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Background The ‘Gonggan’ mandarin, an elite local cultivar from Zhaoqing City, Guangdong Province, combines the qualities of mandarin and sweet orange. A leaf-variegated mutant enhances its ornamental and economic value, providing an excellent model for studying chloroplast development and photosynthetic pigment metabolism in citrus. Results We found that, in this variegated mutant, chloroplasts are severely deficient or absent in mesophyll cells. Physiological assessments revealed lower levels of chlorophyll, carotenoids, net photosynthetic rate ( Pn ), and stomatal conductance ( Gs ), alongside significantly higher non-photochemical quenching (NPQ) and the non-photochemical quenching coefficient ( qN ), reflecting increased photoprotective energy dissipation. To uncover the molecular basis of leaf variegation, high-quality genome assemblies and transcriptomes were generated for both the normal and variegated ‘Gonggan’ mandarin, enabling comparative multi-omics analysis. Key genes involved in chloroplast development, such as TOC159 , PDV2 , THA8 , and SIG5 , were downregulated in the variegated leaves. Similarly, structural genes linked to chlorophyll degradation, including CLH2 , SGR , NOL , and NYC1 , exhibited altered expression. Downregulation of transcription factors GLK, GNC, and GNC-LIKE (GNL), known regulators of chloroplast development and chlorophyll biosynthesis, was also observed. Conclusions These findings suggest that disrupted expression of critical genes impacts chloroplast development and pigment metabolism, causing the leaf variegation phenotype. Overall, this study lays a foundation for functional genomics research and potential germplasm improvement of ‘Gonggan’ mandarin, and provides new insights into the mechanisms driving color variation in citrus.

The number of studies on plant transcriptomes using ONT RNAseq technology is rapidly increasing in recent. It is a powerful method to decipher transcriptomic complexity, particularly alternative splicing (AS) event detection. Citrus plants are the most important widely grown fruit crops. Exploring different AS events in citrus contributes to transcriptome improvement and functional genome study. Here, we performed ONT RNAseq in 9 species ( Atalantia buxifolia , Citrus clementina , C. grandis , C. ichangensis , C. reticulata , C. sinensis , Clausena lansium , Fortunella hindsii , and Poncirus trifoliata ), accompanied with Illumina sequencing. Non-redundant full-length isoforms were identified between 41,957 and 76,974 per species. Systematic analysis including different types of isoforms, number of isoforms per gene locus, isoform distribution, ORFs and lncRNA prediction and functional annotation were performed mainly focused on novel isoforms, unraveling the capability of novel isoforms detection and characterization. For AS events prediction, A3, RI, and AF were overwhelming types across 9 species. We analyzed isoform similarity and evolutionary relationships in all species. We identified that multiple isoforms derived from orthologous single copy genes among different species were annotated as enzymes, nuclear-related proteins or receptors. Isoforms with extending sequences on 5’, 3’, or both compared with reference genome were filtered out to provide information for transcriptome improvement. Our results provide novel insight into comprehending complex transcriptomes in citrus and valuable information for further investigation on the function of genes with diverse isoforms.

N6-methyladenosine (m6A) RNA modification is a conserved mechanism that regulates the fate of RNA across eukaryotic organisms. Despite its significance, a comprehensive analysis of m6A-related genes in non-model plants, such as kiwifruit, is lacking. Here, we identified 36 m6A-related genes in the kiwifruit genome according to homology and phylogenetic inference. We performed bioinformatics and evolutionary analyses of the writer, eraser, and reader families of m6A modification. Reanalysis of public RNA-seq data collected from samples under various biotic and abiotic stresses indicated that most m6A-related genes were remarkably expressed under different conditions. Through construction of gene co-expression networks, we found significant correlations between several m6A-related genes and transcription factors (TFs) as well as receptor-like genes during the development and ripening of kiwifruit. Furthermore, we performed ATAC-seq assays on diverse kiwifruit tissues to investigate the regulatory mechanisms of m6A-related genes. We identified 10 common open chromatin regions that were present in at least two tissues, and these regions might serve as potential binding sites for MADS protein, C2H2 protein, and other predicted TFs. Our study offers comprehensive insights into the gene family of m6A-related components in kiwifruit, which will lay foundation for exploring mechanisms of post-transcriptional regulation involved in development and adaptation of kiwifruit.

With the rapid growth of economy, the environmental pollution problem is becoming increasingly prominent. How to promote the coordinated and balanced development of economy and environment is a strategic problem of great significance that we face urgently. Taking Wuhan City of China as the research object, this paper selects the key indexes of economic growth and environmental pollution and studies the interactive influence between economic growth and environmental pollution in Wuhan. On the one hand, the impact of Wuhan’s economic growth on environmental pollution is analyzed by the proposed time-delay correlation analysis method and the time-delay EKC (Environment Kuznets Curve) models. On the other hand, the impact of Wuhan’s environmental pollution on environmental growth is studied. By establishing the LARS-LASSO (least angle regression-least absolute shrinkage and selection operator) regression model and the stepwise regression model, the main factors affecting economic growth in preliminary environmental pollution indexes are analyzed, and then, an interaction model is established to study the impact of the interaction between any two main environmental factors on economic growth. The results of empirical analysis show that the main factors affecting economic growth are industrial wastewater emissions, industrial waste gas emissions, and industrial smoke and dust emissions, and the interaction between industrial waste gas emissions and industrial wastewater emissions restrains economic growth.

This study presents the design and implementation of a surface plasmon resonance (SPR) sensor in the Kretschmann configuration, employing a gold film deposited on a flexible polydimethylsiloxane (PDMS) substrate as the SPR chip. The refractive-index sensitivity of the SPR sensor was evaluated with sodium chloride solutions of varying concentrations. Optimizing for both sensitivity and detection accuracy, the incident angle was fixed at 13°. The sensor exhibited a sensitivity of 3385.5 nm/RIU. Remarkably, the sensitivity variation was merely 1% after subjecting the sensor chip to 50 bending cycles in both forward and reverse directions. The sensor’s efficacy was further validated through the detection of alcohol content in three different Chinese Baijiu samples, yielding a maximum relative error of 4.04% and a minimum error of 0.17%. Additionally, the sensor was utilized to study the adsorption behavior of glutathione (GSH) on the gold film under varying pH conditions. The findings revealed optimal immediate adsorption at pH = 12, attributed to the complete deprotonation of mercapto groups, facilitating the formation of Au-S bonds with gold atoms. The best film-forming effect was observed at pH = 7, where the interplay of attractive and repulsive forces among different molecular groups led to the gradual extension of the molecular chain, resulting in a thicker molecular film.

Under the background of new-type urbanization and rural revitalization strategy, how to promote the development of urban–rural integration has become an important issue in today’s society. This paper designed a new evaluation mechanism for the development level of urban–rural integration. Specifically, a three-level evaluation index system of urban–rural integration development level was established from four aspects: spatial integration, economic integration, social integration and living environment integration. By combining the entropy weight method with the ranking method, a combination weighting method was proposed to determine the weight of each index in the index system. Furthermore, an improved TOPSIS method based on relative entropy and grey relational degree was proposed to evaluate the development level of urban–rural integration, which considering proximity from the perspectives of distance and shape and solving the problem that some situations cannot be compared through the original model. Then, the established evaluation mechanism was applied to make an empirical analysis for evaluating the development level of urban–rural integration in Hubei Province, China. Cluster analysis and obstacle factor analysis were used to further analyze the evaluation results. Finally, according to the evaluation results, some effective countermeasures and policy implications were provided to improve the development level of urban–rural integration in Hubei Province.

The material realization of charge-4e/6e superconductivity (SC) is a big challenge. Here, we propose to realize charge-4e SC in maximally-twisted homobilayers, such as 45 ∘ -twisted bilayer cuprates and 30 ∘ -twisted bilayer graphene, referred to as twist-bilayer quasicrystals (TB-QC). When each monolayer hosts a pairing state with the largest pairing angular momentum, previous studies have found that the second-order interlayer Josephson coupling would drive chiral topological SC (TSC) in the TB-QC. Here we propose that, above the T c of the chiral TSC, either charge-4e SC or chiral metal can arise as vestigial phases, depending on the ordering of the total- and relative-pairing-phase fields of the two layers. Based on a thorough symmetry analysis to get the low-energy effective Hamiltonian, we conduct a combined renormalization-group and Monte-Carlo study and obtain the phase diagram, which includes the charge-4e SC and chiral metal phases. Liu et al. theoretically study maximally-twisted homo-bilayers, such as 45 ∘ -twisted bilayer cuprates and 30 ∘ -twisted bilayer graphene. Through renormalization group and Monte-Carlo calculations, the authors reveal the presence of charge-4e superconductivity and chiral-metal phases in the phase diagram.

Topological superconductivity has been sought in a variety of heterostructure systems, the interest being that a material displaying such a phenomenon could prove to be the ideal platform to support Majorana fermions, which in turn could be the basis for advanced qubit technologies. Recently, the high-Tc family of superconductors, FeTe1−xSeₓ, have been shown to exhibit the property of topological superconductivity and further, evidence has been found for the presence of Majorana fermions. We have studied the interplay of topology, magnetism, and superconductivity in the FeTe1−xSeₓ family using high-resolution laser-based photoemission. At the bulk superconducting transition, a gap opens at the chemical potential as expected. However, a second gap is observed to open at the Dirac point in the topological surface state. The associated mass acquisition in the topological state points to time-reversal symmetry breaking, probably associated with the formation of ferromagnetism in the surface layer. The presence of intrinsic ferromagnetism combined with strong spin–orbit coupling provides an ideal platform for a range of exotic topological phenomena.

Coronary heart disease (CHD) is a major public health problem affecting a nation’s economic and social development. Risk assessing CHD in a timely manner helps to stop, reverse, and reduce the spread of many chronic diseases and health hazards. This paper proposes a cloud-random forest (C-RF) model combining cloud model and random forest to assess the risk of CHD. In this model, based on the traditional classification and regression trees (CART), a weight determining algorithm based on the cloud model and decision-making trial and evaluation laboratory is applied to obtain the weights of the evaluation attributes. The attribute weight and the gain value of the smallest Gini coefficient corresponding to the same attribute are weighted and summed. The weighted sum is then used to replace the original gain value. This value rule is used as a new CART node split criterion to construct a new decision tree, thus forming a new random forest, namely, the C-RF. The Framingham dataset of the Kaggle platform is the research sample for the empirical analysis. Comparing the C-RF model with CART, support vector machine (SVM), convolutional neural network (CNN), and random forest (RF) using standard performance evaluation indexes such as accuracy, error rates, ROC curve and AUC value. The result shows that the classification accuracy of the C-RF model is 85%, which is improved by 8, 9, 4 and 3% respectively compared with CART, SVM, CNN and RF. The error rate of the first type is 13.99%, which is 6.99, 7.44, 4.47 and 3.02% lower than CART, SVM, CNN and RF respectively. The AUC value is 0.85, which is also higher than other comparison models. Thus, the C-RF model is more superior on classification performance and classification effect in the risk assessment of CHD.