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The effect of gluten peptides (GPs) isolated from a gluten proteolysate on in vitro amylolysis of gelatinized wheat starch was investigated. GPs in a pepsin hydrolysate were fractionated into fractions with molecular weights (MWs) of 500−3000, 3500−7000, 10−17, and 35−48 kDa. The fractions containing peptides with MW > 10 kDa had a strong inhibitory effect on enzyme activity and amylolysis of starch, whereas GPs with MW <10 kDa had no inhibitory effect. Binding constants estimated by surface plasmon resonance showed that peptides in the fractions with MW > 10 kDa bound more strongly to α-amylase, in contrast to peptides of MW <10 kDa. Significant correlations were observed between digestion parameters and equilibrium binding affinity. We conclude that peptides with MW >10 kDa in a pepsin digest of gluten have a strong inhibitory effect on in vitro enzymatic hydrolysis of starch due to their strong binding affinity to α-amylase.

In this work, we propose a framework for singleview hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence. Specifically, for 2D encoding, we propose lightweight yet effective stacked structures. Regarding 3D decoding, we provide an efficient graph operator, namely depth-separable spiral convolution. Moreover, we present a novel feature lifting module for bridging the gap between 2D and 3D representations. This module starts with a map-based position regression (MapReg) block to integrate the merits of both heatmap encoding and position regression paradigms to improve 2D accuracy and temporal coherence. Furthermore, MapReg is followed by pose pooling and pose-to-vertex lifting approaches, which transform 2D pose encodings to semantic features of 3D vertices. Overall, our hand reconstruction framework, called MobRecon, comprises affordable computational costs and miniature model size, which reaches a high inference speed of 83FPS on Apple A14 CPU. Extensive experiments on popular datasets such as FreiHAND, RHD, and HO3Dv2 demonstrate that our MobRecon achieves superior performance on reconstruction accuracy and temporal coherence. Our code is publicly available at https://github. com/SeanChenxy/HandMesh.