Publications

Background: Early detection of postoperative complications after colorectal cancer (CRC) surgery is associated with improved outcomes. The aim was to investigate early metabolomics signatures capable to detect patients at risk for severe postoperative complications after CRC surgery. Materials and methods: Prospective cohort study of patients undergoing CRC surgery from 2015 to 2018. Plasma samples were collected before and after surgery, and analyzed by mass spectrometry obtaining 188 metabolites and 21 ratios. Postoperative complications were registered with Clavien-Dindo Classification and Comprehensive Complication Index. Results: One hundred forty-six patients were included. Surgery substantially modified metabolome and metabolic changes after surgery were quantitatively associated with the severity of postoperative complications. The strongest positive relationship with both Clavien-Dindo and Comprehensive Complication Index (beta=4.09 and 63.05, P<0.001) corresponded to kynurenine/tryptophan, against an inverse relationship with lysophosphatidylcholines (LPCs) and phosphatidylcholines (PCs). Patients with LPC18:2/PCa36:2 below the cut-off 0.084 mu M/mu M resulted in a sevenfold higher risk of major complications (OR=7.38, 95% CI: 2.82-21.25, P<0.001), while kynurenine/tryptophan above 0.067 mu M/mu M a ninefold (OR=9.35, 95% CI: 3.03-32.66, P<0.001). Hexadecanoylcarnitine below 0.093 mu M displayed a 12-fold higher risk of anastomotic leakage-related complications (OR=11.99, 95% CI: 2.62-80.79, P=0.004). Conclusion: Surgery-induced phospholipids and amino acid dysregulation is associated with the severity of postoperative complications after CRC surgery, including anastomotic leakage-related outcomes. The authors provide quantitative insight on metabolic markers, measuring vulnerability to postoperative morbidity that might help guide early decision-making and improve surgical outcomes.

JTD Keywords: Acids, Anastomotic leakage, Bypass, Clinical-practice guidelines, Colon, Colorectal cancer, Metabolomics, Postoperative complications, Predict, Sepsis, Trauma, Tryptophan degradation

Prion-like domains (PrLDs) are intrinsically disordered regions (IDRs) of low sequence complexity with a similar composition to yeast prion domains. PrLDs-containing proteins have been involved in different organisms' regulatory processes. Regions of moderate amyloid propensity within IDRs have been shown to assemble autonomously into amyloid fibrils. These sequences tend to be rich in polar amino acids and often escape from the detection of classical bioinformatics screenings that look for highly aggregation-prone hydrophobic sequence stretches. We defined them as cryptic amyloidogenic regions (CARs) and recently developed an integrated database that collects thousands of predicted CARs in IDRs. CARs seem to be evolutionary conserved among disordered regions because of their potential to stablish functional contacts with other biomolecules. Here we have focused on identifying and characterizing CARs in prion-like proteins (pCARs) from plants, a lineage that has been poorly studied in comparison with other prionomes. We confirmed the intrinsic amyloid potential for a selected pCAR from Arabidopsis thaliana and explored functional enrichments and compositional bias of pCARs in plant prion-like proteins.Copyright © 2023 Pintado-Grima, Santos, Iglesias, Manglano-Artuñedo, Pallarès and Ventura.

JTD Keywords: aggregation, aromatic residues, bioinformatics, domains, functional interactions, identify proteins, plants, prediction, prion-like domains, q/n-rich, regulator, sup35, yeast, Bioinformatics, Cryptic amyloidogenic regions, Functional interactions, Plants, Prion-like domains, Rna-binding proteins

Chemically responsive polymers are macromolecules that respond to local variations of the chemical composition of the solution by changing their conformation, with notable examples including polyelectrolytes, proteins, and DNA. The polymer conformation changes can occur in response to changes in the pH, the ionic strength, or the concentration of a generic solute that interacts with the polymer. These chemical stimuli can lead to drastic variations of the polymer flexibility and even trigger a transition from a coil to a globule polymer conformation. In many situations, the spatial distribution of the chemical stimuli can be highly inhomogeneous, which can lead to large spatial variations of polymer conformation and of the rheological properties of the mixture. In this paper, we develop a theory for the flow of a mixture of solute and chemically responsive polymers. The approach is valid for generic flows and inhomogeneous distributions of polymers and solutes. To model the polymer conformation changes introduced by the interactions with the solute, we consider the polymers as linear elastic dumbbells whose spring stiffness depends on the solute concentration. We use Onsager's variational formalism to derive the equations governing the evolution of the variables, which unveils novel couplings between the distribution of dumbbells and that of the solute. Finally, we use a linear stability analysis to show that the governing equations predict an equilibrium phase separation and a distinct shear-induced phase separation whereby a homogeneous distribution of solute and dumbbells spontaneously demix. Similar phase transitions have been observed in previous experiments using stimuli-responsive polymers and may play an important role in living systems. (C) 2022 The Society of Rheology.

