New Insights into Cell Encapsulation and the Role of Proteins During Flow Cytometry

peer-reviewedModern approaches to science tend to follow divergent paths. On one hand, instruments and technologies are developed to capture as much information as possible with the need for complex data analysis to identify problematic issues. On the other hand, formulation focused, minimalistic approaches that gather only the most pertinent data for specific questions also represent a powerful methodology. This chapter will provide many examples of the latter by integrating Flow Cytometry (FACS - Fluorescence-Activated Cell Sorting) technology with high throughput screening (HTS) of encapsulation systems with extensive utility of one-dimensional (1-D) imaging for protein localisation. In this regard, less information is acquired from each cell, data files will be more manageable, easier to analyse and throughput screening will be significantly enhanced beyond traditional HTS analysis, irrespective of the protein concentration present in the background or delivery media.Some of the cytometric work presented in this chapter was supported by the Irish Dairy Research Trust project NU518 “Probiotic Protection”, the Irish National Development Plan 2007 to 2013 and Science Foundation Ireland (SFI)

β-Lactoglobulin-linoleate complexes: In vitro digestion and the role of protein in fatty acids uptake

peer-reviewedThe dairy protein β-lactoglobulin (BLG) is known to bind fatty acids such as the salt of the essential longchain fatty acid linoleic acid (cis,cis-9,12-octadecadienoic acid, n-6, 18:2). The aim of the current study was to investigate how bovine BLG-linoleate complexes, of various stoichiometry, affect the enzymatic digestion of BLG and the intracellular transport of linoleate into enterocyte-like monolayers. Duodenal and gastric digestions of the complexes indicated that BLG was hydrolyzed more rapidly when complexed with linoleate. Digested as well as undigested BLG-linoleate complexes reduced intracellular linoleate transport as compared with free linoleate. To investigate whether enteroendocrine cells perceive linoleate differently when part of a complex, the ability of linoleate to increase production or secretion of the enteroendocrine satiety hormone, cholecystokinin, was measured. Cholecystokinin mRNA levels were different when linoleate was presented to the cells alone or as part of a protein complex. In conclusion, understanding interactions between linoleate and BLG could help to formulate foods with targeted fatty acid bioaccessibility and, therefore, aid in the development of food matrices with optimal bioactive efficacyS. Le Maux is currently supported by a Teagasc Walsh Fellowship and the Department of Agriculture, Fisheries and Food (FIRM project 08/RD/TMFRC/650). We also acknowledge funding from IRCSET-Ulysses Travel Grant

Covalent labelling of β-casein and its effect on the microstructure and physico-chemical properties of emulsions stabilized by β-casein and whey protein isolate

peer-reviewedThe objective of this work was to investigate the effect of covalent labelling on the physico-chemical properties of β-casein (β-CN) in solution and in emulsions stabilized by β-CN and whey protein isolate (WPI). β-CN was covalently labelled by 5-(and 6)-carboxytetramethylrhodamine, succinimidyl ester (NHS-Rhodamine). The effect of conjugating β-CN with NHS-Rhodamine on the spectroscopic properties of labelled β-CN (β-CNlabelled) was examined. No significant difference in interfacial tension (p > 0.05) was found between mixture of WPI and β-CNlabelled (0.5% w/w WPI/β-CNlabelled) and of WPI and β-CN (0.5% w/w WPI/β-CN) in 10 mM phosphate buffer (pH 7.0) at 20 °C. Oil-in-water emulsions stabilized with either WPI/β-CN or WPI/β-CNlabelled (0.5% w/w) were also investigated using laser-light scattering, analytical centrifugation, rheometry and CLSM. It was shown that labelling had no significant effect on the physico-chemical properties of emulsions (p > 0.05) in terms of droplet size, creaming stability, viscosity or zeta-potential. Confocal micrographs of emulsions made with WPI/β-CNlabelled showed that both β-CN and whey proteins could be observed simultaneously, and were co-localized at the surface of fat globules. Furthermore, it was found through image analysis that β-CN produced a thicker interfacial layer than WPI

