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PurposeRadiotherapy (RT) plays an important role in the treatment of hepatocellular carcinoma (HCC). To screen patients who benefit most from RT, a nomogram for survival prediction of RT based on a large sample of patients with HCC was created and validated.MethodsA total of 2,252 cases collected from the Surveillance, Epidemiology, and End Results (SEER) database were separated into a training or an internal validation cohort in a 7:3 ratio (n = 1,565:650). An external validation cohort of cases from our institute was obtained (n = 403). LASSO regression and Cox analyses were adopted to develop a nomogram for survival prediction. The decision curve analysis (DCA), calibration curve, and time-dependent receiver operating characteristic curves (TROCs) demonstrated the reliability of the predictive model.ResultsFor patients with HCC who received RT, the analyses revealed that the independent survival prediction factors were T stage {T2 vs. T1, hazard ratio (HR) =1.452 [95% CI, 1.195–1.765], p < 0.001; T3 vs. T1, HR = 1.469 [95% CI, 1.168–1.846], p < 0.001; T4 vs. T1, HR = 1.291 [95% CI, 0.951–1.754], p = 0.101}, N stage (HR = 1.555 [95% CI, 1.338–1.805], p < 0.001), M stage (HR = 3.007 [95% CI, 2.645–3.418], p < 0.001), max tumor size (>2 and ≤5 vs. ≤2 cm, HR = 1.273 [95% CI, 0.992–1.633], p = 0.057; >5 and ≤10 vs. ≤2 cm, HR = 1.625 [95% CI, 1.246–2.118], p < 0.001; >10 vs. ≤2 cm, HR = 1.784 [95% CI, 1.335–2.385], p < 0.001), major vascular invasion (MVI) (HR = 1.454 [95% CI, 1.028–2.057], p = 0.034), alpha fetoprotein (AFP) (HR = 1.573 [95% CI, 1.315–1.882], p < 0.001), and chemotherapy (HR = 0.511 [95% CI, 0.454–0.576], p < 0.001). A nomogram constructed with these prognostic factors demonstrated outstanding predictive accuracy. The area under the curve (AUC) in the training cohort for predicting overall survival (OS) at 6, 12, 18, and 24 months was 0.824 (95% CI, 0.803–0.846), 0.824 (95% CI, 0.802–0.845), 0.816 (95% CI, 0.792–0.840), and 0.820 (95% CI, 0.794–0.846), respectively. The AUCs were similar in the other two cohorts. The DCA and calibration curve demonstrated the reliability of the predictive model.ConclusionFor patients who have been treated with RT, a nomogram constructed with T stage, N stage, M stage, tumor size, MVI, AFP, and chemotherapy has good survival prediction ability.

Abstract Background Berberine (BBR) is a commonly used anti-intestinal inflammation drug, and its anti-cancer activity has been found recently. BBR can intervene and control malignant colorectal cancer (CRC) through intestinal microbes, but the direct molecular target and related mechanism are unclear. This study aimed to identify the target of BBR and dissect related mechanisms against the occurrence and development of CRC from the perspective of intestinal microorganisms. Results Here, we found that BBR inhibits the growth of several CRC-driving bacteria, especially Peptostreptococcus anaerobius. By using a biotin-conjugated BBR derivative, we identified the protein FtfL (formate tetrahydrofolate ligase), a key enzyme in C1 metabolism, is the molecular target of BBR in P. anaerobius. BBR exhibits strong binding affinity and potent inhibition on FtfL. Based on this, we determined the crystal structure of PaFtfL (P. anaerobius FtfL)-BBR complex and found that BBR can not only interfere with the conformational flexibility of PaFtfL tetramer by wedging the tetramer interface but also compete with its substrate ATP for binding within the active center. In addition, the enzymatic activities of FtfL homologous proteins in human tumor cells can also be inhibited by BBR. Conclusions In summary, our study has identified FtfL as a direct target of BBR and uncovered molecular mechanisms involved in the anti-CRC of BBR. BBR interferes with intestinal pathogenic bacteria by targeting FtfLs, suggesting a new means for controlling the occurrence and development of CRC.

Abstract Background To establish a prognostic model to predict the overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) treated with intensity modulated radiotherapy (IMRT). Methods The unresectable HCC patients treated with IMRT were retrospectively analyzed and randomized into development cohort (n = 237) and validation cohort (n = 103) in a 7:3 ratio. We developed a prognosis model with the multivariate Cox regression analysis in the development cohort to derive the predictive nomogram, which was then validated in the validation cohort. Model performance was evaluated by the c-index, the area under curve(AUC) and the calibration plot. Results A total of 340 patients were enrolled. Tumor numbers > 3 (HR = 1.69, 95% CI = 1.21–2.37), AFP ≥ 400 ng/ml (HR = 1.52, 95% CI = 1.10–2.10), PLT 150U/L (HR = 1.65, 95% CI = 1.15–2.37) and prior surgery (HR = 0.63, 95% CI = 0.43–0.93) were independent prognostic factors. The nomogram based on independent factors was constructed. The c-index for OS prediction was 0.658 (95% CI, 0.647–0.804) and 0.683 (95% CI, 0.580–0.785) in the development and validation cohort, respectively. The nomogram demonstrated good discriminative ability with AUC rates of 0.726, 0.739 and 0.753 at 1-year, 2-year and 3-year models in the development cohort, and 0.715, 0.756 and 0.780 in the validation cohort, respectively. Additionally, good prognostic discrimination of the nomogram is also reflected in stratifying patients into two subgroups with distinct prognosis. Conclusions We constructed a prognostic nomogram for predicting the survival of patients with unresectable HCC treated with IMRT.

The biosynthesis of flavonoids and anthocyanidins has been exclusively investigated in angiosperms but largely unknown in ferns. This study integrated metabolomics and transcriptome to analyze the fronds from different development stages (S1 without spores and S2 with brown spores) of Cyclosorus parasiticus. About 221 flavonoid and anthocyanin metabolites were identified between S1 and S2. Transcriptome analysis revealed several genes encoding the key enzymes involved in the biosynthesis of flavonoids, and anthocyanins were upregulated in S2, which were validated by qRT-PCR. Functional characterization of two chalcone synthases (CpCHS1 and CpCHS2) indicated that CpCHS1 can catalyze the formation of pinocembrin, naringenin, and eriodictyol, respectively; however, CpCHS2 was inactive. The crystallization investigation of CpCHS1 indicated that it has a highly similar conformation and shares a similar general catalytic mechanism to other plants CHSs. And by site-directed mutagenesis, we found seven residues, especially Leu199 and Thr203 that are critical to the catalytic activity for CpCHS1.

PurposeCongenital hypothyroidism (CH) is the most common neonatal endocrine disease; its early detection ensures successful treatment and prevents complications. However, its molecular etiology remains unclear.MethodsWe used second-generation sequencing to detect 28 pathogenic genes in 15 Chinese Han patients with CH in Shenzhen, China, and analyzed the genetic pattern of the pathogenic genes through their pedigrees. The pathogenicity assessment of gene mutations was performed based on the American College of Medical Genetics and Genomics (ACMG) classification guidelines, inheritance models, and published evidence.ResultsMutations in several target genes were identified in 14 of 15 patients (93.33%); these mutations were distributed in eight genes (DUOX2, DUOXA2, TPO, TG, TSHR, FOXE1, KDM6A, and POU1F1). DUOX2 exhibited the highest mutation frequency (44%, 11/25), followed by TPO (16%, 4/25) and TG (16%, 4/25). DUOX2 exhibited the highest biallelic mutation (7/15). Eight out of 25 variants verified by the ACMG guidelines were classified as pathogenic (P, category 1) or possibly pathogenic (LP, Type 2), namely six variants of DUOX2, and one variant of TPO and DUOXA2. Five new mutations were detected: one in DUOX2, which was located in the splicing region of mRNA (c.1575-1G>A), three new missense mutants, p.A291T, p.R169W, and p. S1237dup, and one new TPO missense variant c.2012G>T (p.W671L). The main criteria for determining the genotype–phenotype relationship were a diagnostic detection rate of 53.33% (8/15) and combination of three or more gene mutations.ConclusionsCH gene mutations in the population may be mainly manifested in genes influencing thyroid hormone synthesis, such as DUOX2 compound heterozygous mutations, which exhibited a high detection rate. The clinical manifestations are diverse, and mainly include transient CH. Therefore, genetic screening is recommended for CH patients to determine the correlation between clinical phenotypes and gene mutations, which will assist in clinical management.

Phenolamides represent one of the largest classes of plant-specialized secondary metabolites and function in diverse physiological processes, including defense responses and development. The biosynthesis of phenolamides requires the BAHD-family acyltransferases, which transfer acyl-groups from different acyl-donors specifically to amines, the acyl-group acceptors. However, the mechanisms of substrate specificity and multisite-acylation of the BAHD-family acyltransferases remain poorly understood. In this study, we provide a structural and biochemical analysis of AtSHT and AtSDT, two representative BAHD-family members that catalyze the multisite acylation of spermidine but show different product profiles. By determining the structures of AtSHT and AtSDT and using structure-based mutagenesis, we identified the residues important for substrate recognition in AtSHT and AtSDT and hypothesized that the acyl acceptor spermidine might adopt a free-rotating conformation in AtSHT, which can undergo mono-, di-, or tri-acylation; while the spermidine molecule in AtSDT might adopt a linear conformation, which only allows mono- or di-acylation to take place. In addition, through sequence similarity network (SSN) and structural modeling analysis, we successfully predicted and verified the functions of two uncharacterized Arabidopsis BAHD acyltransferases, OAO95042.1 and NP_190301.2, which use putrescine as the main acyl-acceptor. Our work provides not only an excellent starting point for understanding multisite acylation in BAHD-family enzymes, but also a feasible methodology for predicting possible acyl acceptor specificity of uncharacterized BAHD-family acyltransferases.

Diterpene glycosyltransferase UGT76G1 from Stevia rebaudiana (SrUGT76G1) is key to the generation of economically important steviol glycosides (SGs), a group of natural sweeteners with high-intensity sweetness. SrUGT76G1 accommodates a wide range of steviol-derived substrates and many other small molecules. We report here the crystal structures of SrUGT76G1 in complex with multiple ligands to answer how this enzyme recognizes diterpenoid aglycones and catalyzes the 1,3-sugar chain branching. A spacious pocket for sugar-acceptor binding was observed from the determined SrUGT76G1 structures, which can explain its broad substrate spectrum. Residues Gly87 and Leu204 lining the pocket play key roles in switching between diterpenoid and flavonoid glucosylation. An engineered mutant of SrUGT76G1, T284S, could catalyze a selectively increased production of next-generation sweetener rebaudioside M, with diminished side product of rebaudioside I. Taken together, these resutls provide significant insights into molecular basis of the substrate specificity of scarcely documented diterpenoid glycosyltransferases and would facilitate the structure-guided glycoengineering to produce diversified diterpenoids with new activities.

Idiopathic central precocious puberty (ICPP) is a relatively common condition in preadolescent girls, and its pathogenesis remains to be uncovered. A variety of studies have highlighted the association of gut microbiota (GM) with endocrine diseases, such as obesity, which is commonly associated with ICPP. However, the relationship between GM and ICPP remains unexplored. Feces samples were collected from 25 girls with ICPP (ICPP group) and 23 healthy girls (Control group). We applied 16S rDNA sequencing to compare the GM between two groups. The ICPP group had higher GM diversity and was enriched for several GM species, including Ruminococcus gnavus, Ruminococcus callidus, Ruminococcus bromii, Roseburia inulinivorans, Coprococcus eutactus, Clostridium leptum, and Clostridium lactatifermentans, which are known to be associated with obesity and are related to the production of short-chain fatty acids. Additionally, 36 candidate GM biomarkers for patients with ICPP screening were identified with high accuracy (AUC = 0.95, 95% CI 0.88 to 1). We observed that the GM of the ICPP group was enriched for the microbial functions of cell motility, signal transduction, and environmental adaptation. Positive correlations were also detected between Fusobacterium and follicle-stimulating hormone, and Gemmiger and luteinizing hormone. This study documents relationships between GM and ICPP, and the implication of these findings remains to be determined.

Global climate change has intensified pressure on China's agricultural carbon emissions reduction. This study investigates the impact of agricultural technological progress on carbon emissions using panel data from 30 Chinese provinces (2002–2021). The research employs the DEA-Malmquist index to measure agricultural total factor productivity, the emission factor method to calculate carbon emissions, and two-tier stochastic frontier models for analysis. Results reveal that agricultural technological progress exhibits a significant double-edged effect on carbon emission intensity, with a positive effect increasing intensity by 9.01% and a negative effect reducing it by 12.18%, resulting in a net reduction of 3.17%. Different technological types show varying impacts: mechanical progress increases emission intensity, while resource-saving and biochemical technologies reduce it. Notable regional heterogeneity exists, with Southwest, South, Central, Northeast, and Northwest regions showing negative net effects, while North and East China exhibit positive effects. The findings suggest the need for differentiated regional policies and targeted agricultural emission reduction strategies to achieve China's agricultural “dual carbon” goals.

From the perspective of rural household income, this paper discusses the relationship between the proportion of land transfer proceeds for rural development and the debt risk of rural households and carries out the empirical testing by using a two-way fixed-effects model based on the data from the Chinese Household Finance Survey (CHFS) and the data matching the prefecture-level city data. The study suggests that the debt risk of rural households can be inhibited significantly in case of an increase in the proportion of proceeds from land transfer supporting rural development, and there is a “U”-shaped relationship between the proportion and the risk, which indicates that the proportion should be controlled reasonably to maximum the use efficiency of land transfer fund, and according to the system analysis, the increase in the proportion can enhance the debt-repayment ability of the rural households and reduce their debt risk by increasing their transfer income and agriculture income. Moreover, thus, this paper is of great importance for resolving the debt risk of rural households, consolidating the achievements of poverty alleviation, preventing large-scale relapse into poverty, and promoting rural revitalization.

