In this paper we present new algorithms based on region analysis of grey and distance differences of images that successfully circumvent these problems. Two key parameters of this analysis, window width and logical threshold, are automatically extracted for use in logical thresholding, and spurious regions are detected and removed through use of a hierarchical window filter. The efficacy of the developed algorithms is demonstrated here through an analysis of cultured brain neurons from newborn mice.
A person's pattern of walking or gait is a defining characteristics of Parkinson's disease (PD). There is no cure for Parkinsonian gait, which continues to progress with time and varies among patients with PD. Detection of gait disorder can help identify PD at an early stage for optimal treatment and reducing adverse effects on the patient's well-being and daily activities. This paper presents the extraction of features of gait dynamics recorded from single wearable sensors, in frequency and spatial domains, for gait classification using sequential deep learning. Experimental results suggest that the proposed method is very promising for detecting gait in PD using single sensor-induced data in comparison with others using signals recorded from multiple wearable sensors. The implication is that the minimized deployment of sensors can avoid physical discomfort in patients and be cost-effective.
Abstract As information about the world traverses the brain, the signals exchanged between neurons are passed and modulated by synapses, or specialized contacts between neurons. While neurotransmitter-based synapses tend to be either relay excitatory or inhibitory pulses of influence on the postsynaptic neuron, electrical synapses, composed of plaques of gap junction channels, are always-on transmitters that can either excite or inhibit a coupled neighbor. A growing body of evidence indicates that electrical synapses, similar to their chemical counterparts, are modified in strength during physiological neuronal activity. The synchronizing role of electrical synapses in neuronal oscillations has been well established, but their impact on transient signal processing in the brain is much less understood. Here we constructed computational models based on the canonical feedforward neuronal circuit, and included electrical synapses between inhibitory interneurons. We provided discrete closely-timed inputs to the circuits, and characterize the influence of electrical synapses on both the subthreshold summation and spike trains in the output neuron. Our simulations highlight the diverse and powerful roles that electrical synapses play even in simple circuits. Because these canonical circuits are represented widely throughout the brain, we expect that these are general principles for the influence of electrical synapses on transient signal processing across the brain. Author Summary The role that electrical synapses play in neural oscillations, network synchronization and rhythmicity is well established, but their role neuronal processing of transient inputs is much less understood. Here we used computational models of canonical feedforward circuits and networks to investigate how the strength of electrical synapses regulates the flow of transient signals passing through those circuits. We show that because the influence of electrical synapses on coupled neighbors can be either inhibitory or excitatory, their role in network information processing is heterogeneous.. Because of the widespread existence of electrical synapses between interneurons as well as a growing body of evidence for their plasticity, we expect such effects play a significant role in how the brain processes transient inputs.
The mitral complex is a functional entity composed of the annulus, valve leaflets, chordae, and papillary muscles. The mechanical properties of the complex are dependent on the unique structural relations of the collagen in the leaflets and chordae. In the chordae the collagen is arranged in avascular columns. These columns interdigitate between muscle fibers in the papillary muscles, and the collagen is anchored to the myofiber membrane by microfibrils. In the leaflet the chordae are continuous with the dense fibrous tissue, forming a sheet of collagen which merges with the annulus. Within the leaflet there are cardiac muscle fibers in direct continuity with left atrial muscle. Contraction of isolated valve preparations can be initiated by electrical stimulation and is preceded by a propagated depolarization. Action potentials from cells in the middle third of the leaflet have a slow upstroke velocity, prominent plateau, and a characteristic positive afterpotential. Valve muscle electromechanical properties are markedly altered by 1 x 10 -7 M acetylcholine; this concentration has little effect on working left atrial muscle. In preparations containing portions of the left atrium and valve leaflet, the excitation wave spreads into the leaflet after electrical stimulation of the atrial muscle. This suggests that the accompanying contractile event may occur in situ before the initiation of systole.
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