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We previously found that B and AGL6 proteins form L (OAP3-2/OAGL6-2/OPI) and SP (OAP3-1/OAGL6-1/OPI) complexes to determine lip/sepal/petal identities in orchids. Here, we show that the functional L’ (OAP3-1/OAGL6-2/OPI) and SP’ (OAP3-2/OAGL6-1/OPI) complexes likely exist and AP3 / PI/AGL6 genes have acquired additional functions during evolution. We demonstrate that the presumed L’ complex changes the structure of the lower lateral sepals and helps the lips fit properly in the center of the flower. In addition, we find that OAP3-1/OAGL6-1/OPI in SP along with presumed SP’ complexes regulate anthocyanin accumulation and pigmentation, whereas presumed L’ along with OAP3-2/OAGL6-2/OPI in L complexes promotes red spot formation in the perianth. Furthermore, the B functional proteins OAP3-1/OPI and OAGL6-1 in the SP complex could function separately to suppress sepal/petal senescence and promote pedicel abscission, respectively. These findings expand the current knowledge behind the multifunctional evolution of the B and AGL6 genes in plants. B class AP3/PI and AGL6-like MADS proteins determine lips and sepals/petals identities in orchids. Here, the authors characterize the extended function of OAP3/OPI/OAGL6 in regulating the specific structure of the lateral sepals, pigmentation/senescence of the perianth and abscission of the pedicel.

Against the backdrop of dynamic transformations in the financial sector and prominent corporate diversification trends, credit risk prediction becomes significantly more challenging. On one hand, this study focuses on optimizing the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm for corporate credit risk prediction, thereby enhancing financial institutions’ risk management capabilities. The study systematically examines corporate diversification strategies, revealing discrepancies between theoretical frameworks and practical implementations. These strategies complicate corporate financial structures, generating divergent profitability, capital allocation, and risk profiles across business units. Such heterogeneity leads to uneven resource distribution, ultimately impacting enterprise operations, debt servicing capacity, and credit performance. Consequently, financial institutions increasingly prioritize cross-sectoral risk monitoring, business synergy evaluation, and dynamic financial tracking. On the other hand, regarding algorithmic innovation, this study conducts an in-depth analysis of SMOTE’s fundamental principles, encompassing its sample generation mechanics and optimized variants. Especially in terms of optimization content, this study innovatively introduces an adaptive boundary adjustment mechanism that automatically defines minority class boundaries based on data distribution characteristics. Meanwhile, it precisely targets critical oversampling areas while avoiding arbitrary sample generation in irrelevant regions. Moreover, an optimized weight allocation protocol during synthetic sample creation incorporates feature relevance and class distribution to produce more representative new samples. The experimental framework utilizes four benchmark datasets: German Credit, Australian Credit Approval, Taiwan Credit Card Default, and Corporate Credit Risk Assessment. The rigorous methodology ensures research validity through scientific data partitioning, appropriate hardware configuration, an advanced software environment, and comprehensive evaluation indices (accuracy, precision, recall, F1-score). The study mainly focuses on two aspects. One is to analyze corporate diversification strategy; the other is to explore the optimization of the SMOTE algorithm and its application in credit risk prediction. Empirical results demonstrate the optimized SMOTE algorithm’s superiority over six comparison models, such as random over-sampling, under-sampling, etc. The accuracy rate is improved by more than 21%, and the highest is close to 38%. Precision is enhanced by over 28%, peak nearly 35%; Recall is increased by more than 31% and peaked at 42%; F1 score is boosted by approximately 33%, with a maximum of about 39%. This study provides financial institutions with an advanced algorithmic solution for credit risk assessment in diversified corporate environments. Also, it is expected to improve decision-making accuracy, strengthen risk resilience, and promote financial market stability.

With the fast‐growing demand for green and safe energy sources, rechargeable ion batteries have gradually occupied the major current market of energy storage devices due to their advantages of high capacities, long cycling life, superior rate ability, and so on. Metallic Sn‐based anodes are perceived as one of the most promising alternatives to the conventional graphite anode and have attracted great attention due to the high theoretical capacities of Sn in both lithium‐ion batteries (LIBs) (994 mA h g−1) and sodium‐ion batteries (847 mA h g−1). Though Sony has used Sn–Co–C nanocomposites as its commercial LIB anodes, to develop even better batteries using metallic Sn‐based anodes there are still two main obstacles that must be overcome: poor cycling stability and low coulombic efficiency. In this review, the latest and most outstanding developments in metallic Sn‐based anodes for LIBs and SIBs are summarized. And it covers the modification strategies including size control, alloying, and structure design to effectually improve the electrochemical properties. The superiorities and limitations are analyzed and discussed, aiming to provide an in‐depth understanding of the theoretical works and practical developments of metallic Sn‐based anode materials. To overcome the main obstacles of poor cycling stability and low coulombic efficiency faced by metallic Sn‐based anodes, a lot of modification methods have been developed, including size control, alloying, and structure design. In this review, the state‐of‐the‐art works of metallic Sn‐based anodes are summarized and classified, and the superiorities and limitations are analyzed and discussed.

Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion). Therefore, in this study, an anomaly detection neural network, dual auto-encoder generative adversarial network (DAGAN), was developed to solve the problem of sample imbalance. With skip-connection and dual auto-encoder architecture, the proposed method exhibited excellent image reconstruction ability and training stability. Three datasets, namely public industrial detection training set, MVTec AD, with mobile phone screen glass and wood defect detection datasets, were used to verify the inspection ability of DAGAN. In addition, training with a limited amount of data was proposed to verify its detection ability. The results demonstrated that the areas under the curve (AUCs) of DAGAN were better than previous generative adversarial network-based anomaly detection models in 13 out of 17 categories in these datasets, especially in categories with high variability or noise. The maximum AUC improvement was 0.250 (toothbrush). Moreover, the proposed method exhibited better detection ability than the U-Net auto-encoder, which indicates the function of discriminator in this application. Furthermore, the proposed method had a high level of AUCs when using only a small amount of training data. DAGAN can significantly reduce the time and cost of collecting and labeling data when it is applied to industrial detection.

HighlightsThe pursuit of high specific energy and high safety has promoted the transformation of lithium metal batteries from liquid to solid-state systems. In addition to high reactivity and mobile interface, all-solid-state lithium metal batteries (ASSLMBs) still faces severe inhomogeneity in mechanical and electrochemical properties.The inherent trade-off in ASSLMBs lies between ionic conductivity and electrochemical window, mechanical strength and interface contact adequacy.The widespread adoption of lithium-ion batteries has been driven by the proliferation of portable electronic devices and electric vehicles, which have increasingly stringent energy density requirements. Lithium metal batteries (LMBs), with their ultralow reduction potential and high theoretical capacity, are widely regarded as the most promising technical pathway for achieving high energy density batteries. In this review, we provide a comprehensive overview of fundamental issues related to high reactivity and migrated interfaces in LMBs. Furthermore, we propose improved strategies involving interface engineering, 3D current collector design, electrolyte optimization, separator modification, application of alloyed anodes, and external field regulation to address these challenges. The utilization of solid-state electrolytes can significantly enhance the safety of LMBs and represents the only viable approach for advancing them. This review also encompasses the variation in fundamental issues and design strategies for the transition from liquid to solid electrolytes. Particularly noteworthy is that the introduction of SSEs will exacerbate differences in electrochemical and mechanical properties at the interface, leading to increased interface inhomogeneity—a critical factor contributing to failure in all-solid-state lithium metal batteries. Based on recent research works, this perspective highlights the current status of research on developing high-performance LMBs.

HighlightsAs an emerging preparation strategy for MXenes, Lewis acidic etching has attracted increasing attention in the past few years benefiting from a series of merits.Lewis acidic etching method is mainly presented from etching mechanism, terminations regulation, in-situ formed metals and delamination of multi-layered MXenes.The applications of MXenes and MXene-based composites obtained by Lewis acidic etching route in energy storage and conversion, sensors and microwave absorption are carefully summarized.Since the discovery in 2011, MXenes have become the rising star in the field of two-dimensional materials. Benefiting from the metallic-level conductivity, large and adjustable gallery spacing, low ion diffusion barrier, rich surface chemistry, superior mechanical strength, MXenes exhibit great application prospects in energy storage and conversion, sensors, optoelectronics, electromagnetic interference shielding and biomedicine. Nevertheless, two issues seriously deteriorate the further development of MXenes. One is the high experimental risk of common preparation methods such as HF etching, and the other is the difficulty in obtaining MXenes with controllable surface groups. Recently, Lewis acidic etching, as a brand-new preparation strategy for MXenes, has attracted intensive attention due to its high safety and the ability to endow MXenes with uniform terminations. However, a comprehensive review of Lewis acidic etching method has not been reported yet. Herein, we first introduce the Lewis acidic etching from the following four aspects: etching mechanism, terminations regulation, in-situ formed metals and delamination of multi-layered MXenes. Further, the applications of MXenes and MXene-based hybrids obtained by Lewis acidic etching route in energy storage and conversion, sensors and microwave absorption are carefully summarized. Finally, some challenges and opportunities of Lewis acidic etching strategy are also presented.

ObjectiveThe systemic spread of colorectal cancer (CRC) is dominated by the portal system and exhibits diverse patterns of metastasis without systematical genomic investigation. Here, we evaluated the genomic evolution of CRC with multiorgan metastases using multiregion sequencing.DesignWhole-exome sequencing was performed on multiple regions (n=74) of matched primary tumour, adjacent non-cancerous mucosa, liver metastasis and lung metastasis from six patients with CRC. Phylogenetic reconstruction and evolutionary analyses were used to investigate the metastatic seeding pattern and clonal origin. Recurrent driver gene mutations were analysed across patients and validated in two independent cohorts. Metastatic assays were performed to examine the effect of the novel driver gene on the malignant behaviour of CRC cells.ResultsBased on the migration patterns and clonal origins, three models were revealed (sequential, branch-off and diaspora), which not only supported the anatomic assumption that CRC cells spread to lung after clonally expanding in the liver, but also illustrated the direct seeding of extrahepatic metastases from primary tumours independently. Unlike other cancer types, polyphyletic seeding occurs in CRC, which may result in late metastases with intermetastatic driver gene heterogeneity. In cases with rapid dissemination, we found recurrent trunk loss-of-function mutations in ZFP36L2, which is enriched in metastatic CRC and associated with poor overall survival. CRISPR/Cas9-mediated knockout of ZFP36L2 enhances the metastatic potential of CRC cells.ConclusionOur results provide genomic evidence for metastatic evolution and indicate that biopsy/sequencing of metastases may be considered for patients with CRC with multiorgan or late postoperative metastasis.

Cancer-associated fibroblasts (CAFs) are the predominant components of the tumor microenvironment (TME) and influence cancer hallmarks, but without systematic investigation on their ubiquitous characteristics across different cancer types. Here, we perform pan-cancer analysis on 226 samples across 10 solid cancer types to profile the TME at single-cell resolution, illustrating the commonalities/plasticity of heterogenous CAFs. Activation trajectory of the major CAF types is divided into three states, exhibiting distinct interactions with other cell components, and relating to prognosis of immunotherapy. Moreover, minor CAF components represent the alternative origin from other TME components (e.g., endothelia and macrophages). Particularly, the ubiquitous presentation of endothelial-to-mesenchymal transition CAF, which may interact with proximal SPP 1 + tumor-associated macrophages, is implicated in endothelial-to-mesenchymal transition and survival stratifications. Our study comprehensively profiles the shared characteristics and dynamics of CAFs, and highlight their heterogeneity and plasticity across different cancer types. Browser of integrated pan-cancer single-cell information is available at https://gist-fgl.github.io/sc-caf-atlas/ . Cancer-associated fibroblasts (CAFs) are a predominant and critical component of the tumour microenvironment. Here, the authors integrate and analyse single-cell RNA-seq data of CAFs across 10 common solid cancer types, identifying their plasticity and interactions with other cell types.