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1.
Aging Clin Exp Res ; 36(1): 87, 2024 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-38578525

RESUMO

BACKGROUND: The multifinger force deficit (MFFD) is the decline in force generated by each finger as the number of fingers contributing to an action is increased. It has been shown to associate with cognitive status. AIMS: The aim was to establish whether a particularly challenging form of multifinger grip dynamometry, that provides minimal tactile feedback via cutaneous receptors and requires active compensation for reaction forces, will yield an MFFD that is more sensitive to cognitive status. METHODS: Associations between measures of motor function, and cognitive status (Montreal Cognitive Assessment [MoCA]) and latent components of cognitive function (derived from 11 tests using principal component analysis), were estimated cross-sectionally using generalized partial rank correlations. The participants (n = 62) were community dwelling, aged 65-87. RESULTS: Approximately half the participants were unable to complete the dynamometry task successfully. Cognitive status demarcated individuals who could perform the task from those who could not. Among those who complied with the task requirements, the MFFD was negatively correlated with MoCA scores-those with the highest MoCA scores tended to exhibit the smallest deficits, and vice versa. There were corresponding associations with latent components of cognitive function. DISCUSSION: The results support the view that neurodegenerative processes that are a feature of normal and pathological aging exert corresponding effects on expressions of motor coordination-in multifinger tasks, and cognitive sufficiency, due to their dependence on shared neural systems. CONCLUSIONS: The outcomes add weight to the assertion that deficits in force production during multifinger tasks are sensitive to cognitive dysfunction.


Assuntos
Disfunção Cognitiva , Força da Mão , Humanos , Força da Mão/fisiologia , Envelhecimento , Dedos/fisiologia , Análise de Componente Principal
2.
Elife ; 122024 Apr 02.
Artigo em Inglês | MEDLINE | ID: mdl-38564239

RESUMO

We have previously shown that after few seconds of adaptation by finger-tapping, the perceived numerosity of spatial arrays and temporal sequences of visual objects displayed near the tapping region is increased or decreased, implying the existence of a sensorimotor numerosity system (Anobile et al., 2016). To date, this mechanism has been evidenced only by adaptation. Here, we extend our finding by leveraging on a well-established covariance technique, used to unveil and characterize 'channels' for basic visual features such as colour, motion, contrast, and spatial frequency. Participants were required to press rapidly a key a specific number of times, without counting. We then correlated the precision of reproduction for various target number presses between participants. The results showed high positive correlations for nearby target numbers, scaling down with numerical distance, implying tuning selectivity. Factor analysis identified two factors, one for low and the other for higher numbers. Principal component analysis revealed two bell-shaped covariance channels, peaking at different numerical values. Two control experiments ruled out the role of non-numerical strategies based on tapping frequency and response duration. These results reinforce our previous reports based on adaptation, and further suggest the existence of at least two sensorimotor number channels responsible for translating symbolic numbers into action sequences.


Assuntos
Individualidade , Reprodução , Humanos , Movimento (Física) , Análise de Componente Principal
3.
Sci Data ; 11(1): 358, 2024 Apr 09.
Artigo em Inglês | MEDLINE | ID: mdl-38594314

RESUMO

This paper presents a standardised dataset versioning framework for improved reusability, recognition and data version tracking, facilitating comparisons and informed decision-making for data usability and workflow integration. The framework adopts a software engineering-like data versioning nomenclature ("major.minor.patch") and incorporates data schema principles to promote reproducibility and collaboration. To quantify changes in statistical properties over time, the concept of data drift metrics (d) is introduced. Three metrics (dP, dE,PCA, and dE,AE) based on unsupervised Machine Learning techniques (Principal Component Analysis and Autoencoders) are evaluated for dataset creation, update, and deletion. The optimal choice is the dE,PCA metric, combining PCA models with splines. It exhibits efficient computational time, with values below 50 for new dataset batches and values consistent with seasonal or trend variations. Major updates (i.e., values of 100) occur when scaling transformations are applied to over 30% of variables while efficiently handling information loss, yielding values close to 0. This metric achieved a favourable trade-off between interpretability, robustness against information loss, and computation time.


Assuntos
Software , Reprodutibilidade dos Testes , Análise de Componente Principal , Fluxo de Trabalho
4.
Spectrochim Acta A Mol Biomol Spectrosc ; 313: 124108, 2024 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-38447442

RESUMO

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.


Assuntos
Prunus persica , Espectroscopia de Luz Próxima ao Infravermelho , Espectroscopia de Luz Próxima ao Infravermelho/métodos , Pós , Calibragem , Análise de Componente Principal , Análise dos Mínimos Quadrados
5.
ACS Sens ; 9(3): 1584-1591, 2024 Mar 22.
Artigo em Inglês | MEDLINE | ID: mdl-38450591

RESUMO

Chemoresistive gas sensors made from SnO2, ZnO, WO3, and In2O3 have been prepared by flame spray pyrolysis. The sensors' response to CO and NO2 in darkness and under illumination at different wavelengths, using commercially available LEDs, was investigated. Operation at room temperature turned out to be impractical due to the condensation of water inside the porous sensing layers and the irreversible changes it caused. Accordingly, for sensors operated at 70 °C, a characterization procedure was developed and proven to deliver consistent data. The resulting data set was so complex that usual univariate data analysis was intricate and, consequently, was investigated by correlation and principal component analysis. The results show that light of different wavelengths affects not only the resistance of each material, both under exposure to the target gases in humidity and in its absence, but also the sensor response to humidity and the target gases. It was found that each of the materials behaves differently under light exposure, and it was possible to identify conditions that need further investigations.


Assuntos
Gases , Análise Multivariada , Umidade , Porosidade , Análise de Componente Principal
6.
PLoS One ; 19(3): e0287187, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38507443

RESUMO

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.


Assuntos
Poluentes Atmosféricos , Neoplasias , Humanos , Análise de Componente Principal , Monitoramento Ambiental , Tailândia , Poluentes Atmosféricos/análise , Poeira/análise , Análise de Regressão , Material Particulado/análise
7.
Huan Jing Ke Xue ; 45(3): 1586-1597, 2024 Mar 08.
Artigo em Chinês | MEDLINE | ID: mdl-38471872

RESUMO

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.


Assuntos
Ecossistema , Tecnologia de Sensoriamento Remoto , Humanos , Monitoramento Ambiental , Conservação dos Recursos Naturais , Análise de Componente Principal , China
8.
BMC Med Res Methodol ; 24(1): 69, 2024 Mar 17.
Artigo em Inglês | MEDLINE | ID: mdl-38494505

RESUMO

BACKGROUND: Intensive longitudinal data (ILD) collected in near real time by mobile health devices provide a new opportunity for monitoring chronic diseases, early disease risk prediction, and disease prevention in health research. Functional data analysis, specifically functional principal component analysis, has great potential to abstract trends in ILD but has not been used extensively in mobile health research. OBJECTIVE: To introduce functional principal component analysis (fPCA) and demonstrate its potential applicability in estimating trends in ILD collected by mobile heath devices, assessing longitudinal association between ILD and health outcomes, and predicting health outcomes. METHODS: fPCA and scalar-to-function regression models were reviewed. A case study was used to illustrate the process of abstracting trends in intensively self-measured blood glucose using functional principal component analysis and then predicting future HbA1c values in patients with type 2 diabetes using a scalar-to-function regression model. RESULTS: Based on the scalar-to-function regression model results, there was a slightly increasing trend between daily blood glucose measures and HbA1c. 61% of variation in HbA1c could be predicted by the three preceding months' blood glucose values measured before breakfast (P < 0.0001, [Formula: see text]). CONCLUSIONS: Functional data analysis, specifically fPCA, offers a unique tool to capture patterns in ILD collected by mobile health devices. It is particularly useful in assessing longitudinal dynamic association between repeated measures and outcomes, and can be easily integrated in prediction models to improve prediction precision.


Assuntos
Diabetes Mellitus Tipo 2 , Telemedicina , p-Cloroanfetamina/análogos & derivados , Humanos , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/terapia , Glicemia , Hemoglobinas Glicadas , Análise de Componente Principal , Avaliação de Resultados em Cuidados de Saúde
9.
Sensors (Basel) ; 24(6)2024 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-38544148

RESUMO

Parkinson's disease is one of the major neurodegenerative diseases that affects the postural stability of patients, especially during gait initiation. There is actually an increasing demand for the development of new non-pharmacological tools that can easily classify healthy/affected patients as well as the degree of evolution of the disease. The experimental characterization of gait initiation (GI) is usually done through the simultaneous acquisition of about 20 variables, resulting in very large datasets. Dimension reduction tools are therefore suitable, considering the complexity of the physiological processes involved. The principal Component Analysis (PCA) is very powerful at reducing the dimensionality of large datasets and emphasizing correlations between variables. In this paper, the Principal Component Analysis (PCA) was enhanced with bootstrapping and applied to the study of the GI to identify the 3 majors sets of variables influencing the postural control disability of Parkinsonian patients during GI. We show that the combination of these methods can lead to a significant improvement in the unsupervised classification of healthy/affected patients using a Gaussian mixture model, since it leads to a reduced confidence interval on the estimated parameters. The benefits of this method for the identification and study of the efficiency of potential treatments is not addressed in this paper but could be addressed in future works.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Humanos , Análise de Componente Principal , Intervalos de Confiança , Doença de Parkinson/terapia , Marcha/fisiologia , Equilíbrio Postural/fisiologia
10.
Sensors (Basel) ; 24(6)2024 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-38544161

RESUMO

There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed changes in the relationships among these variables after a two-month intervention aimed at improving the perception-action link. At baseline, PCA correctly identified three components: one related to cardiac variables, one to trunk motion, and one to Stroop task performance. After the intervention, only two components were found, with trunk symmetry and range of motion, accuracy, time to complete the Stroop task, and low-frequency heart rate variability aggregated into a single component using PCA. Artificial neural network analysis confirmed the effects of both HRV and motor behavior on cognitive Stroop task performance. This analysis suggested that this protocol was effective in investigating embodied cognition, and we defined this approach as "embodimetrics".


Assuntos
Cognição , Análise e Desempenho de Tarefas , Humanos , Feminino , Análise de Componente Principal , Cognição/fisiologia , Teste de Stroop , Coração
11.
Comput Med Imaging Graph ; 113: 102343, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38325245

RESUMO

Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data. In our approach, we derive two abnormality measurements: Dimage and Uimage. The first measurement, Dimage, is based on the variability of the predicted configuration of a well-defined set of landmarks in a subspace formed by the point distribution model of the location of those landmarks in normative data. We create this subspace using Procrustes shape alignment and Principal Component Analysis projection. The second measurement, Uimage, represents the degree of hesitation of the agents when approaching the final location of the landmarks and is based on the distribution of the predicted Q-values of the model for the last ten states. Finally, we unify these measurements in a combined anomaly measurement called Cimage. We compare our method's performance with a 3D convolutional autoencoder technique for abnormality detection using the patch-based mean squared error between the original and the generated image as a basis for classifying abnormal versus normal anatomies. We compare both approaches and show that our method, based on deep reinforcement learning, shows better detection performance for abnormal anatomies on both an artificial and a real clinical CT dataset of various inner ear malformations with an increase of 11.2% of the area under the ROC curve. Our method also shows more robustness against the heterogeneous quality of the images in our dataset.


Assuntos
Orelha Interna , Orelha Interna/diagnóstico por imagem , Análise de Componente Principal , Curva ROC , Tomografia Computadorizada por Raios X
12.
Molecules ; 29(4)2024 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-38398501

RESUMO

This study reports the use of front-face fluorescence spectroscopy with principal component analysis (PCA) as a tool for the characterisation of selected Polish herbhoneys (raspberry, lemon balm, rose, mint, black current, instant coffee, pine, hawthorn, and nettle). Fluorimetric spectra registered in the ranges ascribed to fluorescence of amino acids, polyphenols, vitamins, and products of Maillard's reaction enabled the comparison of herbhoney compositions. Obtained synchronous spectra combined with PCA were used to investigate potential differences between analysed samples and interactions between compounds present in them. The most substantial influence on the total variance had the intensities of polyphenols fluorescence. These intensities were the main factor differentiated by the analysed products.


Assuntos
Polifenóis , Vitamina A , Espectrometria de Fluorescência/métodos , Análise de Componente Principal , Polifenóis/análise , Extratos Vegetais
13.
Sensors (Basel) ; 24(4)2024 Feb 08.
Artigo em Inglês | MEDLINE | ID: mdl-38400276

RESUMO

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.


Assuntos
Imageamento Hiperespectral , Processamento de Imagem Assistida por Computador , p-Cloroanfetamina/análogos & derivados , Humanos , Processamento de Imagem Assistida por Computador/métodos , Máquina de Vetores de Suporte , Análise de Componente Principal
14.
Spectrochim Acta A Mol Biomol Spectrosc ; 311: 123977, 2024 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-38310743

RESUMO

A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.


Assuntos
COVID-19 , Nanopartículas Metálicas , Nanotubos , Ácidos Nucleicos , Humanos , Análise Espectral Raman/métodos , Ouro , Análise de Componente Principal , SARS-CoV-2/genética , COVID-19/diagnóstico , Nucleocapsídeo
15.
Spectrochim Acta A Mol Biomol Spectrosc ; 311: 124046, 2024 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-38364514

RESUMO

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.


Assuntos
Neoplasias da Mama , Nanopartículas Metálicas , Humanos , Feminino , Neoplasias da Mama/diagnóstico , Análise Espectral Raman/métodos , Nanopartículas Metálicas/química , Soro , Prata/química , Análise de Componente Principal
16.
Talanta ; 271: 125733, 2024 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-38309111

RESUMO

Considering the diversity of phosphates and their pivotal roles in physiological processes, detection of various phosphates related to their metabolism is urgent but challenging. Herein, we design a biosensor with zirconium-based MOFs (Zr-MOFs) and fluorophore-modified single-stranded DNA (F-ssDNA) for the analysis of phosphates. Relying on the interaction between Zr clusters and phosphate backbone, F-ssDNA is anchored on the surface of Zr-MOFs, inducing fluorescence resonance energy transfer (FRET) and subsequently quenching the fluorescence of F-ssDNA. Meanwhile, phosphates with different numbers of phosphate groups, molecular structures and coordination environments are able to adjust the FRET between Zr-MOFs and F-ssDNA via a site-occupying effect, recovering the fluorescence of F-ssDNA in distinct cases, which may result in diverse fluorescence signals. Consequently, seventeen phosphates and four phosphate mixtures are discriminated with the assistance of principal component analysis. These results provide new insight into the application of Zr-MOFs and broaden the path for the development of analytical methods for phosphates.


Assuntos
Técnicas Biossensoriais , Estruturas Metalorgânicas , Estruturas Metalorgânicas/química , Zircônio/química , Fosfatos , Análise de Componente Principal , DNA , DNA de Cadeia Simples , Técnicas Biossensoriais/métodos
17.
Nanomedicine ; 57: 102737, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38341010

RESUMO

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.


Assuntos
Neoplasias Encefálicas , Glioblastoma , Neoplasias Meníngeas , Meningioma , Humanos , Meningioma/diagnóstico , Meningioma/patologia , Glioblastoma/diagnóstico , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/patologia , Análise Multivariada , Análise Espectral Raman/métodos , Análise de Componente Principal , Neoplasias Meníngeas/patologia
18.
Food Chem ; 445: 138657, 2024 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-38354640

RESUMO

Rice varieties of different subspecies types (indica rice and japonica rice) across various geographical origins (Hunan, Jiangsu, and Northeast China) were monitored using microsatellite markers (simple sequence repeats, SSR). 110 representative rice cultivars were collected from the main crop areas. Multiple methods including clustering analysis (neighbor-joining (NJ) method, unweighted pair-group method with arithmetic mean (UPGMA) method), principal component analysis (PCA) and model-based grouping were applied. The study revealed that 25 pairs of SSR markers exhibited a broad range of polymorphism information content (PIC) values, ranging from 0.240 to 0.830. Furthermore, our study successfully achieved a higher overall mean correct rate of 99.09% in determining the geographical origin of rice. Simultaneously, it accurately classified indica rice and japonica rice. These findings are significant as they provide an SSR fingerprint of 110 high-quality rice cultivars, serving as a valuable scientific resource for the detection of rice adulteration and traceability of its origin.


Assuntos
Variação Genética , Oryza , Oryza/genética , Polimorfismo Genético , Repetições de Microssatélites/genética , Análise de Componente Principal , Filogenia
19.
BMC Med Inform Decis Mak ; 24(1): 49, 2024 Feb 14.
Artigo em Inglês | MEDLINE | ID: mdl-38355504

RESUMO

BACKGROUND: Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. METHODS: We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. RESULTS: MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. CONCLUSIONS: MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.


Assuntos
Algoritmos , Tomografia Computadorizada por Raios X , Humanos , Análise por Conglomerados , Análise de Componente Principal
20.
Naturwissenschaften ; 111(1): 9, 2024 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-38342817

RESUMO

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.


Assuntos
Quimiometria , Cabelo , Humanos , Masculino , Feminino , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Análise Discriminante , Cabelo/química , Análise de Componente Principal , Proteínas Mutadas de Ataxia Telangiectasia/análise
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