Different genetic strategies to generate high amylose starch mutants by engineering the starch biosynthetic pathways

This review systematically documents the major different strategies of generating high-amylose (HAS) starch mutants aiming at providing high resistant starch, by engineering the starch biosynthesis metabolic pathways. We identify three main strategies based on a new representation of the starch structure: 'the building block backbone model': i) suppression of starch synthases for reduction of amylopectin (AP) side-chains; ii) suppression of starch branching enzymes (SBEs) for production of AM-like materials; and iii) suppression of debranching enzymes to restrain the transformation from over-branched pre-AP to more ordered AP. From a biosynthetic perspective, AM generated through the second strategy can be classified into two types: i) normal AM synthesized mainly by regular expression of granule-bound starch synthases, and ii) modified linear AP chains (AM-like material) synthesized by starch synthases due to the suppression of starch branching enzymes. The application of new breeding technologies, especially CRISPR, in the breeding of HAS crops is also reviewed

Optimization of butter, xylitol, and high‐amylose maize flour on developing a low‐sugar cookie

There is a huge interest to develop low‐sugar baked products for reducing risks of some diseases, such as adiposis, diabetes, and high blood pressure. A low‐sugar cookie was prepared with butter, xylitol, and high‐amylose maize flour (HAMF) through response surface methodology. ANOVA of models for sensory profiles, texture, and digestibility showed the models for sensory attributes, hardness, and resistant starch were significant (p < .05), indicating the reliability of these models. Sensory profiles of cookie were mainly affected by butter and xylitol, while HAMF was not significant. Hardness was negatively related to butter and HAMF. Resistant starch (RS) content was positively correlated with butter, xylitol, and HAMF. The improvement of RS was attributed to high proportions of long amylopectin and amylose chains of starch in HAMF and interactions of starch with butter and xylitol. The predicted model showed the optimal combination of a cookie with the highest sensory and resistant starch and the lowest hardness was intermediate butter, high xylitol, and high HAMF contents

Optimization of butter, xylitol, and high‐amylose maize flour on developing a low‐sugar cookie

There is a huge interest to develop low‐sugar baked products for reducing risks of some diseases, such as adiposis, diabetes, and high blood pressure. A low‐sugar cookie was prepared with butter, xylitol, and high‐amylose maize flour (HAMF) through response surface methodology. ANOVA of models for sensory profiles, texture, and digestibility showed the models for sensory attributes, hardness, and resistant starch were significant (p < .05), indicating the reliability of these models. Sensory profiles of cookie were mainly affected by butter and xylitol, while HAMF was not significant. Hardness was negatively related to butter and HAMF. Resistant starch (RS) content was positively correlated with butter, xylitol, and HAMF. The improvement of RS was attributed to high proportions of long amylopectin and amylose chains of starch in HAMF and interactions of starch with butter and xylitol. The predicted model showed the optimal combination of a cookie with the highest sensory and resistant starch and the lowest hardness was intermediate butter, high xylitol, and high HAMF contents

Decision Aggregation with Reliability Propagation

The diversity of opinions is both cure and curse for the effective use of crowdsourced intelligence. To unify crowd­sourced intelligence for a well­informed decision, we propose an algorithmic approach for decision aggregation that accurately quantifies the reliability of information from multiple sources. The key idea is to model the propagation of reliability in decisions based on an ensemble of relevance graphs, where the optimization of both the reliability propagation and the graph ensemble are mutually reinforced. The propagated reliability is to aggregate intelligence from multiple sources and facilitate decision­making by leveraging various types of intercorrelations of information sources and the subjects. Meanwhile, the optimized graph ensemble can retain the relevance structures with respect to the crowdsourced intelligence. We evaluate our approach with large­scale data sets of stock markets, and find that our approach not only outperforms alternative methods, but also provides interesting insights into the reliability of the information

High Amylose-Based Bio Composites:Structures, Functions and Applications

As biodegradable and eco-friendly bio-resources, polysaccharides from a wide range of sources show steadily increasing interest. The increasing fossil-based production of materials are heavily associated with environmental and climate concerns, these biopolymers are addressing such concerns in important areas such as food and biomedical applications. Among polysaccharides, high amylose starch (HAS) has made major progress to marketable products due to its unique properties and enhanced nutritional values in food applications. While high amylose-maize, wheat, barley and potato are commercially available, HAS variants of other crops have been developed recently and is expected to be commercially available in the near future. This review edifies various forms and processing techniques used to produce HAS-based polymers and composites addressing their favorable properties as compared to normal starch. Low toxic and high compatibility natural plasticizers are of great concern in the processing of HAS. Further emphasis, is also given to some essential film properties such as mechanical and barrier properties for HAS-based materials. The functionality of HAS-based functionality can be improved by using different fillers as well as by modulating the inherent structures of HAS. We also identify specific opportunities for HAS-based food and biomedical fabrications aiming to produce cheaper, better, and more eco-friendly materials. We acknowledge that a multidisciplinary approach is required to achieve further improvement of HAS-based products providing entirely new types of sustainable materials