Publications

Tubular structures made of elastic helical fibers are widely found in nature and in technology. The complex and highly nonlinear mechanical properties of such assemblies have been understood either through minimal models or through complex simulations describing each individual fiber and their interactions. Here, inspired by Chebyshev's geometric model of nets, we propose and experimentally validate a modeling framework that treats tubular braided meshes as continuum surfaces corresponding to the virtual envelope defined by the fibers. The key idea is to relate surface geometry and fiber kinematics, enabling us to follow large deformations. This theory is amenable to efficient computations and, in axisymmetric cases, the problem reduces to finding two scalar fields defined over 1D segments. We validate our model against experiments of axial compression, revealing the existence of a plateau with vanishing stiffness in the axial force-displacement curve, a feature that could prove particularly useful in applications where an applied compressive force needs to be held constant even against settlements of the compressed object.

JTD Keywords: Braided mesh, Chebyshev nets, Computational mechanics, Design, Elastic rods, Envelope surface, Equilibrium, Hélices, Muscle

Transcriptomics and phosphoproteomics were carried out in the cerebral cortex of B6.Cg-Mapttm1(EGFP)Klt (tau knockout: tau-KO) and wild-type (WT) 12 month-old mice to learn about the effects of tau ablation. Compared with WT mice, tau-KO mice displayed reduced anxiety-like behavior and lower fear expression induced by aversive conditioning, whereas recognition memory remained unaltered. Cortical transcriptomic analysis revealed 69 downregulated and 105 upregulated genes in tau-KO mice, corresponding to synaptic structures, neuron cytoskeleton and transport, and extracellular matrix components. RT-qPCR validated increased mRNA levels of col6a4, gabrq, gad1, grm5, grip2, map2, rab8a, tubb3, wnt16, and an absence of map1a in tau-KO mice compared with WT mice. A few proteins were assessed with Western blotting to compare mRNA expression with corresponding protein levels. Map1a mRNA and protein levels decreased. However, β-tubulin III and GAD1 protein levels were reduced in tau-KO mice. Cortical phosphoproteomics revealed 121 hypophosphorylated and 98 hyperphosphorylated proteins in tau-KO mice. Deregulated phosphoproteins were categorized into cytoskeletal (n = 45) and membrane proteins, including proteins of the synapses and vesicles, myelin proteins, and proteins linked to membrane transport and ion channels (n = 84), proteins related to DNA and RNA metabolism (n = 36), proteins connected to the ubiquitin-proteasome system (UPS) (n = 7), proteins with kinase or phosphatase activity (n = 21), and 22 other proteins related to variegated pathways such as metabolic pathways, growth factors, or mitochondrial function or structure. The present observations reveal a complex altered brain transcriptome and phosphoproteome in tau-KO mice with only mild behavioral alterations.

JTD Keywords: computational platform, conformational-changes, cytoskeleton, disease, expression, isoforms, mechanisms, mice, phosphoproteomics, phosphorylation, synapse, tau-ko, tauopathies, transcriptomics, Tau-ko, Tau-protein, Transcriptomics

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

JTD Keywords: computational, engineered living, engineered organs, multicellular, Brain organoids, Cell diversity, Computational, Dynamics, Engineered living, Engineered organs, Heart, Maturation, Model, Multicellular, Mycobacterium-tuberculosis, Quantitative-analysis, Systems, Tissue deformation

Large doses of movement practice have been shown to restore upper extremities' motor function in a significant subset of individuals post-stroke. However, such large doses are both difficult to implement in the clinic and highly inefficient. In addition, an important reduction in upper extremity function and use is commonly seen following rehabilitation-induced gains, resulting in “rehabilitation in vain”. For those with mild to moderate sensorimotor impairment, the limited spontaneous use of the more affected limb during activities of daily living has been previously proposed to cause a decline of motor function, initiating a vicious cycle of recovery, in which non-use and poor performance reinforce each other. Here, we review computational, experimental, and clinical studies that support the view that if arm use is raised above an effective threshold, one enters a virtuous cycle in which arm use and function can reinforce each other via self-practice in the wild. If not, one enters a vicious cycle of declining arm use and function. In turn, and in line with best practice therapy recommendations, this virtuous/vicious cycle model advocates for a paradigm shift in neurorehabilitation whereby rehabilitation be embedded in activities of daily living such that self-practice with the aid of wearable technology that reminds and motivates can enhance paretic limb use of those who possess adequate residual sensorimotor capacity. Altogether, this model points to a user-centered approach to recovery post-stroke that is tailored to the participant's level of arm use and designed to motivate and engage in self-practice through progressive success in accomplishing meaningful activities in the wild. Copyright © 2022 Ballester, Winstein and Schweighofer.

