References of "Goncalves, Jorge 50001877"
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See detailNetwork Identifiability from Intrinsic Noise
Goncalves, Jorge UL; Hayden, David; Yuan, Ye

in IEEE Transactions on Automatic Control (in press)

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See detailA minimal realization technique for the dynamical structure function of a class of LTI systems
Goncalves, Jorge UL; Yuan, Ye; Rai, Anurag et al

in IEEE Transactions on Control of Network Systems (in press)

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See detailAlmost global convergence to practical synchronization in the generalized Kuramoto model on networks over the n-sphere
Markdahl, Johan UL; Proverbio, Daniele UL; Mi, La et al

in Communications Physics (2021), 4

From the flashing of fireflies to autonomous robot swarms, synchronization phenomena are ubiquitous in nature and technology. They are commonly described by the Kuramoto model that, in this paper, we ... [more ▼]

From the flashing of fireflies to autonomous robot swarms, synchronization phenomena are ubiquitous in nature and technology. They are commonly described by the Kuramoto model that, in this paper, we generalise to networks over n-dimensional spheres. We show that, for almost all initial conditions, the sphere model converges to a set with small diameter if the model parameters satisfy a given bound. Moreover, for even n, a special case of the generalized model can achieve phase synchronization with nonidentical frequency parameters. These results contrast with the standard n = 1 Kuramoto model, which is multistable (i.e., has multiple equilibria), and converges to phase synchronization only if the frequency parameters are identical. Hence, this paper shows that the generalized network Kuramoto models for n ≥ 2 displays more coherent and predictable behavior than the standard n = 1 model, a desirable property both in flocks of animals and for robot control. [less ▲]

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See detailModelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden
Kemp, Francoise UL; Proverbio, Daniele UL; Aalto, Atte UL et al

in Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden (2021)

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social ... [more ▼]

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come. [less ▲]

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See detailModelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden
Kemp, Francoise UL; Proverbio, Daniele UL; Aalto, Atte UL et al

in Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden (2021)

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social ... [more ▼]

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come. [less ▲]

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See detailDynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks.
Proverbio, Daniele UL; Kemp, Francoise UL; Magni, Stefano UL et al

in PloS one (2021), 16(5), 0252019

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of ... [more ▼]

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model. [less ▲]

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See detailAutomated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images.
Vlasov, Vanja UL; Bofferding, Marie UL; Marx, Loic Marc UL et al

in Bildverarbeitung für die Medizin 2021 (2021)

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See detailAnalysis and visualisation of tremor dynamics in deep brain stimulation patients
Bremm, René Peter UL; Koch, Klaus Peter; Krüger, Rejko UL et al

in Current Directions in Biomedical Engineering (2020), 6(3), 4

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See detailA rule-based expert system for real-time feedback-control in deep brain stimulation
Bremm, René Peter UL; Koch, Klaus Peter; Krüger, Rejko UL et al

in Current Directions in Biomedical Engineering (2020), 6(3), 4

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See detailFastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields
Baniasadi, Mehri UL; Proverbio, Daniele UL; Goncalves, Jorge UL et al

in NeuroImage (2020)

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes ... [more ▼]

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method–FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable e-field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2s, ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications [less ▲]

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See detailGene regulatory network inference from sparsely sampled noisy data
Aalto, Atte UL; Viitasaari, Lauri; Ilmonen, Pauliina et al

in Nature Communications (2020), 11

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing ... [more ▼]

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life. [less ▲]

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See detailCausal dynamical modelling predicts novel regulatory genes of FOXP3 in human regulatory T cells
Sawlekar, Rucha UL; Magni, Stefano UL; Chapelle, Christophe et al

E-print/Working paper (2020)

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See detailAssessing suppression strategies against epidemicoutbreaks like COVID-19: the SPQEIR model
Proverbio, Daniele UL; Kemp, Francoise UL; Magni, Stefano UL et al

E-print/Working paper (2020)

The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock ... [more ▼]

The current COVID-19 outbreak represents a most serious challenge for societies worldwide. It isendangering the health of millions of people, and resulting in severe socioeconomic challenges dueto lock-down measures. Governments worldwide aim to devise exit strategies to revive the economywhile keeping the pandemic under control. The problem is that the effects of distinct measures arenot well quantified. This paper compares several suppression approaches and potential exit strategiesusing a new extended epidemic SEIR model. It concludes that while rapid and strong lock-down isan effective pandemic suppression measure, a combination of other strategies such as social distanc-ing, active protection and removal can achieve similar suppression synergistically. This quantitativeunderstanding will support the establishment of mid- and long-term interventions. Finally, the paperprovides an online tool that allows researchers and decision makers to interactively simulate diversescenarios with our model. [less ▲]

