In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face ...difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients.
LncRNA AFAP1-AS1 has been corroborated to function in diverse cancers. Our aim was to investigate the molecular mechanism of AFAP1-AS1 in PTX resistance in PCa. The levels of AFAP1-AS1, miR-195-5p, ...and FKBP1A were checked by qRT-PCR. 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-Diphenyltetrazolium Bromide (MTT) assay was employed to assess the resistance of PTX-resistant PCa cells to PTX. Flow cytometry was introduced to evaluate cell apoptosis. The protein levels of C-caspase 3 were determined by western blot. The starBase was used to predict the interaction between miR-195-5p and AFAP1-AS1. Xenograft tumor model was established to investigate the biological role of AFAP1-AS1 in PTX resistance in vivo. The levels of AFAP1-AS1 and FKBP1A were upregulated in PCa tissues and cells, as well as PTX-resistant PCa cells, while the expression of miR-195-5p was declined. Knockdown of AFAP1-AS1 promoted the sensitivity of PTX-resistant PCa cells to PTX, induced apoptosis of PTX-resistant PCa cells, whereas the impacts could be reversed by reducing the expression of miR-195-5p. FKBP1A overexpression could rescue the effects of miR-195-5p-mediated enhancement on the sensitivity of PTX-resistant PCa cells to PTX, promotion on apoptosis of PTX-resistant PCa cells. AFAP1-AS1 interacted with miR-195-5p and miR-195-5p could bind to the 3ʹUTR of FKBP1A. AFAP1-AS1 silencing inhibited the tumor growth in mice implanted with PC3-TXR cell. The protein level of PCNA was decreased in PC3-TXR cells transfected with sh-AFAP1-AS1, while the expression of C-caspase 3 was upregulated. AFAP1-AS1 silencing attenuated the resistance of PTX-resistant PCa cells to PTX by downregulating FKBP1A via sponging miR-195-5p.
Fine ambient aerosols (PM2.5) levels in the atmosphere are continuously worsening over Delhi and National Capital Region (NCR) of India. Complete source profiles are required to be assessed for ...implementation of proper mitigation measures over the NCR. In this study, emission sources of PM2.5 are reported for the NCR of India for samples collected during December 2016 to December 2017 at three sampling sites in Delhi, Uttar Pradesh and Haryana. Organic constituents (n-alkanes, isoprenoid hydrocarbons, polycyclic aromatic hydrocarbons, phthalates, levoglucosan and n-alkanoic acids) in PM2.5 were measured to apportion the sources over the study area. Source apportionment of PM2.5 was performed using organic constituents by Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA). Health risk associated with organic pollutants PAHs and carcinogen BEHP bis(2-ethylhexyl) phthalate demonstrated the threat of PM2.5 exposure via inhalation. Transport pathways of air masses were evaluated using 3-day backward trajectories and observed that some air masses originated from local sources along with long-range transport which influenced the PAHs concentration during most of the study period over the NCR. PMF and PCA resulted in the five major emission sources vehicular emissions (32.2%), biomass burning (30%), cooking emissions (16.8%), plastic burning (13.4%), mixed sources (7.6%) including biogenic and industrial emissions for PM2.5 over the sampling sites. The present study reveals that transport sector is a major source to be targeted to reduce the vehicular emissions and consequent health risks associated with organic pollutants especially PAHs.
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•Emission sources of PM2.5 are reported for three sites in the NCR of India.•Source apportionment was performed using PMF and PCA.•Health risk assessment for organic constituents suggests implementation of effective reduction strategies.•Cluster analysis showed influence of transport routes of air masses on levels of organic constituents at receptor sites.
•Seven parameters for extracting volatile compounds from cabbage were optimized.•A total of 75 volatiles were identified and quantified in the 10 cabbage cultivars.•There were 24 volatiles with the ...odour activity values greater than 1.•Pungent aroma was the strongest odour, followed by green and fruity aromas.•10 cabbage cultivars could be distinguished by odour profile.
Seven parameters of the headspace solid phase micro-extraction (HS-SPME) for extracting volatile compounds from cabbage were optimized comprehensively for the first time. A total of 75 volatiles were identified and quantified in 10 cabbage cultivars, mainly including aldehydes, hydrocarbons, esters, isothiocyanates, alcohols, ethers, nitriles and thiazoles. Dimethyl ether was the most abundant volatile. There were 24 volatiles with the odour activity values (OAVs) greater than 1 making large contributions to the cabbage flavor. Pungent aroma was the strongest odour, followed by green and fruity aromas. In short, the overall OAV of purple cabbages were generally higher than that of green cabbage. The volatile profile of 10 cabbage cultivars could be distinguished on the basis of radar fingerprint chart (RFC), hierarchical cluster analysis (HCA) and principal component analysis (PCA). Therefore, this study not only developed a feasible method to distinguish different cabbage cultivars, but also established a theoretical basis for the genetic improvement of cabbage flavor.
This paper presents a modified Prandtl-Ishlinskii (P-I) (MPI) model for the asymmetric hysteresis description and compensation of piezoelectric actuators. Considering the fact that the classical P-I ...(CPI) model is only efficient for the symmetric hysteresis description, the MPI model is proposed to describe the asymmetric hysteresis nonlinearity of piezoceramic actuators (PCAs). Different from the commonly used approach for the development of asymmetric P-I models by replacing the classical play operator with complex nonlinear operators, the proposed MPI model still utilizes the classical play operator as the elementary operator, while a generalized input function is introduced to replace the linear input function in the CPI model. By this way, the developed MPI model has a relative simple mathematic format with fewer parameters to characterize the asymmetric hysteresis behavior of PCAs. The benefit for the developed MPI model also lies in the fact that an analytic inverse model of the CPI model can be directly applied for the inverse compensation of the asymmetric hysteresis nonlinearity represented by the developed MPI model in real-time applications. To validate the developed MPI model and the inverse hysteresis compensator, simulation, and experimental results on a piezoceramic actuated platform are presented.
•Super-resolution based a hybrid technique for multi-focus image fusion is proposed.•Hybrid technique includes combining of SWT and PCA effectively.•Advantage of method, increasing the detail of ...images using super-resolution method.•Thanks to super-resolution, fused images get more information from source images.•The proposed method is also successfull in colored images looking to metrics.
Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.
Receptor models are useful to understand the chemical and physical characteristics of air pollutants by identifying their sources and by estimating contributions of each source to receptor ...concentrations. In this work, three receptor models based on principal component analysis with absolute principal component scores (PCA–APCS), Unmix and positive matrix factorization (PMF) were applied to study for the first time the apportionment of the airborne particulate matter less or equal than 10
μm (PM10) in Zaragoza, Spain, during 1year sampling campaign (2003–2004). The PM10 samples were characterized regarding their concentrations in inorganic components: trace elements and ions and also organic components: polycyclic aromatic hydrocarbons (PAH) not only in the solid phase but also in the gas phase.
A comparison of the three receptor models was carried out in order to do a more robust characterization of the PM10. The three models predicted that the major sources of PM10 in Zaragoza were related to natural sources (60%, 75% and 47%, respectively, for PCA–APCS, Unmix and PMF) although anthropogenic sources also contributed to PM10 (28%, 25% and 39%). With regard to the anthropogenic sources, while PCA and PMF allowed high discrimination in the sources identification associated with different combustion sources such as traffic and industry, fossil fuel, biomass and fuel-oil combustion, heavy traffic and evaporative emissions, the Unmix model only allowed the identification of industry and traffic emissions, evaporative emissions and heavy-duty vehicles. The three models provided good correlations between the experimental and modelled PM10 concentrations with major precision and the closest agreement between the PMF and PCA models.
•Rate-All-That-Apply (RATA) is a variation of the more widely used CATA question format.•F- and t-tests based on the ANOVA provide reliable analyses of 3-pt and 5-pt RATA data.•Tests for product ...differences usually gave the same results when RATA data were analyzed with ANOVA and Cochran’s Q test.•Correspondence Analysis (treating RATA as CATA) and Principal Component Analysis of RATA data have similar outcomes.
Rate-All-That-Apply (RATA) is a variation of the more widely used CATA question format. For a pre-specified list of terms, consumers indicate whether they apply to a given product, and if they do so, to rate their intensity. For example, a 3-pt scale may be used with anchors ‘low’, ‘medium’ and ‘high’ or a 5-pt scale anchored at 1=‘slightly applicable’ and 5=‘very applicable.’ Given the hierarchical nature of the task and the non-normal distribution of the intensity data, it is not obvious how to analyze RATA data appropriately. In the present work we suggest interpreting RATA data as 4- or 6-point scales, considering a missing check for any attribute as a score of 0. Based on that, randomization tests were applied to investigate potential product differences. We show that the null distribution of these tests for RATA data coincides in practice with the one from classical parametric tests derived from an ANOVA context. Consequently, using the common F- and t-tests provides a valid and easy analysis of RATA data. In four consumer studies, tests for product difference usually gave the same results when RATA data were analyzed with ANOVA and Cochran’s Q test. Graphical display of the data was found to be very similar based on Correspondence Analysis (CA) (treating RATA as CATA data) and Principal Component Analysis (PCA) (treating RATA as continuous data).
•Principal Component Analysis was applied on TiO2 nanoparticle Raman spectra.•A polynomial correlation was found between Sn/Ti percentage and PC-3 scores.•Anatase crystallite size approximation in ...good agreement with XRD calculations.•The method we propose is viable for rapid characterization of TiO2 nanoparticles.
The Raman spectra of anatase/rutile mixed phases of Sn doped TiO2 nanoparticles and undoped TiO2 nanoparticles, synthesised by laser pyrolysis, with nanocrystallite dimensions varying from 8 to 28nm, was simultaneously processed with a self-written software that applies Principal Component Analysis (PCA) on the measured spectrum to verify the possibility of objective auto-characterization of nanoparticles from their vibrational modes. The photo-excited process of Raman scattering is very sensible to the material characteristics, especially in the case of nanomaterials, where more properties become relevant for the vibrational behaviour. We used PCA, a statistical procedure that performs eigenvalue decomposition of descriptive data covariance, to automatically analyse the sample’s measured Raman spectrum, and to interfere the correlation between nanoparticle dimensions, tin and carbon concentration, and their Principal Component values (PCs). This type of application can allow an approximation of the crystallite size, or tin concentration, only by measuring the Raman spectrum of the sample. The study of loadings of the principal components provides information of the way the vibrational modes are affected by the nanoparticle features and the spectral area relevant for the classification.
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