Chemical acylation of pea protein isolate hydrolysate with fatty acid N-hydroxysuccinimide esters: Effect on structure and functional properties.

Applications of pea protein in the food industry have been greatly restricted by its poor functional properties. In order to solve this problem, a novel technique combining enzymatic hydrolysis and fatty acid acylation has been applied in this work to construct a pea protein-fatty acid covalent complex that aims to improve its functional properties. The processed pea protein with increased water solubility tends to decrease the chance of self-aggregation. Additionally, emulsifying and antioxidant properties have also been found after this process. On top of that, the modified pea protein has been characterized by Fourier transform infrared and circular dichroism spectroscopy. These results demonstrate that these properties were mainly caused by the acylation of the amino group from hydrolyzed pea protein and the carboxyl group from the fatty acid. The enzymatic hydrolysis/fatty acid acylation research provides insights into manufacturing high-quality functional lipoproteins from inexpensive pea protein for the food industry.

Stevioside Ameliorates Prenatal Obesity Induced Postpartum Depression: The Potential Role of Gut Barrier Homeostasis.

SCOPE: Postpartum depression and cognitive impairment are the common complications of prenatal obesity. Stevioside is a non-nutritive natural sweetener with antioxidant and anti-inflammatory. However, its effects on depression behaviors and cognitive impairment induced by a high-fat diet (HFD) remain unclear. METHODS AND RESULTS: An 8-week HFD is used to establish a prenatal obesity model in female C57BL/6J mice to explore the improvement effects of stevioside (0.5 mg mL-1 in drinking water) on maternal depression and cognitive dysfunction after weaning. The results demonstrated that stevioside improves behavioral performance of obese maternal mice, and inhibits neuronal damage and 5-hydroxytryptamine (5-HT) abnormality induced by HFD. In addition, stevioside inhibits oxidative stress by reducing malondialdehyde (MDA) and increasing superoxide dismutase (SOD) and glutathione (GSH) activities in the brains of obese maternal mice. Additionally, stevioside improves gut barrier integrity and prevented lipopolysaccharide (LPS) extravasation, and alleviates neuroinflammation. Correlation analysis shows that gut barrier and serum LPS are closely related to behavioral performance and brain biochemical indicators. CONCLUSION: Stevioside is capable to prevent prenatal obesity-induced cognitive and mood disorders by restoring intestinal barrier damage and inhibiting inflammation.

Effect of Adding Fermented Proso Millet Bran Dietary Fiber on Micro-Structural, Physicochemical, and Digestive Properties of Gluten-Free Proso Millet-Based Dough and Cake.

The increasing demand for functional foods has pushed the food industry to produce fiber-enriched products. In this study, rheological, microstructural, physicochemical, and functional characteristics were investigated for whole proso millet dough and cake, fortified with fermented proso millet bran dietary fiber flour (F-DF). Results showed that proso millet flour is less absorbent and stable than the control group. Adding proso millet flour and F-DF reduced the elasticity of the dough and increased its hardness, but had no significant effect on viscosity, cohesion, and resilience. The microstructure analysis exhibited an unformed continuous network formation in proso millet dough. Analyses suggested that proso millet flour combined with the fermented dietary fiber group had significantly higher total phenol content (0.46 GAE mg/g), DPPH• scavenging activity (66.84%), and ABTS•+ scavenging activity (87.01%) than did the other group. In addition, F-DF led to a significant reduction in the predicted released glucose contents of reformulated cakes. In summary, cakes prepared with the involvement of whole proso millet flour and F-DF exhibited less adverse sensory impact and possessed the potential to decrease postprandial blood glucose levels resulting purely from cake consumption.

Whole Grain Proso Millet (Panicum miliaceum L.) Attenuates Hyperglycemia in Type 2 Diabetic Mice: Involvement of miRNA Profile.

This work aimed to investigate the hypoglycemic effects and underlying mechanism of whole grain proso millet (Panicum miliaceum L.; WPM) on type 2 diabetes mellitus (T2DM). The results showed that WPM supplementation significantly reduced fasting blood glucose (FBG) and serum lipid levels in T2DM mice induced by a high-fat diet (HFD) combined with streptozotocin (STZ), with improved glucose tolerance, liver and kidney injury, and insulin resistance. In addition, WPM significantly inhibited the expression of gluconeogenesis-related genes G6pase, Pepck, Foxo1, and Pgc-1α. Further study by miRNA high-throughput sequencing revealed that WPM supplementation mainly altered the liver miRNA expression profile of T2DM mice by increasing the expression of miR-144-3p_R-1 and miR-423-5p, reducing the expression of miR-22-5p_R-1 and miR-30a-3p. GO and KEGG analyses showed that the target genes of these miRNAs were mainly enriched in the PI3K/AKT signaling pathway. WPM supplementation significantly increased the level of PI3K, p-AKT, and GSK3ß in the liver of T2DM mice. Taken together, WPM exerts antidiabetic effects by improving the miRNA profile and activating the PI3K/AKT signaling pathway to inhibit gluconeogenesis. This study implies that PM can act as a dietary supplement to attenuate T2DM.

Lactobacillus plantarum LLY-606 supplementation ameliorates hyperuricemia via modulating intestinal homeostasis and relieving inflammation.

Gut microbiota is associated with hyperuricemia progression and can be regulated by Lactobacillus plantarum. However, the role of Lactobacillus plantarum in hyperuricemia is still unknown. Thus, we constructed the mouse model of hyperuricemia using potassium oxonate and hypoxanthine treatment to explore the effects of Lactobacillus plantarum LLY-606 supplementation on the development of hyperuricemia. The results showed that Lactobacillus plantarum LLY-606 significantly reduced the level of serum uric acid through inhibiting uric acid secretion and regulating uric acid transport. We also found that Lactobacillus plantarum LLY-606 supplementation inhibited the inflammatory response and the activation of the TLR4/MyD88/NF-κB signaling pathway in mice. Microbiome sequencing and analysis suggested the successful colonization of probiotics, which could regulate intestinal flora dysbiosis induced by hyperuricemia. The abundance of Lactobacillus plantarum was significantly negatively correlated with hyperuricemia-related indicators. Notably, the functional abundance prediction of microbiota indicated that lipopolysaccharide biosynthesis protein pathways and lipopolysaccharide biosynthesis pathways were inhibited after the probiotic intervention. In conclusion, Lactobacillus plantarum LLY-606 can serve as a potential functional probiotic to affect the development of hyperuricemia through modulating gut microbiota, downregulating renal inflammation, and regulating uric acid metabolism.

The effect of ball milling on the structure, physicochemical and functional properties of insoluble dietary fiber from three grain bran.

The effects of ball milling processing on the structure, physicochemical, and functional properties of insoluble dietary fiber (IDF) in bran from prosomillet, wheat and rice were investigated. Meanwhile, the effect of IDF on glucose tolerance and blood lipid levels in mice was evaluated as well. With findings, for all three grains, the particle sizes of IDF were significantly reduced after ball milling treatment (p < 0.05). Scanning electron microscopy revealed fragmented fiber with numerous pores and cracks. The reactive groups of three IDF samples were found to be similar by fourier transform infrared spectroscopy. And consistent with X-ray diffraction and thermal analysis, for all three grains, ball milling reduced the crystallinity of IDF and helped to increase the release of free phenol by 23.4 %, 8.9 %, and 12.2 %, respectively. Furthermore, the water holding capacity, glucose delay capacity, glucose, sodium cholate, and cholesterol adsorption capacity, and in vitro digestibility of starch and fat were all improved to varying degrees. Animal experiments showed that ball milling treatment effectively slowed the postprandial rise in blood sugar (especially IDF of rice bran) and blood lipids (especially IDF of prosomillet bran). As a result, ball milling treatment is a potential method for dietary fiber modification in the food industry.

Genomic survey of NPF and NRT2 transporter gene families in five inbred maize lines and their responses to pathogens infection.

Besides manipulating nitrate uptake and allocation, nitrate transporters (NRTs) are also known to play crucial roles in pathogen defense and stress response. By blasting with the model NRT genes of poplar and Arabidopsis, a total of 408 gene members were identified from 5 maize inbred lines in which the number of NRTs ranged from 72 to 88. Phylogenetic analysis showed that the NRT genes of maize were classified into NRT1/PTR (NPF), NRT2 and NRT3 subfamilies, respectively. Marked divergence of the duplication patterns of NRT genes were identified, which may be a new basis for classification and identification of maize varieties. In terms of biotic stress, NRT2.5A showed an enhanced expression during the pathogen infection of Colletotrichum graminicola, while NRT1c4C was down-regulated, suggesting that maize NRT transporters may have both positive and negative roles in the disease resistance response. This work will promote the further studies of NRT gene families in maize, as well as be beneficial for further understanding of their potential roles in plant-pathogen interactions.

Seamless Texture Optimization for RGB-D Reconstruction.

Restoring high-fidelity textures for 3D reconstructed models are an increasing demand in AR/VR, cultural heritage protection, entertainment, and other relevant fields. Due to geometric errors and camera pose drifting, existing texture mapping algorithms are either plagued by blurring and ghosting or suffer from undesirable visual seams. In this paper, we propose a novel tri-directional similarity texture synthesis method to eliminate the texture inconsistency in RGB-D 3D reconstruction and generate visually realistic texture mapping results. In addition to RGB color information, we incorporate a novel color image texture detail layer serving as an additional context to improve the effectiveness and robustness of the proposed method. First, we select an optimal texture image for each triangle face of the reconstructed model to avoid texture blurring and ghosting. During the selection procedure, the texture details are weighted to avoid generating texture chart partitions across high-frequency areas. Then, we optimize the camera pose of each texture image to align with the reconstructed 3D shape. Next, we propose a tri-directional similarity function to resynthesize the image context within the boundary stripe of texture charts, which can significantly diminish the occurrence of texture seams. Finally, we introduce a global color harmonization method to address the color inconsistency between texture images captured from different viewpoints. The experimental results demonstrate that the proposed method outperforms state-of-the-art texture mapping methods and effectively overcomes texture tearing, blurring, and ghosting artifacts.

Rank-PointRetrieval: Reranking Point Cloud Retrieval via a Visually Consistent Registration Evaluation.

Point cloud-based place recognition is a fundamental part of the localization task, and it can be achieved through a retrieval process. Reranking is a critical step in improving the retrieval accuracy, yet little effort has been devoted to reranking in point cloud retrieval. In this paper, we investigate the versatility of rigid registration in reranking the point cloud retrieval results. Specifically, after obtaining the initial retrieval list based on the global point cloud feature distance, we perform registration between the query and point clouds in the retrieval list. We propose an efficient strategy based on visual consistency to evaluate each registration with a registration score in an unsupervised manner. The final reranked list is computed by considering both the original global feature distance and the registration score. In addition, we find that the registration score between two point clouds can also be used as a pseudo label to judge whether they represent the same place. Thus, we can create a self-supervised training dataset when there is no ground truth of positional information. Moreover, we develop a new probability-based loss to obtain more discriminative descriptors. The proposed reranking approach and the probability-based loss can be easily applied to current point cloud retrieval baselines to improve the retrieval accuracy. Experiments on various benchmark datasets show that both the reranking registration method and probability-based loss can significantly improve the current state-of-the-art baselines.

Point Cloud Completion Via Skeleton-Detail Transformer.

Point cloud shape completion plays a central role in diverse 3D vision and robotics applications. Early methods used to generate global shapes without local detail refinement. Current methods tend to leverage local features to preserve the observed geometric details. However, they usually adopt the convolutional architecture over the incomplete point cloud to extract local features to restore the diverse information of both latent shape skeleton and geometric details, where long-distance correlation among the skeleton and details is ignored. In this work, we present a coarse-to-fine completion framework, which makes full use of both neighboring and long-distance region cues for point cloud completion. Our network leverages a Skeleton-Detail Transformer, which contains cross-attention and self-attention layers, to fully explore the correlation from local patterns to global shape and utilize it to enhance the overall skeleton. Also, we propose a selective attention mechanism to save memory usage in the attention process without significantly affecting performance. We conduct extensive experiments on the ShapeNet dataset and real-scanned datasets. Qualitative and quantitative evaluations demonstrate that our proposed network outperforms current state-of-the-art methods.

