Lipid Peroxidation After Intracortical Injection of Ferric Chloride Increases the Incidence of Seizures in Young Rats

Clinical studies have shown that the incidence of early posttraumatic seizures ishigher in children than in adults and it has been proposed that iron-induced lipidperoxidation has an important role in the development of epileptogenic foci. In this study,we examined some of the hypothesized reasons for the difference in the incidence ofearly posttraumatic seizures between young and adult rats. Twelve young and twelveadult rats were randomized into 4 groups. Group 1 and 2 were control groups, eachcomprising of 6 young rats and 6 adult rats respectively and were given intracorticalinjections of normal saline. Group 3 and 4 were injury groups, again comprising 6 youngrats and 6 adult rats respectively and were given intracortical injections of FeCl3. All ratswere observed for 6 hours post injection for the occurrence of seizures and were thenkilled. The injected hemispheres were extirpated and tested for malondialdehyde (MDA)level and superoxide dismutase (SOD) activity as indices of oxidative damage. Resultsshowed that seizures were observed only in Group 3. Increased MDA level and decreasedSOD activity were observed in Group 3 (ANOVA, p<0.001). Increased MDA levels anddecreased SOD activity were significantly higher in rats with seizures (Group 3) than inthose without seizures (independent t-test, p<0.001). We conclude was that differentlevels of lipid peroxidation induced by intracortical ferric chloride injection may accountfor the different seizure incidence between young and adult rat

Potential role of gut microbiota in induction and regulation of innate immune memory

The gut microbiota significantly regulates the development and function of the innate and adaptive immune system. The attribute of immunological memory has long been linked only with adaptive immunity. Recent evidence indicates that memory is also present in the innate immune cells such as monocytes/macrophages and natural killer cells. These cells exhibit pattern recognition receptors (PRRs) that recognize microbe- or pathogen-associated molecular patterns (MAMPs or PAMPs) expressed by the microbes. Interaction between PRRs and MAMPs is quite crucial since it triggers the sequence of signaling events and epigenetic rewiring that not only play a cardinal role in modulating the activation and function of the innate cells but also impart a sense of memory response. We discuss here how gut microbiota can influence the generation of innate memory and functional reprogramming of bone marrow progenitors that helps in protection against infections. This article will broaden our current perspective of association between the gut microbiome and innate memory. In the future, this knowledge may pave avenues for development and designing of novel immunotherapies and vaccination strategies

A Hybrid Variational Iteration Method for Blasius Equation

The objective of this paper is to present the hybrid variational iteration method. The proposed algorithm is based on the combination of variational iteration and shooting methods. In the proposed algorithm the entire domain is divided into subintervals to establish the accuracy and convergence of the approximate solution. It is found that in each subinterval a three term approximate solution using variational iteration method is sufficient. The proposed hybrid variational iteration method offers not only numerical values, but also closed form analytic solutions in each subinterval. The method is implemented using an example of the Blasius equation. The results show that a hybrid variational iteration method is a powerful technique for solving nonlinear problems

Cancer Niches and Their Kikuchi Free Energy

Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation and specialization are released from their autopoietic homeostasis. This induces the construction of cancer niches and maintains their survival. In this paper, we characterise cancer niche construction as a direct consequence of interactions between clusters of cancer and healthy cells. Explicitly, we evaluate these higher-order interactions between niches of cancer and healthy cells using Kikuchi approximations to the free energy. Kikuchi’s free energy is measured in terms of changes to the sum of energies of baseline clusters of cells (or nodes) minus the energies of overcounted cluster intersections (and interactions of interactions, etc.). We posit that these changes in energy node clusters correspond to a long-term reduction in the complexity of the system conducive to cancer niche survival. We validate this formulation through numerical simulations of apoptosis, local cancer growth, and metastasis, and highlight its implications for a computational understanding of the etiopathology of cancer

Modules or mean-fields?

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised 'modules', each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience

Small bowel malignant melanoma presenting as a perforated jejunal diverticulum: a case report and literature review.

Although usually harmless and asymptomatic, jejuno-ileal diverticulae are associated with various non-specific gastrointestinal symptoms, and rarely cause surgical emergencies. This case report describes the presentation and management of a patient with an acute abdomen, whose jejunal diverticulum was perforated. Unexpectedly, histopathological assessment demonstrated malignant melanoma lining the diverticulum. Whether this was primary or metastatic is discussed, together with a synopsis of the literature on small bowel diverticulae

Unsteady two-layered blood flow through a w-shape stenosed artery using the generalized oldroyd-b fluid model

A theoretical study of unsteady two-layered blood flow through a stenosed artery is presented in this article. The geometry of rigid stenosed artery is assumed to be w-shaped. The flow regime is assumed to be laminar, unsteady and uni-directional. The characteristics of blood are modeled by the generalized Oldroyd-B non-Newtonian fluid model in the core region and a Newtonian fluid in the periphery region. The governing partial differential are derived for each region by using mass and momentum conservation equations. In order to facilitate numerical solutions, the derived differential equations are non-dimensionalized. A well-tested explicit finite difference scheme (FDM) which is forward in time and central in space is employed for the solution of nonlinear initial-boundary value problem corresponding to each region. Validation of the FDM computations is achieved with a variational finite element method (FEM) algorithm. The influence of the emerging geometric and rheological parameters on axial velocity, resistance impedance and wall shear stress are displayed graphically. The instantaneous patterns of streamlines are also presented to illustrate the global behavior of blood flow. The simulations are relevant to hemodynamics of small blood vessels and capillary transport wherein rheological effects are dominant

Stretching a Surface Having a Layer of Porous Medium in a Viscous Fluid

The present analysis deals with the steady, incompressible flow of a viscous fluid over a stretching sheet having a layer of porous medium of uniform thickness. The two-dimensional flow equations are derived in a Cartesian coordinate system. The semi-infinite region filled with a viscous fluid is divided into two regions namely, a clear fluid region and a region having a uniform pores. Darcy\u27s law has been used for the flow of fluid in the porous medium region. An exact similar solution of the problem is obtained. The obtained solution is constrained by a relation between the porosity parameter and the parameter representing the viscosity ratios between the two regions. Our interest lies in determining the influence of porosity parameter, viscosities ratio parameter and thickness of the porous layer on the fluid velocity and the skin friction coefficient. The results for the Crane\u27s problem in a complete clear and a complete porous region are retrieved as special cases of the present solution

Hierarchical generative modelling for autonomous robots

Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks

Active Inference: Demystified and Compared

Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration-and account for uncertainty about their environment-in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents