Publications

Antibodies are highly selective and sensitive, making them the gold standard for recognition affinity tools. However, their production cost is high and their downstream processing is time-consuming. Molecularly imprinted polymers (MIPs) are tailor-made by incorporating specific molecular recognition sites in their structure, thus translating into receptor-like activity mode of action. The interest in molecular imprinting technology, applied to biomacromolecules, has increased in the past decade. MIPs, produced using biomolecules as templates, commonly referred to as "plastic antibodies" or "artificial receptors", have been considered as suitable cheaper and easy to produce alternatives to antibodies. Research on MIPs, designed to recognize proteins or peptides is particularly important, with potential contributions towards biomedical applications, namely biosensors and targeted drug delivery systems. This mini review will cover recent advances on (bio)molecular imprinting technology, where proteins or peptides are targeted or mimicked for sensing and therapeutic applications. Polymerization methods are reviewed elsewhere, being out of the scope of this review. Template selection and immobilization approaches, monomers and applications will be discussed, highlighting possible drawbacks and gaps in research.Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

JTD Keywords: artificial antibodies, assay, biomimetics, biomolecules, biosensors, delivery, diagnostics, drug delivery, electrochemical detection, nanoparticles, receptors, science-and-technology, selective recognition, selective targeting, separation, templates, Artificial antibodies, Biomimetics, Biomolecules, Biosensors, Diagnostics, Drug delivery, Molecularly imprinted polymers, Nanoparticles, Selective targeting, Solid-phase synthesis

Glycans are ubiquitously expressed sugars, coating the cell and protein surfaces. They are found on many proteins as either short and branched chains or long chains sticking out from special membrane proteins, known as proteoglycans. This sugar cushion, the glycocalyx, modulates specific interactions and protects the cell. Here it is shown that both the expression of proteoglycans and the glycans expressed on the surface of both the host and virus proteins have a critical role in modulating viral attachment to the cell. A mathematical model using SARS-Cov-2 as an archetypical virus to study the glycan role during infection is proposed. It is shown that this occurs via a tug-of-war of forces. On one side, the multivalent molecular recognition that viral proteins have toward specific host glycans and receptors. On the other side, the glycan steric repulsion that a virus must overcome to approach such specific receptors. By balancing both interactions, viral tropism can be predicted. In other words, the authors can map out the cells susceptible to virus infection in terms of receptors and proteoglycans compositions.© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.

JTD Keywords: binding, entry, glycocalyx, mechanisms, multiplexing, multivalency, nanoparticles, recognition, super-selectivity, viral infectivity, Functional receptor, Glycans, Glycocalyx, Multiplexing, Multivalency, Nanoparticles, Super-selectivity, Viral infectivity

Photosynthesis is a fundamental process that converts photons into chemical energy, driven by large protein complexes at the thylakoid membranes of plants, cyanobacteria, and algae. In plants, water-soluble plastocyanin (Pc) is responsible for shuttling electrons between cytochrome b6f complex and the photosystem I (PSI) complex in the photosynthetic electron transport chain (PETC). For an efficient turnover, a transient complex must form between PSI and Pc in the PETC, which implies a balance between specificity and binding strength. Here, we studied the binding frequency and the unbinding force between suitably oriented plant PSI and Pc under redox control using single molecule force spectroscopy (SMFS). The binding frequency (observation of binding-unbinding events) between PSI and Pc depends on their respective redox states. The interaction between PSI and Pc is independent of the redox state of PSI when Pc is reduced, and it is disfavored in the dark (reduced P700) when Pc is oxidized. The frequency of interaction between PSI and Pc is higher when at least one of the partners is in a redox state ready for electron transfer (ET), and the post-ET situation (PSIRed-PcOx) leads to lower binding. In addition, we show that the binding of ET-ready PcRed to PSI can be regulated externally by Mg2+ ions in solution.

