RESUMO
Fucoidan refers to a group of sulphated polysaccharides obtained from brown seaweed, with numerous biological activities. In this study, fucoidan was fortified into Chinese steamed bread (CSB) at different concentrations (0, 1%, 3% and 5%) and the effect of fucoidan on the dough properties, structure properties and bioactivity were investigated. The results showed that fucoidan could change the viscosity of unfermented dough, and a high concentration of fucoidan could remove the free radicals produced by the SH-SS exchange reaction (GS-) in the dough, which significantly reduced the content of disulfide bond and reduced the expanded volume of fermented dough (p < 0.05). In addition, fucoidan forms a physical barrier on the surface of starch particles and hinders the reaction between protein-to-protein; therefore, fucoidan increased the hardness, gumminess and chewiness in CSB, and reduced the specific volume in CSB. Furthermore, the fucoidan-fortified CSB samples were found to have both the ability to significantly reduce the predicted glycemic index (pGI) (p < 0.05) and improve antioxidant activity (p < 0.05). Collectively, these findings could provide a theoretical basis for the applications of fucoidan as a functional component in fermented foods.
RESUMO
Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry's process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area, critical path, and energy consumption values from the SPICE simulations at the module level are compared with those from NeuroSim. Some adjustment factors are introduced to account for transistor sizing and wiring area in the layout, gate switching activity, post-layout performance drop, etc. We show that the prediction from NeuroSim is precise with chip-level error under 1% after the calibration. Finally, the system-level performance benchmark is conducted with various device technologies and compared with the results before the validation. The general conclusions stay the same after the validation, but the performance degrades slightly due to the post-layout calibration.
RESUMO
Circular RNAs (circRNAs) are a class of noncoding RNAs with a circular, covalent structure that lack both 5' ends and 3' poly(A) tails, which are stable and specific molecules that exist in eukaryotic cells and are highly conserved. The role of circRNAs in viral infections is being increasingly acknowledged, since circRNAs have been discovered to be involved in several viral infections (such as hepatitis B virus infection and human papilloma virus infection) through a range of circRNA/microRNA/mRNA regulatory axes. These findings have prompted investigations into the potential of circRNAs as targets for the diagnosis and treatment of viral infectionrelated diseases. The aim of the present review was to systematically examine and discuss the role of circRNAs in several common viral infections, as well as their potential as diagnostic markers and therapeutic targets.