scholarly journals Unbiased Phenotype Detection Using Negative Controls

2019 ◽  
Vol 24 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Antje Janosch ◽  
Carolin Kaffka ◽  
Marc Bickle
Keyword(s):  
Sparse Matrix ◽  
Complex Data ◽  
L1 Norm ◽  
Hide Information ◽  
Novel Method ◽  
Two Parameters ◽  
Advanced Computing ◽  
Negative Controls ◽  
Comprehensive Measurement ◽  
Sophisticated Analysis

Phenotypic screens using automated microscopy allow comprehensive measurement of the effects of compounds on cells due to the number of markers that can be scored and the richness of the parameters that can be extracted. The high dimensionality of the data is both a rich source of information and a source of noise that might hide information. Many methods have been proposed to deal with this complex data in order to reduce the complexity and identify interesting phenotypes. Nevertheless, the majority of laboratories still only use one or two parameters in their analysis, likely due to the computational challenges of carrying out a more sophisticated analysis. Here, we present a novel method that allows discovering new, previously unknown phenotypes based on negative controls only. The method is compared with L1-norm regularization, a standard method to obtain a sparse matrix. The analytical pipeline is implemented in the open-source software KNIME, allowing the implementation of the method in many laboratories, even ones without advanced computing knowledge.

CLASSIFICATION OF SIMILAR 2-D OBJECTS BY WAVELET-SPARSE-MATRIX (WSM) METHOD

2001 ◽  
Vol 15 (02) ◽  
pp. 329-345 ◽  
Author(s):  
L. FENG ◽  
T. D. BUI ◽  
Y. Y. TANG
Keyword(s):  
Sparse Matrix ◽  
Zernike Moment ◽  
Fourier Descriptor ◽  
Moment Invariant ◽  
Spatial Features ◽  
Novel Method

This paper proposes a novel method called Wavelet-Sparse-Matrix (WSM) to extract the spatial features of 2-D objects for classifying objects that have subtle differences. The differences between these objects are present in the spatial orientations of the objects, or in the local positions of points on the contours of the objects. The separable wavelets are able to distinguish these differences and to separate them into three sparse subpatterns. Sparse matrix technique has the ability to rearrange nonzero elements in a sparse matrix by moving them as close together as possible. WSM method is a combination of these two techniques which can considerably improve the distinction of slightly dissimilar objects. Experiments are conducted, which include a series of discriminative simulations and comparisons with Fourier descriptor and Zernike moment invariant. These experiments verify the feasibility and effectiveness of the WSM method.

Simple Detection of Binding Events Using an Anchoring Transition of Liquid Crystals on the Immobilized oligoDNAs

2006 ◽  
Vol 915 ◽  
Author(s):  
Hak-Rin Kim ◽  
Min-Geon Choi ◽  
Joo-Eun Kim ◽  
Eui-Yul Choi ◽  
Sang-Wook Oh ◽  
...  
Keyword(s):  
Structural Changes ◽  
Extinction Ratio ◽  
Target Material ◽  
Dna Chip ◽  
Interface Condition ◽  
Steric Interaction ◽  
Optical Signals ◽  
Simple Detection ◽  
Novel Method ◽  
Sophisticated Analysis