JTD Keywords: architecture, binding-site, complexes, ferredoxin, force spectroscopy, induced structural-changes, interprotein electron transfer, light-dependent interaction, mg2+ concentration, photosystem i, plastocyanin, probe, recognition, reduction, Force spectroscopy, Interprotein electron transfer, Light-dependent interaction, Photosynthetic reaction-center, Photosystem i, Plastocyanin, Single molecule measurements

Chronic use of cannabis leads to both motor deficits and the downregulation of CB1 receptors (CB1R) in the cerebellum. In turn, cerebellar damage is often related to impairments in motor learning and control. Further, a recent motor learning task that measures cerebellar-dependent adaptation has been shown to distinguish well between healthy subjects and chronic cannabis users. Thus, the deteriorating effects of chronic cannabis use in motor performance point to cerebellar adaptation as a key process to explain such deficits. We review the literature relating chronic cannabis use, the endocannabinoid system in the cerebellum, and different forms of cerebellar-dependent motor learning, to suggest that CB1R downregulation leads to a generalized underestimation and misprocessing of the sensory errors driving synaptic updates in the cerebellar cortex. Further, we test our hypothesis with a computational model performing a motor adaptation task and reproduce the behavioral effect of decreased implicit adaptation that appears to be a sign of chronic cannabis use. Finally, we discuss the potential of our hypothesis to explain similar phenomena related to motor impairments following chronic alcohol dependency. © 2022

JTD Keywords: adaptation, addiction, alcohol-abuse, cerebellum, chronic cannabis use, cognition, deficits, endocannabinoid system, error processing, explicit, modulation, motor learning, release, synaptic plasticity, Adaptation, Adaptation, physiological, Alcoholism, Article, Behavioral science, Cannabinoid 1 receptor, Cannabis, Cannabis addiction, Cerebellum, Cerebellum cortex, Cerebellum disease, Chronic cannabis use, Computer model, Down regulation, Endocannabinoid, Endocannabinoid system, Endocannabinoids, Error processing, Hallucinogens, Human, Humans, Motor dysfunction, Motor learning, Nerve cell plasticity, Nonhuman, Physiology, Psychedelic agent, Purkinje-cells, Regulatory mechanism, Sensation, Sensory dysfunction, Sensory error processing impairment, Synaptic transmission, Task performance

JTD Keywords: coordination, embodied cognition, human-robot interaction, sensorimotor coupling, Coordination, Embodied cognition, Human-robot interaction, Sensorimotor coupling, Social cognition

Charge separation and transport through the reaction center of photosystem I (PSI) is an essential part of the photosynthetic electron transport chain. A strategy is developed to immobilize and orient PSI complexes on gold electrodes allowing to probe the complex's electron acceptor side, the chlorophyll special pair P700. Electrochemical scanning tunneling microscopy (ECSTM) imaging and current-distance spectroscopy of single protein complex shows lateral size in agreement with its known dimensions, and a PSI apparent height that depends on the probe potential revealing a gating effect in protein conductance. In current-distance spectroscopy, it is observed that the distance-decay constant of the current between PSI and the ECSTM probe depends on the sample and probe electrode potentials. The longest charge exchange distance (lowest distance-decay constant ?) is observed at sample potential 0 mV/SSC (SSC: reference electrode silver/silver chloride) and probe potential 400 mV/SSC. These potentials correspond to hole injection into an electronic state that is available in the absence of illumination. It is proposed that a pair of tryptophan residues located at the interface between P700 and the solution and known to support the hydrophobic recognition of the PSI redox partner plastocyanin, may have an additional role as hole exchange mediator in charge transport through PSI.© 2021 Wiley-VCH GmbH.

JTD Keywords: azurin, current distance decay spectroscopy, cytochrome c(6), electrochemical scanning tunneling microscopy (ecstm), electrochemistry, photosystem i, photosystem-i, plastocyanin, protein electron transfer, recognition, single metalloprotein, single molecules, structural basis, tunneling spectroscopy, 'current, Amino acids, Charge transfer, Chlorine compounds, Current distance decay spectroscopy, Decay spectroscopies, Distance decay, Electrochemical scanning tunneling microscopy, Electrochemical scanning tunneling microscopy (ecstm), Electrodes, Electron transfer, Electron transport properties, Gold compounds, Photosystem i, Photosystems, Protein electron transfer, Protein electron-transfer, Proteins, Scanning tunneling microscopy, Silver halides, Single molecule, Single molecules

Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

Tolerances in the fabrication of metal oxide (MOX) gas sensors lead to inter-device variability in baseline and sensitivity, even for sensors of the same fabrication batch. This has traditionally forced the use of individual calibration models (ICMs) built specifically for each sensor unit, which requires an expensive and time-consuming calibration process and hinders sensor replacement. We propose Global calibration models (GCMs) built using the responses of multiple sensor units, and then applied to a new sensor unit that is not part of the calibration set. GCM have been already successfully applied to transfer calibration models between sensor arrays (electronic noses) for classification tasks. In this work, we investigate the use of such models for regression purposes in temperature-modulated sensors, aiming at the quantification of low concentrations of carbon monoxide (CO) in the presence of variable humidity levels (20–80% r.h. at 26 ± 1 °C). Using a laboratory dataset containing data from 6 replicas of the FIS SB-500–12 model, we evaluate the performance of global models built with data from 1 to 4 sensors when applied to unseen sensor units. Results show that the performance of global models improves with an increasing number of sensors in the calibration set, approaching the performance of individual calibration models (1.38 ± 0.15 ppm for GCM; 1.05 ± 0.24 ppm for ICM), and surpassing their performance only if few calibration conditions per sensor are available (2.09 ± 0.10 ppm for GCM;; 2.76 ± 0.22 ppm for ICM, if only 5 samples per sensor are used).

JTD Keywords: design, recognition, Arrays

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance - This establishes the characterization of success and failure patients in the extubation process. © 2021 IEEE.

JTD Keywords: classification, recognition, Airway extubation, Artificial ventilation, Clinical practices, Clinical process, Diaphragm, Diaphragm muscle, Diaphragms, Electrodes, Electromyographic, Extubation, Frequency domain analysis, Human, Humans, Maximum differences, Mechanical ventilation, New protocol, Respiration, artificial, Respiratory system, Risk of failure, Spontaneous breathing, Surface electrode, Surface emg signals, Thorax, Ventilation, Ventilator weaning

Most lectins bind carbohydrate ligands with relatively low affinity, making the identification of optimal ligands challenging. Here we introduce a point accumulation in nanoscale topography (PAINT) super-resolution microscopy method to capture weak glycan-lectin interactions at the single-molecule level in living cells (Glyco-PAINT). Glyco-PAINT exploits weak and reversible sugar binding to directly achieve single-molecule detection and quantification in cells and is used to establish the relative kon and koff rates of a synthesized library of carbohydrate-based probes, as well as the diffusion coefficient of the receptor-sugar complex. Uptake of ligands correlates with their binding affinity and residence time to establish structure-function relations for various synthetic glycans. We reveal how sugar multivalency and presentation geometry can be optimized for binding and internalization. Overall, Glyco-PAINT represents a powerful approach to study weak glycan-lectin interactions on the surface of living cells, one that can be potentially extended to a variety of lectin-sugar interactions.© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.

JTD Keywords: dc-sign, density, dimerization, endocytosis, lateral mobility, ligand-binding, mannose receptor, proteins, recognition, Animal, Animals, Cell membrane, Cell membrane permeability, Chemistry, Cho cell line, Cho cells, Cricetulus, Cysteine-rich domain, Kinetics, Lectin, Lectins, Ligand, Ligands, Molecular library, Multivariate analysis, Polysaccharide, Polysaccharides, Procedures, Protein binding, Single molecule imaging, Small molecule libraries, Structure activity relation, Structure-activity relationship

The unique ability to identify one's own body and experience it as one's own is fundamental in goal-oriented behavior and survival. However, the mechanisms underlying the so-called body ownership are yet not fully understood. Evidence based on Rubber Hand Illusion (RHI) paradigms has demonstrated that body ownership is a product of reception and integration of self and externally generated multisensory information, feedforward and feedback processing of sensorimotor signals, and prior knowledge about the body. Crucially, however, these designs commonly involve the processing of proximal modalities while the contribution of distal sensory signals to the experience of ownership remains elusive. Here we propose that, like any robust percept, body ownership depends on the integration and prediction across all sensory modalities, including distal sensory signals pertaining to the environment. To test our hypothesis, we created an embodied goal-oriented Virtual Air Hockey Task, in which participants were to hit a virtual puck into a goal. In two conditions, we manipulated the congruency of distal multisensory cues (auditory and visual) while preserving proximal and action-driven signals entirely predictable. Compared to a fully congruent condition, our results revealed a significant decrease on three dimensions of ownership evaluation when distal signals were incongruent, including the subjective report as well as physiological and kinematic responses to an unexpected threat. Together, these findings support the notion that the way we represent our body is contingent upon all the sensory stimuli, including distal and action-independent signals. The present data extend the current framework of body ownership and may also find applications in rehabilitation scenarios.

In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of SM interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize the self (including proximo-distal arm-joint dependencies) and to assess motor to sensory influences, and the other by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger causality (GC), transfer entropy (TE), as well as a novel “Acceleration Transfer” (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed SM graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. This study results show that although TE can capture the directional dependencies, a rectified subtraction operation denoted, in this study, as AT is both sufficient and computationally cheaper.

JTD Keywords: agency, attention, autonomy, cognitive development, computational cognition, developmental psychology, sensorimotor learning, Agency, Attention, Autonomy, Cognitive development, Computational cognition, Developmental psychology, Model, Sensorimotor learning, Theory of mind

We report the design, simulation, synthesis, and reversible self-assembly of nanofibrils using polyhistidine-based oligopeptides. The inclusion of aromatic amino acids in the histidine block produces distinct antiparallel β-strands that lead to the formation of amyloid-like fibrils. The structures undergo self-assembly in response to a change in pH. This creates the potential to produce well-defined fibrils for biotechnological and biomedical applications that are pH-responsive in a physiologically relevant range.

JTD Keywords: peptide, recognition, Amyloid fibrils

© 2020 The Authors Selective attention informs decision-making by biasing perceptual processing towards task-relevant stimuli. In experimental and computational literature, this is most often implemented through top-down excitation of selected stimuli. However, physiological and anatomical evidence shows that in certain situations, top-down signals could instead be inhibitory. In this study, we investigated how such an inhibitory mechanism of top-down attention compares with an excitatory one. We did so in a neurorobotics context where the agent was controlled using an established hierarchical architecture. We augmented the architecture with an attentional system that implemented top-down attention biasing as connection gains. We tested four models of top-down attention on the simulated agent performing a foraging task: without top-down biasing, with only excitatory top-down gain, with only inhibitory top-down gain, and with both excitatory and inhibitory top-down gain. We manipulated the reward-distractor ratio that was presented and assessed the agent's performance using accumulated rewards and the latency of the selection. Using these measures, we provide evidence that excitatory and inhibitory mechanisms of attention complement each other.

JTD Keywords: embodied cognition, foraging, inhibition, Embodied cognition, Foraging, Inhibition, Selective attention

Facial expressions are salient social features that crucial in communication, and humans are capable of reading the messages faces convey and the emotions they display. Robots that interact with humans will need to employ similar communication channels for successful interactions. Here, we focus on the readability of the facial expressions of a humanoid robot. We conducted an online survey where participants evaluated emotional stimuli and assessed the robot’s expressions. Results suggest that the robot’s facial expressions are correctly recognised and the appraisal of the robots expressive elements are consistent with the literature.

JTD Keywords: Emotion recognition, Facial expressions, Human-robot interaction

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

JTD Keywords: Fire detection, Gas sensor, Pattern recognition, Sensor fusion, Machine learning, Toxicants, Carbon monoxide, Hydrogen cyanide, Standard test fires, Transducers, Smoke

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

JTD Keywords: Autobiographical Memory., Biology, Cognition, Cognitive Robotics, Computer architecture, Distributed Adaptive Control, Grounding, Human-Robot Interaction, Humanoid robots, Robot sensing systems, Symbol Grounding

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances in the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building on this analysis, we first propose an embodied cognitive architecture integrating heterogeneous subfields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.

JTD Keywords: Cognitive Architectures, Embodied Artificial Intelligence, Evolutionary Arms Race, Unified Theories of Cognition

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

JTD Keywords: Action, Cognition, Cognitive architecture, Communication, Consciousness, Emotion, Perception

Although discriminating self from nonself is a cardinal animal trait, metazoan allorecognition genes do not appear to be homologous. Here, we characterize the Aggregation Factor (AF) gene family, which encodes putative allorecognition factors in the demosponge Amphimedon queenslandica, and trace its evolution across 24 sponge (Porifera) species. The AF locus in Amphimedon is comprised of a cluster of five similar genes that encode Calx-beta and Von Willebrand domains and a newly defined Wreath domain, and are highly polymorphic. Further AF variance appears to be generated through individualistic patterns of RNA editing. The AF gene family varies between poriferans, with protein sequences and domains diagnostic of the AF family being present in Amphimedon and other demosponges, but absent from other sponge classes. Within the demosponges, AFs vary widely with no two species having the same AF repertoire or domain organization. The evolution of AFs suggests that their diversification occurs via high allelism, and the continual and rapid gain, loss and shuffling of domains over evolutionary time. Given the marked differences in metazoan allorecognition genes, we propose the rapid evolution of AFs in sponges provides a model for understanding the extensive diversification of self-nonself recognition systems in the animal kingdom.