Neighbor Reweighted Local Centroid for Geometric Feature Identification.

Identifying geometric features from sampled surfaces is a significant and fundamental task. The existing curvature-based methods that can identify ridge and valley features are generally sensitive to noise. Without requiring high-order differential operators, most statistics-based methods sacrifice certain extents of the feature descriptive powers in exchange for robustness. However, neither of these types of methods can treat the surface boundary features simultaneously. In this paper, we propose a novel neighbor reweighted local centroid (NRLC) computational algorithm to identify geometric features for point cloud models. It constructs a feature descriptor for the considered point via decomposing each of its neighboring vectors into two orthogonal directions. A neighboring vector starts from the considered point and ends with the corresponding neighbor. The decomposed neighboring vectors are then accumulated with different weights to generate the NRLC. With the defined NRLC, we design a probability set for each candidate feature point so that the convex, concave and surface boundary points can be recognized concurrently. In addition, we introduce a pair of feature operators, including assimilation and dissimilation, to further strengthen the identified geometric features. Finally, we test NRLC on a large body of point cloud models derived from different data sources. Several groups of the comparison experiments are conducted, and the results verify the validity and efficiency of our NRLC method.

Involvement of intestinal flora and miRNA into the mechanism of coarse grains improving type 2 diabetes: an overview.

The prevalence of type 2 diabetes has been growing at an increasing rate worldwide. Dietary therapy is probably the easiest and least expensive method to prevent and treat diabetes. Previous studies have reported that coarse grains have anti-diabetic effects. Although considerable efforts have been made on the anti-diabetic function of different grains, the mechanisms of coarse grains on type 2 diabetes have not been systematically compared and summarized so far. Intestinal flora, reported as the main 'organ' of action underlying coarse grains, is an important factor in the alleviation of type 2 diabetes by coarse grains. Furthermore, microRNA (miRNA), as a new disease marker and 'dark nutrient', plays a likely influential role in cross-border communication among coarse grains, intestinal flora, and hosts. Given this context, this article reviews several possible mechanisms for the role of coarse grains on diabetes, incorporating resistance to inflammation and oxidative stress, repair of insulin signaling and ß-cell dysfunction, and highlights the regulation of intestinal flora disorders and miRNAs expression, along with some novel insights. © 2022 Society of Chemical Industry.

Machine learning and experimental validation to construct a metastasis-related gene signature and ceRNA network for predicting osteosarcoma prognosis.

OBJECTIVE: Osteosarcoma (OS) is more common in adolescents and significantly harmful, and the survival rate is considerably low, especially in patients with metastatic OS. The identification of effective biomarkers and associated regulatory mechanisms, which predict OS occurrence and development as well as improve prognostic accuracy, will help develop more refined protocols for OS treatment. METHODS: In this study, genes showing differential expression in metastatic and non-metastatic types of OS were identified, and the ones affecting OS prognosis were screened from among these. Following this, the functions and pathways associated with the genes were explored via enrichment analysis, and an effective predictive signature was constructed using Cox regression based on the machine learning algorithm, least absolute shrinkage and selection operator (LASSO). Next, a correlative competing endogenous RNA (ceRNA) regulatory axis was constructed after verification by bioinformatics analysis and luciferase reporter gene experiments conducted based on the prognostic signature. RESULTS: Overall, 251 differentially expressed genes were identified and screened using bioinformatics and double luciferase reporter gene experiments. An effective prognostic signature was constructed based on 15 genes associated with OS metastasis, and upstream non-coding RNAs were identified to construct the "NBR2/miR-129-5p/FKBP11" regulatory axis based on the ceRNA networks, which helped identify candidate biomarkers for the OS clinical diagnosis and treatment, drug research, and prognostic prediction, among other applications. The findings of this study provide a novel strategy for determining the mechanism underlying OS occurrence and development and the appropriate treatment.

The effect of chronic exposure to a low concentration of perfluorooctanoic acid on cognitive function and intestinal health of obese mice induced by a high-fat diet.

Perfluorooctanoic acid (PFOA) is a persistent organic pollutant associated with many adverse health risks. Evidence suggests that obese individuals may be more susceptible to environmental substances. In the present work, we explored the effects of PFOA exposure on the cognitive function and intestinal health of obese mice. Obese mice induced by a high-fat diet were exposed to PFOA (0.5 mg/kg (bw)/day) via drinking water for 100 days. After exposure to PFOA, decreased body weight, enlarged liver, abnormal behavior, impaired synapse structure, neuroinflammation, activated glial cell, decreased nerve growth factor, altered gut microbiota, and disturbed serum metabolites were observed, while the gut inflammation and intestinal barrier were not significantly influenced. These results suggest that exposure to PFOA is associated with cognitive impairment in obese mice.

Targeted delivery of emamectin benzoate by functionalized polysuccinimide nanoparticles for the flowering cabbage and controlling Plutella xylostella.

BACKGROUND: Plutella xylostella, one of the most destructive and cosmopolitan pests of cruciferous crops, is especially harmful to the young tissues of the flowering cabbage (Brassica campestris L.). Although emamectin benzoate (EB) has high insecticidal activity against P. xylostella, one major reason of low utilization for EB is the lack of internal transport in the young plants. RESULTS: In this study, four kinds of functional EB/polysuccinimide (PSI) with glycine methylester nanoparticles (EB@PGA NPs) were prepared. The obtained EB@PGA NPs could effectively protect EB from photolysis, and the degradation rate of EB@PGA NPs was <30% in 24 h. Simulating the intestinal pH = 9 of P. xylostella, the highest cumulative release rate of EB@PGA NPs could reach 89.61% in 24 h. Furthermore, EB@PGA NPs could delivery EB into the young tissues of the flowering cabbage through the nanocarrier, and the highest transport efficiency of EB@PGA25 reached 1.437%. The bioactivity of EB@PGA25 against P. xylostella larvae (LC50  = 0.34 µg mL-1 ) was 1.6-fold higher than that of EB (LC50  = 0.53 µg mL-1 ). EB@PGA could easily become 'internalized' into the intestinal wall of P. xylostella, thus increasing the penetration of the drug and enhancing the insecticidal activity. CONCLUSION: The accurate delivery of insecticides by PGA nanocarriers into young tissues of plants could be a promising new method for the efficient management of field pests and diseases. © 2021 Society of Chemical Industry.

Unsupervised Intrinsic Image Decomposition Using Internal Self-Similarity Cues.

Recent learning-based intrinsic image decomposition methods have achieved remarkable progress. However, they usually require massive ground truth intrinsic images for supervised learning, which limits their applicability on real-world images since obtaining ground truth intrinsic decomposition for natural images is very challenging. In this paper, we present an unsupervised framework that is able to learn the decomposition effectively from a single natural image by training solely with the image itself. Our approach is built upon the observations that the reflectance of a natural image typically has high internal self-similarity of patches, and a convolutional generation network tends to boost the self-similarity of an image when trained for image reconstruction. Based on the observations, an unsupervised intrinsic decomposition network (UIDNet) consisting of two fully convolutional encoder-decoder sub-networks, i.e., reflectance prediction network (RPN) and shading prediction network (SPN), is devised to decompose an image into reflectance and shading by promoting the internal self-similarity of the reflectance component, in a way that jointly trains RPN and SPN to reproduce the given image. A novel loss function is also designed to make effective the training for intrinsic decomposition. Experimental results on three benchmark real-world datasets demonstrate the superiority of the proposed method.

Chemical constituents with inhibition against TNF-α from Merrillanthus hainanensis.

Two new steroidal glycosides oxystauntoside A (1) and oxystauntoside B (2), together with sixteen known compounds (3-18) were isolated from the 95% ethanol extract of Merrillanthus hainanensis. Their structures were characterized by extensive spectroscopic analysis including NMR and mass spectra and single crystal X-ray crystallography. The absolute configuration of 1 and 2 were further determined by ECD calculations. All of these compounds were isolated from M. hainanensis for the first time. All the fractions and compounds were tested for the anti-inflammatory activity against the TNF-α factor. The ethyl acetate fraction showed the most potent inhibition (71.3%) at 10 µg/mL and compounds 5 (78.9%) and 9 (73.4%) in this fraction with both carboxyl and phenolic hydroxyl groups showed significant inhibition at 10 µM. Our study provided the first scientific report for the medicinal value of M. hainanensis.

A Novel Visual Representation on Text Using Diverse Conditional GAN for Visual Recognition.

Automatic image visual recognition can make full use of largely available images with text descriptions on social media platforms to build large-scale image labeled datasets. In this paper, we propose a novel visual text representation, named DG-VRT (Diverse GAN-Visual Representation on Text), which extracts visual features from synthetic images generated by a diverse conditional Generative Adversarial Network (DCGAN) on the text, for visual recognition. The DCGAN incorporates the current state-of-the-art text-to-image GANs and generates multiple synthetic images with various prior noises conditioned on a text. Then we extract deep visual features from the generated synthetic images to explore the underlying visual concepts and provide a visual transformation on text in feature space. Finally, we combine image-level visual features, text-level features and visual features based on synthetic images together to recognize the images, and we also extend the proposed work to semantic segmentation. We conduct extensive experiments on two benchmark datasets and the experimental results demonstrate the efficacy of our proposed representation on text for visual recognition.

Effects of insoluble dietary fiber from kiwi fruit pomace on the physicochemical properties and sensory characteristics of low-fat pork meatballs.

ABSTRACT: As beverage industry by product, kiwi fruit pomace is potential but underutilized. In this study, insoluble dietary fiber from kiwi fruit pomace was modified via ultra-fine pulverization. The physicochemical and functional properties of kiwi fruit insoluble dietary fiber (KWIDF) superfine powder and its application in pork meatballs as a fat substitute were investigated. The SEM and droplet size measurement results revealed that the specific surface area of KWIDF increased from 44.4 to 192.9 m2 kg-1. The swelling capacity, water-, oil- and fat-holding capacities increased by 51.61%, 40.21%, 46.09% and 47.01%, respectively. The poisonous substances adsorbing abilities and the inhibition of enzyme activities were also improved. Similarly, KWIDF adsorbed cholesterol and glucose preferably. In addition, KWIDF revealed significant dose-response effects on the nutritional within a meat matrix, quality and sensory characteristics in meatballs (P < 0.05). The addition of 3% KWIDF superfine powder was found most suitable with high acceptability overall.

Phloem Delivery of Fludioxonil by Plant Amino Acid Transporter-Mediated Polysuccinimide Nanocarriers for Controlling Fusarium Wilt in Banana.

Fusarium wilt disease poses a serious threat to the global production of bananas. The targeted delivery of fungicides to banana phloem tissues may offer new hope for controlling this hard-to-treat vascular disease. In this study, fludioxonil (FLU)-loaded glycine methyl ester-conjugated polysuccinimide nanoparticles (PGA) were prepared with a loading efficiency (LE) of 27.9%. The obtained nanoparticles (FLU@PGA) exhibited pH-sensitive controlled release, specifically under an alkaline pH in plant phloem. In vivo experiments in potted bananas demonstrated that FLU@PGA can achieve the downward delivery of FLU to banana rhizomes and roots after foliar application, reducing disease severity by 50.4%. The phloem transport studies showed that the phloem loading of FLU@PGA was involved in an active transport mechanism at the organ level (castor bean seedlings). The observation of fluorescein-5-isothiocyanate cadaverine-labeled PGA nanocarriers showed that they could be absorbed by mesophyll cells and loaded into vascular tissues through the symplastic pathway. Furthermore, the interaction of FLU@PGA with the plant amino acid transporter AtLHT1 was observed to enhance transmembrane uptake at the cellular level (Xenopus oocytes). These results suggested that the phloem-targeted delivery of fungicide by transporter-mediated nanocarriers could be a promising new strategy for the management of Fusarium wilt in bananas.