Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 13.881
Filtrar
1.
Water Sci Technol ; 82(5): 927-939, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33031071

RESUMO

UV/Vis spectrometers are powerful tools for online monitoring of wastewater constituents and processes. However, most studies only focus on typical parameters such as chemical oxygen demand (COD) and total suspended solids. This work presents a multi-parameter approach for calibration of a UV/Vis spectrometer for online monitoring of sewer systems. Parameters studied include soluble and total COD, nitrate, ammonium, sulphate and orthophosphate, as well as total dissolved sulphide, bisulphide and hydrogen sulphide, because they are one of the main causes for odour and corrosion in sewer systems. Two calibration methods are compared: multiple linear regression included in the manufacturer's software, and partial least square (PLS) computed using the pls package of the R library. Performance of the methods is evaluated for calibration and validation data sets employing four different criteria: relative root mean square error (RMSErel), RMSE-observations standard deviation ratio, Nash-Sutcliffe efficiency and percentage bias. A method-parameter dependency was revealed during the calibration phase but, when predicting new data, the PLS method showed higher robustness for almost all parameters. Both methods were able to predict concentration trends associated with sewer processes, some of which are strongly correlated to the sulphide species.


Assuntos
Águas Residuárias , Análise da Demanda Biológica de Oxigênio , Calibragem , Análise dos Mínimos Quadrados , Espectrofotometria Ultravioleta
2.
Artigo em Inglês | MEDLINE | ID: mdl-33017918

RESUMO

In this paper, a novel pre-treatment technique Hilbert Huang Transformation with filtering (HHTF) that is coupling of the Hilbert Huang Transformation and the digital filtering is proposed for the measurement of glucose from near infrared spectroscopy. HHTF comprises of the Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis. In Hilbert spectral analysis, Butterworth filtering was used to eliminate the noise present in the Intrinsic Mode Functions (IMFs). The traditional Partial Least squares Regression (PLSR) has been used as the regression method. The proposed HHTF with the PLSR method has been assessed to determine the concentration of glucose from near infrared spectra of two distinct compositions that are prepared by mixing triacetin, urea and glucose in a phosphate buffer solution (PBS) and another composition of glucose and human serum albumin in a PBS. The efficiency of the proposed method has been compared with the standard normal variate and the 1st derivative preprocessing methods and is shown to outperform both.


Assuntos
Glucose , Espectroscopia de Luz Próxima ao Infravermelho , Calibragem , Humanos , Análise dos Mínimos Quadrados
3.
Artigo em Inglês | MEDLINE | ID: mdl-33017919

RESUMO

The objective of quantitative ultrasound (QUS) is to characterize tissue microstructure by parametrizing backscattered radiofrequency (RF) signals from clinical ultrasound scanners. Herein, we develop a novel technique based on dynamic programming (DP) to simultaneously estimate the acoustic attenuation, the effective scatterer size (ESS), and the acoustic concentration (AC) from ultrasound backscattered power spectra. This is achieved through two different approaches: (1) using a Gaussian form factor (GFF) and (2) using a general form factor (gFF) that is more flexible than the Gaussian form factor but involves estimating more parameters. Both DP methods are compared to an adaptation of a previously proposed least-squares (LSQ) method. Simulation results show that in the GFF approach, the variance of DP is on average 88%, 75% and 32% lower than that of LSQ for the three estimated QUS parameters. The gFF approach also yields similar improvements.


Assuntos
Acústica , Análise dos Mínimos Quadrados , Distribuição Normal , Ultrassonografia
4.
Artigo em Inglês | MEDLINE | ID: mdl-33017922

RESUMO

The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification approach for the VAR and SS models, based on Least Absolute Shrinkage and Selection Operator (LASSO), that has the advantages of maintaining good accuracy even when few data samples are available and yielding as output a sparse matrix of estimated information transfer. The performances of LASSO identification were first tested and compared to those of OLS by a simulation study and then validated on real electroencephalographic (EEG) signals recorded during a motor imagery task. Both studies indicated that LASSO, under conditions of data paucity, provides better performances in terms of network structure. Given the general nature of the model, this work opens the way to the use of LASSO regression for the computation of several measures of information dynamics currently in use in computational neuroscience.


Assuntos
Eletroencefalografia , Entropia , Análise dos Mínimos Quadrados , Modelos Lineares , Distribuição Normal
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2723-2727, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018569

RESUMO

Central aortic blood pressure (CABP) is a very-well recognized source of information to asses the cardiovascular system conditions. However, the clinical measurement protocol of this pulse wave is very intrusive and burdensome as it requires expert staff and complicated invasive settings. On the other hand, the measurement of peripheral blood pressure is much more straightforward and easy-to-get non-invasively. Several mathematical tools have been employed in the past few decades to reconstruct CABP waveforms from distorted peripheral pressure signals. More specifically, the cross-relation approach together with the widely used least-squares method, are shown to be effective as a way to estimate CABP waves. In this paper, we propose an improved cross-relation method that leverages the values of the diastolic and systolic pressures as box constraints. In addition, a mean-matching criterion is introduced to relax the need for the input and output mean values to be strictly equal. Using the proposed method, the root mean squared error is reduced by approximately 20% while the computational complexity is not significantly increased.


Assuntos
Aorta , Determinação da Pressão Arterial , Pressão Sanguínea , Humanos , Análise dos Mínimos Quadrados , Reprodutibilidade dos Testes
6.
Environ Monit Assess ; 192(11): 675, 2020 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-33025222

RESUMO

The largest uranium-phosphate deposit in Brazil also contains considerable levels of rare earth elements (REEs), which allows for the co-mining of these three ores. The most common methods for REE determination are time-consuming and demand complex sample preparation and use of hazardous reagents. Thus, the development of a safer and faster method to predict REEs in soil could aid in the assessment of these elements. We investigated the efficiency of near-infrared (NIR) spectroscopy to predict REEs in the soil of the uranium-phosphate deposit of Itataia, Brazil. We collected 50 composite topsoil samples in a well-distributed sampling grid along the deposit. The NIR measures in the soils ranged from 750 to 2500 nm. Three partial least squares regressions (PLSR) were selected to calibrate the spectra: full-spectrum partial least squares (PLS), interval partial least squares (iPLS), and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The concentrations of REEs were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES). In addition to raw spectral data, we also used spectral pretreatments to investigate the effects on prediction results: multiplicative scatter correction (MSC), Savitzky-Golay derivatives (SG), and standard normal variate transformation (SNV). Positive results were obtained in PLS for La and ΣLREE using MSC pretreatment and in iSPA-PLS for Nd and Ce using raw data. The accuracy of the measurements was related to the REE concentration in soil; i.e., elements with higher concentrations tended to present more accurate results. The results obtained here aim to contribute to the development of NIR spectroscopy techniques as a tool for mapping the concentrations of REEs in topsoil.


Assuntos
Urânio , Brasil , Monitoramento Ambiental , Análise dos Mínimos Quadrados , Fosfatos , Solo , Espectroscopia de Luz Próxima ao Infravermelho
8.
Phys Biol ; 17(6): 065005, 2020 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-32966241

RESUMO

Error analysis and data visualization of positive COVID-19 cases in 27 countries have been performed up to August 8, 2020. This survey generally observes a progression from early exponential growth transitioning to an intermediate power-law growth phase, as recently suggested by Ziff and Ziff. The occurrence of logistic growth after the power-law phase with lockdowns or social distancing may be described as an effect of avoidance. A visualization of the power-law growth exponent over short time windows is qualitatively similar to the Bhatia visualization for pandemic progression. Visualizations like these can indicate the onset of second waves and may influence social policy.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Pandemias , Pneumonia Viral/epidemiologia , China/epidemiologia , Infecções por Coronavirus/transmissão , Bases de Dados Factuais , Progressão da Doença , Humanos , Análise dos Mínimos Quadrados , Modelos Lineares , Modelos Logísticos , Conceitos Matemáticos , Modelos Biológicos , Pandemias/estatística & dados numéricos , Pneumonia Viral/transmissão , Fatores de Tempo
9.
PLoS One ; 15(9): e0236650, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32877445

RESUMO

This study investigates the relationship between supervisory behavior, conflict management strategies, and sustainable employee performance and inquires the mediating effect of conflict management strategies. Data were collected from the SMEs of the manufacturing industry of Pakistan. The significance of the model was assessed using the PLS-SEM (structural equation modeling). The findings of the study revealed a positive and significant relationship between supervisory behavior and sustainable employee behavior. Similarly, conflict management strategies had a positive effect on the relationship between supervisory behavior and sustainable employee behavior. This study adds in the current literature of supervisory behavior as a critical predictor of sustainable employee performance in two ways. Firstly, this study validates Conflict management strategies as an influential mediator between the relationship of supervisory behavior and sustainable employee performance. Secondly, this study provides substantial practical implications for managers at SMEs to enhance sustainable employee performance through supervisory behavior, stimulated by conflict management strategies. This study is based on cross-sectional data; more longitudinal studies can further strengthen the generalizability of relationships between the constructs. The study adds in the current literature of PLS-SEM as an assessment model for direct and mediation relationships.


Assuntos
Negociação , Desempenho Profissional , Adulto , Emprego/organização & administração , Feminino , Humanos , Indústrias/organização & administração , Análise dos Mínimos Quadrados , Masculino , Pessoa de Meia-Idade , Negociação/métodos , Organização e Administração , Paquistão , Desempenho Profissional/organização & administração , Adulto Jovem
10.
N Engl J Med ; 383(8): 711-720, 2020 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-32813947

RESUMO

BACKGROUND: Homozygous familial hypercholesterolemia is characterized by premature cardiovascular disease caused by markedly elevated levels of low-density lipoprotein (LDL) cholesterol. This disorder is associated with genetic variants that result in virtually absent (null-null) or impaired (non-null) LDL-receptor activity. Loss-of-function variants in the gene encoding angiopoietin-like 3 (ANGPTL3) are associated with hypolipidemia and protection against atherosclerotic cardiovascular disease. Evinacumab, a monoclonal antibody against ANGPTL3, has shown potential benefit in patients with homozygous familial hypercholesterolemia. METHODS: In this double-blind, placebo-controlled, phase 3 trial, we randomly assigned in a 2:1 ratio 65 patients with homozygous familial hypercholesterolemia who were receiving stable lipid-lowering therapy to receive an intravenous infusion of evinacumab (at a dose of 15 mg per kilogram of body weight) every 4 weeks or placebo. The primary outcome was the percent change from baseline in the LDL cholesterol level at week 24. RESULTS: The mean baseline LDL cholesterol level in the two groups was 255.1 mg per deciliter, despite the receipt of maximum doses of background lipid-lowering therapy. At week 24, patients in the evinacumab group had a relative reduction from baseline in the LDL cholesterol level of 47.1%, as compared with an increase of 1.9% in the placebo group, for a between-group least-squares mean difference of -49.0 percentage points (95% confidence interval [CI], -65.0 to -33.1; P<0.001); the between-group least-squares mean absolute difference in the LDL cholesterol level was -132.1 mg per deciliter (95% CI, -175.3 to -88.9; P<0.001). The LDL cholesterol level was lower in the evinacumab group than in the placebo group in patients with null-null variants (-43.4% vs. +16.2%) and in those with non-null variants (-49.1% vs. -3.8%). Adverse events were similar in the two groups. CONCLUSIONS: In patients with homozygous familial hypercholesterolemia receiving maximum doses of lipid-lowering therapy, the reduction from baseline in the LDL cholesterol level in the evinacumab group, as compared with the small increase in the placebo group, resulted in a between-group difference of 49.0 percentage points at 24 weeks. (Funded by Regeneron Pharmaceuticals; ELIPSE HoFH ClinicalTrials.gov number, NCT03399786.).


Assuntos
Proteínas Semelhantes a Angiopoietina/antagonistas & inibidores , Anticorpos Monoclonais/uso terapêutico , Anticolesterolemiantes/uso terapêutico , LDL-Colesterol/sangue , Hiperlipoproteinemia Tipo II/tratamento farmacológico , Adolescente , Adulto , Idoso , Anticorpos Monoclonais/efeitos adversos , Anticorpos Monoclonais/sangue , Anticolesterolemiantes/efeitos adversos , Anticolesterolemiantes/sangue , Criança , Método Duplo-Cego , Feminino , Homozigoto , Humanos , Hiperlipoproteinemia Tipo II/sangue , Hiperlipoproteinemia Tipo II/genética , Infusões Intravenosas , Análise dos Mínimos Quadrados , Masculino , Pessoa de Meia-Idade , Mutação , Receptores de LDL/metabolismo , Adulto Jovem
11.
PLoS One ; 15(8): e0238149, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32833991

RESUMO

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.


Assuntos
Agaricales/classificação , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Algoritmos , Confiabilidade dos Dados , Análise Discriminante , Fungos/classificação , Análise dos Mínimos Quadrados , Aprendizado de Máquina , Redes Neurais de Computação , Análise de Componente Principal/métodos , Reprodutibilidade dos Testes , Cogumelos Shiitake , Espectrofotometria Infravermelho/métodos , Máquina de Vetores de Suporte
12.
PLoS One ; 15(8): e0235921, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32750049

RESUMO

Developing a conceptual model is vital for small-scale organic farmer's credit access to sustain the livelihoods. However, smallholders continually face severe problems in getting finance that lead to reduce investment and in turn, challenges the livelihoods. Therefore, the aim of the present study was to establish and empirically test a theoretical model to explore how agility and innovativeness in organic food value chain finance are achieved through ITI, TRST, CG, ICT, and IS, and how these, in turn, can accelerate financial flow in the value chain and enhance competitiveness. The present study used a survey method and collected data from small-scale farmers, traders, and financial institutions. The model and hypothesis are tested using data obtained from 331 respondents through partial least square structure equation modeling techniques. We argue that development of theoretical model show potential to increase creditworthiness of smallholders and overcome uncertainties that impede traditional value chain credit arrangement. Thus, the present study could provide new ways to integrate the value chain partners, through information and communication technology and governance arrangements in the organic food value chain financing. This study demonstrates that the mediations of innovativeness and agility significantly affect the development of new financial products to make agile the financial flow, which in turn positively influences value chain competitiveness. Significant judgments are required for trustworthy relations among the value chain partners to positively harness innovative product development for swifter value chain finance. Therefore, this theoretical model should not be regarded as a quick solution, but a process of testing, error, and learning by doing so.


Assuntos
Alimentos Orgânicos/economia , Agricultura Orgânica/economia , Fazendeiros , Humanos , Análise dos Mínimos Quadrados , Análise Multivariada , Paquistão
13.
BMC Bioinformatics ; 21(1): 357, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32795265

RESUMO

BACKGROUND: Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen. RESULTS: The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables. Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble. CONCLUSIONS: When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.


