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Spectral imaging is of visualization, high precision, and high sensitivity, and suitable for analyzing the spatial distribution of complex materials. While providing rich and detailed information, it makes higher demands on feature extraction and information mining of high-dimensional data. For the convenience of further utilization, our research team has developed a python framework for the multi-component synchronous analysis of spectral imaging based on characteristic band method and fast-NNLS algorithm, helping to handle spectrum data from complex samples and gaining semi-quantitative information of the sample on the scale of pixel based on target components. With the help of the easy-to-use framework, users are leading to choose suitable pretreatment methods for image and spectrum, extract spatial information of tissues/structures account of multi-space, and conduct analysis on target components in an intuitive and timesaving way. The sophisticated functional architecture also makes the framework expedite to add algorithms and supported data format.
This article was published in the following journal.
Name: Analytical chemistry
Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfor...
In the current study, Raman spectroscopy is employed for the identification of the biochemical changes taking place during the development of Hepatitis C. The Raman spectral data acquired from the hum...
The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discrimi...
Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of...
We propose a theoretical framework, based on the theory of Sobolev spaces, that allows for a comprehensive analysis of quadrature rules for integration over the sphere. We apply this framework to the ...
MicroPort Orthopedics (MPO) is conducting this study to investigate the primary stability of its PROFEMUR® Preserve Femoral Components using radiostereometric analysis (RSA). RSA allows p...
The overall objective of this study is to identify potential improvements for a noninvasive method of diagnosing dysplasia and neoplasia in the cervix using Multi-spectral Digital Colposco...
It is a prospective and multi-center clinical research in China to compare the efficacy, safety and related impact factors between TACE alone and TACE combined with synchronous multi-point...
The goal of this clinical research study is to evaluate the use of an imaging technology called spectral diagnosis. Researchers want to find out if a special spectral-diagnosis probe can ...
This study evaluates the efficacy of Mutli-Im® transepithelial components in the inhibition of bacterial adhesion. The control group will be the Multi-Im® transepithelial component with...
Measurement and evaluation of the components of substances to be taken as FOOD.
Components of a cell produced by various separation techniques which, though they disrupt the delicate anatomy of a cell, preserve the structure and physiology of its functioning constituents for biochemical and ultrastructural analysis. (From Alberts et al., Molecular Biology of the Cell, 2d ed, p163)
Multi-step systematic review process used for improving safety by investigation of incidents to find what happened, why it happened, and to determine what can be done to prevent it from happening again.
A method of chemical analysis based on the detection of characteristic radionuclides following a nuclear bombardment. It is also known as radioactivity analysis. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)
Meta-analysis of randomized trials in which estimates of comparative treatment effects are visualized and interpreted from a network of interventions that may or may not have been evaluated directly against each other. Common considerations in network meta-analysis include conceptual and statistical heterogeneity and incoherence.