Proposed respiratory ‘gating’ mechanism for cardiac slowing

Nature ◽  
1976 ◽  
Vol 264 (5585) ◽  
pp. 454-456 ◽  
Author(s):  
O. U. LOPES ◽  
J. F. PALMER
Keyword(s):  
Respiratory Gating ◽  
Gating Mechanism ◽  
Cardiac Slowing

Evidence for a single mechanism gating perceptual and long-term memory information into working memory

2020 ◽  
Author(s):  
Sam Verschooren ◽  
Yoav Kessler ◽  
Tobias Egner
Keyword(s):  
Working Memory ◽  
Long Term Memory ◽  
Sources Of Information ◽  
Term Memory ◽  
Source Selection ◽  
Single Mechanism ◽  
Gating Mechanism ◽  
Opening And Closing ◽  
Selection Of

An influential view of working memory (WM) holds that its’ contents are controlled by a selective gating mechanism that allows for relevant perceptual information to enter WM when opened, but shields WM contents from interference when closed. In support of this idea, prior studies using the reference-back paradigm have established behavioral costs for opening and closing the gate between perception and WM. WM also frequently requires input from long-term memory (LTM), but it is currently unknown whether a similar gate controls the selection of LTM representations into WM, and how WM gating of perceptual vs. LTM sources of information relate to each other. To address these key theoretical questions, we devised a novel version of the reference-back paradigm, where participants switched between gating perceptual and LTM information into WM. We observed clear evidence for gate opening and closing costs in both cases. Moreover, the pattern of costs associated with gating and source-switching indicated that perceptual and LTM information is gated into WM via a single gate, and rely on a shared source-selection mechanism. These findings extend current models of WM gating to encompass LTM information, and outline a new functional WM architecture.

scholarly journals Structure of the full-length human Pannexin1 channel and insights into its role in pyroptosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sensen Zhang ◽  
Baolei Yuan ◽  
Jordy Homing Lam ◽  
Jun Zhou ◽  
Xuan Zhou ◽  
...  
Keyword(s):  
Md Simulation ◽  
Potential Role ◽  
Md Simulations ◽  
Full Length ◽  
Inflammasome Activation ◽  
Simulation Studies ◽  
Cryo Electron Microscopy ◽  
Gating Mechanism ◽  
Atp Efflux

AbstractPannexin1 (PANX1) is a large-pore ATP efflux channel with a broad distribution, which allows the exchange of molecules and ions smaller than 1 kDa between the cytoplasm and extracellular space. In this study, we show that in human macrophages PANX1 expression is upregulated by diverse stimuli that promote pyroptosis, which is reminiscent of the previously reported lipopolysaccharide-induced upregulation of PANX1 during inflammasome activation. To further elucidate the function of PANX1, we propose the full-length human Pannexin1 (hPANX1) model through cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulation studies, establishing hPANX1 as a homo-heptamer and revealing that both the N-termini and C-termini protrude deeply into the channel pore funnel. MD simulations also elucidate key energetic features governing the channel that lay a foundation to understand the channel gating mechanism. Structural analyses, functional characterizations, and computational studies support the current hPANX1-MD model, suggesting the potential role of hPANX1 in pyroptosis during immune responses.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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