Применение терагерцовой спектроскопии для in vivo исследования динамики развития лимфедемы -=SUP=-*-=/SUP=-

AbstractA laboratory model of lymphedema development induced by lymphatic vessel resection in rat extremities is presented. In vivo analysis of lymphedema development (monitoring for 4 weeks) employed reflective terahertz spectroscopy with a Dove prism. The incidence angle for an s -polarized electromagnetic wave directed to the boundary of the prism and the biological tissue was close to the Brewster’s angle. Significant changes in the spectral characteristics of the tissue in the animals’ extremities were detected on days 21–28 of lymphedema development. A predictive model for disease diagnostics based on monitoring the changes of the tissue absorbance curve in the 0.4–1.1 THz range was constructed. Principal component analysis and support vector machines were used in the model.

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A Reliable Method for Identification of Antibiotics by Terahertz Spectroscopy and SVM

Journal of Spectroscopy ◽

10.1155/2020/8811467 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jin Guo ◽

Hu Deng ◽

Quancheng Liu ◽

Linyu Chen ◽

Zhonggang Xiong ◽

...

Keyword(s):

Dimensionality Reduction ◽

Absorption Spectra ◽

Reliable Method ◽

Terahertz Spectroscopy ◽

Principal Component ◽

Support Vector ◽

Thz Spectroscopy ◽

Model Parameters ◽

Training Time ◽

Svm Model

Given the extensive use of antibiotics at present, the identification of antibiotics and production quality monitoring are of high importance. However, conventional antibiotic identification methods have a low sensitivity and a long detection time. Here, we propose an identification method that combines terahertz (THz) spectroscopy and chemometric technology. THz time-domain spectroscopy (THz-TDS) was performed for sixteen types of antibiotics, including β-lactam, cephalosporins, macrolides, and tetracyclines. The absorption spectra within the frequency range of 0.2–1.5 THz were calculated. For dimensionality reduction, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were implemented, respectively. The data after dimensionality reduction were input into a support vector machine (SVM). The model parameters were optimized through grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO) methods, and the optimal identification results were obtained after comparison across these methods. Experiments indicate a differentiation of the THz absorption spectra among the sixteen types of antibiotics. After dimensionality reduction, the training time of the model significantly decreased. The use of the t-SNE-PSO-SVM model achieved the highest average accuracy on the prediction set, which was 99.91%. Thus, our study does not only confirm that the t-SNE-PSO-SVM model proves to be a reliable method for antibiotics identification, but also confirms that the combination of THz-TDS and chemometric pattern recognition has great potential for drug detection.

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Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods

Frontiers in Nutrition ◽

10.3389/fnut.2021.680627 ◽

2021 ◽

Vol 8 ◽

Author(s):

Si Yang ◽

Chenxi Li ◽

Yang Mei ◽

Wen Liu ◽

Rong Liu ◽

...

Keyword(s):

Machine Learning ◽

Terahertz Spectroscopy ◽

Geographic Origin ◽

Principal Component ◽

Support Vector ◽

Thz Spectroscopy ◽

Learning Methods ◽

Linear Discriminant ◽

Machine Learning Methods ◽

Coffee Beans

Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

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Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

Diagnostics ◽

10.3390/diagnostics11101930 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1930

Author(s):

Soumya Prakash Rana ◽

Maitreyee Dey ◽

Riccardo Loretoni ◽

Michele Duranti ◽

Lorenzo Sani ◽

...

Keyword(s):

Radial Basis Function ◽

Frequency Spectrum ◽

Basis Function ◽

Breast Lesion ◽

Principal Component ◽

Lesion Detection ◽

Support Vector ◽

Radial Basis ◽

Microwave Apparatus

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1–9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist’s conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.

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A Terahertz Spectroscopy Nondestructive Identification Method for Rubber Based on CS-SVM

Advances in Condensed Matter Physics ◽

10.1155/2018/1618750 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8

Author(s):

Xianhua Yin ◽

Wei Mo ◽

Qiang Wang ◽

Binyi Qin

Keyword(s):

Terahertz Spectroscopy ◽

Search Algorithm ◽

Principal Component ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Function Parameter ◽

Support Vector ◽

Identification Rate ◽

Nondestructive Identification ◽

Factor C

A method is proposed for rubber identification based on terahertz time-domain spectroscopy (THz-TDS) and support vector machine (SVM). In order to improve the accuracy, the cuckoo search algorithm (CS) is used to optimize the penalty factor C and kernel function parameter g of SVM. The SVM model optimized by the cuckoo search algorithm is abbreviated as CS-SVM. Principal component analysis (PCA) is applied to decrease the dimension of the spectral data. The top ten principal component factors, whose accumulated variance contribution rate reaches 93.93%, are extracted from the original spectra data and then are applied to CS-SVM. The identification rate of testing sets for CS-SVM is 100%, which is significantly higher than 96.67% identification rate of testing sets for PSO-SVM and Grid search. Experimental results show that CS-SVM can accomplish nondestructive identification for different rubber. This method lays a theoretical foundation for the application of terahertz spectroscopy in rubber classification and identification.

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Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Scientific Reports ◽

10.1038/s41598-021-82153-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lokesh Basavarajappa ◽

Jihye Baek ◽

Shreya Reddy ◽

Jane Song ◽

Haowei Tai ◽

...

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Early Stage ◽

Principal Component ◽

Support Vector ◽

High Fat ◽

Imaging Approach ◽

Multiparametric Ultrasound

AbstractLiver disease is increasing in prevalence across the globe. We present here a multiparametric ultrasound (mpUS) imaging approach for assessing nonalcoholic fatty liver disease (NALFD). This study was performed using rats (N = 21) that were fed either a control or methionine and choline deficient (MCD) diet. A mpUS imaging approach that includes H-scan ultrasound (US), shear wave elastography, and contrast-enhanced US measurements were then performed at 0 (baseline), 2, and 6 weeks. Thereafter, animals were euthanized and livers excised for histological processing. A support vector machine (SVM) was used to find a decision plane that classifies normal and fatty liver conditions. In vivo mpUS results from control and MCD diet fed animals reveal that all mpUS measures were different at week 6 (P < 0.05). Principal component analysis (PCA) showed that the H-scan US data contributed the highest percentage to the classification among the mpUS measurements. The SVM resulted in 100% accuracy for classification of normal and high fat livers and 92% accuracy for classification of normal, low fat, and high fat livers. Histology findings found considerable steatosis in the MCD diet fed animals. This study suggests that mpUS examinations have the potential to provide a comprehensive estimation of the main components of early stage NAFLD.

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Statistical feature embedding for heart sound classification

Journal of Electrical Engineering ◽

10.2478/jee-2019-0056 ◽

2019 ◽

Vol 70 (4) ◽

pp. 259-272

Author(s):

Mohammad Adiban ◽

Bagher BabaAli ◽

Saeedreza Shehnepoor

Keyword(s):

Heart Sound ◽

Gaussian Mixture Models ◽

Principal Component ◽

Gaussian Mixture ◽

Support Vector ◽

Sound Classification ◽

Disease Diagnostics ◽

Baseline System ◽

Pca Transform ◽

Mel Frequency Cepstral Coefficient

Abstract Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers’ attention to investigate heart sounds’ patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced size vector is fed to Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) for classification purpose. Experimental results demonstrate the proposed method could achieve a performance improvement of 16% based on Modified Accuracy (MAcc) compared with the baseline system on the Physionet2016 dataset.

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Meiotic CENP-C is a shepherd: bridging the space between the centromere and the kinetochore in time and space

Essays in Biochemistry ◽

10.1042/ebc20190080 ◽

2020 ◽

Vol 64 (2) ◽

pp. 251-261

Author(s):

Jessica E. Fellmeth ◽

Kim S. McKim

Keyword(s):

Chromosome Segregation ◽

New Technologies ◽

Early Prophase ◽

Unique Role ◽

Kinetochore Assembly ◽

M Phase ◽

In Vivo Analysis ◽

Meiosis I

Abstract While many of the proteins involved in the mitotic centromere and kinetochore are conserved in meiosis, they often gain a novel function due to the unique needs of homolog segregation during meiosis I (MI). CENP-C is a critical component of the centromere for kinetochore assembly in mitosis. Recent work, however, has highlighted the unique features of meiotic CENP-C. Centromere establishment and stability require CENP-C loading at the centromere for CENP-A function. Pre-meiotic loading of proteins necessary for homolog recombination as well as cohesion also rely on CENP-C, as do the main scaffolding components of the kinetochore. Much of this work relies on new technologies that enable in vivo analysis of meiosis like never before. Here, we strive to highlight the unique role of this highly conserved centromere protein that loads on to centromeres prior to M-phase onset, but continues to perform critical functions through chromosome segregation. CENP-C is not merely a structural link between the centromere and the kinetochore, but also a functional one joining the processes of early prophase homolog synapsis to late metaphase kinetochore assembly and signaling.

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