JTD Keywords: compensatory movement, computational neurorehabilitation, decision-making, individuals, learned non-use, learned nonuse, monkeys, neurorehabilitation, recovery, rehabilitation, stroke, stroke patients, wearable sensors, wrist, Arm movement, Article, Cerebrovascular accident, Clinical decision making, Clinical practice, Clinical study, Compensatory movement, Computational neurorehabilitation, Computer model, Daily life activity, Decision-making, Experimental study, Human, Induced movement therapy, Learned non-use, Musculoskeletal function, Neurorehabilitation, Paresis, Sensorimotor function, Stroke, Stroke rehabilitation, User-centered design, Vicious cycle, Virtuous cycle, Wearable sensors

The mechanical signals sensed by the alveolar cells through the changes in the local matrix stiffness of the extracellular matrix (ECM) are determinant for regulating cellular functions. Therefore, the study of the mechanical response of lung tissue becomes a fundamental aspect in order to further understand the mechanosensing signals perceived by the cells in the alveoli. This study is focused on the development of a finite element (FE) model of a decellularized rat lung tissue strip, which reproduces accurately the mechanical behaviour observed in the experiments by means of a tensile test. For simulating the complex structure of the lung parenchyma, which consists of a heterogeneous and non-uniform network of thin-walled alveoli, a 3D model based on a Voronoi tessellation is developed. This Voronoi-based model is considered very suitable for recreating the geometry of cellular materials with randomly distributed polygons like in the lung tissue. The material model used in the mechanical simulations of the lung tissue was characterized experimentally by means of AFM tests in order to evaluate the lung tissue stiffness on the micro scale. Thus, in this study, the micro (AFM test) and the macro scale (tensile test) mechanical behaviour are linked through the mechanical simulation with the 3D FE model based on Voronoi tessellation. Finally, a micro-mechanical FE-based model is generated from the Voronoi diagram for studying the stiffness sensed by the alveolar cells in function of two independent factors: the stretch level of the lung tissue and the geometrical position of the cells on the extracellular matrix (ECM), distinguishing between pneumocyte type I and type II. We conclude that the position of the cells within the alveolus has a great influence on the local stiffness perceived by the cells. Alveolar cells located at the corners of the alveolus, mainly type II pneumocytes, perceive a much higher stiffness than those located in the flat areas of the alveoli, which correspond to type I pneumocytes. However, the high stiffness, due to the macroscopic lung tissue stretch, affects both cells in a very similar form, thus no significant differences between them have been observed. © 2021 The Authors

JTD Keywords: rat, scaffolds, stiffness, Afm, Animal cell, Animal experiment, Animal model, Animal tissue, Article, Biological organs, Cell function, Cells, Computational geometry, Cytology, Extracellular matrices, Extracellular matrix, Extracellular-matrix, Geometry, High stiffness, Human, Lung alveolus cell type 1, Lung alveolus cell type 2, Lung parenchyma, Lung tissue, Male, Mechanical behavior, Mechanical modeling, Mechanical simulations, Mechanosensing, Model-based opc, Nonhuman, Physical model, Rat, Rigidity, Stiffness, Stiffness matrix, Tensile testing, Thin walled structures, Three dimensional finite element analysis, Tissue, Type ii, Voronoi tessellations

The use of broadly neutralizing antibodies against human immunodeficiency virus type 1 (HIV-1) has been shown to be a promising therapeutic modality in the prevention of HIV infection. Understanding the b12-gp120 binding mechanism under physiological conditions may assist the development of more broadly effective antibodies. In this work, the main conformations and interactions between the receptor-binding domain (RBD) of spike glycoprotein gp120 of HIV-1 and the IgG1-b12 mAb are studied. Accelerated molecular dynamics (aMD) and ab initio hybrid molecular dynamics have been combined to determine the most persistent interactions between the most populated conformations of the antibody-antigen complex under physiological conditions. The results show the most persistent receptor-binding mapping in the conformations of the antibody-antigen interface in solution. The binding-free-energy decomposition reveals a small enhancement in the contribution played by the CDR-H3 region to the b12-gp120 interface compared to the crystal structure.