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See detailRobust synchronization of heterogeneous robot swarms on the sphere
Markdahl, Johan UL; Proverbio, Daniele UL; Goncalves, Jorge UL

in 2020 59th IEEE Conference on Decision and Control (CDC) (2020)

Synchronization on the sphere is important to certain control applications in swarm robotics. Of recent interest is the Lohe model, which generalizes the Kuramoto model from the circle to the sphere. The ... [more ▼]

Synchronization on the sphere is important to certain control applications in swarm robotics. Of recent interest is the Lohe model, which generalizes the Kuramoto model from the circle to the sphere. The Lohe model is mainly studied in mathematical physics as a toy model of quantum synchronization. The model makes few assumptions, wherefore it is well-suited to represent a swarm. Previous work on this model has focused on the cases of complete and acyclic networks or the homogeneous case where all oscillator frequencies are equal. This paper concerns the case of heterogeneous oscillators connected by a non-trivial network. We show that any undesired equilibrium is exponentially unstable if the frequencies satisfy a given bound. This property can also be interpreted as a robustness result for small model perturbations of the homogeneous case with zero frequencies. As such, the Lohe model is a good choice for control applications in swarm robotics. [less ▲]

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See detailLinear system identification from ensemble snapshot observations
Aalto, Atte UL; Goncalves, Jorge UL

in Proceedings of the IEEE Conference on Decision and Control (2019, December)

Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell ... [more ▼]

Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell experiments have revolutionised genetic studies. These experiments yield gene expression data in single cell resolution for a large number of cells at a time. However, the cells are destroyed in the measurement process, and so the data consist of snapshots of an ensemble evolving over time, instead of time series. The problem studied in this article is how such data can be used in modelling gene regulatory dynamics. Two different paradigms are studied for linear system identification. The first is based on tracking the evolution of the distribution of cells over time. The second is based on the so-called pseudotime concept, identifying a common trajectory through the state space, along which cells propagate with different rates. Therefore, at any given time, the population contains cells in different stages of the trajectory. Resulting methods are compared in numerical experiments. [less ▲]

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See detailData driven discovery of cyber physical systems
Yuan, Ye; Tang, Xiuchuan; Zhou, Wei et al

in Nature Communications (2019)

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved ... [more ▼]

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber- physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance. [less ▲]

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See detailFrom Diagnosing Diseases to Predicting Diseases
Balling, Rudi UL; Goncalves, Jorge UL; Magni, Stefano UL et al

in Betz, Ulrich A.K. (Ed.) Curious2018 (2019)

Chronic diseases can be considered as perturbations of complex adaptive systems. Transitions from healthy states to chronic diseases are often characterized by sudden and unexpected onset of diseases ... [more ▼]

Chronic diseases can be considered as perturbations of complex adaptive systems. Transitions from healthy states to chronic diseases are often characterized by sudden and unexpected onset of diseases. These critical transitions or catastrophic shifts have been studied in theoretical and applied physics, ecology, social science, economics and recently also in biomedical applications. If we could understand the underlying mechanisms and the dynamics of critical transitions involved in the development of diseases, we would be better equipped to predict and eventually prevent them from arising. The current paper gives an overview of the potential application of the concept of critical transitions to biomedical applications. [less ▲]

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See detailA multifactorial evaluation framework for gene regulatory network reconstruction
Mombaerts, Laurent UL; Aalto, Atte UL; Markdahl, Johan UL et al

in Foundations of Systems Biology in Engineering (2019)

In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such ... [more ▼]

In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time series data. However, the applicability and accuracy presumptions of such algorithms remain unclear due to experimental heterogeneity. This paper assesses the performance of recent and successful network inference strategies under a novel, multifactorial evaluation framework in order to highlight pragmatic tradeoffs in experimental design. The effects of data quantity and systems perturbations are addressed, thereby formulating guidelines for efficient resource management. Realistic data were generated from six widely used benchmark models of rhythmic and nonrhythmic gene regulatory systems with random perturbations mimicking the effect of gene knock-out or chemical treatments. Then, time series data of increasing lengths were provided to five state-of-the-art network inference algorithms representing distinctive mathematical paradigms. The performances of such network reconstruction methodologies are uncovered under various experimental conditions. We report that the algorithms do not benefit equally from data increments. Furthermore, at least for the studied rhythmic system, it is more profitable for network inference strategies to be run on long time series rather than short time series with multiple perturbations. By contrast, for the non-rhythmic systems, increasing the number of perturbation experiments yielded better results than increasing the sampling frequency. We expect that future benchmark and algorithm design would integrate such multifactorial considerations to promote their widespread and conscientious usage. [less ▲]

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See detailDynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator
Mombaerts, Laurent UL; Carignano, Alberto; Robertson, Fiona et al

in PLoS Computational Biology (2019)

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The ... [more ▼]

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems. [less ▲]

Detailed reference viewed: 130 (8 UL)