Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 13.776
Filtrar
1.
Ying Yong Sheng Tai Xue Bao ; 31(3): 863-871, 2020 Mar.
Artículo en Chino | MEDLINE | ID: mdl-32537982

RESUMEN

Soil spectral information differ across different land use types. Understanding the appropriate modeling methods for different land use types can efficiently and accurately invert soil organic carbon content. We collected 248 samples from forest, cultivated land and orchard in the north-central part of Fengxin County, Jiangxi Province. First, original spectral reflectance curves were reduced noises with Savitzky-Golay (SG) filter. Then 10 nm resampling method was used to reduce data redundancy. We used partial least squares regression (PLSR), support vector machine regression based on grid search method (GRID-SVR) and support vector machine regression based on particle swarm optimization (PSO-SVR) to construct the inversion models of soil organic carbon content. The results showed that when constructing a single land-use type inversion model, RPD of the PLSR method for forest, cultivated land and orchard was 1.536, 1.315 and 1.493 respectively. RPD of GRID-SVR method increased 0.150, 0.183 and 0.502 than that of PLSR method, respectively. The PSO-SVR method had higher accuracy, with RPD being 20.8%, 10.0% and 2.7% higher than GRID-SVR for forest, cultivated land and orchard, respectively. The RPD of forest and orchard were 2.036 and 2.049, which well predicts soil organic carbon. The RPD of cultivated land was 1.647, which can make a rough estimate of soil organic carbon. The PSO-SVR model had the best prediction effect on soil organic carbon of different land use types, with the prediction accuracy of soil organic carbon content in forest and orchard being close and higher than cultivated land. Soil nutrition diffed acorss different land use types, which affect the prediction of soil organic carbon content. Models for inversion of soil organic carbon should be constructed separately for different land use types.


Asunto(s)
Carbono , Suelo , China , Bosques , Análisis de los Mínimos Cuadrados
2.
Ying Yong Sheng Tai Xue Bao ; 31(3): 987-998, 2020 Mar.
Artículo en Chino | MEDLINE | ID: mdl-32537996

RESUMEN

Ecological land is essential to sustainable development of urban agglomeration. Based on the results of remote sensing image interpretation, we analyzed the spatial-temporal evolution of ecological land in 32 research units of ecological land in Wuhan urban agglomeration in 2000-2005, 2005-2010 and 2010-2015, using the land use transition matrix, exploratory regression analysis, the ordinary least squares (OLS) model, and geographically weighted regression (GWR) model. Then, the best regression model was selected after perfecting the traditional index system of influencing factors by data of the location and quantitative information of companies, enterprises and life services, etc., and conducting exploratory regression analysis. Finally, we analyzed the influencing factors and spatial differentiation rules of different research periods with GWR model. The results showed that, from 2000 to 2015, the amount of transition from ecological land use to non-ecological land use in the urban agglomeration showed an inverted U-shaped change pattern, and the space showing the expanding trend from point to surface. Land use patterns of 8.4% area had changed in the urban agglomeration, among which the conversion of cultivated land, forest land, grassland, water body and unused land to non-ecological land accounted for 41.9% of the total area. The spatial pattern gradually expanded from the central urban area of Wuhan to the periphery of the municipal sub-center and county-level towns. The total number of passing models in the three stages of exploratory regression analysis was 326. The GWR and OLS regression were used for comparative analysis of all models. The adjusted R2 in the three stages of selected models were 0.83, 0.91 and 0.76, respectively. The former improved by 0.02, 0.03 and 0.02, and the AICc decreased by 2.88, 3.42 and 0.83, respectively. The results of GWR model showed substantially spatial differentiation of influencing factors of ecological land evolution in Wuhan urban agglomeration, and that the influence patterns was dominated by gradual transition in different directions in space, with other patterns such as "V" distribution. The effects of spatial factors were significant. The potential information of spatial data enhanced the interpretation of ecological land evolution within the urban agglomeration.


