Extraction,optimisation and characterisation of phenolics from Thymus vulgaris L.: phenolic content and profiles in relation to antioxidant,antidiabetic and antihypertensive properties

The objectives of this study were to examine varying extraction conditions of Thymus vulgaris L. as related to phenolic content and profiles of the extracts and their antioxidant, antihypertensive and antidiabetic properties. Phenolics were extracted under various conditions pertaining to free and bound phenolics, solvent type and combination of extraction time and temperature, and these extracts were evaluated in terms of their antioxidant activities and inhibitory activities of angiotensin‐converting enzyme (ACE), α‐glucosidase and α‐amylase. The acetone–water solvent mixture (1:1; v/v) produced the extract with the greatest phenolic content, antioxidant activity and inhibitory activities of ACE and α‐glucosidase. The optimal extraction temperature for maximum phenolic content and antioxidant activity associated with methanol extraction was 60 °C, whereas a lower temperature at 40 °C was required to maximise inhibitory activities for ACE, α‐glucosidase and α‐amylase. An inverse relationship was seen between antioxidant and glucosidase inhibitory activities vs. the ACE and α‐amylase inhibitory activities, which suggests the need for extractions to be directed to specific bioactivities of thyme extracts. Generally, the results indicate major differences in phenolic profiles among the tested extraction conditions with thymol as the predominant phenolic seen in most extractions, while gallic acid, rosmarinic acid or diosmin also predominated in other extracts. Extracts with the same predominant phenolic compound and similar phenolic content showed major disparities in their ACE, glucosidase and α‐amylase inhibitory activities, indicating that the major phenolic profiles of thyme extracts may not be necessarily related to the degree of inhibition of ACE, glucosidase and α‐amylase enzymes.     

Prediction of Transport Properties of Liquid Ammonia and Its Binary Mixture with Methanol by Molecular Simulation

Transport properties of ammonia and of the binary mixture ammonia + methanol are predicted for a broad range of liquid states by molecular dynamics (MD) simulation on the basis of rigid, non-polarizable molecular models of the united-atom type. These models were parameterized in preceding work using only experimental vapor-liquid equilibrium data. The self- and the Maxwell-Stefan (MS) diffusion coefficients as well as the shear viscosity are obtained by equilibrium MD and the Green-Kubo formalism. Non-equilibrium MD is used for the thermal conductivity. The transport properties of liquid ammonia are predicted for temperatures between 223 K and 473 K up to pressures of 200 MPa and are compared to experimental data and correlations thereof. Generally, good agreement is achieved. The predicted self-diffusion coefficient as well as the shear viscosity deviates on average by less than 15 % from the experiment and the thermal conductivity by less than 6 %. Furthermore, the self- and the MS transport diffusion coefficients as well as the shear viscosity of the liquid mixture ammonia + methanol are studied at different compositions and compared to the available experimental data.     

pH and heat-induced structural changes of bovine apo-α-lactalbumin

The effects of pH and thermal treatments on conformation and association state of Ca²⁺-depleted bovine α-lactalbumin (apo-α-LA) have been studied by fluorescence spectroscopy, and molecular modelling. The experimental results demonstrate a third-state model for heat-induced unfolding of apo-α-LA, at pH 2.0, and an all-or-none transition of apo-α-LA, at pH 4.5 and 7.0, respectively. The heat-induced changes in the secondary and tertiary structure of α-LA were outlined after running molecular dynamics simulations at 25 °C and 80 °C, at neutral pH, therefore supporting the experimental observations. Our data provides insight into the mechanism of pH- and heat-dependent structural changes and oligomerization of α-LA, and will be helpful in understanding the ability of this protein to interact with certain compounds of biological interest.     

Separation of Acylglycerides Obtained by Enzymatic Esterification Using Solvent Extraction

New avenues to add value to glycerol are currently being explored. One of them is the synthesis of structured lipids through glycerol esterification. In this work we have analyzed the recovery and purification of dicaprin obtained by esterification of glycerol with capric acid (C) in heptane, mediated by Lipozyme RM IM. This is an intermediate step to obtain lipids MLM. In the first stage, the diglyceride synthesis MGM (being G a central HC–OH) was carried out. When M = C, the diglyceride is CGC. Recovery of the diglyceride CGC is required to carry out the esterification of the sn-2 position with palmitic acid (P), thus obtaining the triglyceride CPC. Different solvents were evaluated using Ecofac 1.0 (a molecular design software solvent) through a theoretical approach to explore the best solvents for the acylglycerides separation. Then, the performance of the selected solvents to separate dicaprin from mono and tricaprin was experimentally studied in a liquid–liquid extraction process. Previously, the remaining fatty acid had been neutralized. With liquid–liquid extraction in three simple steps, using ethanol/water, 94 % of the dicaprin obtained by enzymatic esterification was recovered with a purity of 89 % (wt%). It was also possible to obtain dicaprin with a purity of 97 % but with a yield of 56 %.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Impact of precursor preparation on density of gadolinium iron garnets synthesized via reactive sintering

Gadolinium iron garnet was obtained from two different precursors, homogenized in isopropyl alcohol and in an aqueous environment with a fixed pH. In the first case, it was a mixture of goethite (FeO(OH)) and gadolinium oxide (Gd₂O₃); in the second, a mixture of GdIP (GdFeO₃) and α-Fe₂O₃. Conditions of homogenization in the aqueous environment were selected based on the zeta (ξ) potential measurements as the function of pH. DSC measurements of the output powder mixtures allowed the identification of the effects observed during the temperature rise. In the case of the material obtained from a mixture of goethite (FeO(OH)) and gadolinium oxide, with the increasing temperature, we observe three effects, the first of which corresponds to the phase transformation of goethite into α-Fe₂O₃, the second corresponds to the reaction of gadolinium iron perovskite (GdIP) formation, and the third to the reaction in which a gadolinium iron garnet (GdIG) is formed. However, in the case of heat treatment of the mixture of GdIP and α-Fe₂O₃, we only observe the effect responsible for a solid state reaction leading to the formation of gadolinium iron garnet. Dilatometric measurements allowed to determine the changes in linear dimensions at various stages of reaction sintering. The resulting materials were sintered at temperatures of 1200, 1300, and 1400 °C. In the case of the material obtained from a mixture of perovskite and iron (III) oxide, already at the temperature of 1300 °C, a density has been obtained at around 95% of the theoretical density, and the temperature of 1400 °C allowed achieving a density of 97% of the theoretical density. Whereas, for the material obtained from a mixture of goethite (FeO(OH)) and gadolinium oxide, a density above 95% of theoretical density was achieved only at 1400 °C.