Assuntos
Biomarcadores/metabolismo , Biologia Computacional/métodos , Teorema de Bayes , Bases de Dados Factuais , Análise Discriminante , Feminino , Humanos , Análise dos Mínimos Quadrados , Modelos Logísticos , Tamanho da Amostra , Neoplasias de Mama Triplo Negativas/diagnóstico , Neoplasias de Mama Triplo Negativas/genética
14.
Ecotoxicol Environ Saf ; 205: 111168, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-32846299

RESUMO

Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level ozone soft sensor. The soft sensor was developed based on a novel technique of damped least squares neural network (DLSNN) with greedy backward elimination (GBE) for the estimation of hazardous ground-level ozone. Only three meteorological factors were used as input variables in the estimation of ground-level ozone and we have used weighted k-nearest neighbors (WkNN) classifier with fast response for development of air quality warning system. We have chosen the urban areas of Taiwan for this study and have analyzed seasonal variations in the ground-level ozone concentration of various cities in Taiwan as part of this work. Moreover, descriptive statistics and linear dependence of ozone concentration based on Spearman correlation coefficient, Kendall's tau coefficient, and Pearson coefficient are calculated. The proposed DLSNN/GBE method exhibited excellent performance resulting in very low mean square error (MSE), mean absolute error (MAE), and high coefficient of determination (R2) compared to other traditional approaches in ozone concentration estimation. We have achieved a good fit in the determination of ozone concentration from meteorological features of atmosphere. Moreover, the excellent performance of proposed urban air quality warning system was evident from the good F1-score value of 0.952 achieved by the WkNN classifier.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Redes Neurais de Computação , Ozônio/análise , Algoritmos , Atmosfera , Cidades , Análise dos Mínimos Quadrados , Estações do Ano , Taiwan
15.
PLoS One ; 15(8): e0237920, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32841258

RESUMO

Adjusted plus minus (APM) measures have redefined our understanding of player value in basketball and hockey, where both are team games featuring player productivity spillovers. APM measures use seasonal play-by-play data to estimate individual player contributions. If a team's overall score margin success is figuratively represented by a pie, APM measures are well-designed to slice the pie and attribute individual contributions accordingly. However, they do not account for the possibility that better players can increase the overall size of the pie and thus increase the size of the slice (overall APM value) for teammates. Herein, we use data from NBA player-season Real Plus Minus (RPM)-a leading APM measure-for all recorded player-seasons from 2013-19 and player lineup data to test whether RPM is related to teammate quality. We run sets of linear fixed effect regression models to explain variation in RPM across player-seasons. We also employ a two-stage least square (2-SLS) method for robustness check. Both empirical approaches address potential endogeneity in the relationship of interest. We find strong evidence that RPM is related to on-court teammate quality. Despite adjusting for teammate and opponent quality, RPM does not control for complementarity effects. As such, RPM is not suited for out-of-sample prediction.


Assuntos
Atletas , Basquetebol , Adulto , Distribuição por Idade , Humanos , Análise dos Mínimos Quadrados , Análise de Regressão , Adulto Jovem
16.
Braz J Microbiol ; 51(3): 1109-1115, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32809115

RESUMO

COVID-19 has killed more than 500,000 people worldwide and more than 60,000 in Brazil. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns, and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population, and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities' efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per million - the average level in which some countries start to relax the stay-at-home measures - was estimated. If the daily new deaths curve was bending today (i.e., about 1250 deaths per day), the predicted date would be on July 5. Forecast predictions allowed the estimation of overall death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve may correspond to additional 1685 deaths at the end of COVID-19 outbreak in Brazil (R2 = 0.9890). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision-making to properly fight COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Previsões/métodos , Pneumonia Viral/epidemiologia , Algoritmos , Brasil/epidemiologia , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/prevenção & controle , Detergentes/provisão & distribução , Educação/estatística & dados numéricos , Humanos , Análise dos Mínimos Quadrados , Dinâmica não Linear , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Pneumonia Viral/prevenção & controle , Política , Densidade Demográfica , Pobreza , Fatores Socioeconômicos , Estatística como Assunto , Fatores de Tempo , Abastecimento de Água/normas
17.
Chemosphere ; 260: 127479, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32758777

RESUMO

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.


