Starch retrogradation is a process in which disaggregated amylose and amylopectin chains in a gelatinized starch paste reassociate to form more ordered structures. Starch retrogradation has been the subject of intensive research over the last 50 years, mainly due to its detrimental effect on the sensory and storage qualities of many starchy foods. However, starch retrogadation is desirable for some starchy food products in terms of textural and nutritional properties. To better understand the effect of starch retrogradation on the quality of starchy foods, measurement methods of starch retrogradation and factors that influence starch retrogradation have been studied extensively. This article provides a comprehensive review of starch retrogradation including the definition of the process, molecular mechanisms of how it occurs, and measurement methods and factors that influence starch retrogradation. The review also discusses the effect of retrogradation on the in vitro enzyme digestibility of starch. Spectroscopic methods such as FTIR and Raman are considered to be very promising in characterizing starch retrogradation at a molecular level, although more studies are needed in the future.

Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and timeconsuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a student model and a teacher model, and the student model learns from the teacher model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher model. We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems 3 .

Foxtail millet (Setaria italica) is an important grain crop that is grown in arid regions. Here we sequenced 916 diverse foxtail millet varieties, identified 2.58 million SNPs and used 0.8 million common SNPs to construct a haplotype map of the foxtail millet genome. We classified the foxtail millet varieties into two divergent groups that are strongly correlated with early and late flowering times. We phenotyped the 916 varieties under five different environments and identified 512 loci associated with 47 agronomic traits by genome-wide association studies. We performed a de novo assembly of deeply sequenced genomes of a Setaria viridis accession (the wild progenitor of S. italica) and an S. italica variety and identified complex interspecies and intraspecies variants. We also identified 36 selective sweeps that seem to have occurred during modern breeding. This study provides fundamental resources for genetics research and genetic improvement in foxtail millet.

Starch is the most important glycemic carbohydrate in foods. The relationship between the rate and extent of starch digestion to produce glucose for absorption into the bloodstream and risk factors for diet-related diseases is of considerable nutritional interest. Native starch is attacked slowly by enzymes, but after hydrothermal processing its susceptibility to enzymatic breakdown is greatly increased. Most starch consumed by humans has undergone some form of processing or cooking, which causes native starch granules to gelatinize, followed by retrogradation on cooling. The extent of gelatinization and retrogradation are major determinants of the susceptibility of starch to enzymatic digestion and its functional properties for food processing. The type and extent of changes that occur in starch as a result of gelatinization, pasting and retrogradation are determined by the type of the starch, processing and storage conditions. A mechanistic understanding of the molecular disassembly of starch granules during gelatinization is critical to explaining the effects of processing or cooking on starch digestibility. This review focuses on the molecular disassembly of starch granules during starch gelatinization over a wide range of water levels, and its consequential effect on in vitro starch digestibility and in vivo glycemic index.

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations.Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders.However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted * Corresponding authors: Yanwu Xu (ywxu@ieee.org) and Xiulan Zhang (

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Acid hydrolysis is an important chemical modification that can significantly change the structural and functional properties of starch without disrupting its granular morphology. A deep understanding of the effect of acid hydrolysis on starch structure and functionality is of great importance for starch scientific research and its industrial applications. During acid hydrolysis, amorphous regions are hydrolyzed preferentially, which enhances the crystallinity and double helical content of acid hydrolyzed starch. This review discusses current understanding of the effect of acid hydrolysis on starch structure and functionality. The effects of acid hydrolysis on amylose content, chain length distribution of amylopectin molecules, molecular and crystalline organization (including lamellar structure) and granular morphology are considered. Functional properties discussed include swelling power, gelatinization, retrogradation, pasting, gel texture, and in vitro enzyme digestibility. The paper also highlights some promising applications of acid hydrolyzed starch (starch nanocrystals) in the preparation of biodegradable nanocomposites, bio-hydrogen, and slowly digestible starch-based healthy foods.

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