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Abstract While the abilities of language models are thoroughly evaluated in areas like general domains and biomedicine, academic chemistry remains less explored. Chemical QA tools also play a crucial role in both education and research by effectively translating complex chemical information into an understandable format. Addressing this gap, we introduce ScholarChemQA, a large-scale QA dataset constructed from chemical papers. Specifically, the questions are from paper titles with a question mark, and the multi-choice answers are reasoned out based on the corresponding abstracts. This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. Correspondingly, we introduce a ChemMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data. Experiments show that Large Language Models (LLMs) still have significant room for improvement in the field of chemistry. Moreover, ChemMatch significantly outperforms recent similar-scale baselines: https://github.com/iriscxy/chemmatch .

Abstract Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy.

Abstract Cyclic di-adenylate monophosphate (cyclic di-AMP) is an important second messenger in microorganisms. Cyclic di-AMP regulates bacterial cell volume and turgor via control of potassium and compatible solute transport but is also involved in many other processes, including the activation of the metazoan innate immune response to bacterial infections. We compare the activity of full-length membrane-embedded CdaA, the enzyme that synthesizes cyclic di-AMP, with the water-soluble catalytic domain CdaA-DAC. Purified CdaA from L. lactis was studied in the detergent-solubilized state, and in lipid nanodiscs and vesicles. We show that CdaA is tetrameric and the membrane-bound complex has more than 2-orders of magnitude higher activity than soluble CdaA-DAC. CdaA activity increases with pH but does not strongly depend on the salt or lipid content, factors that are crucial for the control of osmoregulatory transporters. Cryo-EM and in-silico structure prediction of CdaA show that the two DAC dimers engage in a head-to-head interaction, leading to cyclic-di-AMP formation. The inhibitor phosphoglucomutase prevents this active conformation. We observe dynamic flexibility between the catalytic and membrane domains, even in the presence of ATP or non-hydrolyzable substrate ApCpp. This is the first comprehensive functional and structural characterization of a full-length cyclic di-AMP-specific cyclase.

Abstract Background Recent advancements in spatially resolved transcriptomics (SRT) have opened up unprecedented opportunities to explore gene expression patterns within spatial contexts. Deciphering spatial domains is a critical task in spatial transcriptomic data analysis, aiding in the elucidation of tissue structural heterogeneity and biological functions. However, existing spatial domain detection methods ignore the consistency of expression patterns and spatial arrangements between spots, as well as the severe gene dropout phenomenon present in SRT data, resulting in suboptimal performance in identifying tissue spatial heterogeneity. Results In this paper, we introduce a novel framework, spatially regularized deep graph networks (SR-DGN), which integrates gene expression profiles with spatial information to learn spatially-consistent and informative spot representations. Specifically, SR-DGN employs graph attention networks (GAT) to adaptively aggregate gene expression information from neighboring spots, considering local expression patterns between spots. In addition, the spatial regularization constraint ensures the consistency of neighborhood relationships between physical and embedded spaces in an end-to-end manner. SR-DGN also employs cross-entropy (CE) loss to model gene expression states, effectively mitigating the impact of noisy gene dropouts. Conclusions Experimental results demonstrate that SR-DGN outperforms state-of-the-art methods in spatial domain identification across SRT data from different sequencing platforms. Moreover, SR-DGN is capable of recovering known microanatomical structures, yielding clearer low-dimensional visualizations and more accurate spatial trajectory inferences.

Abstract Understanding biological activities in cells or deep tissues requires high-speed three-dimensional (3D) imaging. Substantial progress has been made with the emergence of 3D random-access microscopy. However, current solutions for fast 3D random-access imaging remain complex and costly. Herein we propose a simple, cost-effective, and fast 3D random-access confocal microscopy with remote focusing system. Our system shows isotropic response times across the x, y, and z axes, with a 34-fold improvement in axial response time over traditional piezo stages. We demonstrate its volumetric imaging performance with fluorescent particles and live cells. Furthermore, we validate the 3D random-access imaging capability of this system by continuously monitoring the signals in three different planes, showing a refresh rate of 500 Hz on two different positions in 3D. The simplicity, versatility, and affordability of our system promise widespread applications in research and industry.

Abstract Surface lattice resonances (SLR) have been demonstrated to enhance the sensitivity and reduce the full width at half maximum (FWHM) of the plasmonic resonances. However, their widespread application in immunoassays has been hindered by limitations of high structural defect sensitivity and fabrication costs. Here, we design a novel three-layer cylindrical SLR array that exhibits high tolerance against structural defects, which would facilitate straightforward fabrication. By integrating metal evaporation and nanoimprint lithography, we demonstrate the replication of the SLR array with exceptional quality. Theoretical simulations indicate that the resonance dips of these arrays exhibit are not sensitive to various structural defects. The experimental results reveal that the FWHM of these arrays can be as low as 5.1 nm while maintaining robust resonance characteristics. Furthermore, we demonstrated the high spectral sensitivity of the SLR array, which enabled the detection of immunoglobulin G (IgG) at concentrations as low as 609 pg/mL. These findings emphasize the potential of the defect-insensitive SLR array as a highly scalable immunoassay platform with exceptional performance.

Abstract Flexible circuit boards are a cornerstone of the modern electronics industry. In automatic defect detection, FPC connectors present challenges such as minimal differences between oxidation defects and the background, easy degradation of Intersection over Union (IoU) scores, and significant variations in the shapes of black defect boundaries. Consequently, existing algorithms perform poorly in this task. We improve model YOLOv9 by introducing Multi-scale Dilated Attention (MSDA) on the output side to enhance the ability to capture features, and Deformable Large Kernel Attention (DLKA) on the other side of the output header to improve the ability to adapt to complex defect boundaries. Our use of IoU loss completely eliminates the risk of IoU degradation or gradient vanishing. Furthermore, we reduce computational overhead with the implementation of Faster Block. Following these improvements, the mean Average Precision (mAP) at 75% IoU (mAP75) for oxidized defects increased by 7.5% relative to the base model. Similarly, the mAP at 50% IoU (mAP50) for black defects increased by 5.7%, validating the relevance and efficacy of our proposed improvements. Overall, the average mAP50, mAP75, and mAP50:95 for all defects improved by 3.8%, 2.0%, and 2.3%, respectively. The performance gain achieved by our enhanced model significantly exceeds the improvement of YOLOv9 relative to YOLOv8.

Raindrop size distribution （RSD） is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation， especially in complex terrain with complicated rainfall mechanism and high spatial and temporal variability.In this paper， the RSD characteristics of different rainfall rates and rain types in the mountainous and plain areas of the Ili River Valley during the summer of 2020 -2022 are investigated using Parsivel2 disdrometer.The results show that the precipitation in the mountainous and plain areas is mainly composed of small raindrops， and medium raindrops are the largest contribution to the rainfall rate R.Precipitation in the mountainous and plain areas mainly occurs during the late afternoon and early evening， and the concentration of large and medium-sized raindrops is higher in the mountainous areas in all time periods.The spectral width and the concentration of drops with diameters increase with increasing rainfall rate.The number concentration of medium and large particles in the mountainous areas is obviously higher than that in the plain areas during light rain and heavy rain.For the same rainfall rate and rain type， precipitation in the mountainous areas has higher mass-weighted mean diameter （Dm） and lower normalized intercept parameter （log10Nw）.In addition， the convective rain spectrum in the mountainous and plain areas of the Ili River Valley tend to be “continental-like convective cluster”.It is found that there are also significant differences in the μ-Λ relationship and Z-R relationship of precipitation between the two areas， and the empirical relationship of Z =300R1.40 clearly overestimates precipitation.The results reveal the microphysical characteristics of precipitation under different terrain conditions in the Ili River Valley， and provide a reliable factual basis for the subsequent retrieval of precipitation using radar data.

Coal is a mixture of organic macerals and minerals. The presence of minerals can lead to differences in the macromolecular structure of coal, resulting in significant heterogeneity in its mechanical properties, which in turn affects the overall coalbed methane production, fracturing and transformation effects, and the large-scale application of mature technologies within the block. Taking typical anthracite and kaolinite minerals from the Qinshui Basin as the research object, the influence of kaolinite and its content on the mechanical properties of anthracite is quantitatively studied through molecular dynamics methods. The molecular structure and pore characteristics of anthracite under different mineral contents are analyzed, and its microscopic mechanism is revealed from the perspective of energy and ordering. The results indicate that: ① There are both van der Waals forces and hydrogen bonding forces during the binding process between coal organic matter and kaolinite, and the intensity of hydrogen bonding is higher than that of van der Waals forces. The probability, interaction strength, and orderliness of kaolinite molecules appearing around coal organic matter increase with the increase of mineral content. ② The porosity of anthracite decreases linearly with the increase of kaolinite content, with φ=−0.229 3a+9.629 5, while the bulk modulus, elastic modulus, and shear modulus of anthracite all increase linearly with the increase of kaolinite content. The Poisson’s ratio decreases exponentially with the increase of kaolinite content. ③ The degree of mechanical heterogeneity of anthracite increases overall with the increase of kaolinite content. The variability of bulk modulus, elastic modulus, and shear modulus under different kaolinite contents can be divided into five parts: very low, low, medium, high, and very high. The coefficient of variation of Poisson's ratio is all less than 0.1, indicating very low variability. ④ As the content of kaolinite increases, the interaction energy between coal organic matter and kaolinite shows a linear increase (Einteraction=4.558 5a+443.929). Compared with pure organic matter, when the content of kaolinite reaches 20%, the bond energy and van der Waals energy of anthracite increase by 63.86% and 48.06%, respectively. ⑤ The internal reason why kaolinite enhances the mechanical properties of coal is that it occupies a certain pore space after entering the coal organic matter and interacts with the aromatic carbon skeleton and different functional groups of the coal organic matter. The higher the content of kaolinite, the greater the degree of filling the pores of the coal macromolecular structure and the energy of the coal macromolecular structure, resulting in a decrease in the porosity of the coal macromolecular structure, an increase in orderliness, and an increase in mechanical strength and resistance to deformation.

Abstract Background Primary cardiac angiosarcoma (PCAS) is a rare and aggressive heart tumour with limited treatment options and a poor prognosis. Understanding cellular heterogeneity and tumour microenvironment (TME) is crucial for the development of effective therapies. Here, we investigated the intratumoural heterogeneity and TME diversity of PCAS using single‐cell RNA sequencing (scRNA‐seq). Methods We performed scRNA‐seq analysis on tumour samples from four patients with PCAS, supplemented with multicolour immunohistochemistry for identification. We used scRNA‐seq data from five normal cardiac tissue samples downloaded from public databases for comparative analyses. Bioinformatic analyses, including Cell Ranger, Seurat, Monocle2, hdWGCNA, SCENIC and NicheNet, were utilized to identify distinct cell populations, transcriptional patterns, and co‐regulating gene modules. Results Our analysis revealed significant intratumoural heterogeneity in PCAS driven by diverse biological processes such as protein synthesis, degradation, and RIG‐I signalling inhibition. The SCENIC analysis identified three primary transcription factors' clusters (CEBPB, MYC and TAL1). T‐cell subset analysis showed exhausted antigen‐specific T‐cells, complicating the efficacy of immune checkpoint blockade. Furthermore, we observed suppressive macrophages (SPP1+ and OLR1+) and reduced mitochondrial gene MT‐RNR2 (MTRNR2L12) expression in TME‐infiltrating cells, indicating impaired mitochondrial function. Conclusion This study elucidates the complex cellular landscape and immune microenvironment of PCAS, highlighting potential molecular targets for the development of novel therapies. These findings underscore the importance of a multifaceted therapeutic approach for addressing the challenges posed by PCAS's heterogeneity and immune evasion. Key points Insights into the heterogeneity and transcriptional patterns of sarcoma cells may explain the challenges in treating primary cardiac angiosarcoma (PCAS) using the current therapeutic modalities. Characterization of the immune microenvironment revealed significant immunosuppression mediated by specific myeloid cell populations (SPP1+ and OLR1+ macrophages). Identification of mitochondrial dysfunction in immune cells within the PCAS microenvironment, particularly the notable downregulation of the MTRNR2L12 protein, suggests a new avenue for therapeutic targeting.

