Stability of milk proteins subjected to UHT treatments: challenges and future perspectives.

Ultra-high temperature (UHT) treatments are of high economic relevance for food industries because they contribute to extending the shelf life of food products and facilitating their distribution. In the dairy segment, UHT treatments are applied to a wide range of products containing variable protein amounts. In this sense, the changes in the molecular structure of milk proteins induced by the severity of UHT treatments may lead to fouling in equipment during processing or sedimentation and/or gelation during storage. Nowadays, these concerns are even more relevant due to the increasing demand for UHT-treated high-protein beverages. This review will discuss the two main strategies used by industries to increase the stability of milk proteins during and/or after UHT treatments: (i) addition of chelating agents and (ii) use of polysaccharides. Moreover, the challenges and opportunities associated with promising strategies to improve the stability of milk proteins during and/or after UHT treatments will be covered in this review. The information compiled will be useful to guide researchers and industries in developing more stable UHT dairy products in harmony with consumers' demands.

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

Evaluation of the synergistic effects of milk proteins in a rapid viscosity analyzer.

Protein systems (PS) are routinely used by companies from Brazil and around the globe to improve the texture, yield, and palatability of processed foods. Understanding the synergistic behavior among the different protein structures of these systems during thermal treatment under the influence of pH can help to better define optimum conditions for products and processes. The interpretation of the reactions and interactions that occur simultaneously among the protein constituents of these systems as dispersions during thermal processing is still a major challenge. Here, using a rapid viscosity analyzer, we observed the rheological changes in the startup viscosities of 5 PS obtained by combining varying proportions of milk protein concentrate and whey protein concentrate under different conditions of pH (5.0, 6.5, and 7.0) and heat processing (85°C/15min and 95°C/5min). The solutions were standardized to 25% of total solids and 17% of protein. Ten analytical parameters were used to characterize each of the startup-viscosity ramps for 35 experiments conducted in a 2×3 × 5 mixed planning matrix, using principal component analysis to interpret behavioral similarities. The study showed the clear influence of pH 5.5 in the elevation of the initial temperature of the PS startup viscosity by at least 5°C, as well as the effect of different milk protein concentrate:whey protein concentrate ratios above 15:85 at pH 7.0 on the viscographic profile curves. These results suggested that the primary agent driving the changes was the synergism among the reactions and interactions of casein with whey proteins during processing. This study reinforces the importance of the rapid viscosity analyzer as an analytical tool for the simulation of industrial processes involving PS, and the use of the startup viscosity ramp as a means of interpreting the interactions of system components with respect to changes related to the treatment temperature.

Parallel fixed point implementation of a radial basis function network in an FPGA.

This paper proposes a parallel fixed point radial basis function (RBF) artificial neural network (ANN), implemented in a field programmable gate array (FPGA) trained online with a least mean square (LMS) algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.

Development and characterization of microsatellite markers for the wild South American Passiflora cincinnata (Passifloraceae).

PREMISE OF THE STUDY: We developed the first set of microsatellite markers for the passion fruit, Passiflora cincinnata, to provide tools for further study of its genetic diversity and to support current conservation and genetic studies. METHODS AND RESULTS: We used a microsatellite-enriched library approach to isolate and characterize 25 new molecular markers. Seven primer pairs successfully amplified polymorphic microsatellite regions, with an average of five alleles per locus. The mean values of expected and observed levels of heterozygosity were 0.516 and 0.525, respectively. CONCLUSIONS: The microsatellite markers identified in this study may be valuable tools for population genetic studies, and this set of markers also may be useful in the design of a genetic conservation strategy, mating system, and hybridization studies.

Electrocorticographic description of the effects of anticonvulsant drugs used to treat lidocaine-induced seizures.

INTRODUCTION: Local anesthetics are widely used in clinical practice. While toxicity is rare, these drugs can cause potentially lethal seizures. OBJECTIVE: In the present study, we investigated the electrocorticographic (ECoG) and electromyographic patterns of seizures induced by acute lidocaine (LA) toxicity and treated with anticonvulsant drugs. The study used adult male Wistar rats to describe of the seizure-related behavior of LA and investigated the treatment with anticonvulsant drugs. RESULTS: The use of LA resulted in clear changes in the ECoG pattern, which presented characteristics of Status epilepticus, with increased intensity in all brainwaves. The decomposition of the cerebral waves showed an increase in the beta and gamma waves that may be related to tonic-clonic seizure. Although the treatment with anticonvulsants drugs reduces the power of brainwaves at frequencies between 1 and 40 Hz compared to the LA group, but only diazepam (DZP) was able to decrease the intensity of oscillations. The muscle contraction power also indicated a difference in the effectiveness of the three treatments. CONCLUSION: The sum of the evidence indicates that LA causes status epilepticus and that DZP is the most effective treatment for the control of these seizures, by restoring the systemic values to levels close to those recorded in the control group.

Reliability and Test-Retest Agreement of Mechanical Variables Obtained During Countermovement Jump.

The countermovement jump (CMJ) is often used as performance measure and monitoring tool. Traditionally, jump height (JH) is most often studied and reported, but other variables (e.g. force, velocity, power) can also be obtained during CMJ testing on a force plate. The aim of this study was to determine the intra-rater reliability of mechanical variables obtained during CMJs. For this, 41 physically active men (24 ± 4 years) performed four CMJs on a force plate with an interval of 48 to 168 hours (test-retest design). Six variables were analyzed: 1) jump height (JH), 2) peak force (PF), 3) peak power (PP), 4) velocity at takeoff (VTO), 5) rate of force (RFD) and 6) power (RPD) development. Five variables showed to be reliable (i.e. CV < 10%), except RFD (CV of 12.9%). Although JH exhibited an acceptable level of reliability (r = 0.94 and CV = 5.8%), better scores were observed for PF, VTO, and PP (CV ranging from 2.5 to 5.1%). The PF showed the best reliability scores (r = 0.99 and CV = 2.5%) and RPD, a relatively unexamined variable compared to the others, showed an acceptable level of reliability (r = 0.96 and CV = 7.8%). Therefore, JH, PF, PP, VTO, and RPD demonstrated acceptable scores of reliability. PF seems to be the most appropriate variable to use when small changes in performance are expected. Future studies should investigate the importance of RPD for performance evaluation.