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Compared with the wheat steamed bread, this article was to explore the quality (color difference, structure, texture, sensory evaluation, etc.) and physiological activity (antioxidant properties, in vitro digestive characteristics and glucose production index) of the steamed breads with different ratios of black rice spent grains (BRSG). The results showed that BRSG had significant effects on the specific volume, color difference, structure and texture of steamed bread (P

Abstract To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

Supply chain carbon abatement is an important way to promote low-carbon transformation of the social economy and address global climate change. This paper analyzes the issue of supply chain carbon abatement under different power structures, as well as the effect of consumers’ low-carbon preference and carbon tax rate on the optimal decisions. This paper constructs five different models, namely ML-NO model, ML-CS model, RL-NO model, RL-CS model and VI model. The research finds that VI model is the most ideal model for promoting supply chain carbon abatement. The optimal abatement efforts, market demand, and total profits in the VI model are all the largest among the five models. Whether the supply chain leader is the manufacturer or the retailer, cost sharing contract can enhance optimal abatement efforts, market demand, and profits of both parties. In any model, the leader in Supply chain earns higher profits than the follower. When consumers’ low-carbon preference increases, the optimal abatement efforts, market demand, and profits of both parties will all increase, and the growth rate is gradually accelerating. For the manufacturer with high carbon emissions, when the carbon tax rate increases, the optimal abatement efforts first increase and then decrease. For the manufacturer with low carbon emissions, when the carbon tax rate increases, the optimal abatement efforts will also increase.

This research explores the relationship between energy poverty, industrial efficiency, and the energy transition within China's digital economy from 2010 to 2022, spanning 30 provinces. Addressing the significant issue of energy poverty, where many lack access to affordable and reliable energy, the study seeks to understand its impact on industrial productivity and the broader imperative of energy transition in the face of rapid digitalization in China. Using panel data analysis, the research examines how energy poverty affects industrial production efficiency and evaluates its influence on China's ability to shift to cleaner energy sources within the digital economic framework. Findings highlight a complex interplay between energy poverty, industrial efficiency, and energy transition. It is revealed that energy poverty significantly impedes industrial productivity, with notable differences across provinces. Furthermore, the study finds a positive link between industrial efficiency and the speed of energy transition, indicating that enhancing industrial efficiency can aid in a smoother shift to cleaner energy sources. The digital economy is identified as a crucial factor in this process, providing innovative solutions to reduce energy poverty, improve productive efficiency, and accelerate the energy transition. The study emphasizes the importance of integrated strategies to tackle energy poverty, enhance industrial efficiency, and support the energy transition, particularly through the utilization of digital economy tools.

Abstract Bromodomain-containing protein 4 (BRD4), the major component of bromodomain and extra-terminal domain (BET) protein family, has important functions in early embryonic development and cancer development. However, the posttranslational modification of BRD4 is not well understood. Multiple approaches were used to explore the mechanism of PRMT1-mediated BRD4 methylation and to determine the biological functions of BRD4 and PRMT1 in ovarian cancer. Here we report that BRD4 is asymmetrically methylated at R179/181/183 by PRMT1, which is antagonized by the Jumonji-family demethylase, JMJD6. PRMT1 is overexpressed in ovarian cancer tissue and is a potential marker for poor prognosis in ovarian cancer patients. Silencing of PRMT1 inhibited ovarian cancer proliferation, migration, and invasion in vivo and in vitro. PRMT1-mediated BRD4 methylation was found to promote BRD4 phosphorylation. Compared to BRD4 wild-type (WT) cells, BRD4 R179/181/183K mutant-expressing cells showed reduced ovarian cancer metastasis. BRD4 arginine methylation is also associated with TGF-β signaling. Our results indicate that arginine methylation of BRD4 by PRMT1 is involved in ovarian cancer tumorigenesis. Targeting PRMT1-mediated arginine methylation may provide a novel diagnostic target and an effective therapeutic strategy for ovarian cancer treatment.

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Tuning electrocatalytic reduction of carbon monoxide to achieve valuable ethanol product with high performance is interesting yet challenging. Here, the authors design a hydrophobic Cu/Cu2O sheet catalyst showing a FE of 68.8% and partial current density of 111 mA cm−2 for CO reduction to ethanol.

Background: Uterine leiomyomas (ULs) is the most common gynecological benign tumor in women. Our previous study showed that the phenomenon of vitamin D deficiency existed in patients with ULs. However, the association of vitamin D anabolism-related gene polymorphisms and susceptibility to ULs was unclear.Methods: Vitamin D anabolism-related gene polymorphisms in 110 patients with ULs and 110 healthy controls were detected by sequencing and the differences of the 92 SNPs were analyzed in the two groups via chi-square test. To verify the association between the significantly different SNPs and the risk of ULs, the SNPs were genotyped in another 340 patients and 340 healthy controls. Additionally, an unconditional logistic regression model was conducted to calculate the odds ratio (OR) of ULs occurrence and the 95% confidence interval (CI), adjusting for age and BMI.Findings: In sequencing samples, there were differences in DHCR7 rs1044482 C > T (p = 0.008) and NADSYN1 rs2276360 G > C (p = 0.025) between patients with ULs and healthy controls. DHCR7 rs1044482 was related to the susceptibility to ULs in validation samples (heterogeneous: adjusted OR = 1.967, p = 0.002; homogenous: adjusted OR = 2.494, p = 0.002; additive: adjusted OR = 1.485, p < 0.041; and dominant: adjusted OR = 2.084, p < 0.001). Stratified analysis further showed that the DHCR7 rs1044482 polymorphisms were associated with ULs risks in women over 40 and with 18.5–25.0 BMI. In contrast to the wild-type CG haplotype vectors, individuals with TC haplotypes had a higher risk of developing ULs.Interpretation: The vitamin D anabolism-related gene DHCR7 rs1044482 C > T polymorphism was a risk factor of ULs, especially in patients over 40 with 18.5–25.0 BMI, while the relationship between NADSYN1 rs2276360 and ULs risk was not clear.

The applications of extreme learning machine (ELM) to the hyperspectral-image (HSI) classification have attracted a great deal of research attention because of its excellent performance and fast learning speed. However, conventional ELM is unable to achieve satisfactory accuracy since it only exploits the spectral information to conduct the HSI classification. To address the above issues, we propose a novel classification algorithm based on both spectral and multiscale spatial information, referred to as ELM with enhanced composite feature (ELM-ECF). To be specific, we adopt the original ELM, exploit a multiscale spatial weighted-mean-filtering-based approach to extract multiple spatial information, and use the majority vote method to select the final classification result. The proposed ELM-ECF significantly improves the classification accuracy of the original ELM. Experimental results on three public HSIs (i.e., Indian Pines data set, Pavia University data set, and Salinas data set) illustrate that the proposed ELM-ECF outperforms a variety of the state-of-the-art HSI classification counterparts in terms of classification accuracy.

With the expansion of the global transnational agricultural planting scale, research on transnational agricultural land investment is growing. In order to analyze the development context and basic characteristics of this research, and to discover the research hotspots and frontiers, this study used documentation and bibliometric methods to examine the achievements of it. The results show the following: (1) Transnational agricultural land investment research is mainly focused on the social sciences, development studies, economics, environmental sciences and geography. (2) The concentration of researchers in this research field is not high, and there is still a lack of authoritative researchers with high influence. The cooperation network has been initially formed between research institutions. Among them, universities and research institutes are the main institutions of transnational agricultural land investment research, but the degree of integration among the research teams is not high. (3) The evolution of the research theme of the field has experienced three stages—an embryonic stage, growth stage and stable stage—and the research content shows a trend of continuous divergence and deepening. (4) From 2005 to 2019, the research hotspots of the research focused on “Land Grabbing, Global Land, Africa, Investment”. At present, the emerging frontier research topics are “Indonesia, Livelihood, Trajectory and Sustainability”. With many years of development, the research has become an obvious "land" attribute, independent from traditional agricultural economic research, and the research topics are becoming more and more mature, refined and diversified. Transnational agricultural land investment research is attracting continuous attention from scholars in multiple disciplines and fields.

Physalis is an important genus in the Solanaceae family. It includes many species of significant medicinal value, edible value, and ornamental value. However, many Physalis species are easily confused because of their similar morphological traits, which hinder the utilization and protection of Physalis resources. Therefore, it is necessary to create fast, sensitive, and reliable methods for the Physalis species authentication. Intended for that, in this study, species-specific sequence-characterized amplified region (SCAR) markers were developed for accurate identification of the closely related Physalis species P. angulata, P. minima, P. pubescens, and P. alkekengi var. franchetii, based on a simple and novel marker system, start codon targeted (SCoT) marker. A total of 34 selected SCoT primers yielded 289 reliable SCoT loci, of which 265 were polymorphic. Four species-specific SCoT fragments (SCoT3-1404, SCoT3-1589, SCoT5-550, and SCoT36-520) from Physalis species were successfully identified, cloned, and sequenced. Based on these selected specific DNA fragments, four SCAR primers pairs were developed and named ST3KZ, ST3MSJ, ST5SJ, and ST36XSJ. PCR analysis of each of these primer pairs clearly demonstrated a specific amplified band in all samples of the target Physalis species, but no amplification was observed in other Physalis species. Therefore, the species-specific SCAR primer pairs developed in this study could be used as powerful tools that can rapidly, effectively, and reliably identify and differentiate Physalis species.

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

The over-collection and habitat destruction of natural Dendrobium populations for their commercial medicinal value has led to these plants being under severe threat of extinction. In addition, many Dendrobium plants are similarly shaped and easily confused during the absence of flowering stages. In the present study, we examined the application of the ITS2 region in barcoding and phylogenetic analyses of Dendrobium species (Orchidaceae). For barcoding, ITS2 regions of 43 samples in Dendrobium were amplified. In combination with sequences from GenBank, the sequences were aligned using Clustal W and genetic distances were computed using MEGA V5.1. The success rate of PCR amplification and sequencing was 100%. There was a significant divergence between the inter- and intra-specific genetic distances of ITS2 regions, while the presence of a barcoding gap was obvious. Based on the BLAST1, nearest distance and TaxonGAP methods, our results showed that the ITS2 regions could successfully identify the species of most Dendrobium samples examined; Second, we used ITS2 as a DNA marker to infer phylogenetic relationships of 64 Dendrobium species. The results showed that cluster analysis using the ITS2 region mainly supported the relationship between the species of Dendrobium established by traditional morphological methods and many previous molecular analyses. To sum up, the ITS2 region can not only be used as an efficient barcode to identify Dendrobium species, but also has the potential to contribute to the phylogenetic analysis of the genus Dendrobium.

Many reports have shown that grains play an important role in our daily lives and can provide energy and nutrients to protect us from various diseases, and they are considered to be indispensable parts of our lives. It has been reported that some constituents in grains could exert functional effects against HIV infections and multiple cancers. Zymolytic grain can produce some new useful molecules and thus support the cell nutrients in the human body. In this study, the effects of zymolytic grain extract (ZGE) supernatants on the changes of nematode indicators were investigated, including lifespan, self-brood size, and body length in environmental conditions (temperature, ultraviolet radiation or 5-fluoro-2′-deoxyuridine (FUDR) stimuli). We found that, compared to the control group, the ZGE supernatant-feeding group could prolong the lifespan of nematodes under normal conditions. More importantly, ZGE supernatants could improve the ability of nematodes to resist stress. When the concentration of FUDR was 400 or 50 μM, the ZGE supernatant-feeding group could prolong lifespan by an average of 38.4% compared to the control group, and the eggs of the ZGE supernatant-feeding group could hatch and develop into adults. These results indicated that ZGE could protect C. elegans from external stress and thus prolong their lifespan and improve the physiological state of nematodes. Therefore, ZGE supernatant has potential to be used as a nutritional product in antioxidant and anti-aging research.

Recently, commercial interest in Physalis species has grown worldwide due to their high nutritional value, edible fruit and potential medicinal properties. However, many Physalis species have similar shapes and are easily confused, and consequently the phylogenetic relationships between Physalis species are poorly understood. This hinders their safe utilization and genetic resource conservation. In this study, the nuclear ribosomal ITS2 region was used to identify species and phylogenetically examine Physalis. Eighty-six ITS2 regions from 45 Physalis species were analyzed. The ITS2 sequences were aligned using Clustal W and genetic distances were calculated using MEGA V6.0. The results showed that ITS2 regions have significant intra- and inter-specific divergences, obvious barcoding gaps, and higher species discrimination rates (82.2% for both the BLASTA1 and nearest distance methods). In addition, the secondary structure of ITS2 provided another way to differentiate species. Cluster analysis based on ITS2 regions largely concurred with the relationships among Physalis species established by many previous molecular analyses, and showed that most sections of Physalis appear to be polyphyletic. Our results demonstrated that ITS2 can be used as an efficient and powerful marker in the identification and phylogenetic study of Physalis species. The technique provides a scientific basis for the conservation of Physalis plants and for utilization of resources.

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.