JTD Keywords: Aggregation factor, Allorecognition, Intron phase, Polymorphism, Porifera, RNA editing

We designed a novel experiment to investigate the modulation of human recognition memory by environmental context. Human participants were asked to navigate through a four-arm Virtual Reality (VR) maze in order to find and memorize discrete items presented at specific locations in the environment. They were later on tested on their ability to recognize items as previously presented or new. By manipulating the spatial position of half of the studied items during the testing phase of our experiment, we could assess differences in performance related to the congruency of environmental information at encoding and retrieval. Our results revealed that spatial context had a significant effect on the quality of memory. In particular, we found that recognition performance was significantly better in trials in which contextual information was congruent as opposed to those in which it was different. Our results are in line with previous studies that have reported spatial-context effects in recognition memory, further characterizing their magnitude under ecologically valid experimental conditions.

JTD Keywords: Context effects, Recognition memory, Spatial behavior, Spatial memory and navigation, Virtual reality

In this paper, we propose a new approach to domestic action recognition based on a set of features which describe the relation between poses and movements of both hands. These features represent a set of basic actions in a kitchen in terms of the mimics of the hand movements, without needing information of the objects present in the scene. They address specifically the intra-class dissimilarity problem, which occurs when the same action is performed in different ways. The goal is to create a generic methodology that enables a robotic assistant system to recognize actions related to daily life activities and then, be endowed with a proactive behavior. The proposed system uses depth and color data acquired from a Kinect-style sensor and a hand tracking system. We analyze the relevance of the proposed hand-based features using a state-space search approach. Finally, we show the effectiveness of our action recognition approach using our own dataset.

JTD Keywords: Histograms, Joints, Robot sensing systems, Thumb, Tracking, Human activity recognition, Disable and elderly assistance

Pose-based features have demonstrated to outperform low-levelappearance features in human action recognition. New RGB-D cameras provide locations of human joints with which geometric correspondences can be easily calculated. In this article, a new geometric correspondence between joints called Trisarea feature is presented. It is defined as the area of the triangle formed by three joints. Relevant triangles describing human pose are identified and it is shown how the variation over time of the selected Trisarea features constitutes a descriptor of human action. Experimental results show a comparison with other methods and demonstrate how this Trisarea-based representation can be applied to human action recognition.

JTD Keywords: Action descriptor, Action recognition, Pose-based feature

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

JTD Keywords: Chemical sensor arrays, Pattern recognition, Chemometrics, Electronic noses, Robustness, Signal and data processing

Stroke is a major cause of disability, usually causing hemiplegic damage on the motor abilities of the patient. Stroke rehabilitation seeks restoring normal motion on the affected limb. However, normality’ of movements is usually assessed by clinical and functional tests, without considering how the motor system responds to therapy. We hypothesized that electromyographic (EMG) recordings could provide useful information for evaluating the outcome of rehabilitation from a neuromuscular perspective. Four healthy subjects were asked to perform 14 different functional movements simulating the action of reaching over a table. Each movement was defined according to the starting and target positions that the subject had to connect using linear trajectories. Bipolar recordings of EMG signals were taken from biceps and triceps muscles, and spectral and temporal characteristics were extracted for each movement. Using pattern recognition techniques we found that only two EMG channels were sufficient to accurately determine the spatial characteristics of motor activity: movement direction, length and execution zone. Our results suggest that muscles may fire in a patterned way depending on the specific characteristics of the movement and that EMG signals may codify such detailed information. These findings may be of great value to quantitatively assess post-stroke rehabilitation and to compare the neuromuscular activity of the affected and unaffected limbs, from a physiological perspective. Furthermore, disturbed movements could be characterized in terms of the muscle function to identify, which is the spatial characteristic that fails, e.g. movement direction, and guide personalized rehabilitation to enhance the training of such characteristic.

JTD Keywords: EMG, Movement spatial characteristics, Pattern recognition, Stroke rehabilitation, Upper-limb

This work introduces an interactive robotic system for assistance, conceived to tackle some of the challenges that domestic environments impose. The system is organized into a network of heterogeneous components that share both physical and logical functions to perform complex tasks. It consists of several robots for object manipulation, an advanced vision system that supplies in-formation about objects in the scene and human activity, and a spatial augmented reality interface that constitutes a comfortable means for interacting with the system. A first analysis based on users' experiences confirms the importance of having a friendly user interface. The inclusion of context awareness from visual perception enriches this interface allowing the robotic system to become a flexible and proactive assistant.

