Modification of pulse proteins for improved functionality and flavor profile: A comprehensive review

Consumers’ preference to have a healthy eating pattern has led to an increasing demand for more nutrient-dense and healthier plant-based foods. Pulse proteins are exceptional quality ingredients with potential nutritional benefits, and might act as health-promoting agents for addressing the new-generation foods. However, the utilization of pulse protein in foods has been hampered by its relatively poor functionality and unpleasant flavor. Protein structure modification has been proved to be a useful means to improve the functionality and flavor profile of pulse protein. This paper begins with a brief introduction of hierarchical structure of pulse protein materials to better understand the structure characteristics. A comprehensive review is presented on the current techniques including chemical and enzymatic modifications and molecular breeding on pulse protein structure and functionality/flavor. The mechanism and the limitations and the toxicological concerns of these approaches are discussed. We conclude that understanding protein structure‒functionality relationship is extremely valuable in tailoring proteins for specific functional outcomes and expanding the availability of pulse proteins. Furthermore, selective protein modification is a valuable in-depth toolkit for generating novel protein constructs with preferable functional attributes and flavor profiles. Innovative structure modification with special focus on the molecular basis for the exquisite protein designs is a pillar of pulse protein access to the desired functionality.     

Multiobjective assessment of distributed energy storage location in electricity networks

This paper presents a methodology to provide information to a decision maker on the associated impacts, both of economic and technical nature, of possible management schemes of storage units for choosing the best location of distributed storage devices, with a multiobjective optimisation approach based on genetic algorithms. The methodology was applied to a case study, a known distribution network model in which the installation of distributed storage units was tested, using lithium-ion batteries. The obtained results show a significant influence of the charging/discharging profile of batteries on the choice of their best location, as well as the relevance that these choices may have for the different network management objectives, for example, for reducing network energy losses or minimising voltage deviations. Results also show a difficult cost-effectiveness of an energy-only service, with the tested systems, both due to capital cost and due to the efficiency of conversion.     

Wavelets and fuzzy relational classifiers: A novel diffusion-weighted image analysis system for pediatric metabolic brain diseases

The diffusion-weighted imaging (DWI) technique can be utilized to investigate a variety of diseases. We propose an automated pilot system, which assists in the diagnosis of metabolic brain diseases, utilizing the DWI. In this study, DWI images are preprocessed and exponential apparent diffusion coefficient (eADC) images are produced. The eADC images are later brain extracted and normalized to a standard brain template. Subsequently, we utilized wavelets to denoise the eADC images. The images are rectified, thresholded and now conspicuous abnormal regions are subsequently identified utilizing different brain atlases. Abnormal regions constitute the features that will be used by a fuzzy relational classifier in order to categorize the diseases. A sensitivity and specificity of 60% and 93.33%, respectively, in detecting metabolic brain diseases have been achieved.     

Recommendation of programming activities by multi-label classification for a formative assessment of students

Computer programming ability is a type of knowledge that is considered to be quite complex because it demands many cognitive skills and extensive practice to be mastered. However, formative assessment is a strategy that can improve learning. For this reason, we developed a recommender system to aid in making choices on programming practices by recommending classes of activities. This system provides instructors with a means of semi-automatic assessment, with more individualised and accurate activities tailored to the needs of their learners. To achieve this goal, the system of recommendations analyses multidimensional profiles of new students and seeks the best match for them among profiles in the logs of previous recommendations, which were made manually. Based on these matched profiles, the system can now recommend to new learners classes of activities that are indicated by similar profiles that have already received recommendations. The recommendation of activities is thus treated by our system as a multi-label classification task in which each student’s profile is associated with one or more classes of programming activities. The results obtained under different evaluation metrics confirm that the chosen algorithm, the ML-kNN, correctly mimics human decisions on the recommendations of classes of activities most of the time. Furthermore, these metrics provide relevant information for instructors to perform better actions with regard to formative assessments.     

The Difference Test Statistic for Two Suppliers with Linear Profiles

Comparing two suppliers for linear profiles is a very important task for supplier management. The difference test statistic based on the process‐yield index is proposed to tackle the better process selection for linear profiles. A simple form of the sampling distribution of the process‐yield index is derived by a simulation study. Therefore, the asymptotic normal distribution of the difference test statistic is established. The results provide useful information to practitioners. An example from the leather industry is presented to illustrate the applicability of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Interplay between gut microbiota,its metabolites and human metabolism: Dissecting cause from consequence

BackgroundAlterations in gut microbiota composition and bacterial metabolites have been increasingly recognized to affect host metabolism and are at the basis of metabolic diseases such as obesity and type 2 diabetes (DM2). Intestinal enteroendocrine cells (EEC's) sense gut luminal content and accordingly secrete hormones that modulate glucose and lipid metabolism and affect satiety. It has become evident that microbial metabolic products significantly affect EEC function.Scope and approachIn this review, we will discuss current insights in the role of the gut microbiota and its metabolites in development of obesity and DM2 and elaborate on interventions that modulate EEC action.Key findings and conclusionsStudies including fecal transplantation and Roux-en-Y gastric bypass (RYGB) in humans and animal models suggest that the gut microbiota and its metabolites causally contribute to development of obesity and DM2. Emerging evidence suggests that the gut microbiota and its metabolites can modulate secretion of EEC hormones that regulate appetite and insulin secretion. Dispersed intestinal expression and low abundance make EEC's difficult to study. Since current intestinal sampling methods in humans are mostly limited to the colon, this leaves a large part of EEC function understudied. It would therefore be relevant to develop means to extend sampling methods throughout to entire GI tract.     

Changes in Some Biochemical Indices of Stability of Broiler Chicken Actomyosin at Different Levels of Sodium Bicarbonate in Presence and Absence of Sodium Chloride

This study examined some physicochemical properties of actomyosin isolated from pre-rigor chicken breast muscle (Pectoralis major) to gain insight on its interaction with 0.2–1.0M sodium bicarbonate (NaHCO₃) either with or without 3% sodium chloride (NaCl). NaHCO₃ has been a frequent quality raiser with respect to water holding capacity of meat, which is adversely affected at low pH. This study document a remarkable inactivation of ATPases and the weakening of actin-myosin interaction with increasing molarity of NaHCO₃, irrespective of the presence or absence of NaCl. Similarly, there was a concomitant increase in turbidity of soluble fraction, increase in total protein solubility and reactive SH groups. The observations on protein solubility/insolubility are supported by SDS-PAGE profiles. Therefore, the conformational changes induced by NaHCO₃ are concentration dependent and appear different from those during thermal transitions. The findings suggest that low concentration of NaHCO₃ may be more suitable component of marinades, since it affects the main myofibrillar contractile complex less adversely.     

Effect of Postmolding Heat Treatment on In Vitro Properties of a Polyanhydride Implant Containing Gentamicin Sulfate

A polyanhydride implant containing gentamicin sulfate was fabricated using a laboratory‐scale injection‐molding machine. After injection molding, the implants were subject to heat treatment at 60°C for various time periods with or without nitrogen protection. The impact of this heat treatment on the in vitro properties of the implants including copolymer molecular weights, mechanical properties, and in vitro drug‐release profiles was investigated. This heat treatment caused a drastic drop in the molecular weight of the copolymer. Heating without nitrogen protection resulted in the hardening of the implant, but heating in the presence of nitrogen rendered the implant less rigid. It was also found that a faster in vitro drug release profile was shown by implants heated without nitrogen protection and a pronounced slowing down in drug release was exhibited by implants heated with nitrogen protection.