JTD Keywords: antibody, complex, functionals, gp120 envelope glycoprotein, hiv, immunodeficiency-virus, noncovalent interactions, simulations, software integration, Ab initio, Accelerated molecular dynamics, Accelerated molecular-dynamics, Antibodies, Antigens, Binding energy, Binding mechanisms, Computational studies, Crystal structure, Diseases, Free energy, Hiv infection, Human immunodeficiency virus, Molecular dynamics, Neutralizing antibodies, Physiological condition, Physiology, Receptor-binding domains, Therapeutic modality, Viruses

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

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal–hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems. © 2021 Elsevier Ltd

JTD Keywords: computational model, dentate gyrus, error backpropagation, granule cells, grid cells, hippocampus, inhibition, input, neural-networks, neurons, transformation, Artificial intelligence, Artificial neural network, Back propagation, Backpropagation, Brain, Cognitive systems, Counter current, Error back-propagation, Error backpropagation, Errors, Expressing interneurons, Hippocampal complex, Hippocampus, Human experiment, Input and outputs, Learning, Mammal, Mammalian hippocampus, Mammals, Neural networks, Nonhuman, Review, Self-supervised learning

The extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) pathway involves a three-step cascade of kinases that transduce signals and promote processes such as cell growth, development, and apoptosis. An aberrant response of this pathway is related to the proliferation of cell diseases and tumors. By using simulation modeling, we document that the protein arginine methyltransferase 5 (PRMT5) modulates the MAPK pathway and thus avoids an aberrant behavior. PRMT5 methylates the Raf kinase, reducing its catalytic activity and thereby, reducing the activation of ERK in time and amplitude. Two minimal computational models of the epidermal growth factor (EGF)-Ras-ERK MAPK pathway influenced by PRMT5 were proposed: a first model in which PRMT5 is activated by EGF and a second one in which PRMT5 is stimulated by the cascade response. The reported results show that PRMT5 reduces the time duration and the expression of the activated ERK in both cases, but only in the first model PRMT5 limits the EGF range that generates an ERK activation. Based on our data, we propose the protein PRMT5 as a regulatory factor to develop strategies to fight against an excessive activity of the MAPK pathway, which could be of use in chronic diseases and cancer.

JTD Keywords: cancer, cell response modulation, computational model, egf-ras-erk signaling route, mapk pathway, methylation, Arginine methyltransferase 5, Cancer, Cell response modulation, Colorectal-cancer, Computational model, Egf-ras-erk signaling route, Epidermal-growth-factor, Factor receptor, Histone h3, Kinase cascade, Mapk pathway, Methylation, Negative-feedback, Pc12 cells, Prmt5, Protein, Signal-transduction

Plaques of the amyloid beta (A beta) peptide are a pathological hallmark of Alzheimer's disease (AD), the most common form of dementia. Mutations in A beta also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of A beta. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in A beta, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in A beta.

JTD Keywords: aggregation, kinetics, oligomers, onset, rates, state, Aggregation, Alzheimer's, Amyloid, Computational biology, Deep mutagenesis, Genetics, Genomics, Kinetics, Nucleation, Oligomers, Onset, Precursor protein, Rates, S. cerevisiae, State, Systems biology

We present a new computational framework for modeling moral decision-making in artificial agents based on the notion of ‘Machine Morality as Cooperation’. This framework integrates recent advances from cross-disciplinary moral decision-making literature into a single architecture. We build upon previous work outlining cognitive elements that an artificial agent would need for exhibiting latent morality, and we extend it by providing a computational realization of the cognitive architecture of such an agent. Our work has implications for cognitive and social robotics. Recent studies in human neuroimaging have pointed to three different decision-making processes, Pavlovian, model-free and model-based, that are defined by distinct neural substrates in the brain. Here, we describe how computational models of these three cognitive processes can be implemented in a single cognitive architecture by using the distributed and hierarchical organization proposed by the DAC theoretical framework. Moreover, we propose that a pro-social drive to cooperate exists at the Pavlovian level that can also bias the rest of the decision system, thus extending current state-of-the-art descriptive models based on harm-aversion.

JTD Keywords: Morality, Moral decision-making, Computational models, Cognitive architectures, Cognitive robotics, Human-robot interaction

A novel ECG-derived respiration (EDR) approach is presented to efficiently estimate the respiratory rate. It combines spatial rotations and magnitude variations of the heart's electrical vector due to respiration. Orthogonal leads X, Y and Z from 10 volunteers were analyzed during a tilt table test. The largest vector magnitude (VM) within each QRS loop was assessed, and its 3D coordinates were converted into unit quaternion qb. Angular distances between these quaternions and the axes of the reference coordinate system, θ x , θ y and θ z , were then computed as EDR signals to track their relative variations caused by respiration. The respiratory rate was estimated on the spectrum of individual EDR signals obtained from the angular distances and VM time-series, but also on EDR signals obtained by principal component analysis (PCA). Relative errors (eR) to the reference respiratory signal exhibited relatively low values. The combination of EDR signals' spectrum {θ X ,θ Y, θ Z , VM} (eR=0.63±4.15%) and individual signals derived from θ X (e R =0.46±8.22%) and PCA (eR=0.36±6.58%) achieved the overall best results. The proposed method represents a computationally efficient alternative to other EDR approaches, but its robustness should be further investigated. The method could be enhanced if combined with other features tracking morphological changes induced by respiration.

JTD Keywords: Heart, Three-dimensional displays, Quaternions, Robustness, Computational efficiency, Cardiology, Principal component analysis

Axonal injury results in regenerative success or failure, depending on whether the axon lies in the peripheral or the CNS, respectively. The present study addresses whether epigenetic signatures in dorsal root ganglia discriminate between regenerative and non-regenerative axonal injury. Chromatin immunoprecipitation for the histone 3 (H3) post-translational modifications H3K9ac, H3K27ac and H3K27me3; an assay for transposase-accessible chromatin; and RNA sequencing were performed in dorsal root ganglia after sciatic nerve or dorsal column axotomy. Distinct histone acetylation and chromatin accessibility signatures correlated with gene expression after peripheral, but not central, axonal injury. DNA-footprinting analyses revealed new transcriptional regulators associated with regenerative ability. Machine-learning algorithms inferred the direction of most of the gene expression changes. Neuronal conditional deletion of the chromatin remodeler CCCTC-binding factor impaired nerve regeneration, implicating chromatin organization in the regenerative competence. Altogether, the present study offers the first epigenomic map providing insight into the transcriptional response to injury and the differential regenerative ability of sensory neurons.

JTD Keywords: Cell biology, Computational biology and bioinformatics, Molecular biology, Neuroscience

Insoluble protein aggregates are the hallmarks of many neurodegenerative diseases. For example, aggregates of TDP-43 occur in nearly all cases of amyotrophic lateral sclerosis (ALS). However, whether aggregates cause cellular toxicity is still not clear, even in simpler cellular systems. We reasoned that deep mutagenesis might be a powerful approach to disentangle the relationship between aggregation and toxicity. We generated >50,000 mutations in the prion-like domain (PRD) of TDP-43 and quantified their toxicity in yeast cells. Surprisingly, mutations that increase hydrophobicity and aggregation strongly decrease toxicity. In contrast, toxic variants promote the formation of dynamic liquid-like condensates. Mutations have their strongest effects in a hotspot that genetic interactions reveal to be structured in vivo, illustrating how mutagenesis can probe the in vivo structures of unstructured proteins. Our results show that aggregation of TDP-43 is not harmful but protects cells, most likely by titrating the protein away from a toxic liquid-like phase.

JTD Keywords: Computational biology and bioinformatics, Genomics, Mechanisms of disease, Neurodegeneration, Systems biology

This paper proposes the integration and analysis of a closed-loop model of the baroreflex and cardiovascular systems, focused on a time-varying estimation of the autonomic modulation of heart rate in Brugada syndrome (BS), during exercise and subsequent recovery. Patient-specific models of 44 BS patients at different levels of risk (symptomatic and asymptomatic) were identified through a recursive evolutionary algorithm. After parameter identification, a close match between experimental and simulated signals (mean error = 0.81%) was observed. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge, enabling to observe the expected autonomic changes induced by exercise testing. In particular, symptomatic patients presented a significantly higher parasympathetic activity during exercise, and an autonomic imbalance was observed in these patients at peak effort and during post-exercise recovery. A higher vagal modulation during exercise, as well as an increasing parasympathetic activity at peak effort and a decreasing vagal contribution during post-exercise recovery could be related with symptoms and, thus, with a worse prognosis in BS. This work proposes the first evaluation of the sympathetic and parasympathetic responses to exercise testing in patients suffering from BS, through the recursive identification of computational models; highlighting important trends of clinical relevance that provide new insights into the underlying autonomic mechanisms regulating the cardiovascular system in BS. The joint analysis of the extracted autonomic parameters and classic electrophysiological markers could improve BS risk stratification.

JTD Keywords: Autonomic nervous system, Brugada syndrome, Computational model, Recursive identification

Fluid deformable surfaces are ubiquitous in cell and tissue biology, including lipid bilayers, the actomyosin cortex or epithelial cell sheets. These interfaces exhibit a complex interplay between elasticity, low Reynolds number interfacial hydrodynamics, chemistry and geometry, and govern important biological processes such as cellular traffic, division, migration or tissue morphogenesis. To address the modelling challenges posed by this class of problems, in which interfacial phenomena tightly interact with the shape and dynamics of the surface, we develop a general continuum mechanics and computational framework for fluid deformable surfaces. The dual solid–fluid nature of fluid deformable surfaces challenges classical Lagrangian or Eulerian descriptions of deforming bodies. Here, we extend the notion of arbitrarily Lagrangian–Eulerian (ALE) formulations, well-established for bulk media, to deforming surfaces. To systematically develop models for fluid deformable surfaces, which consistently treat all couplings between fields and geometry, we follow a nonlinear Onsager formalism according to which the dynamics minimizes a Rayleighian functional where dissipation, power input and energy release rate compete. Finally, we propose new computational methods, which build on Onsager’s formalism and our ALE formulation, to deal with the resulting stiff system of higher-order partial differential equations. We apply our theoretical and computational methodology to classical models for lipid bilayers and the cell cortex. The methods developed here allow us to formulate/simulate these models in their full three-dimensional generality, accounting for finite curvatures and finite shape changes.

JTD Keywords: Capsule/cell dynamics, Computational methods, Membranes

The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

JTD Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness

A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain’s anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity.

JTD Keywords: Brain networks, Complexity measures, Computational neuroscience, Functional connectivity

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behavior relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts.

JTD Keywords: Active inference, Cerebellum, Computational model, Motor control, Perceptual learning

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (α, β, γ, δ) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10−3 seconds with a position error fitness around 3.116 × 10−8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10−9.

JTD Keywords: Robotics, Inverse kinematics, Heuristics, Computational kinematics, Swarm intelligence

Novel strategies to heal discogenic low back pain could highly benefit from comprehensive biophysical studies that consider both mechanical and biological factors involved in intervertebral disc degeneration. A decrease in nutrient availability at the bone-disc interface has been indicated as a relevant risk factor and as a possible initiator of cell death processes. Mechanical behaviour of both healthy and degenerated discs could highly interact with cell death in these compromised situations. In the present study, a mechano-transport finite element model was used to investigate the nature of mechanical effects on cell death processes via load-induced metabolic transport variations. Cycles of static sustained compression were chosen to simulate daily human activity. Healthy and degenerated cases were simulated as well as a reduced supply of solutes and an increase in solute exchange area at the bone-disc interface. Results showed that a reduction in metabolite concentrations at the bone-disc boundaries induced cell death, even when the increased exchange area was simulated. Slight local mechanical enhancements of glucose in the disc centre were capable of decelerating cell death but occurred only with healthy mechanical properties. However, mechanical deformations were responsible for a worsening in terms of cell death in the inner annulus, a disadvantaged zone far from the boundary supply with both an increased cell demand and a strain-dependent decrease of diffusivity. Such adverse mechanical effects were more accentuated when degenerative properties were simulated. Overall, this study paves the way for the use of biophysical models for a more integrated understanding of intervertebral disc pathophysiology.

JTD Keywords: Boundary conditions, Cell nutrition, Cell viability, Computational analysis, Intervertebraldisc, Softtissuebiomechanics

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET- 216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: The olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes.

JTD Keywords: Antennal lobes, Artificial olfaction, Computational neuroscience, Olfactory bulbs, Plume tracking, Abstracting, Actuators, Algorithms, Biomimetic processes, Chemical sensors, Conducting polymers, Data processing, Flavors, Odors, Robots, Smart sensors, Embedded systems

Rat model of Obstructive Sleep Apnea (OSA) is a realistic approach for studying physiological mechanisms involved in sleep. Rats are usually anesthetized and autonomic nervous system (ANS) could be blocked. This study aimed to assess the effect of anesthesia on ANS activity during OSA episodes. Seven male Sprague-Dawley rats were anesthetized intraperitoneally with urethane (1g/kg). The experiments were conducted applying airway obstructions, simulating 15s-apnea episodes for 15 minutes. Five signals were acquired: respiratory pressure and flow, SaO2, ECG and photoplethysmography (PPG). In total, 210 apnea episodes were studied. Normalized power spectrum of Pulse Rate Variability (PRV) was analyzed in the Low Frequency (LF) and High Frequency (HF) bands, for each episode in consecutive 15s intervals (before, during and after the apnea). All episodes showed changes in respiratory flow and SaO₂ signal. Conversely, decreases in the amplitude fluctuations of PPG (DAP) were not observed. Normalized LF presented extremely low values during breathing (median=7,67%), suggesting inhibition of sympathetic system due to anesthetic effect. Subtle increases of LF were observed during apnea. HRV and PPG analysis during apnea could be an indirect tool to assess the effect and deep of anesthesia.

JTD Keywords: electrocardiography, fluctuations, medical disorders, medical signal detection, medical signal processing, neurophysiology, photoplethysmography, pneumodynamics, sleep, ECG, SaO₂ flow, SaO₂ signal, airway obstructions, amplitude fluctuations, anesthesia effects, anesthetized nervous system, autonomic evaluation, autonomic nervous system, breathing, heart rate variability, high-frequency bands, low-frequency bands, male Sprague-Dawley rats, normalized power spectrum, obstructive sleep apnea, photoplethysmography, physiological mechanisms, pulse rate variability, rat model, respiratory flow, respiratory pressure, signal acquisition, sympathetic system inhibition, time 15 min, time 15 s, Abstracts, Atmospheric modeling, Computational modeling, Electrocardiography, Rats, Resonant frequency

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

JTD Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials

In natural tissues, the extracellular matrix composition, cell density and physiological properties are often non-homogeneous. Here we describe a model system, in which the distribution of cells throughout tissue engineering scaffolds after perfusion seeding can be influenced by the pore architecture of the scaffold. Two scaffold types, both with gyroid pore architectures, were designed and built by stereolithography: one with isotropic pore size (412 ± 13 [mu]m) and porosity (62 ± 1%), and another with a gradient in pore size (250-500 [mu]m) and porosity (35%-85%). Computational fluid flow modelling showed a uniform distribution of flow velocities and wall shear rates (15-24 s-1) for the isotropic architecture, and a gradient in the distribution of flow velocities and wall shear rates (12-38 s-1) for the other architecture. The distribution of cells throughout perfusion-seeded scaffolds was visualised by confocal microscopy. The highest densities of cells correlated with regions of the scaffolds where the pores were larger, and the fluid velocities and wall shear rates were the highest. Under the applied perfusion conditions, cell deposition is mainly determined by local wall shear stress, which, in turn, is strongly influenced by the architecture of the pore network of the scaffold.

