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The effects of co-fermentation of yeast and Lactiplantibacillus plantarum 104 on buckwheat starch physical properties were investigated by various analytical techniques. To investigate the regulations of starch modification during fermentation and to provide a foundation for improving the performance of modified properties of buckwheat starch food. The pasting properties were decreased by co-fermentation also resulted in a reduction in the relative crystallinity. Scanning electron microscopy (SEM) demonstrated that more holes and a relatively rough granule surface were seen in the co-fermentation group. Fourier transform-infrared spectroscopy (FT-IR) results suggested that co-fermentation fermentation decreased the degree of short-range order (DO) and degree of t1he double helix (DD). The results demonstrated that co-fermentation altered these properties more rapidly than spontaneous fermentation. In conclusion, Lactiplantibacillus plantarum 104 could be used for buckwheat fermentation to improve food quality.

Abstract Background Vaccination is important in influenza prevention but the immune response wanes with age. The circadian nature of the immune system suggests that adjusting the time of vaccination may provide an opportunity to improve immunogenicity. Our previous cluster trial in Birmingham suggested differences between morning and afternoon vaccination for some strains in the influenza vaccine in older adults. Whether this effect is also seen in a younger age group with less likelihood of compromised immunity is unknown. We therefore conducted an individual-based randomized controlled trial in Guangzhou to test the hypothesis that influenza vaccination in the morning induces a stronger immune response in older adults than afternoon vaccination. We included adults in middle age to determine if the effect was also seen in younger age groups. Results Of the 418 participants randomised, 389 (93.1%, 191 middle-aged adults aged 50–60 years and 198 older adults aged 65–75 years) were followed up. Overall, there was no significant difference between the antibody titers (geometric mean /95% CI) after morning vs afternoon vaccination (A/H1N1: 39.9 (32.4, 49.1) vs. 33.0 (26.7, 40.7), p = 0.178; A/H3N2: 92.2 (82.8, 102.7) vs. 82.0 (73.8, 91.2), p = 0.091; B: 15.8 (13.9, 17.9) vs. 14.4 (12.8, 16.3), p = 0.092), respectively. However, in pre-specified subgroup analyses, post-vaccination titers for morning versus afternoon vaccination in the 65–75 years subgroup were (A/H1N1): 49.5 (36.7, 66.6) vs. 32.9 (24.7, 43.9), p = 0.050; (A/H3N2): 93.5 (80.6, 108.5) vs. 73.1 (62.9, 84.9), p = 0.021; (B): 16.6 (13.8, 20.1) vs. 14.4 (12.3, 17.0), p = 0.095, respectively. Among females, antibody titers for morning versus afternoon vaccination were (A/H1N1): 46.9 (35.6, 61.8) vs. 31.1 (23.8, 40.7), p = 0.030; (A/H3N2): 96.0 (83.5, 110.3) vs. 84.7 (74.4, 96.5), p = 0.176; (B): 14.8 (12.7, 17.3) vs. 13.0 (11.3, 14.9), p = 0.061, respectively. In the 50–60 years old subgroup and males, there were no significant differences between morning and afternoon vaccination. Conclusions Morning vaccination may enhance the immunogenicity to influenza vaccine in adults aged over 65 and women. An intervention to modify vaccination programs to vaccinate older individuals in the morning is simple, cost free and feasible in most health systems.

Lactiplantibacillus plantarum KM1 was screened from natural fermented products, which had probiotic properties and antioxidant function. The survival rate of L. plantarum KM1 was 78.26% at 5 mM H2O2. In this study, the antioxidant mechanism of L. plantarum KM1 was deeply analyzed by using the proteomics method. The results demonstrated that a total of 112 differentially expressed proteins (DEPs) were screened, of which, 31 DEPs were upregulated and 81 were downregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEPs participated in various metabolic pathways such as pyruvate metabolism, carbon metabolism, trichloroacetic acid cycle, amino acid metabolism, and microbial metabolism in diverse environments. These metabolic pathways were related to oxidative stress caused by H2O2 in L. plantarum KM1. Therefore, the antioxidant mechanism of L. plantarum KM1 under H2O2 stress provided a theoretical basis for its use as a potential natural antioxidant.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease, and Gegen Qinlian Decoction (GQD), a Chinese botanical formula, has exhibited beneficial efficacy against UC. However, the mechanisms underlying the effect of GQD still remain to be elucidated. In this study, network pharmacology approach and molecular docking in silico were applied to uncover the potential multicomponent synergetic effect and molecular mechanisms. The targets of ingredients in GQD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) database, while the UC targets were retrieved from Genecards, therapeutic target database (TTD) and Online Mendelian Inheritance in Man (OMIM) database. The topological parameters of Protein-Protein Interaction (PPI) data were used to screen the hub targets in the network. The possible mechanisms were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to verify the binding affinity between the active compounds and hub targets. Network pharmacology analysis successfully identified 77 candidate compounds and 56 potential targets. The targets were further mapped to 20 related pathways to construct a compound-target-pathway network and an integrated network of GQD treating UC. Among these pathways, PI3K-AKT, HIF-1, VEGF, Ras, and TNF signaling pathways may exert important effects in the treatment of UC via inflammation suppression and anti-carcinogenesis. In the animal experiment, treatment with GQD and sulfasalazine (SASP) both ameliorated inflammation in UC. The proinflammatory cytokines (TNF-α, IL-1β, and IL-6) induced by UC were significantly decreased by GQD and SASP. Moreover, the protein expression of EGFR, PI3K, and phosphorylation of AKT were reduced after GQD and SASP treatment, and there was no significance between the GQD group and SASP group. Our study systematically dissected the molecular mechanisms of GQD on the treatment of UC using network pharmacology, as well as uncovered the therapeutic effects of GQD against UC through ameliorating inflammation via downregulating EGFR/PI3K/AKT signaling pathway and the pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6.

Raw point cloud data are often noisy, superfluous, and with topological defects, such as holes. These issues cause inaccurate geometric representation in 3D reconstruction. As a result, surface reconstruction from point cloud data is a highly challenging problem. In this paper, we address the aforementioned issues by taking advantage of the embedded information of segmentation, skeletonization, and user guidance. First, we pre-process the point cloud data with three steps, relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details; second, a segmentation method converts the input cloud into separate parts; finally, we construct curve skeletons for each part and guide the surface reconstruction with minimal user interaction, where the parts of refined smooth shapes are fused to generate the final results. The comparison studies confirmed that the proposed method is able to produce state-of-the-art results in terms of preserving sharp features, handling missing data, and requiring minimal user intervention.

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen.

Net primary productivity (NPP) has been substantially changed under the intense oasification in the urban agglomerations on the northern slopes of mid-Tianshan Mountain (UANSTM) and climate change. However, the temporal variations of NPP under the oasification remain unclear, and the relative contribution of oasification and climate change on annual NPP variation is still under debate. By using remote sensing data, reanalysis data, modified Carnegie–Ames-Stanford Approach (CASA) model, and a machine learning method, we explored the spatial–temporal variation of NPP in the UANSTM region and quantified the contribution of oasification and climate change to NPP variation from 2001 to 2020. Our study indicated that: (1) the NPP presents an overall increasing trend in the most of region and the region presented decreasing trend mainly due to the cropland conversion to the urban area; (2) the oasification-dominated NPP area concentrated in the built-up land and cropland; (3) during 2001–2020, the NPP increased by about 5.4 Tg·C, and the contribution of climatic and oasification to NPP increase were quantified (73.1% and 26.9%, respectively); (4) water-related factors was the main driver of NPP variation in the UANSTM region.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.