Hippocampus-dependent emergence of spatial sequence coding in retrosplenial cortex.

Retrosplenial cortex (RSC) is involved in visuospatial integration and spatial learning, and RSC neurons exhibit discrete, place cell-like sequential activity that resembles the population code of space in hippocampus. To investigate the origins and population dynamics of this activity, we combined longitudinal cellular calcium imaging of dysgranular RSC neurons in mice with excitotoxic hippocampal lesions. We tracked the emergence and stability of RSC spatial activity over consecutive imaging sessions. Overall, spatial activity in RSC was experience-dependent, emerging gradually over time, but, as seen in the hippocampus, the spatial code changed dynamically across days. Bilateral but not unilateral hippocampal lesions impeded the development of spatial activity in RSC. Thus, the emergence of spatial activity in RSC, a major recipient of hippocampal information, depends critically on an intact hippocampus; the indirect connections between the dysgranular RSC and the hippocampus further indicate that hippocampus may exert such influences polysynaptically within neocortex

On Decision Support for Sustainability and Resilience of Infrastructure Systems

An overview of selected contributions across the different sciences to sustainability and resilience research is provided and discussed. A general frame-work for supporting decisions for sustainable and resilient design and management of societal infrastructures is then proposed taking basis in Bayesian decision analysis and probabilistic systems performance modelling. A principal example for decision support at regulatory level is presented for a coupled system comprised of infrastructure, social, hazard and environmental subsystems. The infrastructure systems is modelled as multi-component Daniels system generating benefits over time after deduction of potential losses due to disturbance events. The societal system is represented in terms of the preparedness level with respect to respond, reorganize and rehabilitate functionality after disturbances and the environmental system is represented in terms of local and global scale constraints concerning acceptable emissions

Profiling structural elements of short-chain lipopolysaccharide of non-typeable Haemophilus influenzae

Lipopolysaccharide (LPS) is a major virulence determinant of the human bacterial pathogen Haemophilus influenzae. A characteristic feature of H. influenzae LPS is the extensive intra- and inter-strain heterogeneity of glycoform structure which is key to the role of the molecule in both commensal and disease-causing behaviour of the bacterium. The chemical composition of non-typeable Haemophilus influenzae (NTHi) LPS is highly diverse. It contains a number of different monosaccharides (Neu5Ac, L- glycero-D- manno heptose, D- glycero -D- manno heptose, Kdo, D-Glc, D-Gal, D-GlcNAc, D-GalNAc) and non-carbohydrate substituents. Prominent non-carbohydrate components are O-acetyl groups, glycine and phosphates. We now know that sialic acid ( N-acetylneuraminic acid or Neu5Ac) and certain oligosaccharide extensions are important in the pathogenesis of NTHi; however, the biological implications for many of the various features are still unknown. Electrospray ionization mass spectrometry in combination with separation techniques like CE and HPLC is an indispensable tool in profiling glycoform populations in heterogeneous LPS samples. Mass spectrometry is characterized by its extreme sensitivity. Trace amounts of glycoforms expressing important virulence determinants can be detected and characterized on minute amounts of material. The present review focuses on LPS structures and mass spectrometric methods which enable us to profile these in complex mixtures

Small Local Dynamic Fuzzy Logical Models for Large-Scale Power Systems

In the power system stability problems the primary actors in the mathematical system model are the differential equations defining the dynamic state variables of generation and load. These differential equations are coupled together by load flow equations. Mathematically the load flow equations are nonlinear algebraic equations. These differential equations and nonlinear algebraic equations form the mathematical Differential Algebraic Equations (DAE) model for the power system. The fuzzy set theory is commonly used in analysis of dynamical nonlinear systems. In this paper, we build a set of local dynamical fuzzy logic models for the differential equations, thus transforming the differential equations into nonlinear algebraic equations, the DAE into nonlinear algebraic equations. We try to simulate the system by solving the nonlinear algebraic equations rather than by solving the DAE model. We also compare the application of two types of dynamical fuzzy models: the discrete-time model and discrete-event model in this approach. First we explain the approach by a small DAE example, and then we apply it to a 10-bus power system

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.Comment: 16 pages, 6 EPS figures and 2 PS figure

On the Probabilistic Characterization of Robustness and Resilience

Over the last decade significant research efforts have been devoted to the probabilistic modeling and analysis of system characteristics. Especially performance characteristics of systems subjected to random disturbances, such as robustness and resilience have been in the focus of these efforts and significant insights have been gained. However, as much of the undertaken research and developments aim to fulfill the particular needs of specific application areas and/or societal sectors somewhat diverging perspectives and approaches have emerged. In the present paper we take basis in recent developments in the modeling of robustness and resilience in the research areas of natural disaster risk management, socio-ecological systems and social systems and we propose a generic decision analysis framework for the modeling and analysis of systems across application areas. The proposed framework extends the concept of direct and indirect consequences and associated risks in probabilistic systems modeling formulated by the Joint Committee on Structural Safety (JCSS) to facilitate the modeling and analysis of resilience in addition to robustness and vulnerability. Moreover, based on recent insights in the modeling of robustness, a quantification of resilience is formulated utilizing a scenario based systems benefit modeling where resilience failure is associated with exhaustion of the capital accumulated by the system of time. The proposed framework and modeling concepts are illustrated with basis in a simple interlinked system model comprised by an infrastructure system, a governance system, a regulatory system and a geo-hazards system. It is shown how the robustness and the resilience of the interlinked system may be modeled and quantified, how robustness and resilience are influenced by the stochastic dependency structure of the disturbance events and corresponding resistances, how robustness and resilience depends on the capacity of the social system to plan for and respond to disturbances over time and how robustness and resilience interrelate

Translocator protein in late stage Alzheimer\u27s disease and Dementia with Lewy bodies brains

OBJECTIVE: Increased translocator protein (TSPO), previously known as the peripheral benzodiazepine receptor (PBR), in glial cells of the brain has been used as a neuroinflammation marker in the early and middle stages of neurodegenerative diseases, such as Alzheimer\u27s disease (AD) and Dementia with Lewy Bodies (DLB). In this study, we investigated the changes in TSPO density with respect to late stage AD and DLB. METHODS: TSPO density was measured in multiple regions of postmortem human brains in 20 different cases: seven late stage AD cases (Braak amyloid average: C; Braak tangle average: VI; Aged 74-88, mean: 83 ± 5 years), five DLB cases (Braak amyloid average: C; Braak tangle average: V; Aged 79-91, mean: 84 ± 4 years), and eight age-matched normal control cases (3 males, 5 females: aged 77-92 years; mean: 87 ± 6 years). Measurements were taken by quantitative autoradiography using [ RESULTS: No significant changes were found in TSPO density of the frontal cortex, striatum, thalamus, or red nucleus of the AD and DLB brains. A significant reduction in TSPO density was found in the substantia nigra (SN) of the AD and DLB brains compared to that of age-matched healthy controls. INTERPRETATION: This distinct pattern of TSPO density change in late stage AD and DLB cases may imply the occurrence of microglia dystrophy in late stage neurodegeneration. Furthermore, TSPO may not only be a microglia activation marker in early stage AD and DLB, but TSPO may also be used to monitor microglia dysfunction in the late stage of these diseases

Nanoscale Surface Plasmonics Sensor With Nanofluidic Control

Conventional quantitative protein assays of bodily fluids typically involve multiple steps to obtain desired measurements. Such methods are not well suited for fast and accurate assay measurements in austere environments such as spaceflight and in the aftermath of disasters. Consequently, there is a need for a protein assay technology capable of routinely monitoring proteins in austere environments. For example, there is an immediate need for a urine protein assay to assess astronaut renal health during spaceflight. The disclosed nanoscale surface plasmonics sensor provides a core detection method that can be integrated to a lab-on-chip device that satisfies the unmet need for such a protein assay technology. Assays based upon combinations of nanoholes, nanorings, and nanoslits with transmission surface plasmon resonance (SPR) are used for assays requiring extreme sensitivity, and are capable of detecting specific analytes at concentrations as low as picomole to femtomole level in well-controlled environments. The device operates in a transmission mode configuration in which light is directed at one planar surface of the array, which functions as an optical aperture. The incident light induces surface plasmon light transmission from the opposite surface of the array. The presence of a target analyte is detected by changes in the spectrum of light transmitted by the array when a target analyte induces a change in the refractive index of the fluid within the nanochannels. This occurs, for example, when a target analyte binds to a receptor fixed to the walls of the nanochannels in the array. Independent fluid handling capability for individual nanoarrays on a nanofluidic chip containing a plurality of nanochannel arrays allows each array to be used to sense a different target analyte and/or for paired arrays to analyze control and test samples simultaneously in parallel. The present invention incorporates transmission mode nanoplasmonics and nanofluidics into a single, microfluidically controlled device. The device comprises one or more arrays of aligned nanochannels that are in fluid communication with inflowing and outflowing fluid handling manifolds that control the flow of fluid through the arrays. The array acts as an aperture in a plasmonic sensor. Fluid, in the form of a liquid or a gas and comprising a sample for analysis, is moved from an inlet manifold through the nanochannel array, and out through an exit manifold. The fluid may also contain a reagent used to modify the interior surfaces of the nanochannels, and/or a reagent required for the detection of an analyte