ABSTRACTWith the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

Abstract Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Abstract With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Abstract Background The introduction of non-native species is a primary driver of biodiversity loss in freshwater ecosystems. The redclaw crayfish (Cherax quadricarinatus) is a freshwater species that exhibits tolerance to hypoxic stresses, fluctuating temperatures, high ammonia concentration. These hardy physiological characteristics make C. quadricarinatus a popular aquaculture species and a potential invasive species that can negatively impact tropical and subtropical ecosystems. Investigating the genomic basis of environmental tolerances and immune adaptation in C. quadricarinatus will facilitate the development of management strategies of this potential invasive species. Results We constructed a chromosome-level genome of C. quadricarinatus by integrating Nanopore and PacBio techniques. Comparative genomic analysis suggested that transposable elements and tandem repeats drove genome size evolution in decapod crustaceans. The expansion of nine immune-related gene families contributed to the disease resistance of C. quadricarinatus. Three hypoxia-related genes (KDM3A, KDM5A, HMOX2) were identified as being subjected to positive selection in C. quadricarinatus. Additionally, in vivo analysis revealed that upregulating KDM5A was crucial for hypoxic response in C. quadricarinatus. Knockdown of KDM5A impaired hypoxia tolerance in this species. Conclusions Our results provide the genomic basis for hypoxic tolerance and immune adaptation in C. quadricarinatus, facilitating the management of this potential invasive species. Additionally, in vivo analysis in C. quadricarinatus suggests that the role of KDM5A in the hypoxic response of animals is complex.

Abstract With the fast‐growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi‐omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step‐by‐step plans for various bioinformatics tasks. AutoBA's unique capacity to self‐design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open‐source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end‐to‐end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi‐omic analyses.

ABSTRACT Purpose Upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) share histomorphological and therapeutic features but distinct epidemiologic and clinicopathologic characteristics. We examined alterations of chromatin regulatory genes in molecular subtypes, clonal relatedness, and T‐cell receptor (TCR) diversity in UTUC and UCB. Materials and Methods Targeted next‐generation sequencing or whole‐exome DNA sequencing and TCR sequencing were conducted with 34 UTUC and 49 UCB specimens from 63 patients. Tumors were subtyped based on the expression of CK5 and GATA3. Results of tissue microarray of 78 muscle‐invasive bladder cancer (MIBC) samples were used as prognostic factors of different subtypes of MIBC. Results Chromatin regulatory genes were frequently mutated in both UTUC and UCB. Rapid relapse and progression of non‐MIBC are correlated with alterations of KMT2C and EP300. Frequency of alterations in chromatin regulatory genes is higher in UTUC patients with SBS22 and SBS2 signatures and lower in UCB patients with SBS2 and SBS6 signatures. GATA3 and CK5 double‐positive patients with higher frequencies of SMARCA4, ARID1A, and EP300 mutations have better prognoses than patients with basal subtypes. Although UTUC and UCB in the same patient can be either clonally related or developed independently, mutated genes in chromatin pathway were enriched in the related clones. Compared to UTUC, UCB had more deleterious mutations in DNA damage repair (DDR) genes, higher levels of tumor mutation burden (TMB) and copy number variations (CNVs), as well as higher TCR clonality and lower TCR diversity. Conclusions Since genetic alterations of the chromatin pathway genes are important in both UTUC and UCB, they could serve as potential biomarkers for predicting disease progression and therapeutic targets. Differences in mutation frequencies of DDR pathway, TMB, CNV, and TCR might be the contributing factors for the distinct responses to immune checkpoint inhibitor (ICI) between UTUC and UCB.

Summary: Silicon anodes hold promise for future lithium-ion batteries (LIBs) due to their high capacity, but they face challenges such as severe volume expansion and low electrical conductivity. In this study, we present a straightforward and scalable electrostatic self-assembly method to fabricate WSi@SiOx/Ti3C2 composites for LIBs. Silicon nanosheets and the ultra-thin oxide layer SiOx serve as sufficient buffers against volume changes, while the layered MXene enhances the electrical conductivity of the composite and promoted Li+/e− transport. Additionally, cationic surfactant-treated Ti3C2 provides more active sites for WSi@SiOx attachment and acts as an intercalating agent, enabling WSi@SiOx to enter the interlayer spaces of Ti3C2. The WSi@SiOx/Ti3C2 electrodes significantly improved electrochemical performance, achieving a capacity of 1,130 mAh g−1 after 800 charge/discharge cycles at 500 mA g−1. This study not only presents a straightforward pathway for high-value utilization of silicon waste but also offers a feasible route for preparing high-performance and cost-effective silicon-based LIBs.

