A computational simulation of long-term synaptic potentiation inducing protocol processes with model of CA3 hippocampal microcircuit

An experimental study of computational model of the CA3 region presents cog­nitive and behavioural functions the hippocampus. The main property of the CA3 region is plastic recurrent connectivity, where the connections allow it to behave as an auto-associative memory. The computer simulations showed that CA3 model performs efficient long-term synaptic potentiation (LTP) induction and high rate of sub-millisecond coincidence detection. Average frequency of the CA3 pyramidal cells model was substantially higher in simulations with LTP induction protocol than without the LTP. The entropy of pyramidal cells with LTP seemed to be significantly higher than without LTP induction protocol (p = 0.0001). There was depression of entropy, which was caused by an increase of forgetting coefficient in pyramidal cells simulations without LTP (R = –0.88, p = 0.0008), whereas such correlation did not appear in LTP simulation (p = 0.4458). Our model of CA3 hippocampal formation microcircuit biologically inspired lets you understand neurophysiologic data. (Folia Morphol 2018; 77, 2: 210–220

Memory and forgetting processes with the firing neuron model

The aim of this paper is to present a novel algorithm for learning and forgetting within a very simplified, biologically derived model of the neuron, called firing cell (FC). FC includes the properties: (a) delay and decay of postsynaptic potentials, (b) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, (c) modification of properties of the analog weight memory for each input due to a pattern of long-term synaptic potentiation. The FC model could be used in one of the three forms: excitatory, inhibitory, or receptory (gan­glion cell). The computer simulations showed that FC precisely performs the time integration and coincidence detection for incoming spike trains on all inputs. Any modification of the initial values (internal parameters) or inputs patterns caused the following changes of the interspike intervals time series on the output, even for the 10 s or 20 s real time course simulations. It is the basic evidence that the FC model has chaotic dynamical properties. The second goal is the presentation of various nonlinear methods for analysis of a biological time series. (Folia Morphol 2018; 77, 2: 221–233

Legal and Permit Loads Evaluation for Indiana Bridges

According to federal law, routine commercial vehicles must adhere to certain limits on their load configuration in order to operate legally on interstate highways. However, states may allow for heavier or different load configurations provided that bridges on the state and county highway system are load rated and, if necessary, posted with vehicles that appropriately represent these loads. The state of Indiana allows several classes of vehicles to operate with loads that exceed federal limits, and, presently, several LFD design loads are used to represent these exceptions as state legal loads. This study evaluates the MBE rating loads for their ability to encompass Indiana’s exception vehicles and recommends a set of state rating loads which can replace the current state legal loads and, combined with the MBE rating loads, satisfactorily encompass the load effects due to these exceptions. Comparing moment and shear envelopes on a representative set of bridges, the MBE rating vehicles were found to be insufficient for representing Indiana’s exception vehicles. Three new rating loads are proposed which encompass the exception vehicles efficiently and represent realistic legal loads. Conversely, acceptable HS-20 rating factors are also provided as an alternative to the adoption of these new vehicles. These rating factors, all 1.0 or greater, can ensure a similar level of safety by requiring a specific amount of excess capacity for the HS-20 design load

Data Predictive Control for Peak Power Reduction

Decisions on how best to optimize today\u27s energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs

Phosphorylation of p66Shc and forkhead proteins mediates Aβ toxicity

Excessive accumulation of amyloid β-peptide (Aβ) plays an early and critical role in synapse and neuronal loss in Alzheimer's Disease (AD). Increased oxidative stress is one of the mechanisms whereby Aβ induces neuronal death. Given the lessened susceptibility to oxidative stress exhibited by mice lacking p66Shc, we investigated the role of p66Shc in Aβ toxicity. Treatment of cells and primary neuronal cultures with Aβ caused apoptotic death and induced p66Shc phosphorylation at Ser36. Ectopic expression of a dominant-negative SEK1 mutant or chemical JNK inhibition reduced Aβ-induced JNK activation and p66Shc phosphorylation (Ser36), suggesting that JNK phosphorylates p66Shc. Aβ induced the phosphorylation and hence inactivation of forkhead transcription factors in a p66Shc-dependent manner. Ectopic expression of p66ShcS36A or antioxidant treatment protected cells against Aβ-induced death and reduced forkhead phosphorylation, suggesting that p66Shc phosphorylation critically influences the redox regulation of forkhead proteins and underlies Aβ toxicity. These findings underscore the potential usefulness of JNK, p66Shc, and forkhead proteins as therapeutic targets for AD

The Machine that Changed the World: The Story of Lean Production

Nomenclature A = area E b = black body emission per unit area Fij or F sj = shape factor between surfaces i and j (including; = (') or between gas and surface j q = heat transfer m = mass flow rate N = number of surface elements on the wall and the solid T = temperature Az = size of each axial segment of the kil

Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Zircon ages in granulite facies rocks: decoupling from geochemistry above 850 °C?

Granulite facies rocks frequently show a large spread in their zircon ages, the interpretation of which raises questions: Has the isotopic system been disturbed? By what process(es) and conditions did the alteration occur? Can the dates be regarded as real ages, reflecting several growth episodes? Furthermore, under some circumstances of (ultra-)high-temperature metamorphism, decoupling of zircon U–Pb dates from their trace element geochemistry has been reported. Understanding these processes is crucial to help interpret such dates in the context of the P–T history. Our study presents evidence for decoupling in zircon from the highest grade metapelites (> 850 °C) taken along a continuous high-temperature metamorphic field gradient in the Ivrea Zone (NW Italy). These rocks represent a well-characterised segment of Permian lower continental crust with a protracted high-temperature history. Cathodoluminescence images reveal that zircons in the mid-amphibolite facies preserve mainly detrital cores with narrow overgrowths. In the upper amphibolite and granulite facies, preserved detrital cores decrease and metamorphic zircon increases in quantity. Across all samples we document a sequence of four rim generations based on textures. U–Pb dates, Th/U ratios and Ti-in-zircon concentrations show an essentially continuous evolution with increasing metamorphic grade, except in the samples from the granulite facies, which display significant scatter in age and chemistry. We associate the observed decoupling of zircon systematics in high-grade non-metamict zircon with disturbance processes related to differences in behaviour of non-formula elements (i.e. Pb, Th, U, Ti) at high-temperature conditions, notably differences in compatibility within the crystal structure