References of "Goncalves, Jorge 50001877"
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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 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 detailFunctional observability and subspace reconstruction in nonlinear systems
Montanari, Arthur; Freitas, Leandro; Proverbio, Daniele UL et al

in Physical Review Research (2022), 4

Time-series analysis is fundamental for modeling and predicting dynamical behaviors from time- ordered data, with applications in many disciplines such as physics, biology, finance, and engineering ... [more ▼]

Time-series analysis is fundamental for modeling and predicting dynamical behaviors from time- ordered data, with applications in many disciplines such as physics, biology, finance, and engineering. Measured time-series data, however, are often low dimensional or even univariate, thus requiring embedding methods to reconstruct the original system’s state space. The observability of a system establishes fundamental conditions under which such reconstruction is possible. However, complete observability is too restrictive in applications where reconstructing the entire state space is not necessary and only a specific subspace is relevant. Here, we establish the theoretic condition to reconstruct a nonlinear functional of state variables from measurement processes, generalizing the concept of functional observability to nonlinear systems. When the functional observability condition holds, we show how to construct a map from the embedding space to the desired functional of state variables, characterizing the quality of such reconstruction. The theoretical results are then illustrated numerically using chaotic systems with contrasting observability properties. By exploring the presence of functionally unobservable regions in embedded attractors, we also apply our theory for the early warning of seizure-like events in simulated and empirical data. The studies demonstrate that the proposed functional observability condition can be assessed a priori to guide time-series analysis and experimental design for the dynamical characterization of complex systems. [less ▲]

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See detailDBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Baniasadi, Mehri UL; Petersen, Mikkel V.; Goncalves, Jorge UL et al

in Human Brain Mapping (2022)

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by ... [more ▼]

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage. [less ▲]

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See detailAnalysis and comparison of gait impairments in patients with Parkinson’s disease and normal pressure hydrocephalus using wearable sensors and machine learning algorithms
Magni, Stefano UL; Bremm, René Peter UL; Lecossois, Sylvie et al

Scientific Conference (2022, September 05)

Objectives. Gait impairments in patients with Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) are visually assessed by movement disorders experts for diagnoses and to decide on ... [more ▼]

Objectives. Gait impairments in patients with Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) are visually assessed by movement disorders experts for diagnoses and to decide on pharmaceutical and surgical interventions. Despite standardised tests and clinicians’ expertise, such approaches entail a considerable level of subjectivity. The recent development of wearable sensors and machine learning offers complementary approaches providing more objective, quantitative assessments of gait impairments. We aim to employ the data gathered from an inertial measurement unit synchronized with a novel foot pressure sensor embedded in the patient’s shoes to characterize gait impairments. We focus on distinguishing PD from NPH and on assessing gait impairment before and after surgical intervention. Methods. A cohort of 10 PD and 10 NPH patients was assembled and patients performed standardised walking tests. Measurements were performed employing wearable sensors comprising a three-axes gyroscope, a three-axes accelerometer and eight pressure sensors embedded in each patient’s shoe. To analyse the generated data, existing algorithms were implemented and adapted. These allow to compute gait cycle parameters such as step time and metrics characterizing the swing and stance phases. Machine learning algorithms where employed to identify major changes in gait cycle parameters between the two groups of patients, and for individual patients before and after surgical intervention as DBS implantation in PD and Shunt implantation in NPH. Results. The gait impairments of both disease groups were measured and quantified. An algorithm to extract gait cycle parameters from sensors was implemented, tested and employed on such patients. Gait cycle parameters within and between the groups of PD and NPH patients were compared, assessing what gait cycle parameters allow to distinguish between these groups. Gait cycle impairments of patients before and after surgery were compared, assessing the effect of DBS or Shunt implantation and which gait cycle parameters allow to monitor symptoms improvement. Conclusions. Wearable sensors measuring pressure, combined with gait cycle parameters extraction and machine learning algorithms, have a great potential for objective evaluation of gait impairment. In particular, they allow to characterize what differentiate such impairments between PD and NPH patients, and what allow to assess motor symptoms improvement after surgery. [less ▲]

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See detailRényi Entropy in Statistical Mechanics
Fuentes, Jesús UL; Goncalves, Jorge UL

in Entropy (2022), 24(8), 1080

Rényi entropy was originally introduced in the field of information theory as a parametric relaxation of Shannon (in physics, Boltzmann–Gibbs) entropy. This has also fuelled different attempts to ... [more ▼]

Rényi entropy was originally introduced in the field of information theory as a parametric relaxation of Shannon (in physics, Boltzmann–Gibbs) entropy. This has also fuelled different attempts to generalise statistical mechanics, although mostly skipping the physical arguments behind this entropy and instead tending to introduce it artificially. However, as we will show, modifications to the theory of statistical mechanics are needless to see how Rényi entropy automatically arises as the average rate of change of free energy over an ensemble at different temperatures. Moreover, this notion is extended by considering distributions for isospectral, non-isothermal processes, resulting in relative versions of free energy, in which the Kullback–Leibler divergence or the relative version of Rényi entropy appear within the structure of the corrections to free energy. These generalisa- tions of free energy recover the ordinary thermodynamic potential whenever isothermal processes are considered. [less ▲]

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See detailLinear system identifiability from single-cell data
Aalto, Atte UL; Lamoline, François UL; Goncalves, Jorge UL

in Systems and Control Letters (2022), 165

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See detailInitialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data
Baniasadi, Mehri UL; Husch, Andreas UL; Proverbio, Daniele UL et al

in Bildverarbeitung für die Medizin 2022 (2022)

Following the deep brain stimulation (DBS) surgery, the stimulation parameters are manually tuned to reduce symptoms. This procedure can be timeconsuming, especially with directional leads. We propose an ... [more ▼]

Following the deep brain stimulation (DBS) surgery, the stimulation parameters are manually tuned to reduce symptoms. This procedure can be timeconsuming, especially with directional leads. We propose an automated methodology to initialise contact configurations using imaging techniques. The goal is to maximise the electric field on the target while minimising the spillover, and the electric field on regions of avoidance. By superposing pre-computed electric fields, we solve the optimisation problem in less than a minute, much more efficient compared to finite element methods. Our method offers a robust and rapid solution, and it is expected to considerably reduce the time required for manual parameter tuning. [less ▲]

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See detailBuffering variability in cell regulation motifs close to criticality
Proverbio, Daniele UL; Noronha Montanari, Arthur UL; Skupin, Alexander UL et al

in Physical Review. E. (2022)

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to ... [more ▼]

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. [less ▲]

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See detailPerformance of early warning signals for disease re-emergence: A case study on COVID-19 data
Proverbio, Daniele UL; Kemp, Francoise UL; Magni, Stefano UL et al

in PLoS Computational Biology (2022), 18(3), 1009958

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals ... [more ▼]

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical charac- teristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emer- gence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are sat- isfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active moni- toring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empiri- cal studies, constituting a further step towards the application of EWS indicators for inform- ing public health policies. [less ▲]

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See detailModel-based assessment of COVID-19 epidemic dynamics by wastewater analysis
Proverbio, Daniele UL; Kemp, Francoise UL; Magni, Stefano UL et al

in Science of the Total Environment (2022), 827

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective ... [more ▼]

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics. [less ▲]

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See detailReal-time Monitoring and Early Warning Of Atrial Fibrillation
Gavidia, Marino UL; Montanari, Arthur; Fuentes, Jesús UL et al

in Preprint (2022)

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See detailTherapeutic maps for a sensor-based evaluation of deep brain stimulation programming
Bremm, René Peter UL; Berthold, Christophe; Krüger, Rejko UL et al

in Biomedizinische Technik. Biomedical Engineering (2021)

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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 Journal of Theoretical Biology (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 detailSARS-CoV-2 transmission risk from asymptomatic carriers: Results from a mass screening programme in Luxembourg
Wilmes, Paul UL; Zimmer, Jacques UL; Schulz, Jasmin et al

in The Lancet Regional Health. Europe (2021), 4

Background To accompany the lifting of COVID-19 lockdown measures, Luxembourg implemented a mass screening (MS) programme. The first phase coincided with an early summer epidemic wave in 2020. Methods rRT ... [more ▼]

Background To accompany the lifting of COVID-19 lockdown measures, Luxembourg implemented a mass screening (MS) programme. The first phase coincided with an early summer epidemic wave in 2020. Methods rRT-PCR-based screening for SARS-CoV-2 was performed by pooling of samples. The infrastructure allowed the testing of the entire resident and cross-border worker populations. The strategy relied on social connectivity within different activity sectors. Invitation frequencies were tactically increased in sectors and regions with higher prevalence. The results were analysed alongside contact tracing data. Findings The voluntary programme covered 49 of the resident and 22 of the cross-border worker populations. It identified 850 index cases with an additional 249 cases from contact tracing. Over-representation was observed in the services, hospitality and construction sectors alongside regional differences. Asymptomatic cases had a significant but lower secondary attack rate when compared to symptomatic individuals. Based on simulations using an agent-based SEIR model, the total number of expected cases would have been 42·9 (90 CI [-0·3, 96·7]) higher without MS. Mandatory participation would have resulted in a further difference of 39·7 [19·6, 59·2]. Interpretation Strategic and tactical MS allows the suppression of epidemic dynamics. Asymptomatic carriers represent a significant risk for transmission. Containment of future outbreaks will depend on early testing in sectors and regions. Higher participation rates must be assured through targeted incentivisation and recurrent invitation. Funding This project was funded by the Luxembourg Ministries of Higher Education and Research, and Health. [less ▲]

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See detailProceedings of the AI4Health Lecture Series (2021)
Schommer, Christoph UL; Sauter, Thomas UL; Pang, Jun UL et al

Scientific Conference (2021)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [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

Detailed reference viewed: 90 (4 UL)