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1.
Food Res Int ; 188: 114511, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38823884

RESUMO

This study investigated the relationship between rheological properties, sensory perception, and overall acceptability in healthy young and old groups for dysphagia thickened liquids. Unflavored (UTL) and flavored (FTLP) thickened liquids were prepared using tap water or pomegranate juice at 10 different viscosity levels. The rheological properties were then evaluated via syringe flow test and line spread test (LST). When the apparent viscosity levels of UTL and FTLP were similar, the syringe test and LST results were also similar, indicating consistent flow behavior. Sensory perception evaluations showed that the young group better distinguished viscosity differences between stages compared to the old group. Regarding overall acceptability, the old group preferred samples with higher apparent viscosity than the young group. Principal component analysis and k-means cluster analysis were used to explore correlations between variables and classify thickened liquids into four groups. This can serve the foundation for standardized texture grades of dysphagia thickened liquids, considering rheological characteristics and sensory profiles.


Assuntos
Transtornos de Deglutição , Reologia , Humanos , Viscosidade , Adulto Jovem , Feminino , Masculino , Adulto , Idoso , Paladar , Percepção Gustatória , Pessoa de Meia-Idade , Bebidas , Sucos de Frutas e Vegetais , Análise de Componente Principal , Voluntários Saudáveis
2.
Mol Biol Rep ; 51(1): 715, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38824248

RESUMO

BACKGROUND: Camellia tachangensis F. C. Zhang is a five-compartment species in the ovary of tea group plants, which represents the original germline of early differentiation of some tea group plants. METHODS AND RESULTS: In this study, we analyzed single-nucleotide polymorphisms (SNPs) at the genome level, constructed a phylogenetic tree, analyzed the genetic diversity, and further investigated the population structure of 100 C. tachangensis accessions using the genotyping-by-sequencing (GBS) method. A total of 91,959 high-quality SNPs were obtained. Population structure analysis showed that the 100 C. tachangensis accessions clustered into three groups: YQ-1 (Village Group), YQ-2 (Forest Group) and YQ-3 (Transition Group), which was further consistent with the results of phylogenetic analysis and principal component analyses (PCA). In addition, a comparative analysis of the genetic diversity among the three populations (Forest, Village, and Transition Groups) detected the highest genetic diversity in the Transition Group and the highest differentiation between Forest and Village Groups. CONCLUSIONS: C. tachangensis plants growing in the forest had different genetic backgrounds from those growing in villages. This study provides a basis for the effective protection and utilization of C. tachangensis populations and lays a foundation for future C. tachangensis breeding.


Assuntos
Camellia , Variação Genética , Filogenia , Polimorfismo de Nucleotídeo Único , Camellia/genética , Polimorfismo de Nucleotídeo Único/genética , China , Variação Genética/genética , Genética Populacional/métodos , Genótipo , Análise de Componente Principal , Genoma de Planta
3.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38856173

RESUMO

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.


Assuntos
Análise de Componente Principal , Humanos , Análise Multivariada , Biologia Computacional/métodos , Fenótipo , Algoritmos , Genômica/métodos , Biomarcadores/sangue , Simulação por Computador
4.
Anat Histol Embryol ; 53(4): e13064, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38841825

RESUMO

There are different strains of laboratory mouse used in many different fields. These strains differ anatomically. In order to determine these anatomical differences, shape analysis was conducted according to species. CD-1, C57bl/6 and Balb-c strains were preferred to study these differences. Forty-eight adult mouse strains belonging to these strains were utilized. The bones were photographed and geometric morphometry was applied to these photographs. Principal Component Analysis was applied to determine shape variations. In Principal component 1 for cranium, CD-1 and C57bl/6 strain groups showed different shape variations, while Balb-c strain group showed similar shape variations to the other strain groups. Principal Component 1 for the mandible separated the CD-1 and C57bl/6 strain groups in terms of shape variation. Principal Component 2 explained most of the variation between the C57bl/6 and CD-1 lineage groups. In PC1 for molars, the CD-1 group showed a different shape variation from the other groups. Mahalanobis distances and Procrustes distances were measured using Canonical variance analysis to explain the differences between the lineage groups. These measurements were statistically significant. For cranium, in canonical variate 1, CD-1 group of mouse and Balb-c group of mouse were separated from each other. In canonical variate 2, C57bl/6 group of mouse were separated from the other groups. For mandible, Balb-c group of mouse in canonical variate 1 and CD-1 group of mouse in canonical variate 2 were separated from the other groups. For molars, CD-1 group of mouse in canonical variate 1 and Balb-c group of mouse in canonical variate 2 were separated from the other groups. It was thought that these anatomical differences could be caused by genotypic factors as well as dietary differences and many different habits that would affect the way their muscles work.


