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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. Neuroprosthetic devices have recently emerged as promising solutions to restore sensory-motor functions lost due to injury or neurological diseases. In this perspective, Donati and Valle propose to combine neuroprostheses with neuromorphic technologies for designing more natural human-machine interfaces with possible improvements in device performance, acceptability, and embeddability. [ABSTRACT FROM AUTHOR]

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. Neuroprosthetic devices have recently emerged as promising solutions to restore sensory-motor functions lost due to injury or neurological diseases. In this perspective, Donati and Valle propose to combine neuroprostheses with neuromorphic technologies for designing more natural human-machine interfaces with possible improvements in device performance, acceptability, and embeddability. [ABSTRACT FROM AUTHOR]

Context: Among processes involved in colour polymorphism, geographic variation in morph composition and frequency has been attracting interest since it reflects morph local adaptation. A recent study in the Pyrenees associated the pattern of geographic variation in morph frequency of the common wall lizard with the divergence in climatic niches, supporting the hypothesis that morphs represent alternative local climatic adaptations. However, the Pyrenees represent only a small portion of the species range. Aims: We modelled the ecological niches of Italian morphs using the same procedure adopted for the Pyrenees to check whether the effects detected at local scales (i.e. the Pyrenees) were repeatable at regional scales (i.e. Italy). This generalisation is needed to investigate how natural selection maintains locally adapted polymorphisms. Methods: We classified each locality (120 populations) according to the presence/absence of morphs, and independent Ecological Niche Models (ENMs) against the same background were fitted. Receiver Operating Curves accounting for sampling biases, equivalency and similarity tests were used to check and compare models accounting for spatial distribution of data. Key results: Morph-specific ENMs did not reproduce any of the patterns detected in the Pyrenees. Any difference among morphs disappeared after controlling for morph spatial distribution. Since occurrence points of the rarest morphs were a subsample of the occurrence points of the most common morph, it is not possible to separate the effects of true ecological differences among morphs from the effects of the spatial distribution patterns of morph occurrence. Conclusions: Using presence data not specifically collected for ENM comparisons does not allow reliable assessments of morph niche segregation. Our analysis points out the need to be very cautious in ecological interpretations of ENMs built on presence/background or presence-only data when occurrences are spatially nested. Implications: When dealing with data not specifically collected according to a targeted design, it is not legitimate to compare ENMs with completely nested occurrence points, because this approach can not exclude the possibility that ENM differences were the result of a spatial subsampling. This type of bias is probably largely underestimated, and it may lead to serious misinterpretations as shown in this study. Sampling procedures not exactly designed for a specific purpose ('opportunistic' data) are commonly used to model species distribution and analyse niche segregation among species. By analysising niche overlap among morphs in a polymorphic lizard, we show how opportunistically collected data have serious limitations without properly accounting for the spatial structure of the data. We point out the need of being cautious in interpretation of ENMs when occurrences are spatially nested. Photography by Roberto Sacchi [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. A grand challenge in robotics is realising intelligent agents capable of autonomous interaction with the environment. In this Perspective, the authors discuss the potential, challenges and future direction of research aimed at demonstrating embodied intelligent robotics via neuromorphic technology. [ABSTRACT FROM AUTHOR]

Synapses play a critical role in memory, learning, and cognition. Their main functions include converting presynaptic voltage spikes to postsynaptic currents, as well as scaling the input signal. Several brain‐inspired architectures have been proposed to emulate the behavior of biological synapses. While these are useful to explore the properties of nervous systems, the challenge of making biocompatible and flexible circuits with biologically plausible time constants and tunable gain remains. Here, a physically flexible organic log‐domain integrator synaptic circuit is shown to address this challenge. In particular, the circuit is fabricated using organic‐based materials that are electrically active, offer flexibility and biocompatibility, as well as time constants (critical in learning neural codes and encoding spatiotemporal patterns) that are biologically plausible. Using a 10 nF synaptic capacitor, the time constant reached 126 and 221 ms before and during bending, respectively. The flexible synaptic circuit is characterized before and during bending, followed with studies on the effects of weighting voltage, synaptic capacitance, and disparity in presynaptic signals on the time constant. [ABSTRACT FROM AUTHOR]

