Physicochemical, thermal and rheological properties of pectin extracted from sugar beet pulp using subcritical water extraction process

The objective of this study was to characterize the properties of pectin extracted from sugar beet pulp using subcritical water (SWE) as compared to conventional extraction (CE). The research involved advanced modeling using response surface methodology and optimization of operational parameters. The optimal conditions for maximum yield of pectin for SWE and CE methods were determined by the central composite design. The optimum conditions of CE were the temperature of 90 °C, time of 240 min, pH of 1, and pectin recovery yield of 20.8%. The optimal SWE conditions were liquid-to-solid (L/S) ratio of 30% (v/w) at temperature of 130 °C for 20 min, which resulted in a comparable yield of 20.7%. The effect of obtained pectins on viscoamylograph pasting and DSC thermal parameters of corn starch was evaluated. The contents of galacturonic acid, degree of methylation, acetylation, and ferulic acid content were higher in the pectin extracted by SWE, while the molecular weight was lower. Similar chemical groups were characterized by FTIR in both SWE and CE pectins. Color attributes of both pectins were similar. Solutions of pectins at lower concentrations displayed nearly Newtonian behavior. The addition of both pectins to corn starch decreased pasting and DSC gelatinization parameters, but increased ΔH. The results offered a promising scalable approach to convert the beet waste to pectin as a value-added product using SWE with improved pectin properties.Axencia Galega de Innovación | Ref. IN607A2019 / 0

Neural correlates of boredom in music perception

Introduction: Music can elicit powerful emotional responses, the neural correlates of which have not been properly understood. An important aspect about the quality of any musical piece is its ability to elicit a sense of excitement in the listeners. In this study, we investigated the neural correlates of boredom evoked by music in human subjects. Methods: We used EEG recording in nine subjects while they were listening to total number of 10 short-length (83 sec) musical pieces with various boredom indices. Subjects evaluated boringness of musical pieces while their EEG was recording. Results: Using short time Fourier analysis, we found that beta2 rhythm was (16-20 Hz) significantly lower whenever the subjects rated the music as boring in comparison to nonboring. Discussion: The results demonstrate that the music modulates neural activity of various parts of the brain and can be measured using EEG

Application of Epstein-Barr Virus for Optimization of Immortalized B-lymphocyte Production as a Positive Control in Genetic Studies.

BACKGROUND Infection of B-cells with Epstein-Barr virus (EBV) leads to more and subsequent immortalization. This is considered as the method of choice for generating lymphoblastoid cell lines (LCLs). Producing LCLs, although very useful but is very time consuming and troublesome, drives the requirement for quicker and more reliable methods for EBV-driven B-cell transformation. MATERIALS AND METHODS After successfully production of LCLs, different parameters including temperature, serum concentration, type of culture medium, and CO2 concentration were evaluated on EBV-transformed B-cells. In this study, we were able to produce LCLs and optimize condition. RESULTS The best condition for generating LCLs was 37°C, 5% CO2, 20% fasting blood sugar, and RPMI 1640. The study results were to establish a reliable method for producing LCLs that can be used to produce immortalized B-cells from almost any sources. CONCLUSION This can help with tumorgenecity studies, as well as producing control material for rare genetic disorders and so on. The aim of this study was to determine optimized condition for reliable and reproducible LCLs from different sources

The circadian rhythm: an influential soundtrack in the diabetes story

Type 2 Diabetes Mellitus (T2DM) has been the main category of metabolic diseases in recent years due to changes in lifestyle and environmental conditions such as diet and physical activity. On the other hand, the circadian rhythm is one of the most significant biological pathways in humans and other mammals, which is affected by light, sleep, and human activity. However, this cycle is controlled via complicated cellular pathways with feedback loops. It is widely known that changes in the circadian rhythm can alter some metabolic pathways of body cells and could affect the treatment process, particularly for metabolic diseases like T2DM. The aim of this study is to explore the importance of the circadian rhythm in the occurrence of T2DM via reviewing the metabolic pathways involved, their relationship with the circadian rhythm from two perspectives, lifestyle and molecular pathways, and their effect on T2DM pathophysiology. These impacts have been demonstrated in a variety of studies and led to the development of approaches such as time-restricted feeding, chronotherapy (time-specific therapies), and circadian molecule stabilizers

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood.

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon

Attention control learning in the decision space using state estimation

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information

Learning Active Fusion of Multiple Experts’ Decisions: An Attention-Based Approach

ISSN:0899-7667ISSN:1530-888