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods

Isolation and characterisation of κ-casein/whey protein particles from heated milk protein concentrate and role of κ-casein in whey protein aggregation

peer-reviewedMilk protein concentrate (79% protein) reconstituted at 13.5% (w/v) protein was heated (90 °C, 25 min, pH 7.2) with or without added calcium chloride. After fractionation of the casein and whey protein aggregates by fast protein liquid chromatography, the heat stability (90 °C, up to 1 h) of the fractions (0.25%, w/v, protein) was assessed. The heat-induced aggregates were composed of whey protein and casein, in whey protein:casein ratios ranging from 1:0.5 to 1:9. The heat stability was positively correlated with the casein concentration in the samples. The samples containing the highest proportion of caseins were the most heat-stable, and close to 100% (w/w) of the aggregates were recovered post-heat treatment in the supernatant of such samples (centrifugation for 30 min at 10,000 × g). κ-Casein appeared to act as a chaperone controlling the aggregation of whey proteins, and this effect was stronger in the presence of αS- and β-casein.This work was supported by Dairy Levy Research Trust (project MDDT6261 “ProPart”). S. J. Gaspard was funded under the Teagasc Walsh Fellowship Scheme (reference number 2012211

Complete O(alpha_s^2) Corrections to (2+1) Jet Cross Sections in Deep Inelastic Scattering

Complete next-to leading order QCD predictions for (2+1) jet cross sections and jet rates in deep inelastic scattering (DIS) based on a new parton level Monte Carlo program are presented. All relevant helicity contributions to the total cross section are included. Results on total jet cross sections as well as differential distributions in the basic kinematical variables $x,W^2$ and $Q^2$ are shown for HERA energies and for the fixed target experiment E665 at FERMILAB. We study the dependence on the choices of the renormalization scale $\mu_R$ and the factorization scale $\mu_F$ and show that the NLO results are much less sensitive to the variation of $\mu=\mu_F=\mu_R$ than the LO results. The effect of an additional $p_T$ cut to our jet definition scheme is investigated.Comment: 14 pages, LateX, MAD/PH/821, 15 figures (not included), figures are available upon request. (some essential changes to the figures, minor changes to the text). To appear in Z.Phys.

Next-to-Leading Order QCD Corrections to Jet Cross Sections and Jet Rates in Deeply Inelastic Electron Proton Scattering

Jet cross sections in deeply inelastic scattering in the case of transverse photon exchange for the production of (1+1) and (2+1) jets are calculated in next-to-leading order QCD (here the `+1' stands for the target remnant jet, which is included in the jet definition for reasons that will become clear in the main text). The jet definition scheme is based on a modified JADE cluster algorithm. The calculation of the (2+1) jet cross section is described in detail. Results for the virtual corrections as well as for the real initial- and final state corrections are given explicitly. Numerical results are stated for jet cross sections as well as for the ratio \sigma_{\mbox{\small (2+1) jet}}/\sigma_{\mbox{\small tot}} that can be expected at E665 and HERA. Furthermore the scale ambiguity of the calculated jet cross sections is studied and different parton density parametrizations are compared.Comment: 40 pages, LBL-34147 (Latex file). (figures available by mail on request (send e-mail to [email protected]), please include your address such that it can be used as an address label

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60\u2005min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n\u2005=\u2005713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000\u2005cm 121 were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60\u2005min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60\u2005min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation

Dairy structures and physiological responses: a matter of gastric digestion

Digestion and health properties of food do not solely rely on the sum of nutrients but are also influenced by food structure. Dairy products present an array of structures due to differences in the origin of milk components and the changes induced by processing. Some dairy structures have been observed to induce specific effects on digestion rates and physiological responses. However, the underlying mechanisms are not fully understood. Gastric digestion plays a key role in controlling digestion kinetics. The main objective of this review is to expose the relevance of gastric phase as the link between dairy structures and physiological responses. The focus is on human and animal studies, and physiological relevant in vitro digestion models. Data collected showed that the structure of dairy products have a profound impact on rate of nutrient bioavailability, absorption and physiological responses, suggesting gastric digestion as the main driver. Control of gastric digestion can be a tool for delivering specific rates of nutrient digestion. Therefore, the design of food structure targeting specific gastric behavior could be of great interest for particular population needs e.g. rapid nutrient digestion will benefit elderly, and slow nutrient digestion could help to enhance satiety

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows

peer-reviewedRapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60 min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n = 713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000 cm−1 were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60 min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60 min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation