Experimental Validations

2020 ◽  
pp. 91-103
Author(s):  
Kerim Türe ◽  
Catherine Dehollain ◽  
Franco Maloberti
Keyword(s):  
Experimental Validations

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

scholarly journals A simulation study investigating potential diffusion-based MRI signatures of microstrokes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rafat Damseh ◽  
Yuankang Lu ◽  
Xuecong Lu ◽  
Cong Zhang ◽  
Paul J. Marchand ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Statistical Power ◽  
Barrel Cortex ◽  
Signal Loss ◽  
Nuclear Spins ◽  
Directional Diffusion ◽  
Ischemic Tissue ◽  
Significant Difference ◽  
Before And After ◽  
Experimental Validations

AbstractRecent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits. Due to their small size, identifying measurable biomarkers of these microvascular lesions remains a major challenge. This work aims to simulate potential MRI signatures combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). Driving our hypothesis are recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially-oriented, and optical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n = 5) before and after inducing targeted photothrombosis, were analyzed. Computational vascular graphs combined with a 3D Monte-Carlo simulator were used to characterize the magnetic resonance (MR) response, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. We quantified the minimal intravoxel signal loss ratio when applying multiple gradient directions, at varying sequence parameters with and without ASL. With ASL, our results demonstrate a significant difference (p < 0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p < 0.005) using angiograms measured at week 4. Without ASL, no reliable signal change was found. We found that higher ratios, and accordingly improved significance, were achieved at lower magnetic field strengths (e.g., B0 = 3T) and shorter echo time TE (< 16 ms). Our simulations suggest that microstrokes might be characterized through ASL-DWI sequence, providing necessary insights for posterior experimental validations, and ultimately, future translational trials.

scholarly journals Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

2020 ◽  
Vol 21 (24) ◽  
pp. 9461
Author(s):  
Aurora Savino ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Cancer Biology ◽  
Regulatory Networks ◽  
Tumour Progression ◽  
External Perturbation ◽  
Data Types ◽  
Differential Gene ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Definition Of ◽  
Future Work

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

scholarly journals Investigations of Gas–Particle Two-Phase Flow in Swirling Combustor by the Particle Stokes Numbers

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 951
Author(s):  
Yang Liu ◽  
Guohui Li
Keyword(s):  
Two Phase Flow ◽  
Particle Diameter ◽  
Tangential Direction ◽  
Stokes Number ◽  
Order Moment ◽  
Frictional Stress ◽  
Phase Flow ◽  
Two Phase ◽  
Close Relationship ◽  
Experimental Validations

Gas turbulence modulations and particle dispersions of swirling gas–particle two-phase flow in the combustor is investigated under the large spans of the particle Stokes numbers. To fully consider the preferential concentrations and anisotropic dispersions of a particle, a kinetic frictional stress model coupled with a second-order moment two-phase turbulent model and granular temperature equation is improved. The proposed modeling and simulations are in good agreement with the experimental validations. Results show turbulent modulations and particle dispersions exhibit strongly anisotropic characteristics, keeping a close relationship with flow structure. The axial gas velocity and RMS fluctuation velocity of 45.0-μm EGP was approximately 5.0 times and 3.0 times greater than 1000.0 μm Copper particles, and their axial particle velocity was 0.25 times and twice greater than those of 45.0 μm EGP. The degree of modulation in the axial–radial direction is larger than those of radial–tangential and axial–tangential direction. Particle dispersions are sensitive to particle diameter parameters and intensified by higher Stokes number.

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