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Auditory evoked fields (AEFs) are commonly studied, yet their underlying neural mechanisms remain poorly understood. Here, we used the biophysical modelling software Human Neocortical Neurosolver (HNN) whose foundation is a canonical neocortical circuit model to interpret the cell and network mechanisms contributing to macroscale AEFs elicited by a simple tone, measured with magnetoencephalography. We found that AEFs can be reproduced by activating the neocortical circuit through a layer specific sequence of feedforward and feedback excitatory synaptic drives, similar to prior simulation of somatosensory evoked responses, supporting the notion that basic structures and activation patterns are preserved across sensory regions. We also applied the modeling framework to develop and test predictions on neural mechanisms underlying AEF differences in the left and right hemispheres, as well as in hemispheres contralateral and ipsilateral to the presentation of the auditory stimulus. We found that increasing the strength of the excitatory synaptic cortical feedback inputs to supragranular layers simulates the commonly observed right hemisphere dominance, while decreasing the input latencies and simultaneously increasing the number of cells contributing to the signal accounted for the contralateral dominance. These results provide a direct link between human data and prior animal studies and lay the foundation for future translational research examining the mechanisms underlying alteration in this fundamental biomarker of auditory processing in healthy cognition and neuropathology. [ABSTRACT FROM AUTHOR]

Satellite image segmentation has gotten bunches of consideration of late because of the accessibility of commented on high-goals image informational indexes caught by the last age of satellites. The issue of fragmenting a satellite image can be characterized as ordering (or marking) every pixel of the image as indicated by various classes, for example, structures, streets, water, etc. In this paper centered to build up a satellite image segmenting process by utilizing distinctive optimization methods. The work is prepared dependent on three stages that are RGB change, preprocessing, and division. At first the database images are assembled from the database at that point select the blue band images by performing RGB change. To improve the differentiation and furthermore decreasing the commotion of these chose blue band images, Hopfield neural network (HNN) is utilized. After image upgrade, the images are fragmented dependent on fuzzy C means (FCM) clustering method. The images are clustered and segmented in the way of optimizing the centroid in FCM utilizing oppositional crow search algorithm. The exhibition of the proposed framework is investigated dependent on the presentation measurements, for example, affectability, particularity and accuracy. From the outcomes, the proposed strategy diminished the computational time by expanding the accuracy of 98.3% with HNN system. [ABSTRACT FROM AUTHOR]

We describe here the tunability of the HNN experiment to obtain certain residue specific peak patterns in the spectra of (15N, 13C) labeled proteins. This is achieved by tuning a band-selective 180° pulse on the carbon channel in the pulse sequence, whereby one can tamper with the Cα–Cβ coupling evolutions for the different residues. Specifically, we generate distinctive peak patterns for serine and threonine and their neighbors in the different planes of the three dimensional spectrum. These provide useful anchor points during sequential assignment of backbone resonances. The performance of this experiment, referred to as HNN-ST here, is demonstrated using two proteins, one properly folded and the other completely denatured. With the availability of high field spectrometers, techniques such as TROSY, and ever increasing sensitivities in the probes, this experiment with its large number of check points has a great potential for rapid and unambiguous backbone resonance assignment in large proteins. [ABSTRACT FROM AUTHOR]

Two triple resonance experiments, HNN and HN(C)N, are presented which correlate HN and 15N resonances sequentially along the polypeptide chain of a doubly (13C, 15N) labeled protein. These incorporate several improvements over the previously published sequences for a similar purpose and have several novel features. The spectral characteristics enable direct identification of certain triplets of residues, which provide many starting points for the sequential assignment procedure. The experiments are sensitive and their utility has been demonstrated with a 22 kDa protein under unfolding conditions where most of the standard triple resonance experiments such as HNCA, CBCANH etc. have limited success because of poor amide, Cα and Cβ chemical shift dispersions. [ABSTRACT FROM AUTHOR]

In the present study a reliable and structural decision system for production sequence of polymeric products is developed. Minimization of the amount of off-specs is the main objective in the decision of production sequence to maximize profit. Off-specs are generated when the production sequence of polymeric products is changed. The amount of off-specs depends on changes of product grades. In the present study we applied the traveling salesman problem (TSP) to achieve optimal decision of production sequence. To solve the optimal decision problem formulated by TSP, we employed three different approaches such as Branch and Bound (B&B) method, Dynamic Programming (DP) method and Hopfield Neural Network (HNN) method. Production sequences computed based on the actual plant off-spec data were compared with the sequences employed in the actual plant operation. From the comparison the decision method proposed in the present study showed increased profits and reduced off-specs. [ABSTRACT FROM AUTHOR]