DNA and RNA polymerases (Pols) are central to life, health, and biotechnology because they allow the flow of genetic information in biological systems. Importantly, Pol function and (de)regulation are linked to human diseases, notably cancer (DNA Pols) and viral infections (RNA Pols) such as COVID‐19. In addition, Pols are used in various applications such as synthesis of artificial genetic polymers and DNA amplification in molecular biology, medicine, and forensic analysis. Because of all of this, the field of Pols is an intense research area, in which computational studies contribute to elucidating experimentally inaccessible atomistic details of Pol function. In detail, Pols catalyze the replication, transcription, and repair of nucleic acids through the addition, via a nucleotidyl transfer reaction, of a nucleotide to the 3′‐end of the growing nucleic acid strand. Here, we analyze how computational methods, including force‐field‐based molecular dynamics, quantum mechanics/molecular mechanics, and free energy simulations, have advanced our understanding of Pols. We examine the complex interaction of chemical and physical events during Pol catalysis, like metal‐aided enzymatic reactions for nucleotide addition and large conformational rearrangements for substrate selection and binding. We also discuss the role of computational approaches in understanding the origin of Pol fidelity—the ability of Pols to incorporate the correct nucleotide that forms a Watson–Crick base pair with the base of the template nucleic acid strand. Finally, we explore how computations can accelerate the discovery of Pol‐targeting drugs and engineering of artificial Pols for synthetic and biotechnological applications. This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisStructure and Mechanism > Computational Biochemistry and BiophysicsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have emerged as a promising computational paradigm to emulate the features of natural neural tissue physiology. While hardware implementations of SNNs are being conceived to emulate biological systems, they typically rely on hard and rigid silicon electronics that are not bio-compatible. In the physical, or materials realm, organic electronics offer mechanical flexibility and bio-compatibility, allowing for the construction of neural processing systems that can be directly interfaced to biological networks. This study introduces an organic electronics implementation of an Integrate-and-Fire spiking neuron based on the Axon-Hillock CMOS circuit. The circuit employs organic p-type and n-type field effective transistors and reproduces the behavior of the CMOS neuromorphic counterpart. We demonstrate its operating characteristics measuring its spike rate output as a function of its input current. We show how it properly integrates input currents and demonstrate its computing abilities in a basic current summing experiment. The static and dynamic power dissipation is calculated to be less than 0.4 and 40 µW, respectively. This is the first demonstration of the spiking Axon-Hillock neuromorphic circuit using organic materials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 $\text{84}\%$ and $\text{81}\%$ , respectively, and the spiking learning method achieved $\text{74}\%$. The power consumption of the SNN is $\text{0.05 mW}$ , showing the potential of this approach for ultra-low power processing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Locusts are widely recognized for their jumping performances. Jumping allows locusts to avoid predators and initiate flight. In this study, jumping performances of Locusta migratoria were modelled with a lumped-parameter theoretical model. Results showed that L. migratoria can reach a maximum take-off velocity of 1.83 m/s and maximum acceleration of 66.72 m/s2, with take-off angle of about 36°. While carrying out such a high performance, locusts are prone to slip when taking off from a smooth ground, especially for locusts with only one hind leg. Experiments have been therefore conducted to test the influence of ground roughness on jumping performance. There was no significant relation between ground roughness and jumping success, and this was due to combined action of rigid claws and adhesive pads ensuring static contact between tarsus and ground. It was noted that large take-off angles might help locusts to jump successfully with only one hind leg left, as well as to achieve better performance on smooth surfaces. The excellent contact ability on surfaces with different roughness gives meaningful insights for jumping robot design. [ABSTRACT FROM AUTHOR]

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 < 1; 6.3*10 % < 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. [ABSTRACT FROM AUTHOR]

Variations in male body size are known to affect inter- and intrasexual selection outcomes in a wide range of animals. In mating systems involving sexual signaling before mating, body size often acts as a key factor affecting signal strength and mate choice. We evaluated the effect of male size on courtship displays and mating success of the olive fruit fly, Bactrocera oleae (Diptera: Tephritidae). Wing vibrations performed during successful and unsuccessful courtships by large and small males were recorded by high-speed videos and analyzed through frame-by-frame analysis. Mating success of large and small males was investigated. The effect of male-male competition on mating success was evaluated. Male body size affected both male courtship signals and mating outcomes. Successful males showed wing-borne signals with high frequencies and short interpulse intervals. Wing vibrations displayed by successful large males during copulation attempt had higher frequencies over smaller males and unsuccessful large males. In no-competition conditions, large males achieved higher mating success with respect to smaller ones. Allowing large and small males to compete for a female, large males achieve more mating success over smaller ones. Mate choice by females may be based on selection of the larger males, able to produce high-frequency wing vibrations. Such traits may be indicative of 'good genes,' which under sexual selection could means good social-interaction genes, or a good competitive manipulator of conspecifics. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Single incorporation of T1106 into viral RNA is tolerated, but the RNA is mutagenized • Singly incorporated T1106:U base pairs are observed in the wobble configuration • Consecutive T1106 incorporation causes influenza polymerase stalling • Cryo-EM structures show that stalling leads to backtracking and chain termination The broad-spectrum antiviral, nucleoside analog T1106, is incorporated into nascent RNA by viral polymerases. Kouba et al. report cryo-EM structures of influenza polymerase, showing that consecutive, but not single, T1106 incorporation causes polymerase stalling, backtracking, and chain termination. These results detail one of the diverse mechanisms by which nucleoside analog drugs inhibit viral RNA synthesis. [ABSTRACT FROM AUTHOR]

