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1.
J Sci Food Agric ; 103(6): 3168-3183, 2023 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-36268589

RESUMO

BACKGROUND: Seabuckthorn fruits contains many active subtances, among them, the seabuckthorn polysaccharide is one of the main active ingredients, and exhibits diverse bioactivities. The extraction of polysaccharides from seabuckthorn fruits is the most important step for their wide applications. Ultrasound-assisted aqueous two-phase extraction (UA-ATPE) is a promising green method for extracting polysaccharides. Additionally, physicochemical characterization and antioxidant activities can evaluate the potential functions and applications in the food and medicine industries. RESULTS: Based on the single-factor experiments, 20.70% (w/w) ammonium sulfate ((NH4 )2 SO4 ) and 27.56% (w/w) ethanol were determined as the suitable composition for aqueous two-phase. The optimum conditions of UA-ATPE obtained by response surface methodology were as follows: ultrasonic power (390 W), extraction time (41 min), liquid-to-material ratio (72: 1 mL/g), and the total yield of the polysaccharides reached 34.14 ± 0.10%, The molecular weights of the purified upper-phase seabuckthorn polysaccharide (PUSP) and the purified lower-phase seabuckthorn polysaccharide (PLSP) were 65 525 and 26 776 Da, respectively. PUSP and PLSP contained the same six monosaccharides (galacturonic acid, rhamnose, xylose, mannose, glucose and galactose), but with different molar ratios. Furthermore, PUSP and PLSP displayed certain viscoelastic property, had no triple helical structure, possessed different thermal stability, surface morphology and conformation in aqueous solution. PUSP and PLSP displayed strong antioxidant properties by the assays of scavenging ability of ABTS+ ·, the protection of DNA damage and erythrocyte hemolysis. CONCLUSION: UA-ATPE significantly increased the yield of seabuckthorn polysaccharides. PUSP and PLSP were different in many aspects, such as molar ratio, surface shape and antioxidant activities. Seabuckthornpolysaccharides possess great potential in medicine and functional foods. © 2022 Society of Chemical Industry.


Assuntos
Antioxidantes , Hippophae , Antioxidantes/química , Frutas/química , Polissacarídeos/química , Água/análise
2.
Magn Reson Imaging ; 31(6): 970-5, 2013 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-23587929

RESUMO

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA.


Assuntos
Mapeamento Encefálico/métodos , Potenciais Evocados Visuais/fisiologia , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Análise de Componente Principal , Córtex Visual/fisiologia , Percepção Visual/fisiologia , Algoritmos , Humanos , Aumento da Imagem/métodos , Reconhecimento Automatizado de Padrão/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
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