AbstractTo detect biological events, biosensors require a transducer part where specific biomolecular binding events at a bioreceptor part is converted to measurable quantitative signals. Currently, most of biosensors adopt a fluorescent or radioactive probing technique as a transducer. However, such approaches require expensive and sophisticated analysis procedures with laboratory-based equipment.In this work, we propose a novel method for optically detecting hybridization results in a deoxyribonucleic acid (DNA) chip using an anchoring transition of liquid crystal (LC) alignment. To investigate the effects of structural changes of DNA on the LC alignment, we used a functional substrate on which single-stranded oligonucleotide DNA (ssDNA) was selectively immobilized to a Biotin Chip substrate. In our experiment, we used a 19-mer oligoDNA or p53 tumor suppressor as a bioreceptor and its complementary partner oligoDNA as a target material.Before hybridization, surface nematic LC (NLC) molecules on the immobilized ssDNAs are homeotropically aligned by a steric interaction between the freely penetrated NLC molecules and the ssDNA. After hybridization, the penetration of the NLC molecules is hindered by the double strand DNA (dsDNA) due to their increased packing density. Such an interface condition makes the surface ordering of the NLC molecules very weak, as a result, the NLC in the bulk has a planar inhomogeneous orientation. Although hybridization events of the DNA and the subsequent molecular interaction between the immobilized DNA and the NLC molecules takes place within a layer whose thickness is in the tens of nm, such binding events can be communicated to the NLC bulk beyond a distance of tens of μm though the long-range elastic deformation of the NLC molecules. Thus, the hybridization event is converted to amplified optical signals via birefringent nature of the NLC between crossed polarizers. Our NLC-based DNA chip array showed that the extinction ratio of transmitted light depending on the hybridization results was approximately four, which could be read by the naked eye. Since such anchoring behaviors on the immobilized DNA are very similar to those on the conventional amphiphilic homeotropic surfactant of LCs, it is expected that quantitative analysis of hybridization events can be explored with our simple system.

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

2012 ◽  
Vol 239-240 ◽  
pp. 1027-1032 ◽  
Author(s):  
Qing Guo Wei ◽  
Yan Mei Wang ◽  
Zong Wu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Channel Selection ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Channel Reduction ◽  
Novel Method

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

Hydro Thermo-Baric Processing and Properties of Nano Silica from Rice Husk

2012 ◽  
Vol 152-154 ◽  
pp. 177-182 ◽  
Author(s):  
Ugheoke Benjamin Iyenagbe ◽  
Othman Mamat
Keyword(s):  
Rice Husk ◽  
Low Cost ◽  
Industrial Applications ◽  
Processing Temperature ◽  
Processing Methods ◽  
Persistent Problem ◽  
Bet Analysis ◽  
Novel Method ◽  
Two Parameters ◽  
Nanometric Size

Several processing methods have been used to obtain silica from rice husk with a persistent problem of lack of scalability from laboratory scale to levels of production necessary for commercial or industrial applications, at low cost. To address this draw-back, a novel method- hydro thermo-baric process, was developed and used to process high purity silica from rice husk. Since the suitability of rice husk silica in a given application is dependent on the nature of its structure and morphology and the two parameters are affected by the processing methods used in obtaining the silica, this paper reports the preliminary studies done on the silica obtained from this novel method using XRF, XRD, FESEM and EDX. XRD results show that the silica produced, which by XRF analysis had purity approaching 98%, is amorphous in nature. FESEM images showed that the particles have nanometric size. However, EDX results show an increase in residual carbon in the silica, with increase in the processing temperature. BET analysis showed an increased surface area from 21.42m2/g to 133.94m2/g for the untreated and treated samples, respectively.

scholarly journals Estimating Lorenz Curve for Iran by Using Continuous L1 Norm Estimation

2019 ◽  
Vol 3 (1) ◽  
pp. 11-21
Author(s):  
Bijan Bidabad
Keyword(s):  
Lorenz Curve ◽  
Functional Form ◽  
Linear Models ◽  
Continuous Functions ◽  
L1 Norm ◽  
Statistical Population ◽  
Regression Parameters ◽  
Two Parameters ◽  
Continuous Estimation ◽  
Norm Approximation

In this paper, the L1 norm of continuous functions and corresponding continuous estimation of regression parameters are defined. The continuous L1 norm estimation problem of one and two parameters linear models in the continuous case are solved. We proceed to use the functional form and parameters of the probability distribution function of income to exactly determine the L1 norm approximation of the corresponding Lorenz curve of the statistical population under consideration. Iran family budget data were used to estimate income distribution for the period of 1362-1370.  

L1 Norm SVD-Based Ranking Scheme: A Novel Method in Big Data Mining

2020 ◽  
pp. 17-29
Author(s):  
Rahul Aedula ◽  
Yashasvi Madhukumar ◽  
Snehanshu Saha ◽  
Archana Mathur ◽  
Kakoli Bora ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
L1 Norm ◽  
Big Data Mining ◽  
Ranking Scheme ◽  
Novel Method

scholarly journals SURAA: A Novel Method and Tool for Loadbalanced and Coalesced SpMV Computations on GPUs

2019 ◽  
Vol 9 (5) ◽  
pp. 947 ◽  
Author(s):  
Thaha Muhammed ◽  
Rashid Mehmood ◽  
Aiiad Albeshri ◽  
Iyad Katib
Keyword(s):  
Load Balancing ◽  
Graphics Processing Units ◽  
Sparse Matrix ◽  
Memory Access ◽  
Group Matrix ◽  
The Matrix ◽  
Novel Method ◽  
Coalesced Memory ◽  
Graphics Processing ◽  
Matrix Vector

Sparse matrix-vector (SpMV) multiplication is a vital building block for numerous scientific and engineering applications. This paper proposes SURAA (translates to speed in arabic), a novel method for SpMV computations on graphics processing units (GPUs). The novelty lies in the way we group matrix rows into different segments, and adaptively schedule various segments to different types of kernels. The sparse matrix data structure is created by sorting the rows of the matrix on the basis of the nonzero elements per row ( n p r) and forming segments of equal size (containing approximately an equal number of nonzero elements per row) using the Freedman–Diaconis rule. The segments are assembled into three groups based on the mean n p r of the segments. For each group, we use multiple kernels to execute the group segments on different streams. Hence, the number of threads to execute each segment is adaptively chosen. Dynamic Parallelism available in Nvidia GPUs is utilized to execute the group containing segments with the largest mean n p r, providing improved load balancing and coalesced memory access, and hence more efficient SpMV computations on GPUs. Therefore, SURAA minimizes the adverse effects of the n p r variance by uniformly distributing the load using equal sized segments. We implement the SURAA method as a tool and compare its performance with the de facto best commercial (cuSPARSE) and open source (CUSP, MAGMA) tools using widely used benchmarks comprising 26 high n p r v a r i a n c e matrices from 13 diverse domains. SURAA outperforms the other tools by delivering 13.99x speedup on average. We believe that our approach provides a fundamental shift in addressing SpMV related challenges on GPUs including coalesced memory access, thread divergence, and load balancing, and is set to open new avenues for further improving SpMV performance in the future.

scholarly journals Automatic Branch Decomposition for Tubular Structures

2009 ◽  
Author(s):  
Guanglei Xiong ◽  
Lei Xing ◽  
Charles Taylor
Keyword(s):  
Medical Images ◽  
Ct Images ◽  
Binary Image ◽  
Tubular Structures ◽  
4D Ct ◽  
Motion Trajectories ◽  
Individual Branch ◽  
Novel Method ◽  
Two Parameters

Branches of tubular structures (vasculature, trachea, neuron, etc.) in medical images are critical for the topology of these structures. In many applications, It is very helpful to be able to decompose tubular structures and identify every individual branch. For example, quantification of geometric vascular features, registration of trachea movement due to respiration, tracing of neuron path. However, manual decomposition can be tedious, time-consuming, and subject to operator bias. In this paper, we propose a novel method to decompose tubular structures automatically and describe how to implement it in ITK framework. The input is a 2D/3D binary image that can be obtained from any segmentation techniques, as well as the junctions, which can be generated automatically from our previously contributed ITK class: itk::JunctionDetectionFilter. The output will be branches with their labels and their connection. There are only two parameters which need to be set by the user. We provide here the implementation as a ITK class: itk::BranchDecompositionFilter. Please cite the following paper if you are interested in our work. G. Xiong, C. Chen, J. Chen, Y. Xie, and L. Xing, Tracking the Motion Trajectories of Junction Structures in 4D CT Images of the Lung, Vol. 57, No. 15, pp. 4905-4930, Physics in Medicine and Biology, 2012.

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