JTD Keywords: Coil-globule transition, Constitutive equation, Dilute-solutions, Dumbbell model, Dynamics, Macromolecules, Nonequilibrium thermodynamics, Polyelectrolytes, Polymer migration, Polymer phase separation, Polymers, Predictions, Rheology, Shear-induced phase separation, Solute-polymer interactions, Stress, Viscoelasticity

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.

This paper addresses how impairments in prediction in young adults with autism spectrum disorder (ASD) relate to their behavior during collaboration. To assess it, we developed a task where participants play in collaboration with a synthetic agent to maximize their score. The agent’s behavior changes during the different phases of the game, requiring participants to model the agent’s sensorimotor contingencies to play collaboratively. Our results (n = 30, 15 per group) show differences between autistic and neurotypical individuals in their behavioral adaptation to the other partner. Contrarily, there are no differences in the self-reports of that collaboration.

JTD Keywords: Autism, Prediction, Collaboration, Sensorimotor contingencies, Neurodiversity

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients’ functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge.

JTD Keywords: Home hospitalization, Health risk assessment, Predictive modeling, Chronic care, Integrated care, Modeling, Hospitalization, Health risk, Prediction, Mortality, Clinical decision support

Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.

JTD Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines

Our understanding of body ownership largely relies on the so-called Rubber Hand Illusion (RHI). In this paradigm, synchronous stroking of the real and the rubber hands leads to an illusion of ownership of the rubber hand provided that it is physically, anatomically, and spatially plausible. Self-attribution of an artificial hand also occurs during visuomotor synchrony. In particular, participants experience ownership over a virtual or a rubber hand when the visual feedback of self-initiated movements follows the trajectory of the instantiated motor commands, such as in the Virtual Hand Illusion (VHI) or the moving Rubber Hand Illusion (mRHI). Evidence yields that both when the cues are triggered externally (RHI) and when they result from voluntary actions (VHI and mRHI), the experience of ownership is established through bottom-up integration and top-down prediction of proximodistal cues (visuotactile or visuomotor) within the peripersonal space. It seems, however, that depending on whether the sensory signals are externally (RHI) or self-generated (VHI and mRHI), the top-down expectation signals are qualitatively different. On the one hand, in the RHI the sensory correlations are modulated by top-down influences which constitute empirically induced priors related to the internal (generative) model of the body. On the other hand, in the VHI and mRHI body ownership is actively shaped by processes which allow for continuous comparison between the expected and the actual sensory consequences of the actions. Ample research demonstrates that the differential processing of the predicted and the reafferent information is addressed by the central nervous system via an internal (forward) model or corollary discharge. Indeed, results from the VHI and mRHI suggest that, in action-contexts, the mechanism underlying body ownership could be similar to the forward model. Crucially, forward models integrate across all self-generated sensory signals including not only proximodistal (i.e., visuotactile or visuomotor) but also purely distal sensory cues (i.e., visuoauditory). Thus, if body ownership results from a consistency of a forward model, it will be affected by the (in)congruency of purely distal cues provided that they inform about action-consequences and are relevant to a goal-oriented task. Specifically, they constitute a corrective error signal. Here, we explicitly addressed this question. To test our hypothesis, we devised an embodied virtual reality-based motor task where action outcomes were signaled by distinct auditory cues. By manipulating the cues with respect to their spatial, temporal and semantic congruency, we show that purely distal (visuoauditory) feedback which violates predictions about action outcomes compromises both performance and body ownership. These results demonstrate, for the first time, that body ownership is influenced by not only externally and self-generated cues which pertain to the body within the peripersonal space but also those arising outside of the body. Hence, during goal-oriented tasks body ownership may result from the consistency of forward models.

JTD Keywords: Body ownership, Internal forward model, Goal-oriented behavior, Multisensory integration, Top-down prediction

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

JTD Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

Data from a Gas Sensor based Chromatography instrument is used in order to replicate output from a human panel and the estimation of the total solvent amount measured by and FID device in a packaging application. The system is trained on different packaging sample properties and validated with unseen combinations of materials, varnishes and production processes. This contribution will show the difficulties on the prediction of the output of the human panel, and the success on the prediction of the total amount of solvent in the sample

JTD Keywords: Gas sensors, Solvent prediction