Rural economic development faces significant challenges in the context of rapid urbanization and agricultural transformation, particularly in developing countries like China. Agricultural socialization services (ASSs) play a crucial role in promoting rural economic sustainability by enhancing household income and fostering regional development. This study investigates the impact of ASSs on rural economic sustainability in China from both temporal and spatial perspectives, employing the entropy weight method, double fixed effects model, and Spatial Durbin Model. Analyzing panel data from 30 Chinese provinces from 2011 to 2021 reveals significant positive effects of ASSs on rural income, along with spatial spillovers to neighboring regions. The results highlight regional heterogeneity in the impact of ASSs, with the eastern region benefiting from local spillovers, while the central and western regions gain from intensification and scale effects. These findings suggest that policymakers should adopt region-specific ASSs strategies, such as facilitating technology transfer in the eastern regions while leveraging intensification and scale advantages in the central and western regions, to optimize the effectiveness of agricultural support measures. Moreover, the relationship between ASSs and rural income exhibits a non-linear trend across various urbanization stages, implying that ASS policies should be tailored to the specific challenges and opportunities associated with different levels of urbanization to maximize their impact on rural economic sustainability. These findings underscore the importance of optimizing ASSs, tailoring policies to local conditions, and harnessing the role of ASSs in the urbanization process to promote inclusive rural development and foster sustainable rural economic growth.

This study employs the stochastic frontier model (SFM) to analyze trade potential and efficiency in wheat and maize among Belt and Road Initiative (BRI) countries from 2002 to 2021, encompassing 45 countries for wheat trade and 55 for maize trade. The empirical findings reveal that economic development level, population growth, government efficiency, political stability, and regulatory quality are critical determinants of trade efficiency. Notably, World Trade Organization (WTO) membership exhibits a negative correlation with trade efficiency, potentially reflecting challenges in rule implementation and opportunity utilization among member states. In the context of maize trade, increased arable land area is inversely associated with efficiency, suggesting potential issues in managing large-scale agricultural regions or optimizing land use. The BRI’s impact on trade efficiency varies across countries, with Turkey and Hungary showing improved wheat trade efficiency, while Ethiopia and Georgia experienced declines. During the COVID-19 pandemic, effective disease management strategies and diversified trade mechanisms significantly influenced trade efficiency. Furthermore, the study reveals that larger economies do not necessarily outperform small and medium-sized economies in terms of trade potential. These findings contribute significantly to the literature on agricultural trade and offer valuable insights for policymakers, emphasizing the importance of enhancing government efficiency, political stability, and regulatory quality in the context of regional economic development initiatives such as the BRI. This research underscores the need for tailored approaches to trade policy and agricultural management, considering the unique characteristics and challenges faced by different economies along BRI.

PurposeThis study investigates the impact of e-commerce on high-quality agricultural development (HQAD) in China. As the agricultural sector transitions towards higher quality production in the digital era, understanding the influence pathways and mechanisms of e-commerce becomes crucial. We aim to quantify this influence through a hierarchical approach.MethodsUtilizing provincial panel data from 2000 to 2021, we construct a comprehensive HQAD evaluation system using the entropy method. Parallel mediating effect models are employed to empirically assess the multi-level effects of e-commerce on HQAD.ResultsBenchmark regression analyzes reveal a significant positive effect of e-commerce on HQAD, indicating its role as a key driver in China’s agricultural advancement. Mechanism tests identify several intermediary pathways through which e-commerce indirectly promotes HQAD, including market expansion, agricultural value chain optimization, enhanced social services, and improved infrastructure. Notably, market expansion and value chain optimization demonstrate the most substantial mediation effects, accounting for 43.27 and 14.18% of the total effect, respectively.DiscussionThis research contributes to the literature by establishing a comprehensive HQAD evaluation framework, providing a theoretical foundation for future studies. By incorporating circulation factors into the production system, we elucidate the complex influence mechanisms of e-commerce on agricultural production, addressing a significant research gap. Furthermore, we propose a novel “demand-driven supply optimization” paradigm, offering valuable insights for policy formulation aimed at fostering HQAD in China.

