Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations

Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach

We address a three-tier data-driven approach for the numerical solution of the inverse problem in Partial Differential Equations (PDEs) and for their numerical bifurcation analysis from spatio-temporal data produced by Lattice Boltzmann model simulations using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic dimension of the manifold where the emergent dynamics evolve and for feature selection over the parameter space. In the second step, based on the selected features, we learn the right-hand-side of the effective PDEs using two machine learning schemes, namely shallow Feedforward Neural Networks (FNNs) with two hidden layers and single-layer Random Projection Networks (RPNNs), which basis functions are constructed using an appropriate random sampling approach. Finally, based on the learned black-box PDE model, we construct the corresponding bifurcation diagram, thus exploiting the numerical bifurcation analysis toolkit. For our illustrations, we implemented the proposed method to perform numerical bifurcation analysis of the 1D FitzHugh-Nagumo PDEs from data generated by D1Q3 Lattice Boltzmann simulations. The proposed method was quite effective in terms of numerical accuracy regarding the construction of the coarse-scale bifurcation diagram. Furthermore, the proposed RPNN scheme was ∼ 20 to 30 times less costly regarding the training phase than the traditional shallow FNNs, thus arising as a promising alternative to deep learning for the data-driven numerical solution of the inverse problem for high-dimensional PDEs

Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals

Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others

Nutritional considerations during prolonged exposure to a confined, hyperbaric, hyperoxic environment: Recommendations for saturation divers

Saturation diving is an occupation that involves prolonged exposure to a confined, hyperoxic, hyperbaric environment. The unique and extreme environment is thought to result in disruption to physiological and metabolic homeostasis, which may impact human health and performance. Appropriate nutritional intake has the potential to alleviate and/or support many of these physiological and metabolic concerns, whilst enhancing health and performance in saturation divers. Therefore, the purpose of this review is to identify the physiological and practical challenges of saturation diving and consequently provide evidence-based nutritional recommendations for saturation divers to promote health and performance within this challenging environment. Saturation diving has a high-energy demand, with an energy intake of between 44 and 52 kcal/kg body mass per day recommended, dependent on intensity and duration of underwater activity. The macronutrient composition of dietary intake is in accordance with the current Institute of Medicine guidelines at 45-65 % and 20-35 % of total energy intake for carbohydrate and fat intake, respectively. A minimum daily protein intake of 1.3 g/kg body mass is recommended to facilitate body composition maintenance. Macronutrient intake between individuals should, however, be dictated by personal preference to support the attainment of an energy balance. A varied diet high in fruit and vegetables is highly recommended for the provision of sufficient micronutrients to support physiological processes, such as vitamin B12 and folate intake to facilitate red blood cell production. Antioxidants, such as vitamin C and E, are also recommended to reduce oxidised molecules, e.g. free radicals, whilst selenium and zinc intake may be beneficial to reinforce endogenous antioxidant reserves. In addition, tailored hydration and carbohydrate fueling strategies for underwater work are also advised

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

In vitro Three-Dimensional Sprouting Assay of Angiogenesis using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing

Recent advances in induced pluripotent stem cells (iPSC) and gene editing technologies enable the development of novel human cell-based disease models for phenotypic drug discovery (PDD) programs. Although these novel devices could predict the safety and efficacy of investigational drugs in humans more accurately, their development to the clinic still strongly rely on mammalian data, notably the use of mouse disease models. In parallel to human organoid or organ-on-chip disease models, the development of relevant in vitro mouse models is therefore an unmet need for evaluating direct drug efficacy and safety comparisons between species and in vivo and in vitro conditions. Here, a vascular sprouting assay that utilizes mouse embryonic stem cells differentiated into embryoid bodies (EBs) is described. Vascularized EBs cultured onto 3D-collagen gel develop new blood vessels that expand, a process called sprouting angiogenesis. This model recapitulates key features of in vivo sprouting angiogenesis-formation of blood vessels from a pre-existing vascular network-including endothelial tip cell selection, endothelial cell migration and proliferation, cell guidance, tube formation, and mural cell recruitment. It is amenable to screening for drugs and genes modulating angiogenesis and shows similarities with recently described three-dimensional (3D) vascular assays based on human iPSC technologies.Nephrolog