Asunto(s)
Monitoreo del Ambiente , Regresión Espacial , China , Ciudades , Bosques , Análisis de los Mínimos Cuadrados
3.
N Engl J Med ; 382(24): 2289-2301, 2020 06 11.
Artículo en Inglés | MEDLINE | ID: mdl-32521132

RESUMEN

BACKGROUND: Up-regulation of hepatic delta-aminolevulinic acid synthase 1 (ALAS1), with resultant accumulation of delta-aminolevulinic acid (ALA) and porphobilinogen, is central to the pathogenesis of acute attacks and chronic symptoms in acute hepatic porphyria. Givosiran, an RNA interference therapy, inhibits ALAS1 expression. METHODS: In this double-blind, placebo-controlled, phase 3 trial, we randomly assigned symptomatic patients with acute hepatic porphyria to receive either subcutaneous givosiran (2.5 mg per kilogram of body weight) or placebo monthly for 6 months. The primary end point was the annualized rate of composite porphyria attacks among patients with acute intermittent porphyria, the most common subtype of acute hepatic porphyria. (Composite porphyria attacks resulted in hospitalization, an urgent health care visit, or intravenous administration of hemin at home.) Key secondary end points were levels of ALA and porphobilinogen and the annualized attack rate among patients with acute hepatic porphyria, along with hemin use and daily worst pain scores in patients with acute intermittent porphyria. RESULTS: A total of 94 patients underwent randomization (48 in the givosiran group and 46 in the placebo group). Among the 89 patients with acute intermittent porphyria, the mean annualized attack rate was 3.2 in the givosiran group and 12.5 in the placebo group, representing a 74% lower rate in the givosiran group (P<0.001); the results were similar among the 94 patients with acute hepatic porphyria. Among the patients with acute intermittent porphyria, givosiran led to lower levels of urinary ALA and porphobilinogen, fewer days of hemin use, and better daily scores for pain than placebo. Key adverse events that were observed more frequently in the givosiran group were elevations in serum aminotransferase levels, changes in serum creatinine levels and the estimated glomerular filtration rate, and injection-site reactions. CONCLUSIONS: Among patients with acute intermittent porphyria, those who received givosiran had a significantly lower rate of porphyria attacks and better results for multiple other disease manifestations than those who received placebo. The increased efficacy was accompanied by a higher frequency of hepatic and renal adverse events. (Funded by Alnylam Pharmaceuticals; ENVISION ClinicalTrials.gov number, NCT03338816.).


Asunto(s)
Acetilgalactosamina/análogos & derivados , Ácido Aminolevulínico/orina , Porfobilinógeno/orina , Porfiria Intermitente Aguda/tratamiento farmacológico , Pirrolidinas/uso terapéutico , Tratamiento con ARN de Interferencia , Acetilgalactosamina/efectos adversos , Acetilgalactosamina/uso terapéutico , Adulto , Método Doble Ciego , Fatiga/etiología , Femenino , Humanos , Inyecciones Subcutáneas , Análisis de los Mínimos Cuadrados , Hígado/efectos de los fármacos , Masculino , Náusea/etiología , Dolor/etiología , Evaluación del Resultado de la Atención al Paciente , Porfiria Intermitente Aguda/complicaciones , Porfiria Intermitente Aguda/orina , Pirrolidinas/efectos adversos , Insuficiencia Renal Crónica/inducido químicamente , Transaminasas/sangre
4.
J Environ Manage ; 268: 110646, 2020 Aug 15.
Artículo en Inglés | MEDLINE | ID: mdl-32389899

RESUMEN

Groundwater nitrate contamination has been the main water quality problem threatening the sustainable utilization of water resources in Jeju Island, South Korea. The spatially varying distribution of nitrate levels associated with complex environmental and anthropogenic factors has been a major challenge restricting improved groundwater management. In this study, we applied ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to determine the relationships between the NO3-N concentration and various parameters (topography, hydrology and land use) across the island. A comparison between the OLS regression and GWR prediction models showed that the GWR models outperformed the OLS regression models, with a higher R2 and a lower corrected Akaike Information Criterion (AICc) value than the OLS regression models. Interestingly, the GWR model was able to provide undiscovered information that was not revealed in the OLS regression models. For example, the GWR model found that orchards (OR) and urban (UR) variables significantly contributed to nitrate enrichment in the certain parts of the island, whereas these variables were ignored as a statistically insignificant factor in the OLS regression model. Our study highlighted that GWR models are a useful tool for investigating spatially varying relationships between groundwater quality and environmental factors; therefore, it can be applied to establish advanced groundwater management plans by reflecting the spatial heterogeneity associated with environmental and anthropogenic conditions.


