Statistical Features Based Noise Type Identification

2014 ◽  
pp. 231-241 ◽  
Author(s):  
Sohail Masood ◽  
Ismael Soto ◽  
Ayyaz Hussain ◽  
M. Arfan Jaffar
Keyword(s):  
Statistical Features ◽  
Noise Type

Effects of cytochrome c on the mitochondrial apoptosis-induced channel MAC

2004 ◽  
Vol 286 (5) ◽  
pp. C1109-C1117 ◽  
Author(s):  
Liang Guo ◽  
Dawn Pietkiewicz ◽  
Evgeny V. Pavlov ◽  
Sergey M. Grigoriev ◽  
John J. Kasianowicz ◽  
...  
Keyword(s):  
Cytochrome C ◽  
Outer Membrane ◽  
Cell Types ◽  
Rnase A ◽  
Mitochondrial Outer Membrane ◽  
Dependent Manner ◽  
Mitochondrial Apoptosis ◽  
Noise Type ◽  
Voltage Dependent

Recent studies indicate that cytochrome c is released early in apoptosis without loss of integrity of the mitochondrial outer membrane in some cell types. The high-conductance mitochondrial apoptosis-induced channel (MAC) forms in the outer membrane early in apoptosis of FL5.12 cells. Physiological (micromolar) levels of cytochrome c alter MAC activity, and these effects are referred to as types 1 and 2. Type 1 effects are consistent with a partitioning of cytochrome c into the pore of MAC and include a modest decrease in conductance that is dose and voltage dependent, reversible, and has an increase in noise. Type 2 effects may correspond to “plugging” of the pore or destabilization of the open state. Type 2 effects are a dose-dependent, voltage-independent, and irreversible decrease in conductance. MAC is a heterogeneous channel with variable conductance. Cytochrome c affects MAC in a pore size-dependent manner, with maximal effects of cytochrome c on MAC with conductance of 1.9–5.4 nS. The effects of cytochrome c, RNase A, and high salt on MAC indicate that size, rather than charge, is crucial. The effects of dextran molecules of various sizes indicate that the pore diameter of MAC is slightly larger than that of 17-kDa dextran, which should be sufficient to allow the passage of 12-kDa cytochrome c. These findings are consistent with the notion that MAC is the pore through which cytochrome c is released from mitochondria during apoptosis.

scholarly journals RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 444-455
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Sukhairi Sudin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Statistical Features ◽  
Received Signal Strength Indicator ◽  
Privacy Concerns ◽  
Physical Discomfort ◽  
Learning Technique ◽  
Device Free ◽  
The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data

2018 ◽  
Vol 16 (03) ◽  
pp. 1850001 ◽  
Author(s):  
A. Ranjitha Dhanasekaran ◽  
Katheleen J. Gardiner
Keyword(s):  
Brain Regions ◽  
Large Datasets ◽  
Ease Of Use ◽  
Statistical Features ◽  
Software Suite ◽  
Reverse Phase ◽  
High Throughput Technology ◽  
Phase Protein ◽  
Biological Discovery

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

Sign in / Sign up

Export Citation Format

Share Document