Detection of antibodies against the huntingtin protein in human plasma.

Huntington's disease (HD) is a dominantly inherited neurodegenerative disorder resulting from a CAG expansion in the huntingtin (HTT) gene, which leads to the production and accumulation of mutant huntingtin (mHTT). While primarily considered a disorder of the central nervous system, multiple changes have been described to occur throughout the body, including activation of the immune system. In other neurodegenerative disorders, activation of the immune system has been shown to include the production of antibodies against disease-associated pathological proteins.

Portrait of blood-derived extracellular vesicles in patients with Parkinson's disease.

The production of extracellular vesicles (EV) is a ubiquitous feature of eukaryotic cells but pathological events can affect their formation and constituents. We sought to characterize the nature, profile and protein signature of EV in the plasma of Parkinson's disease (PD) patients and how they correlate to clinical measures of the disease. EV were initially collected from cohorts of PD (n = 60; Controls, n = 37) and Huntington's disease (HD) patients (Pre-manifest, n = 11; manifest, n = 52; Controls, n = 55) - for comparative purposes in individuals with another chronic neurodegenerative condition - and exhaustively analyzed using flow cytometry, electron microscopy and proteomics.

Model of an excitatory synapse based on stochastic processes.

We present a mathematical model of a biological synapse based on stochastic processes to establish the temporal behavior of the postsynaptic potential following a quantal synaptic transmission. This potential form is the basis of the neural code. We suppose that the release of neurotransmitters in the synaptic cleft follows a Poisson process, and that they diffuse according to integrated Ornstein-Uhlenbeck processes in 3-D with random initial positions and velocities.

Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches.

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed.

On the learning potential of the approximated quantron.

The quantron is a hybrid neuron model related to perceptrons and spiking neurons. The activation of the quantron is determined by the maximum of a sum of input signals, which is difficult to use in classical learning algorithms. Thus, training the quantron to solve classification problems requires heuristic methods such as direct search.

A multiscale scheme for approximating the Quantron's discriminating function.

Finding an accurate approximation of a discriminating function in order to evaluate its extrema is a common problem in the field of machine learning. A new type of neural network, the Quantron, generates a complicated wave function whose global maximum value is crucial for classifying patterns. To obtain an analytical approximation of this maximum, we present a multiscale scheme based on compactly supported inverted parabolas.

Hardware implementation of a new artificial neuron.

This paper describes an FPGA (Field Programmable Gate Arrays) implementation of a new type of neuron, the Quantron. The goal is to demonstrate the capability of current technology to closely recreate the human body's reaction to a change of temperature. This is accomplished by creating a function that adds a number of kernels at different frequencies depending on the external temperature.