Asunto(s)
Agua Subterránea , Regresión Espacial , Monitoreo del Ambiente , Análisis de los Mínimos Cuadrados , República de Corea , Calidad del Agua
5.
Virology ; 546: 51-66, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-32452417

RESUMEN

Overlapping genes originate by a mechanism of overprinting, in which nucleotide substitutions in a pre-existing frame induce the expression of a de novo protein from an alternative frame. In this study, I assembled a dataset of 319 viral overlapping genes, which included 82 overlaps whose expression is experimentally known and the respective 237 homologs. Principal component analysis revealed that overlapping genes have a common pattern of nucleotide and amino acid composition. Discriminant analysis separated overlapping from non-overlapping genes with an accuracy of 97%. When applied to overlapping genes with known genealogy, it separated ancestral from de novo frames with an accuracy close to 100%. This high discriminant power was crucial to computationally design variants of de novo viral proteins known to possess selective anticancer toxicity (apoptin) or protection against neurodegeneration (X protein), as well as to detect two new potential overlapping genes in the genome of the new coronavirus SARS-CoV-2.


Asunto(s)
Betacoronavirus/genética , Evolución Molecular , Genes Sobrepuestos , Genes Virales , Algoritmos , Secuencia de Aminoácidos , Secuencia de Bases , Biología Computacional , Simulación por Computador , Análisis Discriminante , Análisis de los Mínimos Cuadrados , Análisis de Componente Principal
6.
Zhongguo Zhong Yao Za Zhi ; 45(2): 250-258, 2020 Jan.
Artículo en Chino | MEDLINE | ID: mdl-32237306

RESUMEN

In this paper, a real time release testing(RTRT) model for predicting the disintegration time of Tianshu tablets was established on the basis of the concept of quality by design(QbD), in order to improve the quality controllability of the production process. First, 49 batches of raw materials and intermediates were collected. Afterwards, the physical quality attributes of all materials were comprehensively characterized. The partial least square(PLS) regression model was established with the 72 physical quality attributes of raw materials and intermediates as input and the disintegration time(DT) of uncoated tablets as output. Then, the variable screening was carried out based on the variable importance in the projection(VIP) indexes. Moisture content of raw materials(%HR), tapped density of wet masses(D_c), hygroscopicity of dry granules(%H), moisture content of milling granules(%HR) and Carr's index of mixed granules(IC) were determined as the potential critical material attributes(pCMAs). According to the effects of interactions of pCMAs on the performance of the prediction model, it was finally determined that the wet masses' D_c and the dry granules'%H were critical material attributes(CMAs). A RTRT model of the disintegration time prediction was established as DT=34.09+2×D_c+3.59×%H-5.29×%H×D_c,with R~2 equaling to 0.901 7 and the adjusted R~2 equaling to 0.893 3. The average relative prediction error of validation set for the RTRT model was 3.69%. The control limits of the CMAs were determined as 0.55 g·cm~(-3)<D_c<0.63 g·cm~(-3) and 4.77<%H<7.59 according to the design space. The RTRT model of the disintegration time reflects the understanding of the process system, and lays a foundation for the implementation of intelligent control strategy of the key process of Tianshu Tablets.


Asunto(s)
Liberación de Fármacos , Medicamentos Herbarios Chinos/química , Composición de Medicamentos , Análisis de los Mínimos Cuadrados , Solubilidad , Comprimidos
7.
Food Chem ; 321: 126503, 2020 Aug 15.
Artículo en Inglés | MEDLINE | ID: mdl-32240914

RESUMEN

The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68-1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm.


Asunto(s)
Cadmio/análisis , Plomo/análisis , Lechuga/química , Máquina de Vectores de Soporte , Análisis de los Mínimos Cuadrados , Hojas de la Planta/química , Análisis de Ondículas
8.
Food Chem ; 321: 126628, 2020 Aug 15.
Artículo en Inglés | MEDLINE | ID: mdl-32259731

RESUMEN

We investigated the potential of front-face synchronous fluorescence spectroscopy (△λ = 75 nm) to non-destructively evaluate beef freshness and quality decline during chilled storage. The total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and total viable count (TVC) values were used as standard freshness indicators. The fluorescent substances, including amino acids, collagen and conjugated Schiff bases, were highly correlated with the chemical and microbial deterioration of the beef. Quantitative models for simultaneously predicting the three freshness indicators were built combined with partial least squares (PLS) algorithm and showed good reliability. For TVB-N and TBARS values, Rc2 and Rp2 were both above 0.900, and for TVC values Rc2 and Rp2 were 0.912 and 0.871, respectively. The qualitative model established by partial least squares discriminant analysis (PLS-DA) algorithm could accurately classify beef samples as fresh, acceptable or spoiled. The accuracy of the calibration and validation sets were 92.54% and 86.96%, respectively.


Asunto(s)
Carne Roja/análisis , Algoritmos , Animales , Calibración , Bovinos , Análisis Discriminante , Análisis de los Mínimos Cuadrados , Nitrógeno/química , Carne Roja/microbiología , Reproducibilidad de los Resultados , Espectrometría de Fluorescencia , Sustancias Reactivas al Ácido Tiobarbitúrico/química
9.
Chemosphere ; 252: 126508, 2020 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-32240857

RESUMEN

Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q2 = 0.648, R2pred or Q2F1 = 0.734 and Q2F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.


Asunto(s)
Peces/fisiología , Plaguicidas/toxicidad , Relación Estructura-Actividad Cuantitativa , Contaminantes Químicos del Agua/toxicidad , Animales , Organismos Acuáticos , Simulación por Computador , Humanos , Análisis de los Mínimos Cuadrados , Plaguicidas/química , Reproducibilidad de los Resultados , Contaminantes Químicos del Agua/química
10.
N Engl J Med ; 382(16): 1497-1506, 2020 04 16.
Artículo en Inglés | MEDLINE | ID: mdl-32294346

RESUMEN

BACKGROUND: An oral compound, SEP-363856, that does not act on dopamine D2 receptors but has agonist activity at trace amine-associated receptor 1 (TAAR1) and 5-hydroxytryptamine type 1A (5-HT1A) receptors, may represent a new class of psychotropic agent for the treatment of psychosis in schizophrenia. METHODS: We performed a randomized, controlled trial to evaluate the efficacy and safety of SEP-363856 in adults with an acute exacerbation of schizophrenia. The patients were randomly assigned in a 1:1 ratio to receive once-daily treatment with SEP-363856 (50 mg or 75 mg) or placebo for 4 weeks. The primary end point was the change from baseline in the total score on the Positive and Negative Symptom Scale (PANSS; range, 30 to 210; higher scores indicate more severe psychotic symptoms) at week 4. There were eight secondary end points, including the changes from baseline in the scores on the Clinical Global Impressions Severity (CGI-S) scale and the Brief Negative Symptom Scale (BNSS). RESULTS: A total of 120 patients were assigned to the SEP-363856 group and 125 to the placebo group. The mean total score on the PANSS at baseline was 101.4 in the SEP-363856 group and 99.7 in the placebo group, and the mean change at week 4 was -17.2 points and -9.7 points, respectively (least-squares mean difference, -7.5 points; 95% confidence interval, -11.9 to -3.0; P = 0.001). The reductions in the CGI-S and BNSS scores at week 4 were generally in the same direction as those for the primary outcome, but the results were not adjusted for multiple comparisons. Adverse events with SEP-363856 included somnolence and gastrointestinal symptoms; one sudden cardiac death occurred in the SEP-363856 group. The incidence of extrapyramidal symptoms and changes in the levels of lipids, glycated hemoglobin, and prolactin were similar in the trial groups. CONCLUSIONS: In this 4-week trial involving patients with an acute exacerbation of schizophrenia, SEP-363856, a non-D2-receptor-binding antipsychotic drug, resulted in a greater reduction from baseline in the PANSS total score than placebo. Longer and larger trials are necessary to confirm the effects and side effects of SEP-363856, as well as its efficacy relative to existing drug treatments for patients with schizophrenia. (Funded by Sunovion Pharmaceuticals; ClinicalTrials.gov number, NCT02969382.).