JTD Keywords: Accessibility, Activity Recognition, Ambient Intelligence, Human-Robot Interaction, Robot Assistance, Augmented reality, Complex networks, Computer vision, User interfaces, Accessibility, Activity recognition, Ambient intelligence, Domestic environments, Heterogeneous component, Interactive robotics, Robot assistance, Spatial augmented realities, Human assistance, Robotics

Cell tracking is an emergent area in nano-biotechnology, promising the study of individual cells or the identification of populations of cultured cells. In our approach, microtools designed for extracellular tagging are prepared, because using biofunctionalized polysilicon barcodes to tag cell membranes externally avoids the inconveniences of cell internalization. The crucial covalent biofunctionalization process determining the ultimate functionality was studied in order to find the optimum conditions to link a biomolecule to a polysilicon barcode surface using a self-assembled monolayer (SAM) as the connector. Specifically, a lectin (wheat germ agglutinin, WGA) was used because of its capacity to recognize some specific carbohydrates present on the surface of most mammalian cells. Self-assembled monolayers were prepared on polysilicon surfaces including aldehyde groups as terminal functions to study the suitability of their covalent chemical bonding to WGA. Some parameters, such as the polysilicon surface roughness or the concentration of WGA, proved to be crucial for successful biofunctionalization and bioactivity. The SAMs were characterized by contact angle measurements, time-of-flight secondary ion mass spectrometry (TOF-SIMS), laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS), and atomic force microscopy (AFM). The biofunctionalization step was also characterized by fluorescence microscopy and, in the case of barcodes, by adhesion experiments to the zona pellucida of mouse embryos. These experiments showed high barcode retention rates after 96 h of culture as well as high embryo viability to the blastocyst stage, indicating the robustness of the biofunctionalization and, therefore, the potential of these new microtools to be used for cell tagging.

JTD Keywords: Self-assembled monolayers, Wheat-germ-agglutinin, Protein immobilization strategies, Mass-spectrometry, Cell-surface, Petide, Binding, Identifications, Nanoparticles, Recognition

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task. Six healthy subjects participated in the study and results showed lower desynchronization during passive movements with another task simultaneously being carried out (compared to passive movements with exclusive attention on the task). In addition, it was proved the feasibility to distinguish between the two conditions.

JTD Keywords: Electrodes, Electroencephalography, Induction motors, Medical treatment, Robot sensing systems, Time frequency analysis, Biomechanics, Cognition, Electroencephalography, Medical robotics, Medical signal detection, Medical signal processing, Patient rehabilitation, Attention, Cognitive aspects, Desynchronization, Engagement, Motivation, Motor learning, Motor task, Motor-related EEG activity, Physical aspects, Robot-assisted passive movement detection, Robot-assisted rehabilitation therapies, Upper limb

Aptamers are single stranded artificial nucleic acid ligands that can be generated against almost any kind of target, such as ions, metabolites aminoacids, drugs, toxins, proteins or whole cells. They are isolated from combinatorial libraries of synthetic nucleic acids by an iterative process of adsorption, recovery and amplification, know as SELEX (Systematic Evolution of Ligands by EXponential enrichment) process. Aptamers, the nucleic acid equivalent to antibodies, are easy to synthesise, is not required the use of animals for its synthesis, for this reason it can be developed again toxins and small molecules that do not produce immune response in animals and can be tuned for affinity in closer to assay conditions permitting recognition out of the physiological state. So, aptamers posses numerous advantages that make them preferred candidates as biorecognition elements. In view of the advantages and simple structure of aptamers, they have been used in a wide range of applications such as therapeutics, diagnosis, chromatography, environmental detection, among other.

JTD Keywords: Aptamers, Biosensors, Protein recognition

In this paper, a multiple-channel extension of a dual-grating Coupler biosensor is presented as a Solution for the problem of resolving different selectivities, Usual when heterogeneous samples are analyzed. Several differently functionalized channels can perform quantitative analysis of competiting recognition events, Suppress shifts due to buffer changes and even monitorize drifts coming from the light Source. Here, the multiple-channel approach is developed and proven for a four-channel configuration, providing a resolution limit of 10(-5) Refractive index Units (RIU) and with an a potentially Unlimited scalability. Finally, a differential HSA recognition event is monitored, at both an IgG functionalized channel and at a blocked one.

JTD Keywords: Optical grating coupler, Multi-channel biorecognition, On-chip reference

We demonstrate simultaneous transverse dynamic force microscopy and molecular recognition imaging using tuning forks as piezoelectric sensors. Tapered aluminum-coated glass fibers were chemically functionalized with biotin and anti-lysozyme molecules and attached to one of the prongs of a 32 kHz tuning fork. The lateral oscillation amplitude of the tuning fork was used as feedback signal for topographical imaging of avidin aggregates and lysozyme molecules on mica substrate. The phase difference between the excitation and detection signals of the tuning fork provided molecular recognition between avidin/biotin or lysozyme/anti-lysozyme. Aggregates of avidin and lysozyme molecules appeared as features with heights of 1-4 nm in the topographic images, consistent with single molecule atomic force microscopy imaging. Recognition events between avidin/biotin or lysozyme/anti-lysozyme were detected in the phase image at high signal-to-noise ratio with phase shifts of 1-2 degrees. Because tapered glass fibers and shear-force microscopy based on tuning forks are commonly used for near-field scanning optical microscopy (NSOM), these results open the door to the exciting possibility of combining optical, topographic and biochemical recognition at the nanometer scale in a single measurement and in liquid conditions.

JTD Keywords: Tuning fork, Atomic force microscopy, Shear-force microscopy, Molecular recognition, Avidin-biotin

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

JTD Keywords: Electrocardiography, Medical signal processing, Neural nets, Pattern recognition, Pneumodynamics, Signal classification, Statistical analysis, ECG, Kurtosis coefficient, Lempel Ziv complexity, Breath durations, Cardiac interbeat durations, Electrocardiography, Multiparameter analysis, Pattern recognition neural network, Power spectral density, Respiratory flow signals, Signal classification, Spontaneous breathing, Statistical analysis, Weaning trials

A first step towards the multidetection of anabolic androgenic steroids by Enzyme-linked immunosorbent assays (ELISA) has been performed in this study. This proposal combines an array of classical ELISA assays with different selectivities and multivariate data analysis techniques. Data has been analyzed by principal component analysis in conjunction with a k-nearest line classifier has been used. This proposal allows to detect simultaneously four different compounds in the range of concentration from 10(-1.5) to 10(3) mM with a total rate of 90.6% of correct detection.

JTD Keywords: Immunoarray, Anabolic androgenic steroid, Multidetection, Pattern recognition, K-nearlest line

In this paper we compare three different spectral estimation techniques for the classification of mental tasks. These techniques are the standard periodogram, the Welch periodogram and the Burg method, applied to electroencephalographic (EEG) signals. For each one of these methods we compute two parameters: the mean power and the root mean square (RMS), in various frequency bands. The classification of the mental tasks was conducted with a linear discriminate analysis. The Welch periodogram and the Burg method performed better than the standard periodogram. The use of the RMS allows better classification accuracy than the obtained with the power of EEG signals.

JTD Keywords: Adult, Algorithms, Artificial Intelligence, Cognition, Electroencephalography, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Task Performance and Analysis, User-Computer Interface

In the past years, the quartz crystal microbalance (QCM) has been successfully applied to follow interfacial physical chemistry phenomena in a label free and real time manner. However, carbohydrate self adhesion has only been addressed partially using this technique. Carbohydrates play an important role in cell adhesion, providing a highly versatile form of attachment, suitable for biologically relevant recognition events in the initial steps of adhesion. Here, we provide a QCM study of carbohydrates' self-recognition in the presence of calcium, based on a species-specific cell recognition model provided by marine sponges. Our results show a difference in adhesion kinetics when varying either the calcium concentration (with a constant carbohydrate concentration) or the carbohydrate concentration (with constant calcium concentration).

JTD Keywords: Biomedical materials, Calcium, Cellular biophysics, Microbalances, Porous materials, Quartz, Surface chemistry/ bio-QCM, Carbohydrates self-interaction, Quartz crystal microbalance, Interfacial physical chemistry phenomena, Carbohydrate self adhesion, Biologically relevant recognition events, Marine sponges, Adhesion kinetics, Calcium concentration, Carbohydrate concentration, Biosensors, Biomedical materials, Surface chemistry, Cellular biophysics