultant forces of orthodontic treatments performed with different orthodontic appliances. In addition to customizing and calibrating the therapy for each patient, this platform could be used to develop new specific instruments able to exert lower treatment forces, thus preventing irreversible damages.

Neuromorphic hardware for somatosensory neuroprostheses.

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller.

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.

PURA syndrome-causing mutations impair PUR-domain integrity and affect P-body association.

Mutations in the human PURA gene cause the neurodevelopmental PURA syndrome. In contrast to several other monogenetic disorders, almost all reported mutations in this nucleic acid-binding protein result in the full disease penetrance. In this study, we observed that patient mutations across PURA impair its previously reported co-localization with processing bodies. These mutations either destroyed the folding integrity, RNA binding, or dimerization of PURA. We also solved the crystal structures of the N- and C-terminal PUR domains of human PURA and combined them with molecular dynamics simulations and nuclear magnetic resonance measurements. The observed unusually high dynamics and structural promiscuity of PURA indicated that this protein is particularly susceptible to mutations impairing its structural integrity. It offers an explanation why even conservative mutations across PURA result in the full penetrance of symptoms in patients with PURA syndrome.

PURA syndrome is a neurodevelopmental disorder that affects about 650 patients worldwide, resulting in a range of symptoms including neurodevelopmental delays, intellectual disability, muscle weakness, seizures, and eating difficulties. The condition is caused by a mutated gene that codes for a protein called PURA. PURA binds RNA ­ the molecule that carries genetic information so it can be translated into proteins ­ and has roles in regulating the production of new proteins. Contrary to other conditions that result from mutations in a single gene, PURA syndrome patients show 'high penetrance', meaning almost every reported mutation in the gene leads to symptoms. Proske, Janowski et al. wanted to understand the molecular basis for this high penetrance. To find out more, the researchers first examined how patient mutations affected the location of the PURA in the cell, using human cells grown in the laboratory. Normally, PURA travels to P-bodies, which are groupings of RNA and proteins involved in regulating which genes get translated into proteins. The researchers found that in cells carrying PURA syndrome mutations, PURA failed to move adequately to P-bodies. To find out how this 'mislocalization' might happen, Proske, Janowski et al. tested how different mutations affected the three-dimensional folding of PURA. These analyses showed that the mutations impair the protein's folding and thereby disrupt PURA's ability to bind RNA, which may explain why mutant PURA cannot localize correctly. Proske, Janowski et al. describe the molecular abnormalities of PURA underlying this disorder and show how molecular analysis of patient mutations can reveal the mechanisms of a disease at the cell level. The results show that the impact of mutations on the structural integrity of the protein, which affects its ability to bind RNA, are likely key to the symptoms of the syndrome. Additionally, their approach used establishes a way to predict and test mutations that will cause PURA syndrome. This may help to develop diagnostic tools for this condition.

Neuromorphic Decoding of Spinal Motor Neuron Behaviour During Natural Hand Movements for a New Generation of Wearable Neural Interfaces.

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN.

Direct observation of backtracking by influenza A and B polymerases upon consecutive incorporation of the nucleoside analog T1106.

The antiviral pseudo-base T705 and its de-fluoro analog T1106 mimic adenine or guanine and can be competitively incorporated into nascent RNA by viral RNA-dependent RNA polymerases. Although dispersed, single pseudo-base incorporation is mutagenic, consecutive incorporation causes polymerase stalling and chain termination. Using a template encoding single and then consecutive T1106 incorporation four nucleotides later, we obtained a cryogenic electron microscopy structure of stalled influenza A/H7N9 polymerase. This shows that the entire product-template duplex backtracks by 5 nt, bringing the singly incorporated T1106 to the +1 position, where it forms an unexpected T1106:U wobble base pair. Similar structures show that influenza B polymerase also backtracks after consecutive T1106 incorporation, regardless of whether prior single incorporation has occurred. These results give insight into the unusual mechanism of chain termination by pyrazinecarboxamide base analogs. Consecutive incorporation destabilizes the proximal end of the product-template duplex, promoting irreversible backtracking to a more energetically favorable overall configuration.

Embodied neuromorphic intelligence.

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

Quercetin and luteolin are single-digit micromolar inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a global health pandemic. Among the viral proteins, RNA-dependent RNA polymerase (RdRp) is responsible for viral genome replication and has emerged as one of the most promising targets for pharmacological intervention against SARS-CoV-2. To this end, we experimentally tested luteolin and quercetin for their ability to inhibit the RdRp enzyme. These two compounds are ancestors of flavonoid natural compounds known for a variety of basal pharmacological activities. Luteolin and quercetin returned a single-digit IC50 of 4.6 µM and 6.9 µM, respectively. Then, through dynamic docking simulations, we identified possible binding modes of these compounds to a recently published cryo-EM structure of RdRp. Collectively, these data indicate that these two compounds are a valid starting point for further optimization and development of a new class of RdRp inhibitors to treat SARS-CoV-2 and potentially other viral infections.

Adaptive Extreme Edge Computing for Wearable Devices.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Spontaneous abdominal bleeding associated with SARS-CoV-2 infection: causality or coincidence?

Authors present 6 cases of abdominal bleeding associated with COVID-19, representing 1.35% of all hospitalized COVID-19 patients and hypothesize that there could be, although not very frequently, a relationship between SARS-CoV2 and bleeding. They excluded a side effect of the low molecular weight heparin therapy that all patients underwent during the course of the disease or other possible causes. Alterations of the coagulation state or a weakness of the vascular wall due toa presumed endotheliitis SARS-CoV-2 infection induced, are hypothesized by the authors. Investigation and follow-up for possible hemorrhagic problems in patients with COVID-19 is recommended. In particular, clinicians should be vigilant about retroperitoneal hemorrhage in COVID-19 patients. In addition to the fact that these patients are being treated with anticoagulants, anemia and abdominal pain are the signs that should lead us to suspect this type of haemorrhage. More studies are needed to understand if COVID-19 can be directly associated with bleeding. (www.actabiomedica.it)

A Spike-Based Neuromorphic Architecture of Stereo Vision.

The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems. As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem. Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences. However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored. Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the stereo matching performance with two datasets. Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications.

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing.

Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network.

An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements, a large number of individual motor neurons are monitored simultaneously, producing large amounts of data to be transferred and processed by the computing devices. In this paper, we follow an alternative approach that can be deployed locally on the sensor side. We propose a neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption. We present experimental results on the input data stream using a mixed-signal analog/digital neuromorphic processor. We performed a thorough investigation on the performance of the SNN implemented on the chip, by: first, calculating PCA on the activity of the silicon neurons at the input and the hidden layers to show how the network helps in separating the samples of different classes; second, performing classification of the data using state-of-the-art SVM and logistic regression methods and a hardware-friendly spike-based read-out. The traditional algorithm achieved a classification rate of [Formula: see text] and [Formula: see text], respectively, and the spiking learning method achieved [Formula: see text]. The power consumption of the SNN is [Formula: see text], showing the potential of this approach for ultra-low power processing.