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1.
Sensors (Basel) ; 21(17)2021 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-34502644

RESUMO

The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author's motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.


Assuntos
Indústria Farmacêutica , Análise de Componente Principal
2.
Sensors (Basel) ; 21(17)2021 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-34502695

RESUMO

Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.


Assuntos
Germinação , Medicago sativa , Análise Discriminante , Análise de Componente Principal , Sementes
3.
Comput Intell Neurosci ; 2021: 1759111, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34471405

RESUMO

As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Humanos , Análise de Componente Principal
4.
Anal Chim Acta ; 1177: 338784, 2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34482902

RESUMO

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.


Assuntos
Carcinoma , Análise Espectral Raman , Análise Discriminante , Humanos , Análise dos Mínimos Quadrados , Análise de Componente Principal
5.
Se Pu ; 39(9): 1006-1011, 2021 Sep.
Artigo em Chinês | MEDLINE | ID: mdl-34486840

RESUMO

Currently, guanosine, adenosine, and uridine contents are specified as the quality criteria for related products in the quality standards for fermented Cordyceps powder preparations included in the 2020 edition of Chinese Pharmacopoeia. However, there are many other nucleosides in fermented Cordyceps powder, whose effect on the quality control has not yet been discussed. In this study, an ultra-performance liquid chromatography-ultraviolet detection (UPLC-UV) method was used for the quantitative analysis of 9 nucleosides (uracil, cytidine, guanidine, uridine, adenine, inosine, guanosine, thymidine, and adenosine) in 19 batches of fermented Cordyceps powder samples and products, and the corresponding fingerprints were established. In addition, a method for analyzing the index components was proposed based on statistics. By optimizing the sample extraction method, ultrasound-assisted extraction was selected to process 19 batches of samples. Chromatographic analysis was performed on an Agilent Eclipse Plus C18 column (150 mm×4.6 mm, 3.5 µm) using methanol and water as the mobile phases under gradient elution. The method was validated based on the calibration curves, accuracy, precision, repeatability, and recovery. The fingerprints of the 19 batches of samples were established, and 16 common peaks were obtained. Among them, nine nucleoside peaks were identified by standards, and their concentrations were determined by the external standard one-point method. Similarity evaluation of the fingerprints was conducted; the similarities of the 19 batches of samples were greater than 0.9. Then, chemical pattern recognition was performed. The same classification results were obtained by hierarchical clustering analysis (HCA) and principal component analysis (PCA). Thus, the samples could be segregated into five classes, and the fermented Cordyceps powders were classified as two types with different fermentation processes. Xinganbao capsules, Bailing capsules and Ningxinbao capsules were each separately classified into one class. This indicated that the chemical recognition pattern could effectively distinguish between the fermented Cordyceps powder and different products. PCA was used to calculate the weight value of each common peak for the first time, and the index components among the samples were selected according to the weight value. Finally, the selected index components were used to re-cluster the samples. The results were consistent with those obtained on the basis of the 16 common peaks, thus verifying the rationality of the index components. Therefore, uridine, guanosine, adenosine, adenine, and uracil are recommended for use as evaluation indicators for fermented Cordyceps powder and products, allowing for better distinction between the products on the market. In summary, the combination of liquid chromatographic fingerprints and chemical pattern recognition can provide a simple and reliable method for the analysis and quality control of fermented Cordyceps powder and products.


Assuntos
Cordyceps , Medicamentos de Ervas Chinesas , Cromatografia Líquida de Alta Pressão , Nucleosídeos , Pós , Análise de Componente Principal
6.
Molecules ; 26(15)2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34361742

RESUMO

The biological activities of propolis samples are the result of many bioactive compounds present in the propolis. The aim of the present study was to determine the various chemical compounds of some selected propolis samples collected from Palestine and Morocco by the High-Performance Liquid Chromatography-Photodiode Array Detection (HPLC-PDA) method, as well as the antioxidant and antibacterial activities of this bee product. The chemical analysis of propolis samples by HPLC-PDA shows the cinnamic acid content in the Palestinian sample is higher compared to that in Moroccan propolis. The results of antioxidant activity demonstrated an important free radical scavenging activity (2,2-Diphenyl-1-picrylhydrazyl (DPPH); 2,2'-azino-bis 3-ethylbenzothiazoline-6-sulphonic acid (ABTS) and reducing power assays) with EC50 values ranging between 0.02 ± 0.001 and 0.14 ± 0.01 mg/mL. Additionally, all tested propolis samples possessed a moderate antibacterial activity against bacterial strains. Notably, Minimum Inhibitory Concentrations (MICs) values ranged from 0.31 to 2.50 mg/mL for Gram-negative bacterial strains and from 0.09 to 0.125 mg/mL for Gram-positive bacterial strains. The S2 sample from Morocco and the S4 sample from Palestine had the highest content of polyphenol level. Thus, the strong antioxidant and antibacterial properties were apparently due to the high total phenolic and flavone/flavonol contents in the samples. As a conclusion, the activities of propolis samples collected from both countries are similar, while the cinnamic acid in the Palestinian samples was more than that of the Moroccan samples.


Assuntos
Antibacterianos/química , Antioxidantes/química , Cinamatos/química , Fenóis/química , Própole/química , Animais , Antibacterianos/isolamento & purificação , Antibacterianos/farmacologia , Antioxidantes/isolamento & purificação , Antioxidantes/farmacologia , Abelhas/fisiologia , Benzotiazóis/antagonistas & inibidores , Compostos de Bifenilo/antagonistas & inibidores , Cinamatos/isolamento & purificação , Cinamatos/farmacologia , Bactérias Gram-Negativas/efeitos dos fármacos , Bactérias Gram-Negativas/crescimento & desenvolvimento , Bactérias Gram-Positivas/efeitos dos fármacos , Bactérias Gram-Positivas/crescimento & desenvolvimento , Testes de Sensibilidade Microbiana , Oriente Médio , Marrocos , Fenóis/isolamento & purificação , Fenóis/farmacologia , Picratos/antagonistas & inibidores , Polifenóis , Análise de Componente Principal , Própole/isolamento & purificação , Ácidos Sulfônicos/antagonistas & inibidores
7.
Molecules ; 26(15)2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34361751

RESUMO

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography-quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.


Assuntos
Metaboloma , Mycobacterium abscessus/química , Complexo Mycobacterium avium/química , Mycobacterium avium/química , Mycobacterium bovis/química , Mycobacterium/química , Compostos Orgânicos Voláteis/isolamento & purificação , Biomarcadores/análise , Cromatografia Gasosa-Espectrometria de Massas/métodos , Aprendizado de Máquina/estatística & dados numéricos , Mycobacterium/metabolismo , Mycobacterium abscessus/metabolismo , Mycobacterium avium/metabolismo , Complexo Mycobacterium avium/metabolismo , Mycobacterium bovis/metabolismo , Análise de Componente Principal , Microextração em Fase Sólida , Compostos Orgânicos Voláteis/classificação , Compostos Orgânicos Voláteis/metabolismo
8.
Molecules ; 26(15)2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34361755

RESUMO

Zinc (Zn) is an essential micronutrient for plant growth, and Zn deficiency is a global issue, especially in tropical soils. This study aimed to investigate the effects of humic acid (HA) and the Zn addition (Zn sulfate + HA) on the growth of maize and brachiaria in two contrasting Oxisols. The potential complexation of Zn sulfate by HA was evaluated by Fourier-transform infrared (FTIR) spectroscopy analysis. Zinc content and its availability in solution and the shoot and root biomass of maize and brachiaria were determined. FTIR spectroscopy revealed the complexation of Zn sulfate by HA through its S and C functional groups. In both Oxisols, solution Zn increased due to the combined use of Zn and HA. In a soil type-dependent manner, maize biomass and Zn in its shoots were affected only by the exclusive use of Zn fertilization. In the Yellow Oxisol, brachiaria growth and Zn accumulated in its shoot were positively affected by the combined use of Zn fertilization with HA. In the Oxisol with lower organic matter content, HA can assure adequate supplying of residual Zn, while increasing growth of brachiaria cultivated in sequence to maize.


Assuntos
Brachiaria/efeitos dos fármacos , Substâncias Húmicas/análise , Solo/química , Zea mays/efeitos dos fármacos , Sulfato de Zinco/farmacologia , Brachiaria/crescimento & desenvolvimento , Brasil , Fertilizantes/análise , Concentração de Íons de Hidrogênio , Raízes de Plantas/efeitos dos fármacos , Raízes de Plantas/crescimento & desenvolvimento , Brotos de Planta/efeitos dos fármacos , Brotos de Planta/crescimento & desenvolvimento , Análise de Componente Principal , Espectroscopia de Infravermelho com Transformada de Fourier , Zea mays/crescimento & desenvolvimento , Sulfato de Zinco/análise , Sulfato de Zinco/química
9.
Molecules ; 26(15)2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34361765

RESUMO

In this study, the aroma profile of 10 single origin Arabica coffees originating from eight different growing locations, from Central America to Indonesia, was analyzed using Headspace SPME-GC-MS as the analytical method. Their roasting was performed under temperature-time conditions, customized for each sample to reach specific sensory brew characteristics in an attempt to underline the customization of roast profiles and implementation of separate roastings followed by subsequent blending as a means to tailor cup quality. A total of 138 volatile compounds were identified in all coffee samples, mainly furan (~24-41%) and pyrazine (~25-39%) derivatives, many of which are recognized as coffee key odorants, while the main formation mechanism was the Maillard reaction. Volatile compounds' composition data were also chemometrically processed using the HCA Heatmap, PCA and HCA aiming to explore if they meet the expected aroma quality attributes and if they can be an indicator of coffee origin. The desired brew characteristics of the samples were satisfactorily captured from the volatile compounds formed, contributing to the aroma potential of each sample. Furthermore, the volatile compounds presented a strong variation with the applied roasting conditions, meaning lighter roasted samples were efficiently differentiated from darker roasted samples, while roasting degree exceeded the geographical origin of the coffee. The coffee samples were distinguished into two groups, with the first two PCs accounting for 73.66% of the total variation, attributed mainly to the presence of higher quantities of furans and pyrazines, as well as to other chemical classes (e.g., dihydrofuranone and phenol derivatives), while HCA confirmed the above results rendering roasting conditions as the underlying criterion for differentiation.


Assuntos
Coffea/química , Café/química , Furanos/química , Odorantes/análise , Pirazinas/química , Compostos Orgânicos Voláteis/química , América Central , Coffea/metabolismo , Café/metabolismo , Etiópia , Furanos/classificação , Furanos/isolamento & purificação , Furanos/metabolismo , Cromatografia Gasosa-Espectrometria de Massas , Temperatura Alta , Humanos , Indonésia , Reação de Maillard , Análise de Componente Principal , Pirazinas/classificação , Pirazinas/isolamento & purificação , Pirazinas/metabolismo , Sementes/química , Paladar/fisiologia , Compostos Orgânicos Voláteis/classificação , Compostos Orgânicos Voláteis/isolamento & purificação , Compostos Orgânicos Voláteis/metabolismo
10.
J Agric Food Chem ; 69(34): 9991-10001, 2021 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-34410111

RESUMO

Mushroom, as a kind of higher fungus, is a precious homology resource of medicine and foods. In this study, total lipids were extracted from eight wild edible mushrooms and subsequently characterized by ultra-high-performance liquid chromatography-Quadrupole-Exactive Orbitrap mass spectrometry. 20 lipid classes and 173 molecular species were identified and quantified. Lipid molecules and their concentrations in Boletus speciosus, Boletus bainiugan, and Tricholoma matsutake exhibited significantly different behaviors compared with the remaining mushrooms. Hierarchical cluster analysis revealed that lipid profiles of B. bainiugan were most similar to B. speciosus followed by T. matsutake, Canthar-ellus cibarius, Sarcodon aspratu, Termitomyces eurrhizus, Laccaria laccata, and Thelephora ganbajun. In addition, several differential lipids can be considered as potential biomarkers to distinguish different mushroom species, for instance, lysophosphatidylethanolamine (16:1) and ceramide non-hydroxy fatty acid-dihydrosphingosine (d23:0-10:0). This study provided a new perspective to discriminate the mushroom species from the perspective of lipidomics.


