The Experts below are selected from a list of 73560 Experts worldwide ranked by ideXlab platform
Lun Wang - One of the best experts on this subject based on the ideXlab platform.
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Sensitive Quantitative Image Analysis of bisulfite based on near-infrared upconversion luminescence total internal reflection platform.
Talanta, 2020Co-Authors: Hongqi Chen, Wanying Xia, Qian Gao, Lun WangAbstract:Abstract Bisulfite (HSO3−), serves as an important additive in food industry, is one of the most widely distributed environmental pollutants. Herein, a fast and efficient Quantitative Image Analysis method for the determination of HSO3− has been developed. The method builds a luminescence energy transfer (LET) system utilized upconversion nanoparticles (UCNPs) as an energy donor and cyanine dye molecules as an energy acceptor. The upconversion luminescence is quenched a lot on the addition of dye molecules and gets recovered well with the addition of HSO3−. All the phenomena can be recorded via traditional luminescence spectrometer and near-infrared upconversion luminescence total internal reflection platform. The Quantitative Image Analysis performed on the near-infrared upconversion luminescence total internal reflection platform can significantly reduce sample consumption (10 μL) as well as make a quick and efficient Analysis (0.1 s) with a large amout of data become easy. Meanwhile, it shows a wider linear range (1–120 μM), lower detection limit (0.070 μM) and higher detection speed than that of the classical luminescence spectrometer.
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A near-infrared upconversion luminescence total internal reflection platform for Quantitative Image Analysis.
Chemical communications (Cambridge England), 2020Co-Authors: Wanying Xia, Bo Ling, Lun Wang, Feng Gao, Hongqi ChenAbstract:A Quantitative Image Analysis method by counting photons from different samples was developed based on a near-infrared upconversion luminescence total internal reflection platform. The proposed method can not only greatly reduce the consumption of samples (10 μL) but also ensure high-throughput and fast (0.1 s) data Analysis.
Eugénio C. Ferreira - One of the best experts on this subject based on the ideXlab platform.
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Sludge volume index and suspended solids estimation of mature aerobic granular sludge by Quantitative Image Analysis and chemometric tools
Separation and Purification Technology, 2020Co-Authors: Cristiano Leal, Daniela P. Mesquita, Angeles Val Del Rio, António L. Amaral, Paula M. L. Castro, Eugénio C. FerreiraAbstract:Abstract Aerobic granular sludge (AGS) is considered a promising technology for wastewater treatment. Furthermore, it is recognized that the stability of the process is related to the balanced growth of the suspended (floccular) and granular fractions. Therefore, the development of adequate techniques to monitor this balance is of interest. In this work the sludge volume index (SVI), volatile suspended solids (VSS) and total suspended solids (TSS) of mature AGS were successfully predicted with multilinear regression (MLR) models using data obtained from Quantitative Image Analysis (QIA) of both fractions (suspended and granular). Relevant predictions were obtained for the SVI (R2 of 0.975), granules TSS (R2 of 0.985), flocs TSS (R2 of 0.971), granules VSS (R2 of 0.984) and flocs VSS (R2 of 0.986). The estimation of the granular fraction ratio from the predicted TSS and VSS was also successful (R2 of 0.985). The predictions help to avoid instability episodes of the AGS system, such as changes in biomass morphology, structure and settling properties.
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Estimation of effluent quality parameters from an activated sludge system using Quantitative Image Analysis
Chemical Engineering Journal, 2016Co-Authors: Daniela P. Mesquita, A. Luís Amaral, Eugénio C. FerreiraAbstract:The efficiency of an activated sludge system is generally evaluated by determining several key parameters related to organic matter removal, nitrification and/or denitrification processes. Off-line methods for the determination of these parameters are commonly labor, time consuming, and environmentally harmful. In contrast, Quantitative Image Analysis (QIA) has been recognized as a prompt method for assessing activated sludge contents and structure. In the present study an activated sludge system was operated under different experimental conditions leading to a variety of operational data. Key parameters such as chemical oxygen demand (COD) and ammonium (N-NH4+), and nitrate (N-NO3-) concentrations, throughout the experimental periods, were measured by classical analytical techniques. QIA was further used for the microbial community characterization. Partial least squares (PLS) models were used to correlate QIA information and the aforementioned key parameters. It was found that the use of the morphological and physiological data allowed predicting, at some extent, the effluent COD, N-NH4+, and N-NO3-concentrations based on chemometric techniques.
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Quantitative Image Analysis as a tool for Yarrowia lipolytica dimorphic growth evaluation in different culture media.