Assuntos
Anfípodes/fisiologia , Propranolol/toxicidade , Sulfonamidas/toxicidade , Triclosan/toxicidade , Poluentes Químicos da Água/toxicidade , Animais , Cromatografia Líquida/métodos , Análise dos Mínimos Quadrados , Espectrometria de Massas/métodos , Metaboloma , Metabolômica/métodos , Preparações Farmacêuticas , Águas Residuárias
18.
PLoS One ; 15(8): e0237997, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32836226

RESUMO

For the first time, ten frequentist estimation methods are considered on stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with the same shape parameter. The start point to estimate the parameter R is the maximum likelihood method. Other than the maximum likelihood method, a nine frequentist estimation methods are used to estimate R, namely: least square, weighted least square, percentile, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, method of Cramér-von Mises, Anderson-Darling and Right-tail Anderson-Darling. We also consider two parametric bootstrap confidence intervals of R. We compare the efficiency of the different proposed estimators by conducting an extensive Mont Carlo simulation study. The performance and the finite sample properties of the different estimators are compared in terms of relative biases and relative mean squared errors. The Mont Carlo simulation study revels that the percentile and maximum product of spacing methods are highly competitive with the other methods for small and large sample sizes. To show the applicability and the importance of the proposed estimators, we analyze one real data set.


Assuntos
Engenharia , Distribuições Estatísticas , Estresse Mecânico , Intervalos de Confiança , Análise dos Mínimos Quadrados , Teste de Materiais , Método de Monte Carlo
19.
J Chromatogr A ; 1627: 461420, 2020 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-32823115

RESUMO

Monitoring preparative protein chromatographic steps by in-line spectroscopic tools or fraction analytics results in medium or large sized data matrices. Multivariate Curve Resolution (MCR) serve to compute or to estimate the concentration values of the pure components only from these data matrices. However, MCR methods often suffer from an inherent solution ambiguity which underlies the factorization problem. The typical unimodality of the chromatographic profiles of pure components can support the chemometric analysis. Here we present the pure components estimation process within the framework of the area of feasible solutions, which is a systematic approach to represent the range of all possible solutions. The unimodality constraint in combination with Pareto optimization is shown to be an effective method for the pure component calculation. Applications are presented for chromatograms on a model protein mixture containing ribonuclease A, cytochrome c and lysozyme and on a two-dimensional chromatographic separation of a monoclonal antibody from its aggregate species. The root mean squared errors of the first case study are 0.0373, 0.0529 and 0.0380 g/L compared to traditional off-line analytics. The second case study illustrates the potential of recovering hidden components with MCR from off-line reference analytics.


Assuntos
Produtos Biológicos/análise , Cromatografia/métodos , Preparações Farmacêuticas/análise , Anticorpos Monoclonais/isolamento & purificação , Estudos de Viabilidade , Análise dos Mínimos Quadrados , Análise Multivariada , Proteínas/isolamento & purificação , Reprodutibilidade dos Testes
20.
Food Chem ; 332: 127424, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-32619947

RESUMO

Celery (Apium graveolens L. var dulce) is a widely cultivated vegetable which is popularly consumed due to its nutrient content and contains bioactive metabolites with positive effects on human physiology. In this study, 1H NMR spectroscopy coupled with multivariate statistical analyses was used to distinguish celery stem and leaf samples from different geographical origins. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the differences between celery extracts from three geographical origins: Australia, Taiwan and China. Sugars, amino acids and organic acids were found to contribute significantly to the differentiation between origins, with mannitol identified as an important discriminating metabolite. It was demonstrated that NMR-based metabolomics is an effective approach for establishing reliable metabolomic fingerprints and profiles, enabling the identification of metabolite biomarkers for the possible discrimination of geographical origin.


Assuntos
Apium/química , Metabolômica/métodos , Espectroscopia de Prótons por Ressonância Magnética/métodos , Aminoácidos/análise , Apium/metabolismo , Austrália , China , Análise Discriminante , Humanos , Análise dos Mínimos Quadrados , Manitol/análise , Manitol/metabolismo , Análise Multivariada , Folhas de Planta/química , Análise de Componente Principal , Verduras/química , Verduras/metabolismo
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA