Oscillatory visual mechanisms revealed by random temporal sampling

It is increasingly apparent that functionally significant neural activity is oscillatory in nature. Demonstrating the implications of this mode of operation for perceptual/cognitive function remains somewhat elusive. This report describes the technique of random temporal sampling for the investigation of visual oscillatory mechanisms. The technique is applied in visual recognition experiments using different stimulus classes (words, familiar objects, novel objects, and faces). Classification images reveal variations of perceptual effectiveness according to the temporal features of stimulus visibility. These classification images are also decomposed into their power and phase spectra. Stimulus classes lead to distinct outcomes and the power spectra of classification images are highly generalizable across individuals. Moreover, stimulus class can be reliably decoded from the power spectrum of individual classification images. These findings and other aspects of the results validate random temporal sampling as a promising new method to study oscillatory visual mechanisms.

Electron ionization of clusters containing the formamide molecule

Studies on electron interactions with formamide (FA) clusters promote scientific interest as a model system to understand phenomena relevant to astrophysical, prebiotic, and radiobiological processes. In this work, mass spectrometric detection of cationic species for both small bare and microhydrated formamide clusters was performed at an electron ionization of 70 eV. Furthermore, a comparative analysis of the cluster spectra with the literature-reported gas-phase spectra is presented and discussed, revealing different reaction channels affected by the cluster environment. This study is essential in developing our understanding of both low-energy electron phenomena in clusters that can bridge the complexity gap between gas and realistic systems and the effect of hydration on electron-induced processes.

What is a visual stimulus?

When two stimuli, s_1 and s_2, are added we get that that the amplitudes in (s_1 + s_2) are smaller than the amplitudes in s_1 plus the amplitudes in s_2 when the two stimuli have different phase spectra. Thus, the full amplitudes in the two stimuli cannot be contained in the amplitudes of the combined stimulus. That is, the amplitudes in s_1 and s_2 are reduced when the stimuli are added. When the amplitudes in a stimulus have been reduced it can no longer be held to be the same stimulus. Consequently, it is possible for two stimuli to have the same appearance but have different amplitudes and can, therefore, not to be held to be the same stimulus. This it would seem, has implications for a number of areas among them visual masking, visual crowding and for the adding of noise to stimuli.

Spectral Matching of Three-Component Seismic Ground Accelerations for Nuclear Power Plants

The aim of the paper is to introduce a new procedure of three-component spectral matching of seismic ground acceleration records. The procedure is straightforward, yet it is general. In principle, the procedure involves varying of both the Fourier amplitude and the phase spectra so that the modified records’ spectra agree with a target. The matching can be performed against either a target Fourier or response spectra. In the former the solution is exact, while in the latter it becomes approximate. A target spectrum representative of three directions should be provided. In the example several three-component records were matched against two target spectra. Good convergence was achieved in velocity and displacement records so that no baseline correction was necessary. The couplings among the components were preserved.

Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains. <br>

Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains. <br>

Oscillatory Visual Mechanisms Revealed by Random Temporal Sampling

It is increasingly apparent that functionally significant neural activity is oscillatory in nature. Demonstrating the implications of this mode of operation for perceptual/cognitive function remains somewhat elusive. This report describes the technique of random temporal sampling for the investigation of visual oscillatory mechanisms. The technique is applied in visual recognition experiments using different stimulus classes (words, familiar objects, novel objects, and faces). Classification images reveal variations of perceptual effectiveness according to the temporal features of stimulus visibility. These classification images are also decomposed into their power and phase spectra. Stimulus classes lead to distinct outcomes and the power spectra of classification images are highly generalizable across individuals. Moreover, stimulus class can be reliably decoded from the power spectrum of individual classification images. These findings and other aspects of the results validate random temporal sampling as a promising new method to study oscillatory visual mechanisms.

Rapid determination of trace Cu2+ by in-syringe membrane SPE and membrane solid-phase spectra technique

A new in-syringe membrane SPE and solid-phase visible spectra method was proposed for the rapid extraction and visible spectra determination of trace Cu2+. The chelation and membrane SPE can be...