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Lycium barbarum polysaccharides (LBPs) have been shown to exert an antiglycemic effect. Emerging evidence suggests that patients with hyperglycemia have a hypercontractility of duodenum, and targeting duodenal contraction of duodenum can be beneficial to glucose metabolism. However, it is unknown whether LBPs can improve glucose metabolism by regulating the hypercontractility of the duodenum. Our aim was to explore the effect of LBPs on duodenal contraction in prediabetic mice and also preliminarily investigate the mechanism. The results showed that LBPs improved glucose homeostasis by decreasing the duodenal amplitude of contraction rather than frequency. Moreover, LBPs ameliorated the gut microbiota composition and the levels of short-chain fatty acids, especially acetic acid, which might bind to the receptor on neurons to regulate the contraction of the duodenum. Acetic acid was hypothesized to play a key role in the above process. Then, acetic acid was determined to exert an antiglycemic effect as expected. In conclusion, LBPs may rely on acetic acid to regulate duodenal contraction to ameliorate glucose metabolism in prediabetic mice, which provides a new therapeutic strategy to treat dysglycemia. [ABSTRACT FROM AUTHOR]

This study was undertaken to identify oat (Avena sativa L.) varieties optimal for cultivation in the Jiuquan region, China, in 2021. A selection of 27 domestic and international oat varieties were analyzed, considering ten key agronomic traits, including plant height, stem diameter, spike length, leaf width, and yield. Employing methods such as cluster analysis, principal component analysis, and grey correlation degree, a comprehensive evaluation was conducted. The principal component analysis distilled the ten indicators to three core components. The most influential factors in the first principal component were plant height, ear length, and hay yield, while leaf length and leaf area index were the highest contributors to the second component. The stem-to-leaf ratio emerged as the principal indicator in the third component. The cluster analysis resulted in the classification of the 27 oat varieties into 3 categories. Following a comprehensive evaluation through the grey correlation degree and principal component analysis methodologies, we found that the oat varieties Sweety 1, Fuyan 1, Dingyan 2, Baler, Quebec, and Longyan 2 received the highest scores. These varieties, hence, appear to be the most suitable for cultivation and promotion in the Jiuquan region. This study thus provides invaluable insights into oat cultivation practices, offering guidance for farmers, agricultural policymakers, and future research in the field. [ABSTRACT FROM AUTHOR]

Low-molecular-weight glutenin subunits (LMW-GS) account for 40% of the total wheat grain gluten protein fraction, which plays a significant role in the formation of noodle processing quality. The goal of this study was to clarify the effects of the major LMW-GS encoded by Glu-A3 on gluten and Chinese fresh noodle (CFN) quality. Four near-isogenic lines (NILs) were used as materials in this study, respectively carrying alleles Glu-A3a, Glu-A3b, Glu-A3c, and Glu-A3e, against the background of wheat variety Xiaoyan 22. The grain protein and its component contents and the gluten content, gluten index, farinograph properties, cooking quality, and textural quality of CFN were investigated. The results show that the ratios of glutenin to gliadin (Glu/Gli) in the NILs ranked them as Glu-A3b > Glu-A3c/Glu-A3a > Glu-A3e, and the unextractable polymeric protein content (UPP%), gluten index (GI), and farinograph quality in the NILs ranked them as Glu-A3b > Glu-A3c > Glu-A3a/Glu-A3e. Compared to Glu-A3b and Glu-A3a, the NILs carrying alleles Glu-A3c and Glu-A3e had better cooking and texture properties in CFN. All these findings suggest that the introduction of alleles Glu-A3c or Glu-A3e is an efficient method for quality improvement in CFN, which provides an excellent subunit selection for improving CFN quality. [ABSTRACT FROM AUTHOR]

Scientific estimations and the dynamic monitoring of the development trend of carbon emissions from energy consumption with a long time series can provide the scientific basis for formulating and implementing regional carbon-reduction strategies. Based on DMSP-OLS and NPP-VIIRS night-time light data, a pixel-scale estimation model of energy-consumption carbon emissions in Jiangsu Province from 2000 to 2019 was constructed. The spatiotemporal evolution characteristics and influencing factors were analyzed using the GIS method and a GTWR (geographically and temporally weighted regression) model. The results showed that: (1) The goodness of fit of the image-fusion correction of the two night-time light data sources from 2012 to 2013 was 0.894; the goodness of fit of the carbon-emission estimation model by stages was above 0.99; and the average relative error was 7.71%, which met the requirement for the estimation accuracy. (2) During the study period, the total carbon emissions from energy consumption in Jiangsu Province continued to increase, rising from 94.7618 million tons to 313.3576 million tons, with an annual growth rate of 6.50%; and the growth rate presented an upward trend of "slow-accelerate-decelerate". Spatially, it showed an unbalanced distribution pattern of "low north and high south". (3) Per-capita GDP and energy intensity were the core driving factors affecting carbon emissions in Jiangsu Province over the past 20 years. Energy intensity had the greatest driving effect on carbon emissions in southern Jiangsu, while per-capita GDP had the greatest influence in central and northern Jiangsu. Coordinating the relationship between central, north, and south Jiangsu is of great significance for the realization of the sustainable economic and social development of the double carbon goal. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the complexity of sperm cell reproduction and maturation, the genetic structure of semen traits remains largely unknown. In our study, we used weighted single-step GWAS to detect genetic regions and further candidate genes related to semen traits in Duroc boars. This study provides in-depth understanding of the genetic structure of semen traits and the biological information provided by gene networks, and can be applied to speed up the genetic process of semen traits in boars. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility—sperm progressive motility, sperm abnormality rate, and total sperm count—explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars. [ABSTRACT FROM AUTHOR]

The corrosion resistance of stainless steel is attributed to the extraordinary protectiveness of the ultrathin native passive film (~3 nanometers) on alloy surface. This protectiveness, independent of alloying, can possibly be further increased by modifying the native film to resist corrosion in harsh conditions. However, the modification based on the film itself is extremely difficult due to its rapid, self-limiting growth. Here we present a strategy by using low-temperature plasma processing so as to follow the growth kinetics of the native film. The native oxide film is restarted and can uniformly grow up to ~15 nanometers in a self-limiting manner. High-resolution TEM found that the film exhibited a well-defined, chemical-ordering layered structure. The following corrosion tests revealed that the anodic current density of the alloy decreased by two orders of magnitude in 0.6 M NaCl solution with a remarkable increase of pitting potential. This enhancement is also observed in Fe-Cr alloys with Cr contents above ~10.5 wt.%. The superior protectiveness of the alloy is thus attributed to the continuous and thickened high-quality ultrathin Cr2O3 layer in the restarted film. [ABSTRACT FROM AUTHOR]

Axially chiral heterobiaryl frameworks are privileged structures in many natural products, pharmaceutically active molecules, and chiral ligands. Therefore, a variety of approaches for constructing these skeletons have been developed. Among them, de novo synthesis, due to its highly convergent and superior atom economy, serves as a promising strategy to access these challenging scaffolds including C-N, C-C, and N-N chiral axes. So far, several elegant reviews on the synthesis of axially chiral heterobiaryl skeletons have been disclosed, however, atroposelective construction of the heterobiaryl subunits by de novo synthesis was rarely covered. Herein, we summarized the recent advances in the catalytic asymmetric synthesis of the axially chiral heterobiaryl scaffold via de novo synthetic strategies. The related mechanism, scope, and applications were also included. [ABSTRACT FROM AUTHOR]

Simple Summary: Insulin-like growth factor 1 (IGF1) promotes mammalian development and growth. The poly(dA:dT) tract usually acts as a promoter element to regulate gene transcription. In this study, it was found that the length of a poly(dA:dT) tract in the porcine IGF1 promoter can regulate gene expression in vivo. Moreover, this polymorphism is associated with porcine growth traits (days to 115 kg and average daily gain). These results suggest that the poly(dA:dT) tract is a genetic marker for porcine growth traits. Insulin-like growth factor 1 (IGF1) is an important regulator of body growth, development, and metabolism. The poly(dA:dT) tract affects the accessibility of transcription factor binding sites to regulate transcription. Therefore, this study assessed the effects of two poly(dA:dT) tracts on the transcriptional activity of porcine IGF1. The luciferase assay results demonstrated that the poly(dA:dT) tract 2 (−264/−255) was a positive regulatory element for IGF1 gene expression, and the activities between the different lengths of the poly(dA:dT) tract 2 were significant ( p < 0.01 ). The transcription factor C/EBPα inhibited the transcription of IGF1 by binding to tract 2, and the expression levels between the lengths of tract 2 after C/EBPα binding were also statistically different ( p < 0.01 ). Only the alleles 10T and 11T were found in the tract 2 in commercial pig breeds, while the 9T, 10T, and 11T alleles were found in Chinese native pig breeds. The allele frequencies were in Hardy–Weinberg equilibrium in all pig breeds. The genotypes of tract 2 were significantly associated with the growth traits (days to 115 kg and average daily gain) ( p < 0.05 ) in commercial pig breeds. Based on these findings, it can be concluded that the tract 2 mutation could be applied as a candidate genetic marker for growth trait selection in pig breeding programs. [ABSTRACT FROM AUTHOR]

AP2/ERF (APETALA2/ethylene responsive factor) is a family of plant-specific transcription factors whose members are widely involved in many biological processes, such as growth, development, and biotic and abiotic stress responses. Here, 20 AP2/ERF genes were identified based on wheat RNA-seq data before and after drought stress, and classified as AP2, ERF, DREB, and RAV. The analysis of gene structure revealed that about 85% of AP2/ERF family members had lost introns, which are presumed to have been lost during the formation and evolution of the wheat genome. The expression of 20 AP2/ERF family genes could be verified by qRT-PCR, which further supported the validity of the RNA-seq data. Subsequently, subcellular localization and transcriptional activity experiments showed that the ERF proteins were mainly located in the nucleus and were self-activating, which further supports their functions as transcription factors. Furthermore, we isolated a novel ERF gene induced by drought, salt, and cold stresses and named it TaERF-6-3A. TaERF-6-3A overexpression increased sensitivity to drought and salt stresses in Arabidopsis, which was supported by physiological and biochemical indices. Moreover, the expression of stress- and antioxidant-related genes was downregulated in TaERF-6-3A-overexpressing plants. Overall, these results contribute to the further understanding of the TaERF-6-3A gene function in wheat. [ABSTRACT FROM AUTHOR]

Financial inclusion plays a positive role in protecting agriculture during or after disaster. This paper focuses on the protective effect of digital financial inclusion on the agricultural supply chain and analyzes three mechanisms of the protective effect: financial widening, financial deepening, and financial services digitization. Based on the Gravity Equation, we conduct an empirical study using agricultural logistics and digital financial inclusion data from China. The regression results indicate that a 1% increase in the digital financial inclusion, measured by the Peking University Digital Inclusion Index, increases agricultural trade during the COVID-19 pandemic by approximately 1.6%. Furthermore, heterogeneous protective effects exist between regions in China. Digital financial inclusion is more effective in the Eastern regions in protecting the ASC than in other regions. This paper enriches the understanding of financial inclusion in helping agriculture supply chain recovery. [ABSTRACT FROM AUTHOR]

Flowering time is one of the most important agronomic traits in wheat production. A proper flowering time might contribute to the reduction or avoidance of biotic and abiotic stresses, adjust plant architecture, and affect the yield and quality of grain. In this study, TaTOE1-B1 in wheat produced three transcripts (TaTOE1-B1-1, TaTOE1-B1-2, and TaTOE1-B1-3) by alternative splicing. Compared to the longest transcript, TaTOE1-B1-1, TaTOE1-B1-3 has a deletion in the sixth exon (1219–1264 bp). Under long-day conditions, the heterologous overexpression of the TaTOE1-B1-3 gene delayed flowering, prolonged the vegetative growth time, and enlarged the vegetative body of Arabidopsis, but that of TaTOE1-B1-1 did not. As typical AP2 family members, TaTOE1-B1-1 and TaTOE1-B1-3 are mainly located in the nucleus and have transcriptional activation activities; the transcriptional activation region of TaTOE1-B1-3 is located in the C-terminal. In TaTOE1-B1-3 overexpression lines, the expression of flowering-related AtFT and AtSOC1 genes is significantly downregulated. In addition, this study confirms the protein–protein interaction between TaTOE1-B1-3 and TaPIFI, which may play an important role in flowering inhibition. These results provide a theoretical basis for the precise regulation of wheat flowering time. [ABSTRACT FROM AUTHOR]

Green spaces play an important role in improving the health and quality of life of urban residents, and environmental justice has become one of the most debated topics for scholars and governments in China. The accessibility of green space has been recognized as an effective method for the assessment of environmental justice. Population and green space are the two basic elements for evaluating green space accessibility. However, high-precision population data are difficult to obtain. To address this issue, we propose a straightforward method to assess green space accessibility using urban artificial building data and investigate how green space distribution is correlated with the population. Our analysis uses urban artificial building data and green space data from 2018 in Nanjing, China. The results show that the overall amount of green space, is not only very low, but also unevenly distributed. From the urban center to the city outskirts, the green space changed representing an M-shaped pattern, while the population sharply declined. Given the serious mismatch between the population and green space, only about 26.4% and 51.8% of Nanjing residents could access green space within 400 and 800 m, respectively, which was far below that in cities in developed countries. For residents' wellbeing, governments must consider increasing green space provision and promoting equal access. [ABSTRACT FROM AUTHOR]

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy. [ABSTRACT FROM AUTHOR]

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches. [ABSTRACT FROM AUTHOR]

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.