JTD Keywords: Scaffolds, Microstructure, Cell adhesion, Confocal microscopy, Image analysis, Computational fluid dynamics

In this study, a three-dimensional (3D) computational simulation of bone regeneration was performed in a human tibia under realistic muscle loading. The simulation was achieved using a discrete lattice modeling approach combined with a mechanoregulation algorithm to describe the cellular processes involved in the healing process namely proliferation, migration, apoptosis, and differentiation of cells. The main phases of fracture healing were predicted by the simulation, including the bone resorption phase, and there was a qualitative agreement between the temporal changes in interfragmentary strain and bending stiffness by comparison to experimental data and clinical results. Bone healing was simulated beyond the reparative phase by modeling the transition of woven bone into lamellar bone. Because the simulation has been shown to work with realistic anatomical 3D geometry and muscle loading, it demonstrates the potential of simulation tools for patient-specific pre-operative treatment planning.

JTD Keywords: Tissue differentiation, Computational analysis, Mechanical conditions, Bone regeneration, Weight-bearing, Proliferation, Osteoblast, Stiffness, Ingrowth, Scaffold

Current analytical instrumentation and continuous sensing can provide huge amounts of data. Automatic signal processing and information evaluation is needed to overcome drowning in data. Today, statistical techniques are typically used to analyse and extract information from continuous signals. However, it is very interesting to note that biology (insects and vertebrates) has found alternative solutions for chemical sensing and information processing. This is a brief introduction to the developments in the European Project: Bio-ICT NEUROCHEM: Biologically Inspired Computation for Chemical Sensing (grant no. 216916) Fp7 project devoted to biomimetic olfactory systems.

JTD Keywords: Signal processing, Chemioception, Neural nets, Computational complexity

Apart from partial or total joint replacement, no surgical procedure is currently available to treat large and deep cartilage defects associated with advanced diseases such as osteoarthritis. In this work, we developed a perfusion bioreactor system to engineer human cartilage grafts in a size with clinical relevance for unicompartmental resurfacing of human knee joints (50 mm diameter x 3 mm thick). Computational fluid dynamics models were developed to optimize the flow profile when designing the perfusion chamber. Using the developed system, human chondrocytes could be seeded throughout large 50 mm diameter scaffolds with a uniform distribution. Following two weeks culture, tissues grown in the bioreactor were viable and homogeneously cartilaginous, with biomechanical properties approaching those of native cartilage. In contrast, tissues generated by conventional manual production procedures were highly inhomogeneous and contained large necrotic regions. The unprecedented engineering of human cartilage tissues in this large-scale opens the practical perspective of grafting functional biological substitutes for the clinical treatment for extensive cartilage defects, possibly in combination with surgical or pharmacological therapies to support durability of the implant. Ongoing efforts are aimed at integrating the up-scaled bioreactor based processes within a fully automated and closed manufacturing system for safe, standardized, and GMP compliant production of large-scale cartilage grafts.

JTD Keywords: Bioreactor, Cartilage repair, Computational fluid dynamics, Scale-up, Regenerative medicine, Tissue engineering

A computational model based on finite element method (FEM) and computational fluid dynamics (CFD) is developed to analyse the mechanical stimuli in a composite scaffold made of polylactic acid (PLA) matrix with calcium phosphate glass (Glass) particles. Different bioreactor loading conditions were simulated within the scaffold. In vitro perfusion conditions were reproduced in the model. Dynamic compression was also reproduced in an uncoupled fluid-structure scheme: deformation level was studied analyzing the mechanical response of scaffold alone under static compression while strain rate was studied considering the fluid flow induced by compression through fixed scaffold. Results of the model show that during perfusion test an inlet velocity of 25mum/s generates on scaffold surface a fluid flow shear stress which may stimulate osteogenesis. Dynamic compression of 5% applied on the PLA-Glass scaffold with a strain rate of 0.005s(-1) has the benefit to generate mechanical stimuli based on both solid shear strain and fluid flow shear stress on large scaffold surface area. Values of perfusion inlet velocity or compression strain rate one order of magnitude lower may promote cell proliferation while values one order of magnitude higher may be detrimental for cells. FEM-CFD scaffold models may help to determine loading conditions promoting bone formation and to interpret experimental results from a mechanical point of view.

