scholarly journals An anterior limit of FGF/Erk signal activity marks the earliest future somite boundary in zebrafish

Development ◽  
2014 ◽  
Vol 141 (5) ◽  
pp. 1104-1109 ◽  
Author(s):  
R. Akiyama ◽  
M. Masuda ◽  
S. Tsuge ◽  
Y. Bessho ◽  
T. Matsui
Keyword(s):  
Signal Activity

Signal Activity Detection of Offset-QPSK in Colored Gaussian Noise

2011 ◽  
Vol E94-A (11) ◽  
pp. 2226-2229
Author(s):  
Sayed Jalal ZAHABI ◽  
Mohammadali KHOSRAVIFARD ◽  
Ali A. TADAION ◽  
T. Aaron GULLIVER
Keyword(s):  
Gaussian Noise ◽  
Activity Detection ◽  
Signal Activity ◽  
Colored Gaussian Noise

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals Chemokines as phosphatidylserine-bound ‘find-me’ signals in apoptotic cell clearance

2020 ◽  
Author(s):  
Sergio M. Pontejo ◽  
Philip M. Murphy
Keyword(s):  
Extracellular Vesicles ◽  
Chemokine Receptors ◽  
Apoptotic Cell ◽  
Apoptotic Cells ◽  
Anionic Phospholipid ◽  
Apoptotic Cell Clearance ◽  
Cell Clearance ◽  
Cell Plasma ◽  
Signal Activity ◽  
Dying Cells

AbstractChemokines are positively charged cytokines that attract leukocytes by binding to anionic glycosaminoglycans (GAGs) on endothelial cells for efficient presentation to leukocyte G protein-coupled receptors (GPCRs). The atypical chemokine CXCL16 has been reported to also bind the anionic phospholipid phosphatidylserine (PS), but the biological relevance of this interaction remains poorly understood. Here we demonstrate that PS binding is in fact a widely shared property of chemokine superfamily members that, like GAG binding, induces chemokine oligomerization. PS is an essential phospholipid of the inner leaflet of the healthy cell plasma membrane but it is exposed in apoptotic cells to act as an ‘eat-me’ signal that promotes engulfment of dying cells by phagocytes. We found that chemokines can bind PS in pure form as well as in the context of liposomes and on the surface of apoptotic cells and extracellular vesicles released by apoptotic cells, which are known to act as ‘find-me’ signals that chemoattract phagocytes during apoptotic cell clearance. Importantly, we show that GAGs are severely depleted from the surface of apoptotic cells and that extracellular vesicles extracted from apoptotic mouse thymus bind endogenous thymic chemokines and activate cognate chemokine receptors. Together these results indicate that chemokines tethered to surface-exposed PS may be responsible for the chemotactic and find-me signal activity previously attributed to extracellular vesicles, and that PS may substitute for GAGs as the anionic scaffold that regulates chemokine oligomerization and presentation to GPCRs on the GAG-deficient membranes of apoptotic cells and extracellular vesicles. Here, we present a new mechanism by which extracellular vesicles, currently recognized as essential agents for intercellular communication in homeostasis and disease, can transport signaling cytokines.

On recent seismic electrical signal activity in northern Greece

1991 ◽  
Vol 188 (3-4) ◽  
pp. 403-405 ◽  
Author(s):  
P. Varotsos ◽  
K. Alexopoulos ◽  
M. Lazaridou
Keyword(s):  
Electrical Signal ◽  
Northern Greece ◽  
Signal Activity

Analysis of Germin-like Protein Genes (OsGLPs) Family in Rice Using Various In silico Approaches

2020 ◽  
Vol 15 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Muhammad Ilyas ◽  
Muhammad Irfan ◽  
Tariq Mahmood ◽  
Hazrat Hussain ◽  
Latif-ur-Rehman ◽  
...  
Keyword(s):  
Cellular Localization ◽  
Physiochemical Properties ◽  
Bacterial Pathogenicity ◽  
Bioinformatics Tools ◽  
Cellular Environment ◽  
Extracellular Region ◽  
Defensive Role ◽  
Signal Activity ◽  
Food Programs ◽  
Search Tool

Background: Germin-like Proteins (GLPs) play an important role in various stresses. Rice contains 43 GLPs, among which many remain functionally unexplored. The computational analysis will provide significant insight into their function. Objective: To find various structural properties, functional importance, phylogeny and expression pattern of all OsGLPs using various bioinformatics tools. Methods: Physiochemical properties, sub-cellular localization, domain composition, Nglycosylation and Phosphorylation sites, and 3D structural models of the OsGLPs were predicted using various bioinformatics tools. Functional analysis was carried out with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Blast2GO servers. The expression profile of the OsGLPs was predicted by retrieving the data for expression values from tissuespecific and hormonal stressed array libraries of RiceXPro. Their phylogenetic relationship was computed using Molecular and Evolutionary Genetic Analysis (MEGA6) tool. Results: Most of the OsGLPs are stable in the cellular environment with a prominent expression in the extracellular region (57%) and plasma membrane (33%). Besides, 3 basic cupin domains, 7 more were reported, among which NTTNKVGSNVTLINV, FLLAALLALASWQAI, and MASSSF were common to 99% of the sequences, related to bacterial pathogenicity, peroxidase activity, and peptide signal activity, respectively. Structurally, OsGLPs are similar but functionally they are diverse with novel enzymatic activities of oxalate decarboxylase, lyase, peroxidase, and oxidoreductase. Expression analysis revealed prominent activities in the root, endosperm, and leaves. OsGLPs were strongly expressed by abscisic acid, auxin, gibberellin, cytokinin, and brassinosteroid. Phylogenetically they showed polyphyletic origin with a narrow genetic background of 0.05%. OsGLPs of chromosome 3, 8, and 12 are functionally more important due to their defensive role against various stresses through co-expression strategy. Conclusion: The analysis will help to utilize OsGLPs in future food programs.

Hardware-Assisted Signal Activity Analysis for Power Estimation in Rapid Prototyped Systems

2003 ◽  
Vol 8 (4) ◽  
pp. 297-308
Author(s):  
R. Ludewig ◽  
A. García Ortiz ◽  
T. Murgan ◽  
M. Glesner
Keyword(s):  
Power Estimation ◽  
Activity Analysis ◽  
Signal Activity
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