Assuntos
Mandíbula , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Crânio , Animais , Crânio/anatomia & histologia , Camundongos/anatomia & histologia , Mandíbula/anatomia & histologia , Camundongos Endogâmicos BALB C/anatomia & histologia , Camundongos Endogâmicos C57BL/anatomia & histologia , Dente/anatomia & histologia , Análise de Componente Principal , Especificidade da Espécie , Masculino
5.
Gigascience ; 132024 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-38832466

RESUMO

BACKGROUND: Due to human error, sample swapping in large cohort studies with heterogeneous data types (e.g., mix of Oxford Nanopore Technologies, Pacific Bioscience, Illumina data, etc.) remains a common issue plaguing large-scale studies. At present, all sample swapping detection methods require costly and unnecessary (e.g., if data are only used for genome assembly) alignment, positional sorting, and indexing of the data in order to compare similarly. As studies include more samples and new sequencing data types, robust quality control tools will become increasingly important. FINDINGS: The similarity between samples can be determined using indexed k-mer sequence variants. To increase statistical power, we use coverage information on variant sites, calculating similarity using a likelihood ratio-based test. Per sample error rate, and coverage bias (i.e., missing sites) can also be estimated with this information, which can be used to determine if a spatially indexed principal component analysis (PCA)-based prescreening method can be used, which can greatly speed up analysis by preventing exhaustive all-to-all comparisons. CONCLUSIONS: Because this tool processes raw data, is faster than alignment, and can be used on very low-coverage data, it can save an immense degree of computational resources in standard quality control (QC) pipelines. It is robust enough to be used on different sequencing data types, important in studies that leverage the strengths of different sequencing technologies. In addition to its primary use case of sample swap detection, this method also provides information useful in QC, such as error rate and coverage bias, as well as population-level PCA ancestry analysis visualization.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Análise de Sequência de DNA , Humanos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de DNA/métodos , Software , Análise de Componente Principal , Biologia Computacional/métodos , Algoritmos
6.
BMC Med Imaging ; 24(1): 137, 2024 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-38844854

RESUMO

BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.


Assuntos
Aprendizado de Máquina , Imagens de Fantasmas , Humanos , Tomografia Computadorizada por Raios X , Tomógrafos Computadorizados , Análise de Componente Principal , Redes Neurais de Computação , Algoritmos , Radiômica
7.
Hum Brain Mapp ; 45(8): e26682, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38825977

RESUMO

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.


Assuntos
Transtorno Bipolar , Imageamento por Ressonância Magnética , Obesidade , Análise de Componente Principal , Humanos , Transtorno Bipolar/diagnóstico por imagem , Transtorno Bipolar/tratamento farmacológico , Transtorno Bipolar/patologia , Adulto , Feminino , Masculino , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Obesidade/diagnóstico por imagem , Esquizofrenia/diagnóstico por imagem , Esquizofrenia/patologia , Esquizofrenia/tratamento farmacológico , Esquizofrenia/fisiopatologia , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/patologia , Análise por Conglomerados , Adulto Jovem , Encéfalo/diagnóstico por imagem , Encéfalo/patologia
8.
PLoS One ; 19(6): e0300938, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38829863

RESUMO

PURPOSE: To clarify the morphological factors of the pelvis in patients with developmental dysplasia of the hip (DDH), three-dimensional (3D) pelvic morphology was analyzed using a template-fitting technique. METHODS: Three-dimensional pelvic data of 50 patients with DDH (DDH group) and 3D pelvic data of 50 patients without obvious pelvic deformity (Normal group) were used. All patients were female. A template model was created by averaging the normal pelvises into a symmetrical and isotropic mesh. Next, 100 homologous models were generated by fitting the pelvic data of each group of patients to the template model. Principal component analysis was performed on the coordinates of each vertex (15,235 vertices) of the pelvic homologous model. In addition, a receiver-operating characteristic (ROC) curve was calculated from the sensitivity of DDH positivity for each principal component, and principal components for which the area under the curve was significantly large were extracted (p<0.05). Finally, which components of the pelvic morphology frequently seen in DDH patients are related to these extracted principal components was evaluated. RESULTS: The first, third, and sixth principal components showed significantly larger areas under the ROC curves. The morphology indicated by the first principal component was associated with a decrease in coxal inclination in both the coronal and horizontal planes. The third principal component was related to the sacral inclination in the sagittal plane. The sixth principal component was associated with narrowing of the superior part of the pelvis. CONCLUSION: The most important factor in the difference between normal and DDH pelvises was the change in the coxal angle in both the coronal and horizontal planes. That is, in the anterior and superior views, the normal pelvis is a triangle, whereas in DDH, it was more like a quadrilateral.


Assuntos
Displasia do Desenvolvimento do Quadril , Imageamento Tridimensional , Curva ROC , Humanos , Feminino , Displasia do Desenvolvimento do Quadril/patologia , Displasia do Desenvolvimento do Quadril/diagnóstico por imagem , Imageamento Tridimensional/métodos , Análise de Componente Principal , Ossos Pélvicos/diagnóstico por imagem , Pelve/patologia , Pelve/diagnóstico por imagem , Modelos Anatômicos , Luxação Congênita de Quadril/diagnóstico por imagem , Luxação Congênita de Quadril/patologia
9.
PLoS One ; 19(6): e0299476, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38829898

RESUMO

In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na+, Ca2+, Mg2+, Cl-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability. Meanwhile, the ISSA was used to optimize the kernel parameters and regularization coefficients in the KELM to establish a mine water inrush source discrimination model based on the KPCA-ISSA-KELM. Then, the mine water source data are input into the model for discrimination in compared with discrimination results of KPCA-SSA-KELM, KPCA-KELM, ISSA-KELM, SSA-KELM and KELM models. The results of the study show as follows: The discrimination results of the KPCA-ISSA-KELM model are in agreement with the actual results. Compared with the other models, the accuracy of the KPCA-ISSA-KELM model is improved by 8.33%, 12.5%, 4.17%, 21.83%, and 25%, respectively. Finally, when these models were applied to discriminate water sources in a coal mine in Shanxi, and the misjudgment rates of each model were 28.57%, 19.05%, 14.29%, 23.81%, 9.52% and 4.76%, respectively. From this, the KPCA-ISSA-KLEM model is the most accurate about discrimination and significantly better than other models in other evaluation indicators, verifying the universality and stability of the model. It can be effectively applied to the discrimination of inrush water sources in mines, providing important guarantees for mine safety production.


Assuntos
Algoritmos , Análise de Componente Principal , Aprendizado de Máquina , Minas de Carvão , Mineração , Modelos Teóricos
10.
Artigo em Inglês | MEDLINE | ID: mdl-38833396

RESUMO

The global trend of population aging presents an urgent challenge in ensuring the safety and well-being of elderly individuals, especially those living alone due to various circumstances. A promising approach to this challenge involves leveraging Human Action Recognition (HAR) by integrating data from multiple sensors. However, the field of HAR has struggled to strike a balance between accuracy and response time. While technological advancements have improved recognition accuracy, complex algorithms often come at the expense of response time. To address this issue, we introduce an innovative asynchronous detection method called Rapid Response Elderly Safety Monitoring (RESAM), which relies on progressive hierarchical action recognition and multi-sensor data fusion. Through initial analysis of inertial sensor data using Kernel Principal Component Analysis (KPCA) and multi-class classifiers, we efficiently reduce processing time and lower the false-negative rate (FNR). The inertial sensor identification serves as a pre-filter, enabling the identification of filtered abnormal signals. Decision-level data fusion is then executed, incorporating skeleton image analysis based on ResNet and the inertial sensor data from the initial step. This integration enables the accurate differentiation between normal and abnormal behaviors. The RESAM method achieves an impressive 97.4% accuracy on the UTD-MHAD database with a minimal delay of 1.22 seconds. On our internally collected database, the RESAM system attains an accuracy of 99%, ranking among the most accurate state-of-the-art methods available. These results underscore the practicality and effectiveness of our approach in meeting the critical demand for swift and precise responses in healthcare scenarios.


Assuntos
Algoritmos , Análise de Componente Principal , Humanos , Idoso , Masculino , Feminino , Reconhecimento Automatizado de Padrão/métodos , Segurança , Idoso de 80 Anos ou mais
11.
Sci Rep ; 14(1): 13353, 2024 06 10.
Artigo em Inglês | MEDLINE | ID: mdl-38858531

RESUMO

Shape of supracondylar fracture of the humeral of pediatric patients is analysed with Procrustes method. XR-images of fractures are considered both in anterio-posterior (AP) view and in a lateral (L) view. Applying Procrustes method for both views mean images are constructed and compared. Variability of shapes is quantified with a shape principal component analysis. Possibility of predictions of typical shape of humeral fracture and its variability using statistical shape analysis offers additional information on injury characteristics important in preoperative planning. Non-parametric tests (permutational and bootstrap) do not indicate statistical difference between Procrustes mean shapes in anterio-posterior and lateral projections. It is shown, however, that AP and L shapes of humeral fractures differ in their variability quantified by shape principal components.


Assuntos
Fraturas do Úmero , Humanos , Fraturas do Úmero/diagnóstico por imagem , Fraturas do Úmero/cirurgia , Criança , Pré-Escolar , Feminino , Masculino , Análise de Componente Principal , Úmero/lesões , Úmero/diagnóstico por imagem
12.
PLoS One ; 19(6): e0304663, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38843239

RESUMO

The productivity of agricultural ecosystems is heavily influenced by soil-dwelling organisms. To optimize agricultural practices and management, it is critical to know the composition, abundance, and interactions of soil microorganisms. Our study focused on Acrobeles complexus nematodes collected from tomato fields in South Africa and analyzed their associated bacterial communities utilizing metabarcoding analysis. Our findings revealed that A. complexus forms associations with a wide range of bacterial species. Among the most abundant species identified, we found Dechloromonas sp., a bacterial species commonly found in aquatic sediments, Acidovorax temperans, a bacterial species commonly found in activated sludge, and Lactobacillus ruminis, a commensal motile lactic acid bacterium that inhabits the intestinal tracts of humans and animals. Through principal component analysis (PCA), we found that the abundance of A. complexus in the soil is negatively correlated with clay content (r = -0.990) and soil phosphate levels (r = -0.969) and positively correlated with soil sand content (r = 0.763). This study sheds light on the bacterial species associated to free-living nematodes in tomato crops in South Africa and highlights the occurrence of various potentially damaging and beneficial nematode-associated bacteria, which can in turn, impact soil health and tomato production.


Assuntos
Produtos Agrícolas , Nematoides , Microbiologia do Solo , Solanum lycopersicum , Animais , Solanum lycopersicum/microbiologia , Solanum lycopersicum/parasitologia , África do Sul , Produtos Agrícolas/parasitologia , Produtos Agrícolas/microbiologia , Nematoides/microbiologia , Bactérias/classificação , Bactérias/genética , Bactérias/isolamento & purificação , Solo/parasitologia , RNA Ribossômico 16S/genética , Análise de Componente Principal
13.
Sci Rep ; 14(1): 13127, 2024 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-38849404

RESUMO

Improvement in the survival rate of gastric cancer, a prevalent global malignancy and the leading cause of cancer-related mortality calls for more avenues in molecular therapy. This work aims to comprehend drug resistance and explore multiple-drug combinations for enhanced therapeutic treatment. An endogenous network modeling clinic data with core gastric cancer molecules, functional modules, and pathways is constructed, which is then transformed into dynamics equations for in-silicon studies. Principal component analysis, hierarchical clustering, and K-means clustering are utilized to map the attractor domains of the stochastic model to the normal and pathological phenotypes identified from the clinical data. The analyses demonstrate gastric cancer as a cluster of stable states emerging within the stochastic dynamics and elucidate the cause of resistance to anti-VEGF monotherapy in cancer treatment as the limitation of the single pathway in preventing cancer progression. The feasibility of multiple objectives of therapy targeting specified molecules and/or pathways is explored. This study verifies the rationality of the platform of endogenous network modeling, which contributes to the development of cross-functional multi-target combinations in clinical trials.


Assuntos
Neoplasias Gástricas , Neoplasias Gástricas/tratamento farmacológico , Neoplasias Gástricas/patologia , Neoplasias Gástricas/metabolismo , Humanos , Resistencia a Medicamentos Antineoplásicos , Modelos Biológicos , Terapia de Alvo Molecular/métodos , Análise por Conglomerados , Análise de Componente Principal , Fator A de Crescimento do Endotélio Vascular/metabolismo , Fator A de Crescimento do Endotélio Vascular/antagonistas & inibidores , Transdução de Sinais/efeitos dos fármacos
14.
Lipids Health Dis ; 23(1): 177, 2024 Jun 08.
Artigo em Inglês | MEDLINE | ID: mdl-38851716

RESUMO

BACKGROUND: Exposure to different concentration levels of fatty acids (FAs) may have an impact on depression. However, previous studies using individual FAs may not reflect the performance of mixtures of various FAs, and the associations of FA patterns with depression remain unclear. METHODS: We conducted the cross-sectional analysis in 792 adults aged 18 and older with available serum FAs and depression screening data in the National Health and Nutrition Examination Survey (NHANES) 2011-2012. The serum concentrations of thirty FAs were measured using gas chromatography-mass spectrometry and their percentage compositions were subsequently calculated. Depression was defined as the Patient Health Questionnaire-9 score ≥ 10. We employed principal component analysis to derive serum FA patterns. We examined the association between these patterns and depression in the overall population and various subgroups through survey-weighted logistic regression. RESULTS: Four distinct patterns of serum FAs were identified: 'high eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA); low docosatetraenoic acid (DTA) and docosapentaenoic acid (DPA) n-6', 'high long-chain saturated FA and long chain FA', 'low median-chain saturated FA and myristoleic acid' and 'low capric acid and lauric acid; high gamma-linolenic acid (GLA) and stearidonic acid (SDA)' pattern. Individuals in the high tertile of 'high EPA and DHA; low DTA and DPA n-6' pattern score had 0.46 (95% CI: 0.22, 0.93) lower odds of developing depression compared to individuals in the lowest tertile after adjusting for confounders such as age, sex, physical activity and total energy intake, etc. The odds ratio (OR) of depression was increased in the population with the highest tertile of 'low capric acid and lauric acid; high GLA and SDA' pattern (OR: 2.45, 95% CI: 1.24, 4.83). In subgroup analyses, we observed that the association between 'high EPA and DHA; low DTA and DPA n-6' and depression persisted among specific demographic and lifestyle subgroups, including females, non-Mexican Americans, non-obese, those aged over 60 years, smokers and drinkers. Similarly, 'low capric acid and lauric acid; high GLA and SDA' showed stable associations in female, non-Mexican Americans and smokers. CONCLUSIONS: Serum FA patterns are associated with depression, and their relationships vary across sex, race, BMI, age, smoking and drinking subgroups, highlighting the importance of considering specific FA patterns within these demographic and lifestyle categories. Utilization of combined FA administration may serve as a mitigation measure against depression in these specific populations.


Assuntos
Depressão , Ácidos Graxos , Inquéritos Nutricionais , Humanos , Feminino , Masculino , Depressão/sangue , Depressão/epidemiologia , Adulto , Pessoa de Meia-Idade , Ácidos Graxos/sangue , Estudos Transversais , Estados Unidos/epidemiologia , Ácidos Decanoicos/sangue , Ácido Eicosapentaenoico/sangue , Idoso , Ácidos Graxos Insaturados/sangue , Adulto Jovem , Adolescente , Análise de Componente Principal
15.
PLoS One ; 19(5): e0304139, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38814958

RESUMO

The present study aimed to assess the use of technical-tactical variables and machine learning (ML) classifiers in the automatic classification of the passing difficulty (DP) level in soccer matches and to illustrate the use of the model with the best performance to distinguish the best passing players. We compared eight ML classifiers according to their accuracy performance in classifying passing events using 35 technical-tactical variables based on spatiotemporal data. The Support Vector Machine (SVM) algorithm achieved a balanced accuracy of 0.70 ± 0.04%, considering a multi-class classification. Next, we illustrate the use of the best-performing classifier in the assessment of players. In our study, 2,522 pass actions were classified by the SVM algorithm as low (53.9%), medium (23.6%), and high difficulty passes (22.5%). Furthermore, we used successful rates in low-DP, medium-DP, and high-DP as inputs for principal component analysis (PCA). The first principal component (PC1) showed a higher correlation with high-DP (0.80), followed by medium-DP (0.73), and low-DP accuracy (0.24). The PC1 scores were used to rank the best passing players. This information can be a very rich performance indication by ranking the best passing players and teams and can be applied in offensive sequences analysis and talent identification.


Assuntos
Desempenho Atlético , Aprendizado de Máquina , Futebol , Máquina de Vetores de Suporte , Humanos , Desempenho Atlético/classificação , Análise de Componente Principal , Algoritmos
16.
J Environ Manage ; 360: 121208, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38788413

RESUMO

Stability of soil organic carbon (SOC) is pre-requisite for stabilization of C leading to long-term C sequestration. However, development of a comprehensive metric of SOC stability is a major challenge. The objectives for the study were to develop novel SOC stability indices by encompassing physical, chemical, and biochemical SOC stability parameters and identifying the most important indicators from a Mollisol, an Inceptisol, a Vertisol, and an Alfisol under long-term manuring and fertilization. The treatments were control, 100%NPK, 50% NPK+ 50% N through either farmyard manure, cereal residue, or green manure. SOC stability indicators were selected, transformed and integrated into unique SOC stability indices via conceptual framework and principal component analysis. Principal component analysis identified Al-macroaggregate, humic acid C-microaggregate, microaggregate-C, particulate organic matter-C-macroaggregate and polyphenol-microaggregate as the important SOC stability indicators. The principal component analysis -based SOC stability index varied from 0.2 to 0.9, 0.1 to 0.5, 0.2 to 0.6, 0.1 to 0.5 for Mollisol, Inceptisol, Vertisol and Alfisol, respectively. The SOC-stability index derived from conceptual framework and principal component analysis significantly lined up well with one another, although NaOCl-Res-C showed a high correlation with both conceptual framework (r = 0.8) and principal component analysis-based (r = 0.7) SOC stability indexes, suggesting that both methods might be used to quickly assess SOC stability in four soil orders. Overall, 50%NPK+50%N by farmyard manure or green manure emerged as the most effective management practices for enhancing stability of SOC in Mollisol, Inceptisol, Vertisol, and Alfisol of India which might act as major C sink in rice-wheat and maize-wheat cropping systems. The other aspect of C sequestration is to enhance agricultural productivity without depending much on expensive chemical fertilizers. The model yardstick thus developed for assessing SOC stability might be useful to other systems as well.


Assuntos
Carbono , Solo , Solo/química , Carbono/análise , Índia , Fertilizantes/análise , Esterco , Agricultura , Análise de Componente Principal , Nitrogênio/análise , Sequestro de Carbono , Substâncias Húmicas/análise
17.
Talanta ; 276: 126209, 2024 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-38728802

RESUMO

The rapid development of nanozymes has offered substantial opportunities for the fields of biomedicine, chemical sensing, and food safety. Among these applications, multichannel sensors, with the capability of simultaneously detecting multiple target analytes, hold promise for the practical application of nanozymes in chemical sensing with high detection efficiency. In this study, Rh-decorated Pd nanocubes (Pd-Rh nanocubes) with significantly enhanced peroxidase-like activity are synthesized through the mediation of underpotential deposition (UPD) and subsequently employed to develop a multichannel colorimetric sensor for discriminating tea polyphenols (TPs) and tea authentication. Based on a single reactive unit of efficient catalytic oxidation of 3,3',5,5'-tetramethylbenzidine dihydrochloride (TMB), the nanozyme-based multichannel colorimetric sensor responds to each analyte in as short as 1 min. With the aid of principal component analysis (PCA) and hierarchical cluster analysis (HCA), various TPs and types of tea can be accurately identified. This work not only provides a new type of simply structured and highly active nanozymes but also develops a concise and rapid multichannel sensor for practical application in tea authentication and quality inspection.


Assuntos
Colorimetria , Paládio , Polifenóis , Chá , Chá/química , Polifenóis/análise , Polifenóis/química , Colorimetria/métodos , Paládio/química , Benzidinas/química , Nanopartículas Metálicas/química , Análise de Componente Principal , Peroxidase/química , Catálise , Oxirredução
18.
J Exp Biol ; 227(11)2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38785337

RESUMO

Predators are not perfect, as some of their prey capture attempts result in failure. Successful attempts may be partly due to predators modulating their capture kinematics in relation to variation in the visual cues of the prey to increase the probability of success. In praying mantises, which have been suggested to possess stereoscopic vision, variation in prey distance has been shown to elicit variation in the probability of an attempt. However, it remains to be examined whether variation in prey distance results in mantises modulating their attempt to successfully capture prey. The goals of this study were to examine these relationships using the praying mantis system. Using 11 adult female Sphodromantis lineola, we recorded 192 prey capture attempts at 1000 Hz with two cameras to examine the 3D kinematics of successful and unsuccessful prey capture attempts. Using a combination of principal component analysis (PCA) and logistic regression, our results show that as prey distance increases, mantises adjust through greater and faster expansion of the forelegs and body (PC1), which significantly predicts capture success. However, PC1 only explains 22% of the variation in all prey capture attempts, suggesting that the other components may be related to additional aspects of the prey. Our results suggest that the distances at which mantises prefer to attempt to capture prey may be the result of their greater probability of successfully capturing the prey. These results highlight the range of motions mantises use when attempting to capture prey, suggesting flexibility in their prey capture attempts in relation to prey position.


Assuntos
Mantódeos , Comportamento Predatório , Fenômenos Biomecânicos , Animais , Feminino , Mantódeos/fisiologia , Análise de Componente Principal , Modelos Logísticos
19.
Sci Rep ; 14(1): 10465, 2024 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-38714823

RESUMO

Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link between balance impairments and joint kinematics during walking remains unclear. Clinical assessments and gait measurements using motion analysis system was conducted in 44 stroke patients. This study utilised principal component analysis to identify key joint kinematics characteristics of patients with stroke during walking using average joint angles of pelvis and bilateral lower limbs in every gait-cycle percentile related to balance impairments. Reconstructed kinematics showed the differences in joint kinematics in both paretic and nonparetic lower limbs that can be distinguished by balance impairment, particularly in the sagittal planes during swing phase. The impaired balance group exhibited greater joint variability in both the paretic and nonparetic limbs in the sagittal plane during entire gait phase and during terminal swing phase respectively compared with those with high balance scores. This study provides a more comprehensive understanding of stroke hemiparesis gait patterns and suggests considering both nonparetic and paretic limb function, as well as bilateral coordination in clinical practice. Principal component analysis can be a useful assessment tool to distinguish differences in balance impairment and dynamic symmetry during gait in patients with stroke.


Assuntos
Marcha , Equilíbrio Postural , Análise de Componente Principal , Acidente Vascular Cerebral , Caminhada , Humanos , Masculino , Feminino , Equilíbrio Postural/fisiologia , Acidente Vascular Cerebral/fisiopatologia , Acidente Vascular Cerebral/complicações , Pessoa de Meia-Idade , Caminhada/fisiologia , Idoso , Fenômenos Biomecânicos , Marcha/fisiologia , Transtornos Neurológicos da Marcha/fisiopatologia , Transtornos Neurológicos da Marcha/diagnóstico , Transtornos Neurológicos da Marcha/etiologia , Adulto
20.
Food Res Int ; 186: 114346, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38729720

RESUMO

Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.


Assuntos
Coffea , Café , Manipulação de Alimentos , Sementes , Brasil , Coffea/química , Sementes/química , Manipulação de Alimentos/métodos , Café/química , Alcaloides/análise , Cromatografia Líquida de Alta Pressão , Humanos , Paladar , Análise de Componente Principal
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