Assuntos
Agaricales , Lipidômica , Basidiomycota , Laccaria , Análise de Componente Principal , Termitomyces
11.
BMC Health Serv Res ; 21(1): 830, 2021 Aug 17.
Artigo em Inglês | MEDLINE | ID: mdl-34404416

RESUMO

BACKGROUND: Undocumented migrants face many hardships in their everyday life such as poor living conditions, discrimination, and lack of access to healthcare. Previous studies have demonstrated considerable health care needs for psychiatric disorders as well as physical diseases. The aim of this paper was to find out the main barriers that undocumented migrants experience in accessing the Swedish healthcare system and to explore their relation with socioeconomic factors. METHODS: A cross-sectional study with adult undocumented migrants was performed in the three largest cities of Sweden in 2014-2016. Sampling was done via informal networks. A socioeconomic questionnaire was constructed including 22 barriers to health care. Trained field workers conducted the interviews. A principal component analysis was conducted of all barriers to reveal central components. Then, Pearson's chi-squared test was used to explore the characteristics of undocumented migrants experiencing barriers to care. RESULTS: Two main components/barriers were extracted: "Fear of being taken by police/authorities", which was related to fear of disclosure by or in relation to seeking health care, and "Structural and psychosocial factors" which was related to practical obstacles or shame of being ill. Lower age (74.1 % vs 56.0 %), lower level of education (75.0 % vs. 45.1 %), and having no children (70.3 % vs. 48.1 %) were significantly related to a higher likelihood of experiencing a barrier. CONCLUSION: Fear of deportation and practical and psychosocial factors constitute hinderance of access to healthcare for undocumented migrants in Sweden. This highlights the importance of clear instructions, both to undocumented migrants and health professionals about the right to health care according to the international law on human rights as well as the law of confidentiality.


Assuntos
Migrantes , Adulto , Estudos Transversais , Acesso aos Serviços de Saúde , Humanos , Análise de Componente Principal , Suécia
12.
Sensors (Basel) ; 21(16)2021 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-34450701

RESUMO

This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data.


Assuntos
Espectroscopia de Luz Próxima ao Infravermelho , Análise Multivariada , Análise de Componente Principal , Estações do Ano
13.
Sensors (Basel) ; 21(16)2021 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-34450710

RESUMO

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel-SVD, EEMD-Hankel-SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.


Assuntos
Algoritmos , Processamento de Sinais Assistido por Computador , Análise de Componente Principal
14.
Sensors (Basel) ; 21(16)2021 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-34450790

RESUMO

There is scarce research into the use of Strive Sense3 smart compression shorts to measure external load with accelerometry and muscle load (i.e., muscle activations) with surface electromyography in basketball. Sixteen external load and muscle load variables were measured from 15 National Collegiate Athletic Association Division I men's basketball players with 1137 session records. The data were analyzed for player positions of Centers (n = 4), Forwards (n = 4), and Guards (n = 7). Nonparametric bootstrapping was used to find significant differences between training and game sessions. Significant differences were found in all variables except Number of Jumps and all muscle load variables for Guards, and all variables except Muscle Load for Forwards. For Centers, the Average Speed, Average Max Speed, and Total Hamstring, Glute, Left, and Right Muscle variables were significantly different (p < 0.05). Principal component analysis was conducted on the external load variables. Most of the variance was explained within two principal components (70.4% in the worst case). Variable loadings of principal components for each position were similar during training but differed during games, especially for the Forward position. Measuring muscle activation provides additional information in which the demands of each playing position can be differentiated during training and competition.


Assuntos
Desempenho Atlético , Basquetebol , Humanos , Masculino , Músculos , Fenômenos Físicos , Análise de Componente Principal
15.
Sensors (Basel) ; 21(16)2021 Aug 19.
Artigo em Inglês | MEDLINE | ID: mdl-34451019

RESUMO

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.


Assuntos
Algoritmos , Redes Neurais de Computação , Humanos , Movimento (Física) , Postura , Análise de Componente Principal
16.
Nutrients ; 13(7)2021 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-34371952

RESUMO

The distinct Tibetan regional diet is strongly influenced by the regional biogeography, indigenous traditions, popular religious beliefs and food taboos. In the context of the nutritional transition in Tibet, studies seldom report on the food consumption and dietary patterns of Tibetan residents. This is a cross-section study of 552 local adults (≥18 years old, 277 men and 275 women) living in 14 agricultural countries along the Yarlung Tsangpo River. Dietary intakes were assessed by a culturally specific FFQ and compared with the Chinese Dietary Pagoda (2016). Dietary Patterns were extracted by using PCA method. The binary logistic regression model was applied to assess the association between independent variables (genders, regions and age groups) and adherence to dietary patterns. With the exception of meat (100 ± 260 g/day) and soybean nuts (42 ± 12 g/day), which exceeded the recommended dietary intakes of CDP, the dietary intake of other foods were not up to the recommended value. In particular, the intake of aquatic products (2 ± 0.1 g/day), vegetables (90 ± 19 g/day), dairy products (114 ± 29 g/day), cereals (117 ± 27 g/day) and fruits (97 ± 25 g/day) were seriously inadequate, which were 95%, 70%, 62%, 53.2% and 51.5% lower than the recommended intakes, respectively. Four dietary patterns were identified. "Local traditional diet" was characterized by a high intake of tsampa (roasted highland barley flour), culturally specific beverages (sweet tea and yak buttered tea), potato and yak beef and was associated with female, rural and older adults (≥51 years old). The male, urban and 18~30 years old group had a higher adherence score with the "Han diet", which was comprised of rice, pork, dumplings, eggs, milk and cabbage. The "Beverage diet", which mainly include tsampa, chang (homemade barley wine) and sweet tea, was associated with the following group: female, urban and aged 18~30 years. The "Out-sourced diet" pattern, consisting of mainly rice, steam bread and some processed meat, was associated with being male, urban and 18-30 years of age. These findings indicate that the dietary practice of the Tibetan people still has strong local characteristics, but it is also undergoing a dietary transition with the penetration of the Chinese Han diet and the increased consumption of outsourced (processed) foods. The unbalanced dietary intake of Tibetan residents should be taken seriously by all parties.


Assuntos
Dieta , Comportamento Alimentar , Adolescente , Adulto , Fatores Etários , Bebidas , Estudos Transversais , Inquéritos sobre Dietas , Ingestão de Alimentos , Feminino , Frutas , Humanos , Masculino , Pessoa de Meia-Idade , Análise de Componente Principal , Recomendações Nutricionais , Rios , População Rural , Fatores Sexuais , Tibet , População Urbana , Verduras , Adulto Jovem
17.
Spectrochim Acta A Mol Biomol Spectrosc ; 263: 120234, 2021 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-34343842

RESUMO

Serum protein is generally used to assess the severity of disease, as well as cancer progression and prognosis. Herein, a simple and rapid serum proteins analysis method combined with surface-enhanced Raman spectroscopy (SERS) technology was applied for breast cancer detection. The cellulose acetate membrane (CA) was employed to extract human serum proteins from 30 breast cancer patients and 45 healthy volunteers and then extracted proteins were mixed with silver nanoparticles for SERS measurement. Additionally, we also mainly assessed the use of different ratios of proteins-silver nanoparticles (Ag NPs) mixture to generate maximum SERS signal for clinical samples detection. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-support vector machines (PLS-SVM) were used to analyze the obtained serum protein SERS spectra and establish the diagnostic model. The results demonstrate that the PLS-SVM model provides superior performance in the classification of breast cancer diagnosis compared with PCA-LDA. This exploratory work demonstrates that the label-free SERS analysis technique combined with CA membrane purified serum proteins has great potential for breast cancer diagnosis.


Assuntos
Neoplasias da Mama , Nanopartículas Metálicas , Proteínas Sanguíneas , Neoplasias da Mama/diagnóstico , Feminino , Humanos , Análise de Componente Principal , Prata , Análise Espectral Raman
18.
J Photochem Photobiol B ; 222: 112280, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34375907

RESUMO

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm-1 and 2800 to 3000 cm-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.


Assuntos
Neoplasias da Mama/patologia , Microscopia Confocal/métodos , Análise Espectral Raman , Neoplasias da Mama/química , Neoplasias da Mama/metabolismo , Análise por Conglomerados , Análise Discriminante , Progressão da Doença , Feminino , Humanos , Análise de Componente Principal
19.
Zhongguo Zhong Yao Za Zhi ; 46(14): 3614-3624, 2021 Jul.
Artigo em Chinês | MEDLINE | ID: mdl-34402285

RESUMO

The volatile oil of Curcumae Rhizoma has many active components,which are the key to the quality of Curcumae Rhizoma. Exploring the difference between volatile oil of different kinds of Curcumae Rhizoma facilitates the quality control and rational application of resources. In this study,GC-MS was applied to realize online qualitative and semi-quantitative analysis of the chemical composition spectrum of volatile oil from Curcuma wenyujin( CW),C. phaeocaulis( CP),and C. kwangsiensis( CK). Forty components were identified and their fingerprints were compared and evaluated. Hierarchical cluster analysis( HCA),principal component analysis( PCA),and orthogonal partial least squares discrimination analysis( OPLS-DA) were adopted to analyze the overall and outlier data. The results showed that the whole data could be divided into three kinds according to each analysis mode,and the volatile components of Curcumae Rhizoma vary greatly among species. PCA explored the difference between outliers and the mean value of the group and found that some volatile oils from CW may be greatly affected by the origin. By OPLS-DA,the samples from Zhejiang were able to gather,but those from Guizhou remained isolated,indicating the influence of growing environment on Curcumae Rhizoma metabolites. Based on VIP results combined with the heat map,characteristic volatile oil components of Curcumae Rhizoma from different varieties were screened out: curdione and linalool for CW; 2-undecanone for CP; humulene,γ-selinene,and zederone for CK. The GCMS method established in this study describes Curcumae Rhizoma samples comprehensively and accurately,and the characteristic components screened based on chemometrics can be used to distinguish Curcumae Rhizoma from different varieties and give them unique pharmacodynamic significance,which is fast,convenient,stable,and reliable and supports the rational application of Curcu-mae Rhizoma resources. It is found that the region of origin has great influence on CW,which is worthy of further study.


Assuntos
Óleos Voláteis , Curcuma , Cromatografia Gasosa-Espectrometria de Massas , Análise de Componente Principal , Rizoma
20.
J Photochem Photobiol B ; 222: 112259, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34274827

RESUMO

Zostera marina, a widespread seagrass, evolved from a freshwater ancestor of terrestrial monocots and successfully transitioned into a completely submerged seagrass. We found that its oxygen-evolving complex (OEC) was partially inactivated in response to light exposure, as evidenced by both the increment of the relative variable fluorescence at the K-step and the downregulation of the OEC genes and proteins. This photosynthetic regulation was further addressed at both proteome and physiology levels by an in vivo study. The unchanged content of the ΔpH sensor PsbS protein and the non-photochemical quenching induction dynamics, described by a single exponential function, verified the absence of the fast qE component. Contents and activities of chlororespiration, Mehler reaction, malic acid synthesis, and photorespiration key enzymes were not upregulated, suggesting that alternative electron flows remained unactivated. Furthermore, neither significant production of singlet oxygen nor increment of total antioxidative capacity indicated that reactive oxygen species were not produced during light exposure. In summary, these low electron consumptions may allow Z. marina to efficiently use the limited electrons caused by partial OEC photoinactivation to maintain a normal carbon assimilation level.


Assuntos
Fotossíntese , Zosteraceae/metabolismo , Antioxidantes/química , Antioxidantes/metabolismo , Carbono/química , Carbono/metabolismo , Clorofila/química , Transporte de Elétrons , Metabolismo Energético , Luz , Oxigênio/metabolismo , Fotossíntese/efeitos da radiação , Complexo de Proteína do Fotossistema II/metabolismo , Folhas de Planta/metabolismo , Análise de Componente Principal , Proteoma/análise , Oxigênio Singlete/metabolismo
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