As the global climate crisis intensifies, improving agricultural carbon emission efficiency has become crucial for achieving the sustainable development goals (SDGs). This study investigates the complex, non-linear relationship between China’s digital economy and agricultural carbon emission efficiency, utilizing panel data from Chinese provinces spanning 2012–2022. We employ a multi-method approach, including the Super-SBM model for efficiency measurement, two-way fixed effects models, quantile regression, and Generalized Additive Models (GAMs) for empirical analysis. Our findings reveal: (1) The digital economy significantly enhances agricultural carbon emission efficiency, but with distinct non-linear characteristics across different dimensions. (2) The impact varies among digital economy aspects: the digital economy foundation shows the most substantial influence, followed by the rural digital industry level, while rural digital infrastructure has a relatively minor effect. (3) A threshold effect is observed, with the digital economy’s impact more pronounced in regions with higher agricultural carbon emission efficiency. (4) GAM analysis unveils complex non-linear patterns: the rural digital industry’s impact initially decreases before increasing, the digital economy foundation shows an overall increasing trend with plateaus, and rural digital infrastructure exhibits a near-linear relationship. (5) Sensitivity analysis indicates that agricultural carbon emission efficiency is most responsive to changes in the digital economy foundation, followed by the rural digital industry level. These findings provide nuanced insights into the digital economy’s role in enhancing agricultural sustainability. We propose targeted policy recommendations, including accelerating rural digital infrastructure development, optimizing the rural digital industry structure, and implementing context-specific digital facility construction. These strategies aim to fully leverage the digital economy’s potential in improving agricultural carbon emission efficiency, contributing to China’s “dual carbon” goals and sustainable agricultural development.

In the context of sustainable agricultural development and rural revitalization in China, understanding and addressing psychological poverty traps among rural farm households is crucial. The poverty mindset represents a crucial factor affecting rural poverty. This study focuses on two key questions: first, whether and how material poverty influences the poverty mindset; and second, whether this psychological state affects economic behavior, potentially intensifying material poverty. Using data from the China Family Panel Studies (CFPS) spanning 2014–2018, the data collection employed a multi-stage stratified sampling approach. Multiple methods, including questionnaire surveys and in-depth interviews, were utilized to gather information. Through matching and merging processes based on personal questionnaire IDs, a total of 30,143 observations were obtained over a three-year period. We employ Causal Mediation Analysis (CMA) to examine the micro-level mechanisms between material and psychological poverty among rural farm households. Our findings reveal three key insights. First, material poverty significantly reduces aspiration levels and behavioral capabilities of rural farm households, with impoverished groups scoring approximately 10% lower than non-poor groups. Second, this negative impact operates through two primary channels: stigma effects (self-stigmatization 11.29%, social stigma 4.71%) and psychological resource depletion (negative emotions 1.5%, psychological stress 1.27%). Third, psychological poverty reinforces material poverty through aspiration failure (72.3%) and capability deficiency (75.68%), creating a self-perpetuating “psychological poverty trap” that particularly affects agricultural production efficiency. These findings suggest that sustainable agricultural development requires addressing both material and psychological dimensions of rural poverty. Policy recommendations include strengthening psychological support for farm households, enhancing agricultural capacity building, mitigating stigma effects in rural communities, and reconstructing psychological resources for sustainable development. This integrated approach can help break psychological poverty traps, improve agricultural productivity, and support rural revitalization in China.

After the merger of China’s Ministry of Culture and the National Tourism Administration into the Ministry of Culture and Tourism in 2018, there was a strong push to advance the strategy of culture–tourism integration, which has shown significant effects on the economy and society. Building on this success, the Chinese government proposed the strategy of agriculture–culture–tourism integration in 2023. However, its impact on the agricultural sector remains unclear. This study employs field theory to explain the impact of culture–tourism integration on the agricultural sector. Drawing from the relevant literature, we construct and statistically analyze the Culture–Tourism Coupling Coordination Degree (CTCCD). Utilizing provincial panel data from China from 2000 to 2022, we employ two-way fixed-effects panel regression to empirically estimate and analyze the effects of the CTCCD on the agricultural GDP, Total Factor Productivity (TFP), and various aspects of crop farming. Our findings reveal several key insights: (i) the CTCCD exhibits a significant negative impact on both agricultural GDP and TFP; (ii) the CTCCD negatively affects the proportion of crop farming within the agricultural sector, suggesting that culture–tourism integration results in weaker development of crop farming compared to forestry, animal husbandry, and fisheries; (iii) the CTCCD demonstrates significant negative effects on the sown area, yield per unit area, and total production of food crops, while positively influencing the per capita income of agricultural workers. These empirical results highlight the complex relationship between culture–tourism integration and agricultural development. Our analysis provides quantitative evidence of the trade-offs involved in pursuing integrated development strategies, adding to the existing research on rural development. The findings indicate that promoting agriculture–culture–tourism integration should be balanced with efforts to accelerate agricultural technological progress.

Food security serves as the cornerstone of national security, intricately linked to social stability and economic progress. Currently, with the swift evolutions in social economy, logistics and transport, information dissemination, and technological advancements, there has been a marked increase in the cross-regional flow of food production, distribution, and consumption. Consequently, the spatial interdependence of food security across different regions has grown increasingly salient. This paper investigates the spatial interrelationship of food security levels in China through a network analysis framework, examining its determinants and network dynamics. The findings offer valuable insights for decision-makers aiming to optimize agricultural resource allocation and enhance national food security levels. This research establishes a comprehensive evaluation index system for assessing food security levels in China across four dimensions: production security, distribution security, supply security, and consumption security. Employing data from 30 provinces between 2008 and 2022, the entropy method quantifies food security levels, while a modified gravity model underpins the construction of a spatial association network. This framework subsequently examines the network’s structural characteristics and the factors influencing its formation. The results reveal that: (1) China’s food security levels demonstrate a consistent upward trajectory over the study period, though significant regional disparities persist. The central region surpasses the national average, while the eastern and western regions lag. Recently, the western region has shown accelerated improvements in food security, followed by the central area, with the eastern region maintaining steady growth. (2) A structurally robust spatial correlation network of food security has emerged, characterized by variations in the number of network relationships, fluctuations in network density, and a decline in network efficiency while still exhibiting pronounced small-world characteristics. (3) The network displays a clear core-periphery structure, with Shanghai, Beijing, and Jiangsu positioned centrally, playing pivotal intermediary roles, whereas remote provinces such as Gansu, Ningxia, and Liaoning occupy the periphery. (4) The four major regions demonstrate sparse internal connectivity yet robust inter-regional ties, resulting in pronounced spillover effects. (5) Various factors, including geographic distance, provincial proximity, disparities in economic development levels, variations in marketization, differences in agricultural human capital, and disparities in land productivity, significantly impact the establishment of spatial correlations in food security. The affirmative influences of geographic distance and neighboring relations, along with the beneficial shifts in economic development disparities, suggest that the flow of technology and resources plays a crucial role in reinforcing spatial connections.

The digital economy’s transformative impact on agriculture presents both opportunities and challenges for China’s pursuit of high-quality agricultural and rural development. This study investigates the complex interplay between digital economy, industrial structure transformation, and agricultural advancement using panel data from 31 Chinese provinces spanning 2012–2021. We employed mediation analysis and threshold effect models to uncover several key findings: (1) The digital economy influences high-quality agricultural and rural development through the dual-mediating mechanisms of industrial structure intensification and upgrading in China. (2) These mediating effects exhibit heterogeneous patterns: while industrial intensification positively channels the digital economy’s impact, industrial upgrading shows an initial negative indirect effect, suggesting potential short-term disruptions. (3) The relationship between digital economy and agricultural development is nonlinear, characterized by significant threshold effects. The digital economy’s positive impact becomes more pronounced as industrial structure surpasses certain sophistication and advancement thresholds. Our findings reveal the nuanced dynamics of digital-driven agricultural transformation, highlighting the need for targeted policies that leverage industrial-structure changes while mitigating potential adverse effects. This research contributes to a more comprehensive understanding of how digitalization can be harnessed to promote sustainable and high-quality agricultural and rural development in China, with implications for other developing economies navigating similar transitions.

IntroductionExport quality improvement is a crucial path for a country or region to climb up the advanced division of labor in the global value chain. For developing countries that have long been at the lower end of the global value chain division of labor, the dominance of agricultural trade is often controlled by international retailers, leading to higher costs of agricultural trade and hindering the upgrading of the quality of agricultural exports. As a traditional agricultural exporting country, China still needs a competitive advantage despite the large scale of its agricultural exports. Currently, e-commerce can provide buyers and sellers with comprehensive transaction information and technical support, allowing them to realize rational allocation of resources and export upgrading. Therefore, studying how e-commerce can promote the quality upgrade of agricultural exports is crucial for China to build a trade power.MethodsThis paper clarifies the mechanism of e-commerce’s theoretical impact on the quality of agricultural exports and then empirically tests it using a high-dimensional fixed-effects model with Chinese customs data from 2000 to 2020 as a sample.ResultsThe results show that e-commerce significantly promotes the quality of agricultural exports in all regions of China, and this conclusion still holds after various robustness tests; the heterogeneity test shows that e-commerce fosters the quality of agricultural exports in eastern China, densely populated regions, and economically underdeveloped regions, especially in the case of heterogeneous products; the mechanism analysis confirms that e-commerce promotes the quality of agricultural exports mainly through the optimization of the agricultural industry chain and supply chain, and the enhancement of the level of servicing. Mechanism analysis confirms that e-commerce improves agricultural export quality mainly through optimizing the agricultural industry chain and supply chain, improving the level of servitization, and docking the demand information of the consumer market, etc. Expanded analysis shows that constructing cross-border e-commerce platforms also significantly improves the quality of micro enterprises’ agricultural exports. This paper enriches the research on e-commerce and agricultural trade.DiscussionThis paper provides an essential reference for constructing a robust agricultural trade country and developing high-quality agriculture. Despite encompassing data up to 2020, in the context of today’s global economic volatility directly affecting agricultural trade, future investigations will broaden the scope to delve deeper into how e-commerce can help countries cope with global economic uncertainty.

Three types of variable lymphocyte receptor (VLR) genes, VLRA, VLRB, and VLRC, encode antigen recognition receptors in the extant jawless vertebrates, lampreys and hagfish. The somatically diversified repertoires of these VLRs are generated by serial stepwise copying of leucine-rich repeat (LRR) sequences into an incomplete germline VLR gene. Lymphocytes that express VLRA or VLRC are T cell–like, while VLRB-expressing cells are B cell–like. Here, we analyze the composition of the VLRB locus in different jawless vertebrates to elucidate its configuration and evolutionary modification. The incomplete germline VLRB genes of two hagfish species contain short noncoding intervening sequences, whereas germline VLRB genes in six representative lamprey species have much longer intervening sequences that exhibit notable genomic variation. Genomic clusters of potential LRR cassette donors, fragments of which are copied to complete VLRB gene assembly, are identified in Japanese lamprey and sea lamprey. In the sea lamprey, 428 LRR cassettes are located in five clusters spread over a total of 1.7 Mbp of chromosomal DNA. Preferential usage of the different donor cassettes for VLRB assemblage is characterized in our analysis, which reveals evolutionary modifications of the lamprey VLRB genes, elucidates the organization of the complex VLRB locus, and provides a comprehensive catalog of donor VLRB cassettes in sea lamprey and Japanese lamprey. [ABSTRACT FROM AUTHOR]

This paper explores the significance of digital governance for sustainable rural development in China, emphasizing the collaborative efforts of village administrative organizations, new agricultural business entities, and peasant households. Utilizing an evolutionary game approach, we examine the decision-making behaviors and stability points of these three entities within the context of rural digital governance. Our analysis is grounded in a mechanism of interest linkage among the stakeholders, with numerical simulations used to assess the impact of key variables and parameters on their evolutionary outcomes. The paper reveals that village administrative organizations are highly sensitive to changes in performance gains, special subsidies, penalty losses, and benefit distribution coefficients. Enhancing these variables can significantly motivate these organizations to engage in digital governance. In contrast, new agricultural business entities and peasant households demonstrate a stronger and more consistent willingness to collaborate, minimally affected by variable changes, which suggests a solid economic and social foundation for rural digital governance in China. Our paper underscores the need for positive incentives and a robust fault-tolerance mechanism to foster collaboration among village administrative organizations. It also highlights the importance of integrating new agricultural business entities into the digital governance framework to promote sustainable rural development. These insights provide valuable theoretical and practical implications for policymakers aiming to enhance the efficacy and inclusivity of digital governance in rural China.

The integration of agriculture and tourism has emerged as a potential strategy to boost agricultural productivity and promote sustainable rural development in China. This study investigates the impact of agricultural tourism integration (ATI) on agricultural green total factor productivity (AGTFP) in China, filling a research gap in understanding the complex dynamic nexus. We examine the mediating role of the agricultural industrial structure adjustment index (AISAI) and the threshold effects of ATI and human capital level (HCL) using a panel dataset of 30 Chinese provinces from 2011 to 2021. The empirical results reveal three key findings: (1) ATI has a significant positive impact on AGTFP growth; (2) the AISAI partially mediates the relationship between ATI and AGTFP; and (3) ATI and HCL have a double-threshold effect and a single-threshold effect on the ATI-AGTFP nexus, respectively. These findings suggest that the effectiveness of ATI in promoting AGTFP varies across different stages of integration and levels of human capital development. The empirical results contribute to the literature on rural development strategies by providing an in-depth understanding of how ATI influences agricultural productivity and sustainability. Based on these findings, we propose targeted policy recommendations to optimize ATI strategies and invest in human capital to improve the resilience and sustainability of rural economies in China.

Promoting green and sustainable agriculture is of great significance for ensuring food security and addressing global challenges. Meanwhile, health has increasingly become a global concern. Nutrition and health are the purpose of agricultural production. As two major global issues, how agriculture empowers human health has long been discussed. Based on the provincial panel data of China from 2003 to 2021, this paper studies the impact of agricultural green transformation (AGT) on residents’ health and explores its impact mechanism. The empirical results show that: (1) AGT in China has significantly reduced the average mortality rate and maternal mortality rate and significantly increased the average life expectancy, indicating that AGT in China has significantly improved the health level. The conclusions of robustness testing methods such as replacing AGT indicators and controlling endogeneity are still valid; and (2) The impact of AGT on residents’ health depends on the regional economic level, and there is a threshold effect. Compared with low-income areas, the positive effects of AGT in high-income areas on residents’ health are more pronounced; and (3) Agricultural carbon emissions play an intermediary effect between AGT and residents’ health, and AGT can improve residents’ health by reducing carbon emissions. The level of local education development plays a moderating role in the relationship between AGT and residents’ health. Agricultural policy implications include enhancing the ability to protect and utilize agricultural resources, promoting the green and low-carbon transformation of agriculture, and taking a more scientific and systematic approach towards the complex diversity of health risk factors.

Large scale agricultural business entities (hereinafter referred to as “the large scale entities”) are newly sprouted in the process of China’s agricultural transformation from traditional production to modern mode, while the improvement of agricultural total factor productivity is an important driving force to realize agricultural transformation in China. Therefore, it is of great significance to explore whether the former has a promoting effect on the latter. First of all, the effective improvement of agricultural total factor productivity by the large scale entity development is analyzed from the two aspects of scale effect and technology utilization effect, and the effect mechanism is discussed in depth in this paper. Secondly, empirical test was carried out by using the panel data of prefecture-level cities from 2015 to 2020. The benchmark regression results show that the development of large scale entities can significantly improve agricultural total factor productivity. The mechanism test results indicate that the development of large scale entities improves the agricultural total factor productivity by raising the agricultural mechanization level. The results of heterogeneity analysis show that the difference between large scale entities would affect the benchmarking results. Therefore, it is believed through the study as discussed in this paper that devoting greater effort to support the development of large scale entities is an important measure to improve agricultural total factor productivity and promote China’s agricultural transformation, but it is necessary to act according to local conditions based on the difference of entity types in the process.

Is it possible for the imposition of environmental regulation to develop the textile industry? Is it possible to balance quality and efficiency with energy conservation and emission reduction and achieve the sustainable development of the textile industry? The main objective of this study is to analyze the effects of the environmental regulation on industrial agglomeration and industrial efficiency within the textile sector. The findings reveal that, first, environmental regulation has significantly facilitated the agglomeration of the textile industry to regions with less stringent environmental standards. The restructuring and optimization of domestic value chains have also been promoted. Second, environmental policies in the process of the promotion of the industrial agglomeration are accompanied by a significant improvement in industrial efficiency. This improvement has contributed to the achievement of sustainability goals in the textile domain. Third, the influence of environmental regulations on industrial agglomeration and industrial efficiency improvement of the textile industry is strongly heterogeneous in terms of ownership. This impact is more significant in state-owned industries and private industries. This study holds substantial theoretical significance and practical relevance in promoting environmentally friendly and sustainable progress in the Chinese textile industry. First published online 12 October 2023

In the rapidly evolving business landscape of Thailand, the design and facilities of office buildings play a crucial role in enhancing employee satisfaction and productivity. This study seeks to answer the question: “How can office building facilities be optimized to meet the diverse preferences of occupants in Thailand, thereby improving their satisfaction and productivity”? This study employs a copula-based ordinal regression model combined with machine learning techniques to investigate the determinants of facility preferences in office buildings in Thailand. By analyzing data from 372 office workers in Bangkok, we identify the factors influencing facility needs and preferences, and measure the correlation between these preferences. Our findings reveal that safety and security are the highest-rated amenities, indicating their importance in the workplace. The findings reveal distinct preferences across demographic groups: age negatively influences the demand for certain amenities like lounges, while higher education levels increase the preference for cafeteria services. Employees in smaller firms show a higher preference for lounges and fitness centers but lower for restaurants and cafeterias. Interestingly, the size of the enterprise does not significantly affect preferences for fundamental facilities like security and cleaning. The study also uncovers the significant role of gender and income in shaping preferences for certain facilities. These results suggest that while basic amenities are universally valued, luxury or leisure-oriented facilities are more appreciated in smaller, possibly more community-focused work environments. This study highlights the need for tailored facility management in office buildings, considering the diverse needs of different employee groups, which has significant implications for enhancing workplace satisfaction and productivity.

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of “high in the east and low in the west”. The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran’s index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time.

Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers’ incomes, and promoting sustainable agri-food supply chains.