JTD Keywords: Bone tissue engineering, Scaffold, Finite element analysis, Computational fluid dynamics, Mechanical stimuli

Tissue engineering scaffolds provide temporary mechanical support for tissue regeneration and transfer global mechanical load to mechanical stimuli to cells through its architecture. In this study the interactions between scaffold pore morphology, mechanical stimuli developed at the cell microscopic level, and culture conditions applied at the macroscopic scale are studied on two regular scaffold structures. Gyroid and hexagonal scaffolds of 55% and 70% porosity were modeled in a finite element analysis and were submitted to an inlet fluid flow or compressive strain. A mechanoregulation theory based on scaffold shear strain and fluid shear stress was applied for determining the influence of each structures on the mechanical stimuli on initial conditions. Results indicate that the distribution of shear stress induced by fluid perfusion is very dependent on pore distribution within the scaffold. Gyroid architectures provide a better accessibility of the fluid than hexagonal structures. Based on the mechanoregulation theory, the differentiation process in these structures was more sensitive to inlet fluid flow than axial strain of the scaffold. This study provides a computational approach to determine the mechanical stimuli at the cellular level when cells are cultured in a bioreactor and to relate mechanical stimuli with cell differentiation.

JTD Keywords: Tissue engineering, Scaffold, Rapid prototyping, Computational fluid dynamics, Finite element

BACKGROUND:Ribonucleotide reductases (RNRs) catalyse the only known de novo pathway for deoxyribonucleotide synthesis, and are therefore essential to DNA-based life. While ribonucleotide reduction has a single evolutionary origin, significant differences between RNRs nevertheless exist, notably in cofactor requirements, subunit composition and allosteric regulation. These differences result in distinct operational constraints (anaerobicity, iron/oxygen dependence and cobalamin dependence), and form the basis for the classification of RNRs into three classes.DESCRIPTION:In RNRdb (Ribonucleotide Reductase database), we have collated and curated all known RNR protein sequences with the aim of providing a resource for exploration of RNR diversity and distribution. By comparing expert manual annotations with annotations stored in Genbank, we find that significant inaccuracies exist in larger databases. To our surprise, only 23% of protein sequences included in RNRdb are correctly annotated across the key attributes of class, role and function, with 17% being incorrectly annotated across all three categories. This illustrates the utility of specialist databases for applications where a high degree of annotation accuracy may be important. The database houses information on annotation, distribution and diversity of RNRs, and links to solved RNR structures, and can be searched through a BLAST interface. RNRdb is accessible through a public web interface at http://rnrdb.molbio.su.se.CONCLUSION:RNRdb is a specialist database that provides a reliable annotation and classification resource for RNR proteins, as well as a tool to explore distribution patterns of RNR classes. The recent expansion in available genome sequence data have provided us with a picture of RNR distribution that is more complex than believed only a few years ago; our database indicates that RNRs of all three classes are found across all three cellular domains. Moreover, we find a number of organisms that encode all three classes.

JTD Keywords: Enzymology (Biochemistry and Molecular Biophysics), Computer Applications (Computational Biology)

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. In this work, the state of the art concerning biologically inspired computation for chemical sensing will be reviewed. Instead of reviewing the whole body of computational neuroscience of olfaction, we restrict this review to the application of models to the processing of real chemical sensor data.

JTD Keywords: Computational Intelligence, Chemical Sensors

This 167-page book is volume 188 in the series 'Studies in computational intelligence.' This volume contain 9 extensive chapters written in English. This volume presents a collection of research advances in biologically inspired signal processing for chemical sensing. The olfactory system, and the gustatory system to a minor extent, has been taken in the last decades as a source of inspiration to develop artificial sensing systems. The recognition of odors by the olfactory system entails a number of signal processing functions such as preprocessing, dimensionality reduction, contrast enhancement, and classification. Using mathematical models to mimic the architecture of the olfactory system, these processing functions can be applied to chemical sensor signals. This book provides background on the olfactory system including a review on information processing in the insect olfactory system along with a proposed signal processing architecture based on the mammalian cortex. It also provides some bio-inspired approaches to process chemical sensor signals such as an olfactory mucosa to improve odor separation and a model of olfactory receptor neuron convergence to correlated sensor responses to an odor and his organoleptic properties. This book will useful to those working or studying in the areas of sensory reception and computational biology.

JTD Keywords: Nervous System (Neural Coordination), Computer Applications (Computational Biology), Sense Organs (Sensory Reception)