Journal of biotechnology, 2015Co-Authors: Adelaide Braga, Daniela P. Mesquita, Eugénio C. Ferreira, António L. Amaral, Isabel BeloAbstract:Yarrowia lipolytica, a yeast strain with a huge biotechnological potential, capable to produce metabolites such as γ-decalactone, citric acid, intracellular lipids and enzymes, possesses the ability to change its morphology in response to environmental conditions. In the present study, a Quantitative Image Analysis (QIA) procedure was developed for the identification and quantification of Y. lipolytica W29 and MTLY40-2P strains dimorphic growth, cultivated in batch cultures on hydrophilic (glucose and N-acetylglucosamine (GlcNAc) and hydrophobic (olive oil and castor oil) media. The morphological characterization of yeast cells by QIA techniques revealed that hydrophobic carbon sources, namely castor oil, should be preferred for both strains growth in the yeast single cell morphotype. On the other hand, hydrophilic sugars, namely glucose and GlcNAc caused a dimorphic transition growth towards the hyphae morphotype. Experiments for γ-decalactone production with MTLY40-2P strain in two distinct morphotypes (yeast single cells and hyphae cells) were also performed. The obtained results showed the adequacy of the proposed morphology monitoring tool in relation to each morphotype on the aroma production ability. The present work allowed establishing that QIA techniques can be a valuable tool for the identification of the best culture conditions for industrial processes implementation.
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Monitoring intracellular polyphosphate accumulation in enhanced biological phosphorus removal systems by Quantitative Image Analysis
Water science and technology : a journal of the International Association on Water Pollution Research, 2014Co-Authors: Daniela P. Mesquita, A. Luís Amaral, Mónica Carvalheira, Cristiano Leal, Adrian Oehmen, Jorge Ricardo Cunha, Maria A.m. Reis, Eugénio C. FerreiraAbstract:A rapid methodology for intracellular storage polyphosphate (poly-P) identification and monitoring in enhanced biological phosphorus removal (EBPR) systems is proposed based on Quantitative Image Analysis (QIA). In EBPR systems, 4',6-diamidino-2-phenylindole (DAPI) is usually combined with fluorescence in situ hybridization to evaluate the microbial community. The proposed monitoring technique is based on a QIA procedure specifically developed for determining poly-P inclusions within a biomass suspension using solely DAPI by epifluorescence microscopy. Due to contradictory literature regarding DAPI concentrations used for poly-P detection, the present work assessed the optimal DAPI concentration for samples acquired at the end of the EBPR aerobic stage when the accumulation occurred. Digital Images were then acquired and processed by means of Image processing and Analysis. A correlation was found between average poly-P intensity values and the analytical determination. The proposed methodology can be seen as a promising alternative procedure for quantifying intracellular poly-P accumulation in a faster and less labour-intensive way.
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Prediction of intracellular storage polymers using Quantitative Image Analysis in enhanced biological phosphorus removal systems
Analytica chimica acta, 2013Co-Authors: Daniela P. Mesquita, A. Luís Amaral, Cristiano Leal, Adrian Oehmen, Jorge Ricardo Cunha, Maria A.m. Reis, Eugénio C. FerreiraAbstract:The present study focuses on predicting the concentration of intracellular storage polymers in enhanced biological phosphorus removal (EBPR) systems. For that purpose, Quantitative Image Analysis techniques were developed for determining the intracellular concentrations of PHA (PHB and PHV) with Nile blue and glycogen with aniline blue staining. Partial least squares (PLS) were used to predict the standard analytical values of these polymers by the proposed methodology. Identification of the aerobic and anaerobic stages proved to be crucial for improving the assessment of PHA, PHB and PHV intracellular concentrations. Current Nile blue based methodology can be seen as a feasible starting point for further enhancement. Glycogen detection based on the developed aniline blue staining methodology combined with the Image Analysis data proved to be a promising technique, toward the elimination of the need for analytical off-line measurements.
Hongqi Chen - One of the best experts on this subject based on the ideXlab platform.
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Sensitive Quantitative Image Analysis of bisulfite based on near-infrared upconversion luminescence total internal reflection platform.
Talanta, 2020Co-Authors: Hongqi Chen, Wanying Xia, Qian Gao, Lun WangAbstract:Abstract Bisulfite (HSO3−), serves as an important additive in food industry, is one of the most widely distributed environmental pollutants. Herein, a fast and efficient Quantitative Image Analysis method for the determination of HSO3− has been developed. The method builds a luminescence energy transfer (LET) system utilized upconversion nanoparticles (UCNPs) as an energy donor and cyanine dye molecules as an energy acceptor. The upconversion luminescence is quenched a lot on the addition of dye molecules and gets recovered well with the addition of HSO3−. All the phenomena can be recorded via traditional luminescence spectrometer and near-infrared upconversion luminescence total internal reflection platform. The Quantitative Image Analysis performed on the near-infrared upconversion luminescence total internal reflection platform can significantly reduce sample consumption (10 μL) as well as make a quick and efficient Analysis (0.1 s) with a large amout of data become easy. Meanwhile, it shows a wider linear range (1–120 μM), lower detection limit (0.070 μM) and higher detection speed than that of the classical luminescence spectrometer.
