Maillard reaction between pea protein isolate and maltodextrin via wet-heating route for emulsion stabilisation

Pea protein isolate (PPI)-maltodextrin (MD) conjugates were prepared by using controlled Maillard reaction at 90 °C in the solution state (wet-heating route). The degree of conjugation between PPI and MD was measured in terms of evolved colour, UV absorbance, and molecular weight change. Results showed that the degree of Maillard reaction increased with the increase in pH and reaction time; however, the PPI-MD conjugation was optimised and was successfully controlled within the initial stage at pH 7.5 and 8.0 to avoid the formation of melanonids. The random coil content of PPI significantly increased upon conjugation with MD producing more flexible structure. The functional properties of PPI in terms of solubility, surface change (zeta-potential), and emulsifying properties of PPI were significantly improved after conjugation with MD. The highest solubility PPI-MD conjugates was observed at 5 h of reaction. The canola oil-in-water (O/W) emulsion stabilised by PPI-MD 5 h conjugate at 2% of emulsifier concentration and homogenised at 60 MPa for 3 passes had the highest physical stability when subjected to 25–60 °C. These findings indicate that Maillard reaction can be controlled in the initial stage via the wet-heating route and the resulting conjugates can be much effective emulsifiers than plant proteins

Role of Dorsal Root Ganglion Glutaminase in Acute and Chronic Inflammatory Pain

Scope and methods of the study.The purpose of this study was to provide further evidence supporting the concept that glutaminase, the synthetic enzyme for the neurotransmitter glutamate found in dorsal root ganglion (DRG) neurons, is a potential target for analgesic therapeutics. Behavioral assays, fluorescent microscopy and quantitative image analysis were used to investigate the temporal expression of DRG glutaminase after peripheral inflammation. The role of basal glutaminase and glutamate levels in initiating acute and chronic inflammatory pain was also explored by inhibition with a glutaminase inhibitor, 6-diazo-5-oxo-L-norleucine (DON).Findings and Conclusions. Significant elevation of glutaminase was observed in subpopulations of DRG neurons labeled with nociceptive-related neuropeptide markers in small- to medium-sized neuronal cell bodies. Elevated glutaminase was also found in peripheral nerve during the acute phase of inflammation. These results indicate that inflammation stimulates glutaminase synthesis in neuronal cell bodies for transport to peripheral nerve terminals at the inflamed site. Glutamate production would be augmented and increased release would contribute to peripheral sensitization. Inhibition of glutaminase with DON at the peripheral nerve terminal prior to inflammation 1) achieved robust long-term anti-edema and anti-nociceptive effects; 2) inhibited the inflammation-induced elevation of glutaminase in the peptidergic DRG neuronal cell bodies. These results support the notion that, at the onset of inflammation, tonic or early glutamate production in peripheral nerve terminals has a "feed-forward" mechanism by up-regulating glutaminase synthesis in DRG neuronal cell bodies. I suggest that early treatment of inflammation targeting glutamate production in the peripheral terminal might prevent the development of chronic pain and/or provide more effective alleviation if the pain continues into the chronic phase with the pathology. With further understanding of the role of dorsal root ganglion glutaminase in acute and chronic inflammatory pain, it is rational to propose that glutaminase and glutamate metabolism can be novel potential targets for analgesic therapeutics.Microbiology, Cell, & Molecular Biolog

Investigating the effectiveness of coacervates produced from conjugated and unconjugated Spirulina protein in delivering unstable oil to the intestinal phase of digestion