Asunto(s)
Antipsicóticos/uso terapéutico , Esquizofrenia/tratamiento farmacológico , Enfermedad Aguda , Administración Oral , Adulto , Antipsicóticos/efectos adversos , Método Doble Ciego , Esquema de Medicación , Femenino , Humanos , Análisis de los Mínimos Cuadrados , Masculino , Receptores de Dopamina D2 , Receptores Acoplados a Proteínas G/agonistas , Esquizofrenia/clasificación , Psicología del Esquizofrénico , Agonistas del Receptor de Serotonina 5-HT1/uso terapéutico , Índice de Severidad de la Enfermedad , Resultado del Tratamiento
11.
Fa Yi Xue Za Zhi ; 36(1): 35-40, 2020 Feb.
Artículo en Inglés, Chino | MEDLINE | ID: mdl-32250076

RESUMEN

Abstract: Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy (FTIR-MSP) combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm-1, 2 854 cm-1, 1 623 cm-1, and 1 535 cm-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.


Asunto(s)
Algoritmos , Aprendizaje Automático , Animales , Análisis de Fourier , Análisis de los Mínimos Cuadrados , Porcinos
12.
Food Chem ; 319: 126536, 2020 Jul 30.
Artículo en Inglés | MEDLINE | ID: mdl-32146292

RESUMEN

Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI.


Asunto(s)
Lycium/química , Antocianinas/análisis , Calibración , Color , Aprendizaje Profundo , Flavonoides/análisis , Análisis de los Mínimos Cuadrados , Análisis de Componente Principal , Espectroscopía Infrarroja Corta , Máquina de Vectores de Soporte
13.
PLoS One ; 15(3): e0229113, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32126111

RESUMEN

In CT (computerized tomography) imaging reconstruction, the acquired sinograms are usually noisy, so artifacts will appear on the resulting images. Thus, it is necessary to find the adequate filters to combine with reconstruction methods that eliminate the greater amount of noise possible without altering in excess the information that the image contains. The present work is focused on the evaluation of several filtering techniques applied in the elimination of artifacts present in CT sinograms. In particular, we analyze the elimination of Gaussian and Speckle noise. The chosen filtering techniques have been studied using four functions designed to measure the quality of the filtered image and compare it with a reference image. In this way, we determine the ideal parameters to carry out the filtering process on the sinograms, prior to the process of reconstruction of the images. Moreover, we study their application on reconstructed noisy images when using noisy sinograms and finally we select the best filter to combine with an iterative reconstruction method in order to test if it improves the quality of the images. With this, we can determine the feasibility of using the selected filtering method for our CT reconstructions with projections reduction, concluding that the bilateral filter is the filter that behaves best with our images. We will test it when combined with our iterative reconstruction method, which consists on the Least Squares QR method in combination with a regularization technique and an acceleration step, showing how integrating this filter with our reconstruction method improves the quality of the CT images.


Asunto(s)
Algoritmos , Procesamiento de Imagen Asistido por Computador , Fantasmas de Imagen , Tomografía Computarizada por Rayos X , Artefactos , Humanos , Procesamiento de Imagen Asistido por Computador/instrumentación , Procesamiento de Imagen Asistido por Computador/métodos , Procesamiento de Imagen Asistido por Computador/normas , Análisis de los Mínimos Cuadrados , Distribución Normal , Fantasmas de Imagen/normas , Control de Calidad , Relación Señal-Ruido , Tomografía Computarizada por Rayos X/instrumentación , Tomografía Computarizada por Rayos X/métodos , Tomografía Computarizada por Rayos X/normas
14.
PLoS One ; 15(3): e0229873, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32134971

RESUMEN

BACKGROUND: The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE's for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes. METHODS: Following method validation, three independent experiments were conducted to develop AEE's using various least squares linear or nonlinear, and median-based linear regression techniques. SD's were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 ("small" sample sets) or 20 ("large" sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE's were developed by combining the SD's of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated. RESULTS: Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil's regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD's, including SD's below the lower limit of quantification. CONCLUSIONS: In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.


Asunto(s)
Cromatografía Liquida/métodos , Monitoreo de Drogas/métodos , Modelos Biológicos , Medicina de Precisión/métodos , Espectrometría de Masas en Tándem/métodos , Carbamazepina/química , Exactitud de los Datos , Fluconazol/química , Humanos , Lamotrigina/química , Análisis de los Mínimos Cuadrados , Levetiracetam/química , Límite de Detección , Concentración Osmolar , Suero/química , Programas Informáticos
15.
PLoS One ; 15(3): e0228500, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32160185

RESUMEN

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.