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A near-infrared upconversion luminescence total internal reflection platform for Quantitative Image Analysis.
Chemical communications (Cambridge England), 2020Co-Authors: Wanying Xia, Bo Ling, Lun Wang, Feng Gao, Hongqi ChenAbstract:A Quantitative Image Analysis method by counting photons from different samples was developed based on a near-infrared upconversion luminescence total internal reflection platform. The proposed method can not only greatly reduce the consumption of samples (10 μL) but also ensure high-throughput and fast (0.1 s) data Analysis.
Cristiano Leal - One of the best experts on this subject based on the ideXlab platform.
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Sludge volume index and suspended solids estimation of mature aerobic granular sludge by Quantitative Image Analysis and chemometric tools
Separation and Purification Technology, 2020Co-Authors: Cristiano Leal, Daniela P. Mesquita, Angeles Val Del Rio, António L. Amaral, Paula M. L. Castro, Eugénio C. FerreiraAbstract:Abstract Aerobic granular sludge (AGS) is considered a promising technology for wastewater treatment. Furthermore, it is recognized that the stability of the process is related to the balanced growth of the suspended (floccular) and granular fractions. Therefore, the development of adequate techniques to monitor this balance is of interest. In this work the sludge volume index (SVI), volatile suspended solids (VSS) and total suspended solids (TSS) of mature AGS were successfully predicted with multilinear regression (MLR) models using data obtained from Quantitative Image Analysis (QIA) of both fractions (suspended and granular). Relevant predictions were obtained for the SVI (R2 of 0.975), granules TSS (R2 of 0.985), flocs TSS (R2 of 0.971), granules VSS (R2 of 0.984) and flocs VSS (R2 of 0.986). The estimation of the granular fraction ratio from the predicted TSS and VSS was also successful (R2 of 0.985). The predictions help to avoid instability episodes of the AGS system, such as changes in biomass morphology, structure and settling properties.
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Microbial-based evaluation of foaming events in full-scale wastewater treatment plants by microscopy survey and Quantitative Image Analysis
Environmental science and pollution research international, 2016Co-Authors: Cristiano Leal, António L. Amaral, Maria De Lourdes CostaAbstract:Activated sludge systems are prone to be affected by foaming occurrences causing the sludge to rise in the reactor and affecting the wastewater treatment plant (WWTP) performance. Nonetheless, there is currently a knowledge gap hindering the development of foaming events prediction tools that may be fulfilled by the Quantitative monitoring of AS systems biota and sludge characteristics. As such, the present study focuses on the assessment of foaming events in full-scale WWTPs, by Quantitative protozoa, metazoa, filamentous bacteria, and sludge characteristics Analysis, further used to enlighten the inner relationships between these parameters. In the current study, a conventional activated sludge system (CAS) and an oxidation ditch (OD) were surveyed throughout a period of 2 and 3 months, respectively, regarding their biota and sludge characteristics. The biota community was monitored by microscopic observation, and a new filamentous bacteria index was developed to quantify their occurrence. Sludge characteristics (aggregated and filamentous biomass contents and aggregate size) were determined by Quantitative Image Analysis (QIA). The obtained data was then processed by principal components Analysis (PCA), cross-correlation Analysis, and decision trees to assess the foaming occurrences, and enlighten the inner relationships. It was found that such events were best assessed by the combined use of the relative abundance of testate amoeba and nocardioform filamentous index, presenting a 92.9 % success rate for overall foaming events, and 87.5 and 100 %, respectively, for persistent and mild events.
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Monitoring intracellular polyphosphate accumulation in enhanced biological phosphorus removal systems by Quantitative Image Analysis
Water science and technology : a journal of the International Association on Water Pollution Research, 2014Co-Authors: Daniela P. Mesquita, A. Luís Amaral, Mónica Carvalheira, Cristiano Leal, Adrian Oehmen, Jorge Ricardo Cunha, Maria A.m. Reis, Eugénio C. FerreiraAbstract:A rapid methodology for intracellular storage polyphosphate (poly-P) identification and monitoring in enhanced biological phosphorus removal (EBPR) systems is proposed based on Quantitative Image Analysis (QIA). In EBPR systems, 4',6-diamidino-2-phenylindole (DAPI) is usually combined with fluorescence in situ hybridization to evaluate the microbial community. The proposed monitoring technique is based on a QIA procedure specifically developed for determining poly-P inclusions within a biomass suspension using solely DAPI by epifluorescence microscopy. Due to contradictory literature regarding DAPI concentrations used for poly-P detection, the present work assessed the optimal DAPI concentration for samples acquired at the end of the EBPR aerobic stage when the accumulation occurred. Digital Images were then acquired and processed by means of Image processing and Analysis. A correlation was found between average poly-P intensity values and the analytical determination. The proposed methodology can be seen as a promising alternative procedure for quantifying intracellular poly-P accumulation in a faster and less labour-intensive way.