Abstract Nonlinear nanophotonic devices have shown great potential for on‐chip information processing, quantum source, 3D microfabrication, greatly promoting the developments of integrated optics, quantum science, nanoscience and technologies, etc. To promote the applications of nonlinear nanodevices, improving the nonlinear efficiency, expanding the spectra region of nonlinear response and reducing device thickness are three key issues. Herein, this study focuses on the nonlinear effect of third‐harmonic generation (THG), and present a thin Si meta‐sructure to improve the THG efficiency in the ultraviolet (UV) region. The measured THG efficiency is up to 10−5 at an emission wavelength of 309 nm. Also, the THG nanosystem is only 100 nm in thickness, which is two–five times thinner than previous all‐dielectric nanosystems applied in THG studies. These findings not only present a powerful thin meta‐structure with highly efficient THG emission in UV region, but also provide a constructive avenue for further understanding the light–matter interactions at subwavelength scales, guiding the design and fabricating of advanced photonic devices in future.

Abstract Intriguing “slidetronics” has been reported in van der Waals (vdW) layered non-centrosymmetric materials and newly-emerging artificially-tuned twisted moiré superlattices, but correlative experiments that spatially track the interlayer sliding dynamics at atomic-level remain elusive. Here, we address the decisive challenge to in-situ trace the atomic-level interlayer sliding and the induced polarization reversal in vdW-layered yttrium-doped γ-InSe, step by step and atom by atom. We directly observe the real-time interlayer sliding by a 1/3-unit cell along the armchair direction, corresponding to vertical polarization reversal. The sliding driven only by low energetic electron-beam illumination suggests rather low switching barriers. Additionally, we propose a new sliding mechanism that supports the observed reversal pathway, i.e., two bilayer units slide towards each other simultaneously. Our insights into the polarization reversal via the atomic-scale interlayer sliding provide a momentous initial progress for the ongoing and future research on sliding ferroelectrics towards non-volatile storages or ferroelectric field-effect transistors.

Maize is an important food crop, and 100-kernel weight (HKW) is one of the three key components of yield. In this study, 200 maize inbred lines were used as the material, and HKW was evaluated over three consecutive years in two environments. A genome-wide association study (GWAS) was conducted using the Blink and FarmCPU models with 44,935 SNP markers evenly distributed across the maize genome. A total of 25 SNPs significantly associated with HKW were identified, with three SNPs detected in both models. Six significant SNPs were located within previously mapped QTL bins associated with grain weight. In the linkage disequilibrium (LD) regions of the significant SNP loci, 198 candidate genes were identified, of which 74 had annotation information. Further analysis revealed 21 candidate genes related to HKW, such as GRMZM2G010555 (alternative oxidase), GRMZM2G102471 (ubiquitin-conjugating enzyme), GRMZM2G060669 (histone deacetylase), GRMZM2G090156 (methyltransferase), GRMZM2G002075 (BZIP transcription factor), and GRMZM2G138454 (bHLH transcription factor). The SNP loci and candidate genes identified in this study provide important references for marker-assisted selection, fine mapping, and gene cloning related to HKW in maize.

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection.

Authorship style transfer aims to modify the style of neutral text to match the unique speaking or writing style of a particular individual. While Large Language Models (LLMs) present promising solutions, their effectiveness is limited by the small number of in-context learning demonstrations, particularly for authorship styles not frequently seen during pre-training. In response, this paper proposes an inverse transfer data augmentation (ITDA) method, leveraging LLMs to create (neutral text, stylized text) pairs. This method involves removing the existing styles from stylized texts, a process made more feasible due to the prevalence of neutral texts in pre-training. We use this augmented dataset to train a compact model that is efficient for deployment and adept at replicating the targeted style. Our experimental results, conducted across four datasets with distinct authorship styles, establish the effectiveness of ITDA over traditional style transfer methods and forward transfer using GPT-3.5. For further research and application, our dataset and code are openly accessible at https://github.com/Vicky-Shao/ITDA.

Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately capturing small abandoned cropland, particularly in regions, such as southern China, with fragmentized farmland, poses a significant challenge using the traditional optical image-based mapping methods due to their coarse spatial resolution. In addition, irregular and chaotic textures of abandoned cropland further complicate the accurate prediction using very high resolution (VHR) data. In this article, we propose a novel deep learning network termed pixel attention contrastive network (PACnet) to map fine-grained abandoned cropland based on VHR data. Cross-image pixel contrast learning is introduced to discern distinctive features distinguishing abandoned cropland from other land types across various interimages. Moreover, a criss-cross attention module is embedded to enhance the contrasting characteristics within individual intraimages. Experimental outcomes validate the efficacy of PACnet, showcasing the highest accuracy (OA = 93.8% and mIOU = 71.7%) when compared with classical semantic segmentation networks. Our proposal not only underscores the potency of VHR remote sensing data in finely delineating abandoned cropland but also carries significant implications for cropland abandonment impact analysis and informed policy formulation.

This study experimentally investigates the mechanical behavior of additively manufactured (AM) alumina ceramics by stereolithography technique. The AM alumina specimens with two different printing orientations (POs) are tested under quasi-static and dynamic loading rates. The material shows a quasi-static (i.e., a strain rate of 10−3 s−1) compressive strength of 1640.54 ± 99.33 MPa and 1494.25 ± 260.08 MPa for the PO1 and PO2, respectively, and a dynamic (i.e., a strain rate of 640–730 s−1) compressive strength of 3077.25 ± 174.07 MPa and 3107.33 ± 97.03 MPa for the PO1 and PO2, respectively, which are among the highest reported values for AM alumina due to the higher density and finer grain size. The strain-rate-dependent compressive strength of the material is slightly affected by the PO which is alleviated with the increase in strain rate from quasi-static to dynamic loading conditions. In contrast, the PO noticeably affects the macro-scale failure pattern. The fractography analysis shows the dominant contribution of the intergranular failure mechanism and a combination of intergranular and transgranular mechanisms under quasi-static and dynamic loading, respectively. The crack speed propagation is found to be 785 ± 174 m/s on average which is ∼68% less than that of conventional ones in the literature. The current AM alumina shows a hardness of 24.45 ± 0.88 GPa which is higher than that of the majority of other AM alumina. Overall, this study discusses the potentiality of using AM ceramics in engineering fields replacing the conventionally-made counterparts, and provides implications for designing better-performing AM ceramics.

Abstract Background The widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT. Methods We retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022. Results For the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively. Conclusions Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.

Individual tree segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned aerial vehicles (UAVs) have become an essential data source for (ITS) due to their capability to capture ultra-fine details. However, current deep-learning-based ITS methods struggle to accurately handle densely overlapping fruit tree distributions with similar characteristics in UAVs images, primarily due to the intricate nature of spatial arrangements in such scenarios. In this article, we propose CEDAnet, a context enhancement, and density adjustment network, to address the challenge of dense fruit trees segmentation. Specifically, a transformer-based contextual aggregation module is designed to distinguish different instances and refine the boundary of the instances. We have proposed a density-guided nonmaximum suppression method to adaptively generate sufficient candidate bounding boxes, aiming to retain more potential instances in dense trees. To evaluate the effectiveness and robustness of our proposal, we curated two ITS datasets constructed with imagery captured by UAVs, namely instance segmentation in Conghua images dataset (iSCHID) and instance segmentation in Maoming images dataset (iSMMID) based on their respective spatial characteristics. Experimental results on both two datasets demonstrated that CEDAnet yields competitive results in ITS tasks, with the bounding box AP of 0.498, segmentation AP of 0.493 in iSCHID, and the bounding box AP of 0.706, segmentation AP of 0.703 in iSMMID.

Hyperspectral image superresolution (HSI-SR) has become an essential step of data preprocessing for tasks such as classification and change detection in remote sensing. For HSI-SR tasks, the state-of-the-art methods lie in how to learn effective spatial and spectral characteristics. More deeply mining the shallow and deep spatial–spectral features is vital for the performance improvement of HSI-SR. Hereby, we provide an end-to-end multiscale spatial–spectral network (M3SN) driven by a hybrid spectral attention mechanism (HSAM) for HSI-SR, aiming to fully dig shallow and deep spatial–spectral characteristics. Precisely, considering the importance of shallow spatial–spectral features at early stages, a multiscale information block consisting of a 3-D convolution, three parallel 2-D convolutions with different scale sizes, and SE-Net is first designed to extract informative multiscale shallow spatial–spectral features and recalibrate the channel weights of features. Then, a dual-path multiscale spatial–spectral feature block is set up to explore the deep spatial and spatial–spectral features. While one path using 2-D convolutions extracts spatial features, the other path employing 3-D-Res2Net as well as an updated HSAM module mines multiscale deep spatial–spectral features. Finally, we design a multiscale fusion block based on the channel reduction-scaling operation to fuse the extracted hierarchical feature maps for the final reconstruction. It is demonstrated that the M3SN outperforms existing methods by extensive experiments on four publicly available hyperspectral datasets.

Abstract Recently, ground coverings change detection (CD) driven by bitemporal hyperspectral images (HSIs) has become a hot topic in the remote sensing community. There are two challenges in the HSI‐CD task: (1) attribute feature representation of pixel pairs and (2) feature extraction of attribute patterns of pixel pairs. To solve the above problems, a novel spectral‐spatial sequence characteristics‐based convolutional transformer (S3C‐CT) method is proposed for the HSI‐CD task. In the designed method, firstly, an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns. Then, a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs. Next, a fusion framework of the convolutional neural network (CNN) and Transformer encoder (TE) is designed to extract high‐order sequence semantic features, taking into account both local context information and global sequence dependencies. Specifically, a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE. Finally, the binary change map is determined according to the fully‐connected layer. Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.

Abstract Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients’ right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients’ private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.

Abstract Repetitive DNA sequences playing critical roles in driving evolution, inducing variation, and regulating gene expression. In this review, we summarized the definition, arrangement, and structural characteristics of repeats. Besides, we introduced diverse biological functions of repeats and reviewed existing methods for automatic repeat detection, classification, and masking. Finally, we analyzed the type, structure, and regulation of repeats in the human genome and their role in the induction of complex diseases. We believe that this review will facilitate a comprehensive understanding of repeats and provide guidance for repeat annotation and in-depth exploration of its association with human diseases.

BackgroundDeep vein thrombosis (DVT) is associated with aberrant gene expression that is a common peripheral vascular disease. Here, we aimed to elucidate that the epigenetic modification of forkhead box protein 3 (FOXP3) at the post-transcriptional level, which might be the key trigger leading to the down-regulation of FOXP3 expression in DVT.MethodsIn order to explore the relationship between microRNAs (miRNAs) and FOXP3, mRNA and microRNA microarray analysis were performed. Dual luciferase reporter assay was used to verify the upstream miRNAs of FOXP3. Quantitative real-time polymerase chain reaction, flow cytometry and Western blot were used to detect the relative expression of miR-6132 and FOXP3. Additionally, DVT models were established to investigate the role of miR-6132 by Murine Doppler Ultrasound and Hematoxylin-Eosin staining.ResultsMicroarray and flow cytometry results showed that the FOXP3 expression was decreased while miR-6132 level was increased substantially in DVT, and there was significant negative correlation between miR-6132 and FOXP3. Moreover, we discovered that overexpressed miR-6132 reduced FOXP3 expression and aggravated DVT formation, while miR-6132 knockdown increased FOXP3 expression and alleviated DVT formation. Dual luciferase reporter assay validated the direct binding of miR-6132 to FOXP3.ConclusionCollectively, our data elucidate a new avenue through which up-regulated miR-6132 contributes to the formation and progression of DVT by inhibiting FOXP3 expression.

Abstract Objective The purpose of this systematic review is to collect, appraise, and synthesize existing evidence from systematic reviews and meta-analyses (SRs/MAs) on the effectiveness of tolvaptan for water retention in heart failure. Methods A comprehensive literature search was performed on PubMed, EMBASE, web of science, Cochrane reviews for SRs/Mas published between the databases’ establishment to November 17, 2021. All the records were managed with Endnote 20. Standardized forms were used to extract data. Revman 5.3 was used to make forest plots to show the characteristics of outcomes. The methodological and evidence quality were respectively evaluated by AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews 2) and GRADE (Grading of Recommendation of Assessment, Development, and Evaluation) system. Results A total of 9 SRs/Mas between 2015 to 2020 met inclusion criteria. Serum sodium concentration and urine output were considered as primary outcomes and body weight change and all-cause mortality as second outcomes. Through conducting forest plots, it appeared that tolvaptan brought more positive effect than conventional therapies. It was pessimistic when it comes to the quality of the 9 studies. all the 9 articles were rated as low-quality because AMSTAR 2 evaluation showed that they each had at least one critical item (items 2, 4, 7, 9, 11, 13 and 15) defect. Besides, every article had a few non-critical item defects too. The result of GRADE assessment was not optimistic, so the overall quality of the evidences was low as well. Conclusion Tolvaptan can be recommended for water retention in HF patients, but more evidence is needed.

Three polysaccharides (SnNG, SnFS and SnFG) were purified from the body wall of Stichopus naso. The physicochemical properties, including monosaccharide composition, molecular weight, sulfate content, and optical rotation, were analyzed, confirming that SnFS and SnFG are sulfated polysaccharides commonly found in sea cucumbers. The highly regular structure {3)-L-Fuc2S-(α1,}n of SnFS was determined via a detailed NMR analysis of its oxidative degradation product. By employing β-elimination depolymerization of SnFG, tri-, penta-, octa-, hendeca-, tetradeca-, and heptadeca-saccharides were obtained from the low-molecular-weight product. Their well-defined structures confirmed that SnFG possessed the backbone of {D-GalNAc4S6S-β(1,4)-D-GlcA}, and each GlcA residue was branched with Fuc2S4S. SnFS and SnFG are both structurally the simplest version of natural fucan sulfate and fucosylated glycosaminoglycan, facilitating the application of low-value sea cucumbers S. naso. Bioactivity assays showed that SnFG and its derived oligosaccharides exhibited potent anticoagulation and intrinsic factor Xase (iXase) inhibition. Moreover, a comparative analysis with the series of oligosaccharides solely branched with Fuc3S4S showed that in oligosaccharides with lower degrees of polymerization, such as octasaccharides, Fuc2S4S led to a greater increase in APTT prolongation and iXase inhibition. As the degree of polymerization increases, the influence from the sulfation pattern diminishes, until it is overshadowed by the effects of molecular weight.

ObjectiveTo explore the bidirectional causal relationship between Ankylosing Spondylitis (AS) and Osteoarthritis (OA) at the genetic level within the European ancestry.MethodsWe implemented a series of quality control steps to select instrumental variables (IVs) related to the exposure. We conducted two-sample Mendelian randomization (MR) using the inverse-variance weighted method as the primary approach. We adjusted significance levels using Bonferroni correction, assessed heterogeneity using Cochrane’s Q test. Sensitivity analysis was conducted through leave-one-out method. Additionally, external datasets and relaxed IV selection criteria were employed, and multivariate MR analyses were performed for validation purposes. Finally, Bayesian colocalization (COLOC) analysis identified common genes, validating the MR results.ResultsThe investigation focused on the correlation between OA and AS in knee, hip, and hand joints. MR results revealed that individuals with AS exhibit a decreased risk of knee OA (OR = 0.9882, 95% CI: 0.9804-0.9962) but no significant increase in the risk of hip OA (OR = 0.9901, 95% CI: 0.9786-1.0018). Conversely, AS emerged as a risk factor for hand OA (OR = 1.0026, 95% CI: 1.0015-1.0036). In reverse-direction MR analysis, OA did not significantly influence the occurrence of AS. Importantly, minimal heterogeneity was observed in our MR analysis results (p > 0.05), and the robustness of these findings was confirmed through sensitivity analysis and multivariate MR analysis. COLOC analysis identified four colocalized variants for AS and hand OA (rs74707996, rs75240935, rs181468789, and rs748670681).ConclusionIn European population, individuals with AS have a relatively lower risk of knee OA, whereas AS serves as a risk factor for hand OA. However, no significant causal relationship was found between AS and hip OA. Additionally, it offers novel insights into genetic research on AS and OA.

Signal transducer and activator of transcription (STAT) proteins, pivotal regulators of signaling cascades, undergo activation in response to the stimulation of cytokines and growth factors, and participate in biological processes, including inflammation, immune responses, cell proliferation, and differentiation. During the process of pregnancy, STAT signaling is involved in regulating embryonic implantation, endometrial decidualization, and establishing and maintaining maternal-fetal immune tolerance. Increasing evidence suggests that aberrant STAT signaling contributes to the occurrence and development of pregnancy disorders, including repeated implantation failure (RIF), preeclampsia (PE), recurrent spontaneous abortion (RSA), preterm birth (PTB) and gestational diabetes mellitus (GDM). Elucidating the molecular mechanisms of the STAT signaling pathway holds promise for further understanding the establishment and maintenance of normal pregnancy, and thereby providing potent targets and strategic avenues for the prevention and management of ailments associated with pregnancy. In this review, we summarized the roles of the STAT signaling pathway and its related regulatory function in embryonic implantation, endometrial decidualization, and maternal-fetal immune tolerance. In conclusion, in-depth research on the mechanism of the STAT signaling pathway not only enhances our understanding of normal pregnancy processes but also offers STAT-based therapeutic approaches to protect women from the burden of pregnancy-related disorders.

We propose an amorphous metal oxide thin film transistor photo-capacitance model in the depletion region that takes Fermi level splitting and band-bending rearrangement into consideration. The split Fermi level is used to characterize the variation in trapped electrons under illumination. Those trapped electrons are excited by optical energy transport under the electric field induced by gate voltage, changing the charge density in the space charge and inducing the rearrangement of band bending. By comparing the data calculated from the model with the test data under three different illumination conditions, that is, 808, 635, and 520 nm, we verify the correctness of this model. Furthermore, the fitting results were in accordance with the general law: the higher the photon energy, the higher the energy level splitting.

The spatial distribution of precipitation in Xinjiang, China, is extremely heterogeneous due to its complex subsurface, and microphysical studies of cloud precipitation over Xinjiang are severely constrained by observational means. The Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) provides improved three-dimensional observation data of strong and weak precipitation and performs effective observations in alpine mountain regions. Therefore, this study used GPM/DPR Level-3 data from January 2015 to December 2022 to analyze the convective and stratiform precipitation characteristics over Xinjiang. The results showed that (1) the range of maximum precipitation reflectivity factors (PRF_max) near the ground and at different heights were larger in summer and maximum PRF_max values appeared in areas along the Altai, Tianshan, and Kunlun Mountains and plains on the respective mountain slopes. In summer, the PRF_max of stratiform (convective) precipitation nearsurface ranged from 24 to 34 dBZ (28–40 dBZ). (2) The two types of precipitation exhibited the highest storm top heights in summer, with convective precipitation having higher values than stratiform precipitation and mountainous areas having higher values than the surrounding basins due to the effects of topographic uplift. (3) The value domains of the PRF, particle mass-weighted diameter (Dm), and particle number concentration for stratiform (convective) precipitation were more concentrated (wider), that is, PRF: 14–30 dBZ (12–40 dBZ), Dm: 0.9–1.4 mm (0.7–2.1 mm), and the particle concentration: 29–35 (24–39). The height of the center of the probability density of each variable was higher in the three major mountain ranges than in the two major basins owing to the influence of topography. PRF increased and then decreased with height. Convective raindrops were in an active state of high concentration and exhibited a large scale in the lower layers, whereas the particle spectral parameters of stratiform precipitation showed a more concentrated and symmetric distribution and insignificant changes with height.

The diurnal variation characteristics of raindrop size distribution (RSD) in the Ili River Valley are investigated in this study, using the RSD data from May to September during 2020-2021 collected by a Parsivel2 disdrometer in Zhaosu. Significant diurnal variations (02–07, 08–13, 14–19, and 20-01 local standard time (LST)) of precipitation and RSD in Zhaosu are revealed during the rainy seasons. Precipitation mainly occurs in the late afternoon and early evening. A higher concentration of small raindrops is observed in the morning, whereas more mid-size and large raindrops are observed in the afternoon. The RSD exhibits diurnal differences between different rainfall rate classes; the diurnal difference of RSD is more pronounced in the case of high rainfall rates. Stratiform precipitation can occur at any time of the day, yet convective precipitation mainly occurs during the late afternoon and early evening. The RSD of stratiform rainfall shows a similar distribution over the four time periods. For convective rainfall, the concentration of small raindrops is the highest (lowest) over 02–07 (14–19) LST, while the highest (lowest) concentration of medium and large drops is observed over 14–19 (02–07) LST. Convective rain in the Ili River Valley over 14–19 LST can be characterized as the continental convective cluster, while in the rest time of the day, it is neither in the maritime cluster nor in the continental cluster. The empirical relationships between the radar reflectivity factor and rainfall rate (Z-R) for stratiform and convective rain types are also derived. The purpose of this study is to advance our understanding of precipitation microphysics in arid mountainous region.

Abstract Photoacoustic remote sensing has been recently developed as an all-optical imaging modality capable of imaging a variety of endogenous contrast agents label-free. Initially predicted laser pulse-induced refractive index perturbation-based interrogation beam reflectivity modulations have been found to be orders of magnitude smaller than those typically observed experimentally. In this report we utilize a 10 million frames-per-second camera to further investigate these predicted reflectivity modulations, while also exploring other potential mechanisms of laser pulse-induced reflectivity modulations. Laser-induced motion is demonstrated both laterally for gold wires suspended and submerged in air and water, respectively, and carbon fibers submerged in water, and axial motion is observed in gold wires submerged in a depth gradient of intralipid solution. This laser-induced sample motion is anticipated to cause reflectivity modulations local to the interrogation beam profile in microscopy set-ups. Non-motion-based maximum intensity modulations of 3% are also observed in gold wires submerged in water, indicating the presence of the originally predicted reflectivity modulations. Overall, these observations are important as they provide a widefield view of laser-pulse interactions unavailable in previous point scanning-based photoacoustic remote sensing microscopy configurations, where observed mechanisms occur on time-scales orders of magnitude faster than equivalent field of view point scanning capabilities.

This study comprehensively benchmarks 18 state-of-the-art methods for cellular deconvolution of spatial transcriptomics and provide decision-tree-style guidelines and recommendations for method selection.

We use a difference-in-difference research design to examine the effect of an exogenous shock to a firm's R&D investment from Chinese regulations regarding technology exports revised in 2008. We document that these technology export restrictions (TERs) decrease a firm's R&D investment. Specifically, the TERs reduce corporate R&D investment by 114 percent compared with that by an average firm in the sample. Additional analysis suggests that the TERs also reduce outbound direct investment and the number of firms' patent applications. Overall, TERs prevent firms from investing in innovation projects. Hence, relaxing TERs would enhance corporate R&D investment and promote general economic development.

We have developed a three-dimensional hybrid finite-discrete element model to investigate the mode I tensile opening failure of alumina ceramic. This model implicitly considers the flaw system in the material and explicitly shows the macroscopic failure patterns. A single main crack perpendicular to the loading direction is observed during the tensile loading simulation. Some fragments appear near the crack surfaces due to crack branching. The tensile strength obtained by our model is consistent with the experimental results from the literature. Once validated with the literature, the influences of the distribution of the flaw system on the tensile strength and elastic modulus are explored. The simulation results show that the material with more uniform flaw sizes and fewer big flaws has stronger tensile strength and higher elastic modulus.

Manual selection of the distribution target and determination of its spatial location is not only time consuming but also has a serious impact on the polarimetric channel imbalances assessment if the area is incorrect. However, the existing automatic extraction methods are affected by the morphology of the features, and their local feature extraction capability is limited. On the other hand, only local information is considered, while the feature channels and global information are ignored, which in turn leads to the limited application scenarios, especially in some urban areas. The polarimetric channel imbalance estimation accuracy is so low that it exceeds the system tolerance limit. Therefore, in order to more effectively mine the polarimetric features of distributed targets in polarimetric synthetic aperture radar images, we propose an attention-based multipath hybrid convolutional distribution target extraction network for polarimetric channel imbalance assessment. First, in order to develop the local feature extraction capability of the hierarchical network, we design a hybrid convolutional module with adaptive adjustment of the receptive field size. Second, a polarimetric feature channel reconstruction module is constructed in order to utilize the spatial information of the polarimetric feature channels. Then, considering that the polarimetric information of the ground feature is sensitive to the relative geometry of the target attitude to the radar line of sight, the vision transformer architecture is used to capture polarimetric global information and extend it to multipath. Finally, Gaofen-3(GF-3) full-polarimetric data are used for experimental verification. Experimental results demonstrate the effectiveness and reliability of the proposed method.

Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts, structured knowledge graphs and relational databases, semi-structured web tables, or even hybrid data sources. However, although the ideas behind these approaches show similarity to some extent, there is not yet a consistent strategy to deal with various data sources. In this survey, we carefully examine how complex factual question answering has evolved across various data sources. We list the similarities among these approaches and group them into the analysis–extend–reason framework, despite the various question types and data sources that they focus on. We also address future directions for difficult factual question answering as well as the relevant benchmarks.

We examine the impact of air pollution on underpricing of initial public offerings (IPOs) and its underlying mechanisms. Our findings suggest that IPO underpricing increases when air pollution is more severe in cities where bidding investors are located. Mechanism tests show that air pollution influences investors' mood and cognition, leading them to make pessimistic bids and rely more on anchors. Additional tests document that the impact of air pollution on IPO underpricing is more salient when information asymmetry is more severe and when the IPO firm also faces severe air pollution. Our research contributes to the literature on the causal influence of air pollution on pricing efficiency in primary markets by linking air pollution to potential bias embedded in investors’ bid prices, which carries important implications for environmental regulations in emerging markets, given the economic cost of air pollution imposed on IPO firms.

Summary: Cognitive impairment has been associated with an age-related decline in adult hippocampal neurogenesis (AHN). The molecular basis of declining neurogenesis in the aging hippocampus remains to be elucidated. Here, we show that pleiotrophin (PTN) expression is decreased with aging in neural stem and progenitor cells (NSPCs). Mice lacking PTN exhibit impaired AHN accompanied by poor learning and memory. Mechanistically, we find that PTN engages with protein tyrosine phosphatase receptor type Z1 (PTPRZ1) to promote NSPC proliferation and differentiation by activating AKT signaling. PTN overexpression or pharmacological activation of AKT signaling in aging mice restores AHN and alleviates relevant memory deficits. Importantly, we also find that PTN overexpression improves impaired neurogenesis in senescence-accelerated mouse prone 8 (SAMP8) mice. We further confirm that PTN is required for enriched environment-induced increases in AHN. These results corroborate the significance of AHN in aging and reveal a possible therapeutic intervention by targeting PTN.

Fluorescence is widely used in biological imaging and biosensing. Rich information can be revealed from the fluorescence spectrum of fluorescent molecules, such as pH, viscosity and polarity of the molecule’s environment, and distance between two FRET molecules. However, constructing the fluorescence spectrum of a single fluorescent molecule typically requires a significant number of photons, which can suffer from photobleaching and, therefore, limit its potential applications. Here, we propose a deep learning-enhanced single-molecule spectrum imaging method (SpecGAN) for improving the single-molecule spectrum imaging efficiency. In SpecGAN, the photon flux required to extract a single-molecule fluorescence spectrum can be reduced by 100 times, which enables two orders of magnitude higher temporal resolution compared to the conventional single-molecule spectrometer. The concept of SpecGAN was validated through numerical simulation and single Nile Red molecule spectrum imaging on support lipid bilayers (SLBs). With SpecGAN, the super-resolution spectrum image of the COS-7 membrane can be reconstructed with merely 12 000 frames of single-molecule localization images, which is almost half of the previously reported frame count for spectrally resolved super-resolution imaging. The low photon flux requirement and high temporal resolution of SpecGAN make it a promising tool for investigating the molecular spectrum dynamics related to biological functions or biomolecule interactions.