Multi-Timescale spectra as Features for continuous Workload estimation in Realistic Settings

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Validation of fNIRS System as a Technique to Monitor Cognitive Workload

CognitiveWorkload (CW) is a key factor in the human learning context. Knowing the optimal amount of CW is essential to maximise cognitive performance, emerging as an important variable in e-learning systems and Brain-Computer Interfaces (BCI) applications. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising avenue of brain discovery because of its easy setup and robust results. It is, in fact, along with Electroencephalography (EEG), an encouraging technique in the context of BCI. Brain- Computer Interfaces, by tracking the user’s cognitive state, are suitable for educational systems. Thus, this work sought to validate the fNIRS technique for monitoring different CW stages. For this purpose, we acquired the fNIRS and EEG signals when performing cognitive tasks, which included a progressive increase of difficulty and simulation of the learning process. We also used the breathing sensor and the participants’ facial expressions to assess their cognitive status. We found that both visual inspections of fNIRS signals and power spectral analysis of EEG bands are not sufficient for discriminating cognitive states, nor quantify CW. However, by applying machine learning (ML) algorithms, we were able to distinguish these states with mean accuracies of 79.8%, reaching a value of 100% in one specific case. Our findings provide evidence that fNIRS technique has the potential to monitor different levels of CW. Furthermore, our results suggest that this technique allied with the EEG and combined via ML algorithms is a promising tool to be used in the e-learning and BCI fields for its skill to discriminate and characterize cognitive states.O esforço cognitivo (CW) é um factor relevante no contexto da aprendizagem humana. Conhecer a quantidade óptima de CW é essencial para maximizar o desempenho cognitivo, surgindo como uma variável importante em sistemas de e-learning e aplicações de Interfaces Cérebro-Computador (BCI). A Espectroscopia Funcional de Infravermelho Próximo (fNIRS) emergiu como uma via de descoberta do cérebro devido à sua fácil configuração e resultados robustos. É, de facto, juntamente com a Electroencefalografia (EEG), uma técnica encorajadora no contexto de BCI. As interfaces cérebro-computador, ao monitorizar o estado cognitivo do utilizador, são adequadas para sistemas educativos. Assim, este trabalho procurou validar o sistema de fNIRS como uma técnica de monitorização de CW. Para este efeito, adquirimos os sinais fNIRS e EEG aquando da execução de tarefas cognitivas, que incluiram um aumento progressivo de dificuldade e simulação do processo de aprendizagem. Utilizámos, ainda, o sensor de respiração e as expressões faciais dos participantes para avaliar o seu estado cognitivo. Verificámos que tanto a inspeção visual dos sinais de fNIRS como a análise espectral dos sinais de EEG não são suficientes para discriminar estados cognitivos, nem para quantificar o CW. No entanto, aplicando algoritmos de machine learning (ML), fomos capazes de distinguir estes estados com exatidões médias de 79.8%, chegando a atingir o valor de 100% num caso específico. Os nossos resultados fornecem provas da prospecção da técnica fNIRS para supervisionar diferentes níveis de CW. Além disso, os nossos resultados sugerem que esta técnica aliada à de EEG e combinada via algoritmos ML é uma ferramenta promissora a ser utilizada nos campos do e-learning e de BCI, pela sua capacidade de discriminar e caracterizar estados cognitivos

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" worksho

Systems biology of energy metabolism in skeletal muscle

The primary function of skeletal muscle tissue is to produce force or cause motion. To perform this task chemical energy stored in nutrients (glucose and fatty acids) has to be converted into an energy currency that can drive muscle contraction (adenosine-tri-phosphate, ATP). This process is known as the energy metabolism of skeletal muscle and consists of a large number of chemical reactions that are organized in metabolic pathways. Unraveling this complex network is important from a fundamental biological perspective, but also essential to understand how a disturbance of muscle bioenergetics can cause metabolic disorders. ?? 31P magnetic resonance spectroscopy (MRS) has emerged as one of the premier methods to study skeletal muscle energy metabolism in vivo. It, however, remains challenging to relate the observed metabolite dynamics to an understanding of the underlying processes at the level of the metabolic pathways. A possible solution for bridging this gap between macroscopic measurements and mechanistic understanding at pathway level is the application of mechanistic computational modeling. This dissertation describes a series of studies in which a mechanistic model of ATP metabolism was developed and applied in the analysis of skeletal muscle bioenergetics. Skeletal muscle cells contain two primary processes that are responsible for the conversion of glucose and fatty acids into ATP. These processes are known as glycolysis and oxidative phosphorylation in mitochondria. The initial mathematical models of these processes were obtained by integration of known enzyme kinetics and thermodynamics. Testing of these models, however, showed that they failed to reproduce many of the in vivo observed metabolite dynamics, as has been described in chapter 1 and 2. These results indicated that the models might be missing essential regulatory mechanisms or that the model parameterization required changes. First, the physiological implications of necessary model adaptations were investigated in a series of studies described in chapters 2 – 5. ?? Numerical analysis of the initial glycolysis model revealed that the experimentally observed slow turnover rate of phosphorylated sugars post exercise could only be explained by rapid deactivation of phosphofructokinase (PFK) and pyruvate kinase (PK) in non-contracting muscle. In particular the deactivation of PFK was crucial for adequate control of pathway flux. Therefore, in a follow-up study, it was tested if the missing regulation at the level of PFK could be explained by calcium – calmodulin mediated activation of this enzyme. To this end, pathway behavior, represented by phosphocreatine (PCr) and pH dynamics, was measured in ischemic skeletal muscle for a wide variety of muscle excitation frequencies (0 – 80 Hz). Next, it was shown that addition of the calcium – calmodulin mediated activation of PFK was necessary to accurately reproduce these data. These results provided important new quantitative support for the hypothesis that this particular mechanism has a key role in the regulation of glycolytic flux in skeletal muscle.?? The initial model of oxidative phosphorylation was first tested against empirically determined mitochondrial input – output relations, i.e., [ADP] – mitochondrial ATP synthesis flux (Jp) and phosphate potential (¿Gp) – Jp. These empirically determined relations were derived from 31P MRS measurements of metabolite dynamics post-exercise. They capture key features of the regulation of oxidative phosphorylation in vivo and are therefore considered relevant for testing the quality of the mathematical model. Numerical model analysis (i.e., parameter sensitivity analysis) was applied to investigate which components significantly influenced predictions of these input – output relations. Based on these results it was concluded that the adenine nucleotide transporter (which facilitates the exchange of ATP and ADP across the inner mitochondrial membrane) has a dominant role in controlling the ADP sensitivity of mitochondria. Furthermore, we identified that Pi feedback control of respiratory chain activity was essential to explain measurements of ¿Gp at low metabolic rates. These insights were used to improve the predictive power of the model, as described in chapters 4 and 5. ?? In the studies described in chapters 2 - 5 the glycolytic and mitochondrial model components were tested for conditions in which only one of the two processes was active (ischemia and post exercise recovery, respectively). It remained therefore unknown if the control mechanisms included in these models could also explain the contribution of mitochondrial versus glycolytic ATP synthesis for conditions in which both processes are active (aerobic exercise). In an attempt to answer this question, dynamics of ATP metabolism were recorded during a full rest – exercise – recovery protocol under aerobic conditions and subsequently used for testing of the integrated mitochondrial + glycolytic model. The results presented in chapter 8 showed that the integrated model could accurately reproduce the observed metabolite and pH dynamics for varying exercise intensities. The main physiological implications of these results were that, substrate feedback control (ADP + Pi) of oxidative phosphorylation combined with substrate feedback control (ADP + AMP + Pi) and control by parallel activation (calcium – calmodulin mediated activation of PFK) of glycolysis, provides a set of key control mechanisms that can explain the regulation of ATP metabolism in skeletal muscle in vivo for a wide range of physiological conditions. By application of several cycles of model development it was possible to improve the models performance to the point it was consistent with 31P MRS measurements of muscle bioenergetics in both healthy humans and animals. As described in chapters 6 and 7, it is was investigated if the model could be applied to analyze the adaptations of muscle physiology that underlie changes in mitochondrial capacity that occur in for instance type 2 diabetes patients or with aging. A decrease of mitochondrial capacity in these subjects can be diagnosed accurately by determining the rate of PCr recovery post exercise. However, the changes in muscle physiology responsible for any observed difference in oxidative capacity cannot be deduced from these measurements. Therefore additional muscle biopsy samples are collected and analyzed for in vitro markers of oxidative capacity. State-of-the-art analyses of these data are typically limited to statistical or intuitive approaches. We investigated if the insight obtained from the combined in vivo + in vitro data sets could be increased by application of our mathematical model. To this end, first, the model was extended from a single uniform cell type model to a three types cell model (type I, IIA, and IIX), capturing the microscopic heterogeneity of muscle tissue. In addition, several key validation tests were conducted, as described in chapter 6. Subsequently, we demonstrated that the model could explain the prolongation of PCr recovery period observed in type 2 diabetes patients by integrating available literature data of in vitro markers of mitochondrial function. Although this result was already very promising, it was also concluded that the approach could be tested more rigorously by obtaining all data (in vivo + in vitro) in a single study. Therefore, the method was further tested in an animal model of decreased mitochondrial function: 8 versus 25 week old Wistar rats. The first main result of this study was that the mathematical model could accurately reproduce the delayed PCr recovery kinetics in 25 week old animals based on in vitro determined changes in muscle physiology. In addition, model predictions provided quantitative insight in the individual contribution of the different factors responsible for the decreased oxidative capacity. This type of information is considered very relevant for the design of (pharmaceutical) therapies aimed at improving mitochondrial function. For example, model predictions of the physiological changes that contribute the most to the decrease in oxidative capacity provide potentially promising targets for therapy design. Based on these considerations it was concluded that application of the mathematical model provides new promising opportunities for future studies of mitochondrial (dys)function in skeletal muscle. ?? In conclusion, through application of a series of iterative cycles of model development combined with multiple new experimental studies it was possible to develop a detailed mechanistic model of ATP metabolism that was consistent with in vivo observations of skeletal muscle bioenergetics for a wide range of physiological conditions. This process provided new insight in the key control mechanisms embedded in the metabolic pathways that have a dominant role in regulating ATP metabolism in skeletal muscle in vivo. In addition, we successfully demonstrated the feasibility and added value of application of the model for integration of in vivo and in vitro measurements of oxidative capacity in future studies of mitochondrial (dys)function in, for example, type 2 diabetes, aging or mitochondrial myopathy

Vibration-based damage localisation: Impulse response identification and model updating methods

Structural health monitoring has gained more and more interest over the recent decades. As the technology has matured and monitoring systems are employed commercially, the development of more powerful and precise methods is the logical next step in this field. Especially vibration sensor networks with few measurement points combined with utilisation of ambient vibration sources are attractive for practical applications, as this approach promises to be cost-effective while requiring minimal modification to the monitored structures. Since efficient methods for damage detection have already been developed for such sensor networks, the research focus shifts towards extracting more information from the measurement data, in particular to the localisation and quantification of damage. Two main concepts have produced promising results for damage localisation. The first approach involves a mechanical model of the structure, which is used in a model updating scheme to find the damaged areas of the structure. Second, there is a purely data-driven approach, which relies on residuals of vibration estimations to find regions where damage is probable. While much research has been conducted following these two concepts, different approaches are rarely directly compared using the same data sets. Therefore, this thesis presents advanced methods for vibration-based damage localisation using model updating as well as a data-driven method and provides a direct comparison using the same vibration measurement data. The model updating approach presented in this thesis relies on multiobjective optimisation. Hence, the applied numerical optimisation algorithms are presented first. On this basis, the model updating parameterisation and objective function formulation is developed. The data-driven approach employs residuals from vibration estimations obtained using multiple-input finite impulse response filters. Both approaches are then verified using a simulated cantilever beam considering multiple damage scenarios. Finally, experimentally obtained data from an outdoor girder mast structure is used to validate the approaches. In summary, this thesis provides an assessment of model updating and residual-based damage localisation by means of verification and validation cases. It is found that the residual-based method exhibits numerical performance sufficient for real-time applications while providing a high sensitivity towards damage. However, the localisation accuracy is found to be superior using the model updating method

Decoding subjective emotional arousal from eeg during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience