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.     

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.     

Optimization of Sesame Oil Extraction by Screw-Pressing at Low Temperature

Box-Behnken designs were used to optimize a process for sesame oil extraction by screw-pressing at low temperature (50 °C). Experimental designs included seed moisture content (SMC), pressing speed (PS), and restriction die (RD) as the main processing parameters. Extractions at pilot plant scale showed a peak in oil recovery (OR, 71.1 ± 2.80%) at 12.3% SMC, 4 mm RD, and 20 rpm PS. Theoretical models were scanned against experimental data in order to scale up the proposed oil extraction process to industrial scale. A fitted model for OR showed a maximum predicted value similar to the highest experimental value (74.4 ± 1.23%) under the following conditions: 8.03% SMC, 10 mm RD, and 20 rpm PS. Chemical quality parameters of oils obtained at both pilot and industrial scales were in the ranges stated in Codex (FAO/WHO) standards for non-refined sesame oil.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.