Asunto(s)
Algoritmos , Proteínas de Granos/análisis , Tecnología de Sensores Remotos/métodos , Imágenes Satelitales/métodos , Triticum/química , Triticum/metabolismo , Análisis de los Mínimos Cuadrados
16.
Food Chem ; 317: 126448, 2020 Jul 01.
Artículo en Inglés | MEDLINE | ID: mdl-32114274

RESUMEN

The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm-1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.


Asunto(s)
Análisis de los Alimentos/métodos , Contaminación de Alimentos/análisis , Fraude , Origanum/química , Análisis Discriminante , Análisis de los Alimentos/estadística & datos numéricos , Contaminación de Alimentos/estadística & datos numéricos , Análisis de los Mínimos Cuadrados , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Espectroscopía Infrarroja Corta/métodos
17.
N Engl J Med ; 382(13): 1219-1231, 2020 03 26.
Artículo en Inglés | MEDLINE | ID: mdl-32212518

RESUMEN

BACKGROUND: Patients with transfusion-dependent ß-thalassemia need regular red-cell transfusions. Luspatercept, a recombinant fusion protein that binds to select transforming growth factor ß superfamily ligands, may enhance erythroid maturation and reduce the transfusion burden (the total number of red-cell units transfused) in such patients. METHODS: In this randomized, double-blind, phase 3 trial, we assigned, in a 2:1 ratio, adults with transfusion-dependent ß-thalassemia to receive best supportive care plus luspatercept (at a dose of 1.00 to 1.25 mg per kilogram of body weight) or placebo for at least 48 weeks. The primary end point was the percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval. Other efficacy end points included reductions in the transfusion burden during any 12-week interval and results of iron studies. RESULTS: A total of 224 patients were assigned to the luspatercept group and 112 to the placebo group. Luspatercept or placebo was administered for a median of approximately 64 weeks in both groups. The percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval was significantly greater in the luspatercept group than in the placebo group (21.4% vs. 4.5%, P<0.001). During any 12-week interval, the percentage of patients who had a reduction in transfusion burden of at least 33% was greater in the luspatercept group than in the placebo group (70.5% vs. 29.5%), as was the percentage of those who had a reduction of at least 50% (40.2% vs. 6.3%). The least-squares mean difference between the groups in serum ferritin levels at week 48 was -348 µg per liter (95% confidence interval, -517 to -179) in favor of luspatercept. Adverse events of transient bone pain, arthralgia, dizziness, hypertension, and hyperuricemia were more common with luspatercept than placebo. CONCLUSIONS: The percentage of patients with transfusion-dependent ß-thalassemia who had a reduction in transfusion burden was significantly greater in the luspatercept group than in the placebo group, and few adverse events led to the discontinuation of treatment. (Funded by Celgene and Acceleron Pharma; BELIEVE ClinicalTrials.gov number, NCT02604433; EudraCT number, 2015-003224-31.).


Asunto(s)
Receptores de Activinas Tipo II/uso terapéutico , Transfusión de Eritrocitos/estadística & datos numéricos , Hematínicos/uso terapéutico , Fragmentos Fc de Inmunoglobulinas/uso terapéutico , Proteínas Recombinantes de Fusión/uso terapéutico , Talasemia beta/tratamiento farmacológico , Receptores de Activinas Tipo II/efectos adversos , Adolescente , Adulto , Anciano , Método Doble Ciego , Femenino , Ferritinas/sangre , Hematínicos/efectos adversos , Humanos , Fragmentos Fc de Inmunoglobulinas/efectos adversos , Análisis de Intención de Tratar , Análisis de los Mínimos Cuadrados , Masculino , Persona de Mediana Edad , Oportunidad Relativa , Proteínas Recombinantes de Fusión/efectos adversos , Esplenectomía , Adulto Joven , Talasemia beta/genética , Talasemia beta/cirugía , Talasemia beta/terapia
18.
J Chromatogr A ; 1618: 460905, 2020 May 10.
Artículo en Inglés | MEDLINE | ID: mdl-32008825

RESUMEN

Retention time shifts in second-order calibration-assisted chromatographic analysis seriously impact the modeling and quantitative accuracies in complex systems. In this work, three second-order methods, i.e. alternating trilinear decomposition (ATLD) algorithm, multivariate curve resolution-alternating least squares (MCR-ALS), alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR), were compared their performance to process liquid chromatographic data in the presence of retention time shifts and overlapped peaks. Firstly, the validation samples contain five tea polyphenols at three concentrate levels within the calibration ranges, helped to understand, visualize and interpret these features of three second-order multivariate methods. Secondly, experimental data were studied concerning the determination of polyphenols in Chinese tea samples by HPLC-DAD. The results showed that all three second-order multivariate methods realized satisfactory quantification for five targeted analytes in Pu-Er ripe tea samples and Green tea samples even with the interference of slight retention time shifts, average recoveries were 91.23% -113.16% for ATLD, 89.96%-115.96% for ATLD-MCR, 90.64%-117.60% for MCR-ALS, respectively. However, ATLD was disappointing in the case of larger time shifts (approx. 4.00 s and 6.40 s) occurring for the quantitative analysis of Black tea and Clinacanthus nutans tea, the average recoveries were just 67.33-84.05%. Relatively, MCR-ALS and ATLD-MCR were more significantly excellent, satisfactory results still can be obtained, the average recoveries for MCR-ALS and ATLD-MCR were in the range of 86.04-117.60% and 89.96-115.96%, respectively.


Asunto(s)
Cromatografía Líquida de Alta Presión/métodos , Polifenoles/análisis , Té/química , Algoritmos , Calibración , Camellia sinensis/química , Cromatografía Líquida de Alta Presión/normas , Análisis de los Mínimos Cuadrados , Análisis Multivariante
19.
J Chromatogr A ; 1618: 460938, 2020 May 10.
Artículo en Inglés | MEDLINE | ID: mdl-32081486

RESUMEN

This work presents and evaluates an algorithmic approach to deconvolving the elution profiles of chemical components of vapor mixtures that have been sampled and desorbed from a novel preconcentrator based on highly ordered silicon nanowire arrays. The arrays provide a medium for both preconcentration and partial chromatographic resolution, which is then further leveraged with multichannel detection. Here, mixtures of nitro aromatic vapors are sampled and then thermally desorbed from the device, at which point they are detected by a conventional mass selective detector. The overlapping elution profiles observed from the array are sequentially extracted using a chemometric analysis approach based on evolving factor analysis and multivariate curve resolution by alternating least squares, enabling qualitative and quantitative analysis of individual components without target analyte libraries or complete chromatographic separation. This work examines the analytical capabilities conferred to multichannel detection by silicon nanowire array pre-concentration and partial separation and discusses the technique's limitations, illustrated by both experimental and simulated data.


Asunto(s)
Cromatografía , Nanocables , Silicio/química , Algoritmos , Gases/química , Análisis de los Mínimos Cuadrados , Espectrometría de Masas , Temperatura
20.
PLoS One ; 15(2): e0227510, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32023261

RESUMEN

While many stakeholders believe worker wages in global supply chains are too low, there is disagreement about what, if anything, can be done to raise wages. Through a two-year quasi-experiment in an operating apparel factory, we assess the effects on productivity and profits of raising worker wages with a re-designed compensation system. We show that, even within current factory margins and constraints, important wage gains (4.2-9.7%) are possible and profitable. Productivity increased 8-10%-points while turnover decreased markedly. Workers were motivated by the potential for increased wages from an accelerating group rate as well as increased engagement and sense of fair compensation. Workers focused their increased effort on reducing quality defects and tardiness, two behaviors which individual workers largely control. Additional productivity-increasing behaviors were constrained by skill, position, and conflicts arising from free riders. Advanced apparel manufacturing demands a more engaged workforce; this research provides early evidence that compensation systems can be a critical tool to meet multiple needs.


Asunto(s)
Industrias/economía , Salarios y Beneficios/economía , Textiles/economía , Indemnización para Trabajadores/economía , Conducta , Eficiencia , Humanos , Análisis de los Mínimos Cuadrados
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA