The role that choice of model plays in predictions for epilepsy surgery

This is the final version. Available on open access from Nature Research via the DOI in this recordMathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.Engineering and Physical Sciences Research Council (EPSRC)Medical Research Council (MRC)Epilepsy Research UKWellcome Trus

Safety-Aware Apprenticeship Learning

Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure safety while retaining performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.Comment: Accepted by International Conference on Computer Aided Verification (CAV) 201

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The code and synthetic networks generated are available upon request.Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.Medical Research Council (MRC)Epilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustInnovate U

Probabilistic Guarantees for Safe Deep Reinforcement Learning

Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in probabilistic environments due to, for example, hardware faults or noisy sensors. We propose MOSAIC, an algorithm for measuring the safety of deep reinforcement learning agents in stochastic settings. Our approach is based on the iterative construction of a formal abstraction of a controller's execution in an environment, and leverages probabilistic model checking of Markov decision processes to produce probabilistic guarantees on safe behaviour over a finite time horizon. It produces bounds on the probability of safe operation of the controller for different initial configurations and identifies regions where correct behaviour can be guaranteed. We implement and evaluate our approach on agents trained for several benchmark control problems

Leaf hyperspectral reflectance as a potential tool to detect diseases associated with vineyard decline.

Grape production in the Serra Gaúcha region, south of Brazil, is severily constrained by several diseases such as the decline and death syndrome caused grapevine trunk (fungal) diseases (GTDs) and the grapevine leafroll-associated virus (GLRaV). As pathogens induce changes in leaf tissue that modify the reflectance, the spectral signature of asymptomatic and symptomatic grapevine leaves infected by GTDs and GLRaV was analyzed to check whether spectral responses could be useful for disease identification. This work aims at (a) defining the spectral signature of grapevine leaves asymptomatic and symptomatic to GTDs and GLRaV; b) analyzing whether the spectral response of asymptomatic leaves can be distinguished from symptomatic; and (c) defining the most useful wavelengths for discriminating spectral responses. For such, reflectance of leaves in either condition collected in a ?Merlot? vineyard during three growing seasons was measured using a spectroradiometer. Principal components and partial least square discriminant analyses confirmed the spectral separation and classes discrimination. The average spectra, difference spectra, and first-order derivative (FOD) spectra indicated differences between asymptomatic and symptomatic leaves in the green peak (520?550 nm), chlorophyll-associated wavelengths (650?670 nm), red edge (700?720 nm), beginning of nearinfrared (800?900 nm), and shortwave infrared. Hyperspectral data was linked to biochemical and physiological changes described for GTD and GLRaV. Variable importance in the projection (VIP) analysis showed that some wavelengths allowed to differentiate the tested pathosystems and could serve as a basis for further validation and disease classification studies. Keywords Grapevine leafroll-associated virus . Grapevine trunk diseases . Vitis vinifera L. . Principal components analysis . Variable importance in the projectio

Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordObjective: The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. Methods: We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network’s ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. Results: The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.Medical Research CouncilEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustEngineering and Physical Sciences Research Council (EPSRC)Innovate UKEuropean Union’s Horizon 2020Alzheimer's SocietyMedical Research Counci

Plaster Layout Process in Civil Works with a Focus on Clean Production

The constant expansion of civil construction and the increasing use of plaster gives rise to a solid waste generation problem causing difficulties for the disposal or reuse of this material. The generation of plaster waste represents an economic problem, with serious consequences and impacts. In order to contribute to sustainability, this study sought to evaluate the reduction of plaster waste in an apartment construction project, employing the layout method. With the adequate arrangement of plates, a reduction of 4.41% in the use of plaster could be obtained, This reduction will consequently result in the minimization of waste from civil works, bringing invaluable economic and environmental benefits

Adsorption and incorporation of the zinc oxide nanoparticles in seeds of corn: germination performance and antimicrobial protection

The treatments of the seeds are important procedures applied by the agronomical area to improve the culture yield. From these procedures the micronutrients are available for the seeds before and during the germination stages. One high challenge is make efficient these treatment processes and to ensure the adsorption and the incorporation of these micronutrients in the seeds and to improve its performance in the germination phase. In this work studies explored the optimization of the incorporation process and the characteristics of the zinc oxide clusters adsorbed on the surface of the seed. The results were associated with the agronomic responses during the germinations stages of the seeds of corn. The seeds were treated in suspensions containing different concentrations of nanoparticles of zinc oxide and during different treatment times. The adsorptions in the corn surface and the absorption of the nanoparticles for the inner of the seeds were studied together with its antibacterial characteristics and correlated with the germinations indicators. The results showed that is possible to incorporate nanoparticles of zinc oxide in inner of the seeds of corn and improve the germinations indicators. Antibacterial protection was aggregated on the seeds of corn. It´s possible to incorporate 0.280 mg of zinc oxide nanoparticle per seed mass in inner of seeds with the optimal treatment conditions with nanoparticle concentration of 50 mg/L in the suspension and with treatment time of 180 minutes. With the optimal treatment concentration the normal plant percentage increase of 2.70% in relationship to the seeds not treated