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• An acoustic-to-kinematic model to project acoustic dynamics to jaw-tongue kinematics has been proposed. • A weight estimation method based on gradient-descent has been used in reducing estimation errors. • A weight optimization method based on signal realignment has been defined. • Time delays from male and female Parkinson's Disease patients have been estimated. • Large and small movement ranges in hypokinetic dysarthria have been obtained from acoustic signals. Speech signal analysis is a powerful tool that facilitates the monitoring and tracking of symptom deterioration caused by neurodegenerative disorders, typically achieved using either sustained vowels, diadochokinetic exercises or running speech. This study expands our previous work on the study of the movement produced by the jaw-tongue biomechanical system. The aim is to further investigate the effects of neuromotor activity during muscular exertion that translates formant acoustics into speech articulatory movements affected by hypokinetic dysarthria in Parkinson's Disease (PD). The objective of this study is to estimate the parameters of an inverse acoustic-to-kinematic projection model that takes as an input the variations of the first and second formants and estimates as output the spatial variation of the jaw-tongue biomechanical system. The spatial variations have been extracted from 3D accelerometry (3DAcc). These serve as ground truth for comparison with the estimated activity projected from speech kinematics, as a measure of fitness of the inverse model. The estimation method is a two step process: first initial weight values are produced using multiple regression between each of the formant dynamic signals (acoustical analysis) and the estimated spatial variations (accelerometry). The second step uses a weight refinement method based on gradient-descent. Additionally, a time-realignment study has been carried out on the acoustic-to-kinematic projection model, based on the estimation of relative time displacements as to maximize the cross-correlation between signals. The study is complemented with an estimation of the model weights on a dataset from PD participants and Healthy Controls (HC). This methodology opens up new ways to investigate the underlying physiological voice production mechanism which may offer new insights into PD symptoms. [ABSTRACT FROM AUTHOR]

• Biomechanical description of phonation in Down syndrome (DS) from acoustical correlates. • Classical acoustic analysis is not sharp enough to observe phenomena underlying neurological dysfunctions in DS. • Distortions may be linked to neuromotor alterations in DS. • Tremor is larger in Down syndrome group (DSG) compared to Normative group (NG). • Laryngeal tremor may be related with neuromotor dysfunctions of DS. Down Syndrome (DS) has been the aim of numerous studies, although its phonation specificities have not received much attention. DS is a genetic disorder caused by the presence of a third copy of chromosome 21, being the explanation of different behavioral and clinical anomalies: developmental disorders, hearing, speech and language impairments and neuromotor and morphological alterations of the oro-naso-pharyngeal (ONP) and laryngeal (ONL) structures. The goal of the current study is to carry on a first approach to the biomechanics of phonation in DS children from an acoustic perspective, trying to describe its specificities and to establish a link with the syndrome. A specific application designed to analyze the glottal source and its relationship to vocal fold biomechanics has been used. Nineteen biomechanical features from vocal fold dynamics have been used, related to fundamental frequency, jitter, shimmer, noise-to-harmonic ratio, mass and stiffness profiles, unbalance, contact gaps, and tremor. On one hand, the non-normophonic corpus is composed of eight children with DS with age range between five and six years old. On the other hand, a normative or normophonic population was taken. The last group consisted of eight children with similar ages to the previous ones. The results achieved show relevant differences (p <.05) in larynx biomechanics regarding the following features: deviations in the fundamental frequency, vocal fold tension (regarding musculus vocalis), and phonation tremor. These distortions could be directly related with the neuromotor alterations known to be associated to DS and could reflect underlying factors of DS phonation features. [ABSTRACT FROM AUTHOR]

• A multi-agent autonomous broker (COLDPower'16) for energy markets, is proposed. • The strategies for retail market use MDPs and reinforcement learning. • The strategy for wholesale market estimates future amount of energy and prices. • COLDPower'16 is competitive by achieving the 2nd place in PowerTAC tournament. • COLDPower'16 is profitable by earning over 86 million EUR in PowerTAC tournament. Free and competitive energy markets are a recent and increasing phenomenon in several countries. Understanding these new energy markets and estimating their possible evolutions are current challenges of the research community. To avoid real market risks, the research community has developed autonomous traders and tested them in the Power Trading Agent Competition (Power TAC), a sophisticated energy market simulator. In this paper, we present COLDPower'16, a competitive autonomous trader composed of expert agents in specific kinds of markets and customers that combines local strategies into a global strategy to maximize profit. The local strategy of each tariff expert agent uses reinforcement learning algorithms, while the local strategy of the wholesale expert agent estimates future energy prices and the amount of energy that can be negotiated to buy energy when prices are low and sell energy when prices are high. COLDPower'16 was tested in Power TAC 2016. It achieved 2nd place in the final round of this international competition with 7 autonomous agent brokers. [ABSTRACT FROM AUTHOR]

Coronavirus stands for a large family of viruses characterized by protruding spikes surrounding a lipidic membrane adorned with proteins. The present study explores the adhesion of transmissible gastroenteritis coronavirus (TGEV) particles on a variety of reference solid surfaces that emulate typical virus-surface interactions. Atomic force microscopy informs about trapping effectivity and the shape of the virus envelope on each surface, revealing that the deformation of TGEV particles spans from 20% to 50% in diameter. Given this large deformation range, experimental Langmuir isotherms convey an unexpectedly moderate variation in the adsorption-free energy, indicating a viral adhesion adaptability which goes beyond the membrane. The combination of an extended Helfrich theory and coarse-grained simulations reveals that, in fact, the envelope and the spikes present complementary adsorption affinities. While strong membrane-surface interaction lead to highly deformed TGEV particles, surfaces with strong spike attraction yield smaller deformations with similar or even larger adsorption-free energies., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

The recent COVID19 pandemic has remarkably boosted the research on in vitro diagnosis assays to detect biomarkers in biological fluids. Specificity and sensitivity are mandatory for diagnostic kits aiming to reach clinical stages. Whilst the modulation of sensitivity can significantly improve the detection of biomarkers in liquids, this has been scarcely explored. Here, we report on the proof of concept and parametrization of a novel biosensing methodology based on the changes of AC magnetic hysteresis areas observed for magnetic nanoparticles following biomolecular recognition in liquids. Several parameters are shown to significantly modulate the transducing capacity of magnetic nanoparticles to detect analytes dispersed in saline buffer at concentrations of clinical relevance. Magnetic nanoparticles were bio-conjugated with an engineered recognition peptide as a receptor. Analytes are engineered tetratricopeptide binding domains fused to the fluorescent protein whose dimerization state allows mono- or divalent variants. Our results unveil that the number of receptors per particle, analyte valency and concentration, nanoparticle composition and concentration, and field conditions play a key role in the formation of assemblies driven by biomolecular recognition. Consequently, all these parameters modulate the nanoparticle transduction capacity. Our study provides essential insights into the potential of AC magnetometry for customizing biomarker detection in liquids.

Nanoparticle aggregation is a driving principle of innovative materials and biosensing methodologies, improving transduction capabilities displayed by optical, electrical or magnetic measurements. This aggregation can be driven by the biomolecular recognition between target biomolecules (analytes) and receptors bound onto nanoparticle surface. Despite theoretical advances on modelling the entropic interaction in similar systems, predictions of the fractal morphologies of the nanoclusters of bioconjugated nanoparticles are lacking. The morphology of resulting nanoclusters is sensitive to the location, size, flexibility, average number of receptors per particle f̄ , and the analyte-particle concentration ratio. Here we considered bioconjugated iron oxide nanoparticles (IONPs) where bonds are mediated by a divalent protein that binds two receptors attached onto different IONPs. We developed a protocol combining analytical expressions for receptors and linker distributions, and Brownian dynamics simulations for bond formation, and validated it against experiments. As more bonds become available ( e.g. , by adding analytes), the aggregation deviates from the ideal Bethe's lattice scenario due to multivalence, loop formation, and steric hindrance. Generalizing Bethe's lattice theory with a (not-integer) effective functionality f eff leads to analytical expressions for the cluster size distributions in excellent agreement with simulations. At high analyte concentration steric impediment imposes an accessible limit value f acc to f eff , which is bounded by f acc < f eff < f̄ . A transition to gel phase, is correctly captured by the derived theory. Our findings offer new insights into quantifying analyte amounts by assessing nanocluster size, and predicting nanoassembly morphologies accurately is a first step towards understanding variations of physical properties in clusters formed after biomolecular recognition.