Lysiphlebus testaceipes (Hymenoptera: Braconidae: Aphidiinae) is a generalist endoparasitoid attacking more than 100 aphid species. In L. testaceipes, wing fanning is a main male courtship display evoked by a female-borne sex pheromone. However, no information is available on the characteristics and behavioral role of male fanning during courtship in this parasitoid. Here, the courtship behavior of a wild strain of L. testaceipes was quantified and the male wing fanning performances were analyzed through high-speed video recordings and examined in relation to mating success. Courtship sequence of wild L. testaceipes did not substantially differ from that previously reported for other populations mass reared on aphids. We observed that the male courtship duration did not affect mating success. However, video analysis revealed that the males producing high-frequency fanning signals achieved higher mating success over those that display low-frequency fanning. Wing fanning before successful and unsuccessful courtship differed in amplitude of wing movements and alignment toward the mate, highlighting that frontal courtship positively influence the female mating decisions. This study increases knowledge on sexual behavior in a key parasitoid of aphids, highlighting the importance of wing fanning among the range of sensory modalities used in the sexual communication of L. testaceipes. From a practical point of view, this information is useful in L. testaceipes-based biocontrol strategies, since it can help to establish parameters for quality checking of mass-reared wasps over time. [ABSTRACT FROM AUTHOR]

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 waspLeptomastidea 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aphidius colemani Viereck (Hymenoptera: Braconidae) is a pan-tropical broadly oligophagous endoparasitoid of many aphids of economic importance, including Aphis gossypii Glover and Myzus persicae (Sulzer). While the trophic interactions occurring among A. colemani and its hosts have been extensively studied, little is known about the male- and female-borne cues that guide mating dynamics. Male wing fanning has been found to play a key role in A. colemani courtship, as successful mounting of females without initial wing fanning has never been observed. In this research, we analyzed wing fanning performance and mating ability of males from three different strains of A. colemani: wasps commercially mass-reared on A. gossypii, wild wasps from parasitized A. gossypii, and wild wasps from parasitized Aphis nerii Boyer de Fonscolombe. Results showed that virgin females did not rely on particular male fanning features during mate choice. Moreover, when A. colemani individuals developed on A. gossypii, no major differences were detected in courtship and mating ability between field collected and mass-reared wasps. In contrast, courtship performance and mating success varied between wild A. colemani males reared on different hosts, with those developing on A. nerii having lower quality wing fanning performance during the mounting attempt phase, and reduced ability to compete for females with other males reared on A. gossypii. [ABSTRACT FROM AUTHOR]

These authors contributed equally: Chiara Bartolozzi, Giacomo Indiveri, Elisa Donati. The correct version states 'Neuromorphic tactile system encompassing healable materials and memristive elements to perform proof-of-concept edge tactile sensing, demonstrated in a robotic task that is further applicable to prosthetic applications'. [Extracted from the article]

Objectives: The objective was to study genotypic correlates of discordant interpretations of amprenavir (APV) resistance between a rules-based algorithm and either recombinant phenotype or virtual phenotype. Methods: HIV resistance mutations found in patients from the GenPheRex study were interpreted with VGI-TRUGENE (version 5.0; VGI) and compared with either recombinant-phenotype (Antivirogram, r-PHT) or virtual-phenotype (Virtual-Phenotype, v-PHT) interpreted through Virco biological cut-offs. Results: Among 180 samples available, 56 (31.1%) were discordant with the observed genotype interpretation results, as a result of being judged as sensitive by r-PHT or v-PHT but resistant by VGI (S/R). Only the I84V mutation was almost invariably found in concordant resistant isolates compared with S/R isolates (60% versus 0%, respectively; P < 0.0001). Notwithstanding this, the number of multi-protease inhibitor-associated mutations (PAMs) was significantly higher in the concordant resistant isolates; the prevalence of >3 PAMs was 56.52% versus 33.93% in R/R and S/R isolates, respectively (P = 0.01). Correspondence analysis confirmed the relevance of PAMs, although additional mutations appeared to be correlated with APV resistance. Conclusions: The rate of discordance between rules-based and either r-PHT or v-PHT interpretations for APV was high. Mutation I84V and accumulation of >3 PAMs were found to be associated with resistance as interpreted with all systems tested. However, our results indicate that a number of mutations may have an impact on APV resistance, but that they are missed by current interpretation algorithms and this merits further investigations. [ABSTRACT FROM AUTHOR]

The cover image is based on the Advanced Review I Computational investigations of polymerase enzymes: Structure, function, inhibition, and biotechnology i by Inacrist Geronimo et al., https://doi.org/10.1002/wcms.1534. GLO:BDD0/01nov21:wcms1586-toc-0001.jpg PHOTO (COLOR): . gl. [Extracted from the article]

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback. Designing efficient and scalable specialized neuromorphic circuits to integrate raw nervous stimuli and respond identically to biological neurons remains a challenge. Here, the authors propose an analog programming strategy to emulate biological neurons in silico. [ABSTRACT FROM AUTHOR]