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Prediction of intracellular storage polymers using Quantitative Image Analysis in enhanced biological phosphorus removal systems
Analytica chimica acta, 2013Co-Authors: Daniela P. Mesquita, A. Luís Amaral, Cristiano Leal, Adrian Oehmen, Jorge Ricardo Cunha, Maria A.m. Reis, Eugénio C. FerreiraAbstract:The present study focuses on predicting the concentration of intracellular storage polymers in enhanced biological phosphorus removal (EBPR) systems. For that purpose, Quantitative Image Analysis techniques were developed for determining the intracellular concentrations of PHA (PHB and PHV) with Nile blue and glycogen with aniline blue staining. Partial least squares (PLS) were used to predict the standard analytical values of these polymers by the proposed methodology. Identification of the aerobic and anaerobic stages proved to be crucial for improving the assessment of PHA, PHB and PHV intracellular concentrations. Current Nile blue based methodology can be seen as a feasible starting point for further enhancement. Glycogen detection based on the developed aniline blue staining methodology combined with the Image Analysis data proved to be a promising technique, toward the elimination of the need for analytical off-line measurements.
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Biopolymer monitoring using Quantitative Image Analysis techniques
2013Co-Authors: Mónica Carvalheira, Daniela P. Mesquita, Eugénio C. Ferreira, Cristiano Leal, Adrian Oehmen, Gilda Carvalho, Jorge Ricardo Cunha, A. L. Amaral, Maria A.m. ReisAbstract:76 PS 1.19 Biopolymer monitoring using Quantitative Image Analysis techniques Monica Isabel Carvalheira, Daniela P. Mesquita, Cristiano Leal, Adrian Oehmen, Gilda Carvalho, Jorge R. Cunha, A. Luis Amaral, Eugenio C. Ferreira, Maria A. M. Reis Faculdade de Ciencias e Tecnologia Universidade Nova de Lisboa, Portugal; Instituto de Biologia Experimental e Tecnologica (IBET), Portugal; IBB-Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Portugal; Instituto Superior de Engenharia de Coimbra, Instituto Politecnico de Coimbra, Portugal mic16141@campus.fct.unl.pt
Wanying Xia - One of the best experts on this subject based on the ideXlab platform.
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Sensitive Quantitative Image Analysis of bisulfite based on near-infrared upconversion luminescence total internal reflection platform.
Talanta, 2020Co-Authors: Hongqi Chen, Wanying Xia, Qian Gao, Lun WangAbstract:Abstract Bisulfite (HSO3−), serves as an important additive in food industry, is one of the most widely distributed environmental pollutants. Herein, a fast and efficient Quantitative Image Analysis method for the determination of HSO3− has been developed. The method builds a luminescence energy transfer (LET) system utilized upconversion nanoparticles (UCNPs) as an energy donor and cyanine dye molecules as an energy acceptor. The upconversion luminescence is quenched a lot on the addition of dye molecules and gets recovered well with the addition of HSO3−. All the phenomena can be recorded via traditional luminescence spectrometer and near-infrared upconversion luminescence total internal reflection platform. The Quantitative Image Analysis performed on the near-infrared upconversion luminescence total internal reflection platform can significantly reduce sample consumption (10 μL) as well as make a quick and efficient Analysis (0.1 s) with a large amout of data become easy. Meanwhile, it shows a wider linear range (1–120 μM), lower detection limit (0.070 μM) and higher detection speed than that of the classical luminescence spectrometer.
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A near-infrared upconversion luminescence total internal reflection platform for Quantitative Image Analysis.
Chemical communications (Cambridge England), 2020Co-Authors: Wanying Xia, Bo Ling, Lun Wang, Feng Gao, Hongqi ChenAbstract:A Quantitative Image Analysis method by counting photons from different samples was developed based on a near-infrared upconversion luminescence total internal reflection platform. The proposed method can not only greatly reduce the consumption of samples (10 μL) but also ensure high-throughput and fast (0.1 s) data Analysis.