This study investigated the potential of complex coacervates produced using Spirulina protein concentrate (SPC) conjugated with maltodextrin (MD) and carrageenan (CG) for encapsulating and delivering sensitive oils. A wet-heating Maillard reaction was employed to conjugate SPC with MD, followed by coacervation with CG to form the conjugate-based coacervates. Additionally, a mixture of unconjugated SPC and MD was coacervated with CG to produce mixture-based coacervates. Both types of coacervates were utilised as wall materials for encapsulating canola oil. The in-vitro digestion of the resulting microcapsules was assessed in oral, gastric, and intestinal phases, focusing on physicochemical parameters such as droplet size, zeta-potential, microstructure, proteolysis, oil release and lipolysis. The findings revealed that microcapsules prepared using both (SPC-MD mixture)-CG and (SPC-MD conjugate)-CG coacervates were remarkably stable against gastric digestion, as evidenced by the minimal production of free amino acids (15 mM). Most of the encapsulated oil (62–67%) was released during the intestinal phase due to the breakdown of the coacervates. Notably, the microcapsules produced with (SPC-MD conjugate)-CG coacervates demonstrated a lower degree of lipolysis (41.77% free fatty acid content) compared to those prepared with (SPC-MD mixture)-CG coacervates (53.35% free fatty acid content). These results highlight the potential of complex coacervates produced using conjugated SPC as promising materials for the encapsulation and delivery of sensitive oils

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.We have made the code publicly available at https://github.com/HuiChen24/NER-GRN.Comment: This paper is accepted by AAAI 201

Double-edged sword of diabetes mellitus for abdominal aortic aneurysm

IntroductionDiabetes mellitus (DM) has been proved to contribute to multiple comorbidities that are risk factors for abdominal aortic aneurysm (AAA). Remarkably, evidences from epidemiologic studies have demonstrated a negative association between the two disease states. On the other hand, hyperglycemic state was linked to post-operative morbidities following AAA repair. This review aims to provide a thorough picture on the double-edged nature of DM and major hypoglycemic medications on prevalence, growth rate and rupture of AAA, as well as DM-associated prognosis post AAA repair.MethodsWe performed a comprehensive search in electronic databases to look for literatures demonstrating the association between DM and AAA. The primary focus of the literature search was on the impact of DM on the morbidity, enlargement and rupture rate, as well as post-operative complications of AAA. The role of antidiabetic medications was also explored.ResultsRetrospective epidemiological studies and large database researches associated the presence of DM with decreased prevalence, slower expansion and limited rupture rate of AAA. Major hypoglycemic drugs exert similar protective effect as DM against AAA by targeting pathological hallmarks involved in AAA formation and progression, which were demonstrated predominantly by animal studies. Nevertheless, presence of DM or postoperative hyperglycemia was linked to poorer short-term and long-term prognosis, primarily due to greater risk of infection, longer duration of hospital stays and death.ConclusionWhile DM is a positive factor in the formation and progression of AAA, it is also associated with higher risk of negative outcomes following AAA repair. Concomitant use of antidiabetic medications may contribute to the protective mechanism of DM in AAA, but further studies are still warranted to explore their role following AAA repair

Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China

Against the background of large-scale urbanisation and rural land expropriation, rural resettlement residential housing has been built to accommodate local rural residents in the peripheral areas of China. To explore the context-specific policy implications for improving neighbourhood satisfaction (NS) of residents in rural resettlement residential communities (RRRCs), this paper examines the determinants of NS, and their spatial effects, in rural resettlement residential neighbourhoods using Suqian, in Jiangsu Province, as a case study. This study contributes to the current literature in two ways: it constitutes the first attempt to examine NS among RRRCs; second, our spatial model helps to gain further understanding of horizontal and vertical spatial dependence effects. Our results indicate that income, gender, age, family structure, number of years living in a community, transport and architectural age all have significant effects on NS in RRRCs

Joint Image-Text Hashing for Fast Large-Scale Cross-Media Retrieval Using Self-Supervised Deep Learning

Recent years have witnessed the promising future of hashing in the industrial applications for fast similarity retrieval. In this paper, we propose a novel supervised hashing method for large-scale cross-media search, termed Self-Supervised Deep Multimodal Hashing (SSDMH), which learns unified hash codes as well as deep hash functions for different modalities in a self-supervised manner. With the proposed regularized binary latent model, unified binary codes can be solved directly without relaxation strategy while retaining the neighborhood structures by the graph regularization term. Moreover, we propose a new discrete optimization solution, termed as Binary Gradient Descent, which aims at improving the optimization efficiency towards real-time operation. Extensive experiments on three benchmark datasets demonstrate the superiority of SSDMH over state-of-the-art cross-media hashing approaches