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Abstract Background Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown cause. N6-methyladenosine (m6A) is the most common mRNA modification and participates in various immune processes such as interferon production and immune cell regulation. However, the role of m6A in dysregulated immune response of SLE remains unknown. Methods PBMCs from SLE patients were collected to compare the m6A modification profile by methylated RNA immunoprecipitation sequencing (MeRIP-seq). Interferon regulatory factor 4 (IRF4) was identified by combination with MeRIP-seq and RNA-Seq. IRF4 and methyltransferase 3 (METTL3) were detected using qRT-PCR and WB. Clinical significance of IRF4 in SLE patients was explored subsequently. IRF4 expression in B cell subsets of female MRL/lpr mice was detected by flow cytometry. Adeno-associated viruses (AAV) including AAV9-METTL3-OE and/or AAV9-IRF4-sh were treated with female MRL/lpr mice. Autoantibody levels and kidney injury were tested by ELISA, pathological staining, and immunofluorescence. m6A level of IRF4 was detected by MeRIP-qPCR. The downstream effectors of IRF4 contributing to renal pathology were explored by RNA-seq and verified by qRT-PCR. Results m6A methylation features were obviously aberrant in SLE patients, and IRF4 was the upregulated gene modified by m6A. METTL3 and IRF4 expressions were elevated in SLE patients and kidney of MRL/lpr mice. Clinical analysis indicated that SLE patients with high IRF4 level were more prone to kidney damage. IRF4 expression was especially increased in plasma cells of MRL/lpr mice. METTL3 induced renal IRF4 expression, plasma creatinine, ANA and urine ALB levels, IgG and C3 deposition, and renal damage and plasma cell infiltration were aggravated in MRL/lpr mice. However, IRF4 depletion could partially reduce METTL3-induced kidney damage. Meanwhile, m6A level of IRF4 elevated with METTL3 overexpression. Also, the expression of Cxcl1, Bcl3, and Fos mRNA were significantly reduced after knockdown of IRF4, which were mainly involved in TNF signaling pathway. Conclusions Our study confirmed that upregulated METTL3 promoting IRF4 expression in an m6A-dependent manner, thus causing plasma cell infiltration-mediated kidney damage of SLE. This provides new evidence for the role of m6A in SLE kidney injury.

Abstract Under a warmer climate, the enhancement of dry-wet abrupt alternation (DWAA) risk poses a great challenge for sustainable development. Here, we introduce a novel framework for DWAA detection based on our proposed soil moisture concentration index. By the end of this century, over humid southern China, the shift of soil moisture time series from anomalously wet to anomalously dry pattern, or the other way around, will be more abrupt. In addition, the proposed framework driven by Coupled Model Intercomparison Project Phase 6 simulations projects more widespread DWAA-affected areas over southwestern China, coastal regions of southeastern China, and the lower reaches of the Yangtze River, especially under a high emission scenario. The framework proposed in this study provides an efficient system for DWAA detection and prediction, and the findings of this study provide a reference for upgrading hydraulic infrastructure and mitigating future DWAA events.

Abstract Background BAK1 (Brassinosteroid insensitive 1-associated receptor kinase 1) plays an important role in disease resistance in plants. However, the function of BAK1 family in cucumber and the decisive genes for disease-resistance remain elusive. Results Here, we identified 27 CsBAK1s in cucumber, and classified them into five subgroups based on phylogenetic analysis and gene structure. CsBAK1s in the same subgroup shared the similar motifs, but different gene structures. Cis-elements analysis revealed that CsBAK1s might respond to various stress and growth regulation. Three segmentally duplicated pairwise genes were identified in cucumber. In addition, Ka/Ks analysis indicated that CsBAK1s were under positive selection during evolution. Tissue expression profile showed that most CsBAK1s in Subgroup II and IV showed constitutive expression, members in other subgroups showed tissue-specific expression. To further explore whether CsBAK1s were involved in the resistance to pathogens, the expression patterns of CsBAK1s to five pathogens (gummy stem blight, powdery mildew, downy mildew, grey mildew, and fusarium wilt) reveled that different CsBAK1s had specific roles in different pathogen infections. The expression of CsBAK1-14 was induced/repressed significantly by five pathogens, CsBAK1-14 might play an important role in disease resistance in cucumber. Conclusions 27 BAK1 genes were identified in cucumber from a full perspective, which have important functions in pathogen infection. Our study provided a theoretical basis to further clarify the function of BAK1s to disease resistance in cucumber.

Abstract Purpose This study evaluates the efficacy of intrauterine hCG perfusion for RIF, as defined by ESHRE 2023 guidelines, highlighting hCG as a cost-effective alternative to other immunotherapies, especially suitable for less developed regions. It aims to clarify treatment guidance amidst previous inconsistencies. Methods This meta-analysis, registered with PROSPERO (CRD42024443241) and adhering to PRISMA guidelines, assessed the efficacy and safety of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in RIF. Comprehensive literature searches were conducted through December 2023 in major databases including PubMed, Web of Science, Embase, the Cochrane Library, and key Chinese databases, without language restrictions. Inclusion and exclusion criteria were strictly aligned with the 2023 ESHRE recommendations, with exclusions for studies lacking robust control, clear outcomes, or adequate data integrity. The risk of bias was evaluated using the Newcastle-Ottawa Scale, ROBINS-I, and RoB2 tools. Data analysis was performed in R using the ‘meta’ package, employing both fixed and random effect models to account for study variability. Subgroup analyses by dosage, volume, hCG concentration, timing of administration, and type of embryo transfer were conducted to deepen insights, enhancing the reliability and depth of the meta-analysis in elucidating the role of hCG perfusion in RIF treatments. Results Data from 13 studies, comprising six retrospective and six prospective studies from single centers, along with one multi-center RCT, totaling 2,157 participants, were synthesized to evaluate the effectiveness of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in patients with RIF. Significant improvements were observed in clinical pregnancy and embryo implantation rates across various dosages, timing of administration, and embryo developmental stages, without impacting miscarriage rates. Notably, the most significant efficacy within subgroups occurred with a 500 IU dosage and perfusion parameters of ≤ 500µL volume and ≥ 2 IU/µL concentration. Additionally, a limited number of studies showed no significant increases in ectopic pregnancy or multiple pregnancy rates, and a modest improvement in live birth rates, although the small number of these studies precludes definitive conclusions. Conclusions The analysis suggests that intrauterine hCG perfusion probably enhances embryo implantation, clinical pregnancy, and live birth rates slightly in RIF patients. Benefits are indicated with a dosage of 500 IU and a maximum volume of 500µL at concentrations of at least 2 IU/µL. However, substantial heterogeneity from varying study types and the limited number of studies necessitate cautious interpretation. These findings underscore the need for more rigorously designed RCTs to definitively assess the efficacy and safety.

Abstract Background With the increasing incidence of obesity and the childbearing-age delay among women, a debate over obesity’s impacts on pregnancy and neonatal outcomes becomes hot. The potential negative effects of obesity and aging on fertility lead to an idea, whether an obese female pursuing IVF treatment can benefit from an ideal BMI achieved over a long-time weight loss process at the cost of aging? We aimed to assess the association between body mass index (BMI) and clinical or neonatal outcomes in patients undergoing in vitro fertilization (IVF) treatment, for answering whether it is necessary to lose weight first for obese patients, particularly those at advanced age. Methods A retrospective cohort study was performed using multicentered data from China. The women were stratified into 5 groups in terms of pre-gravid BMI (kg/m2) with the WHO obesity standard (group 1: BMI

Forest fragmentation is a significant issue in China; however, a comprehensive understanding and assessment of the temporal trends and spatial modes of forest fragmentation remain lacking. For the first time, this study employs an object-based morphological image processing method to conduct multiscale spatiotemporal identification and analysis of China’s forest landscape from 1985 to 2023. Observations reveal an overall improvement in the integrity of China’s forest landscape from 1985 to 2023, with 79 % of counties showing a positive trend in forest landscape integrity and 5 % experiencing forest fragmentation. The forest core area in China has almost tripled from 1985 to 2023, expanding from 0.57 to 1.53 million km2. Nevertheless, 32.04 % of forests in China remain within 100 m of a non-forest edge, significantly higher than the global average (∼20 %). From 1985 to 2023, out of the 0.33 million km2 of newly planted forests, 29.5 % became forest cores, while the remaining 70.5 % constituted forest edges. This underscores the need for government planning to focus not only on expanding forest areas but also on enhancing the integrity of forest landscapes, thereby creating more ecologically valuable forest ecosystems. Analysis indicates that 25 % of forest fragmentation in China is associated with agriculture (16 %) and urbanization (9 %), while the remaining portion is linked to local topography, climate, and other natural boundaries for forest distribution. Although natural boundaries play a predominant role in forest fragmentation, there is a growing trend of agricultural activities and urbanization encroaching upon the edges. It is recommended that government policies prioritize strategic planning for forest landscape integrity, considering both quantitative expansion and qualitative optimization, to ensure the sustainable development of China’s forest ecosystems.

Abstract JC polyomavirus (JCPyV) is a human polyomavirus that can establish lifelong persistent infection in the majority of adults. It is typically asymptomatic in immunocompetent individuals. However, there is a risk of developing progressive multifocal leukoencephalopathy (PML) in immunocompromised or immunosuppressed patients. Though JCPyV commonly resides in the kidney-urinary tract, its involvement in urinary system diseases is extremely rare. Here, we reported a case of a 60-year-old male patient with coronavirus disease 2019 (COVID-19) infection who developed hemorrhagic cystitis after receiving treatment with nirmatrelvir 300 mg/ritonavir 100 mg quaque die (QD). Subsequent metagenomic next-generation sequencing (mNGS) confirmed the infection to be caused by JCPyV type 2. Then, human immunoglobulin (PH4) for intravenous injection at a dose of 25 g QD was administered to the patient. Three days later, the hematuria resolved. This case illustrates that in the setting of compromised host immune function, JCPyV is not limited to causing central nervous system diseases but can also exhibit pathogenicity in the urinary system. Moreover, mNGS technology facilitates rapid diagnosis of infectious etiology by clinical practitioners, contributing to precise treatment for patients.

Abstract Objective IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies. Methods We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD. Results Thirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS. Conclusions This study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape.

Abstract The northern terrestrial biomes are being remarkably altered by climate change. Higher springtime temperature induces the earlier greening of vegetation, which may further influence ecosystem functions during the subsequent season. However, the response of summer net ecosystem productivity to spring vegetation greenness and phenology changes has not yet been quantified. To understand the impact of such phenological changes on terrestrial carbon sink of the following season, here we integrate remotely-sensed vegetation data and model simulations of carbon flux with an explainable machine learning approach. We find that the lagged effects of widespread earlier spring greening are increasing the summer ecosystem carbon sink across the northern vegetated areas (30° to 90°N) from 1982 to 2015. In particular, response disparities exist in non-agricultural biomes, and the vegetation with moderate tree coverage is more sensitive to earlier spring greening. Furthermore, modest tree restoration can strengthen the beneficial effects of earlier spring greening. This study improves our understanding of interseasonal vegetation-climate-carbon coupling that drives the key ecological feedback within climate change projections.

Abstract Background Southwest China is one of the largest karst regions in the world. Karst environment is relatively fragile and vulnerable to human activities. Due to the discharge of sewage and domestic garbage, the karst system may be polluted by pathogenic bacteria. The detection of bacterial distribution and identification of phage capable of infecting them is an important approach for environmental assessment and resource acquisition. Methods Bacteria and phages were isolated from karst water in southwest China using the plate scribing and double plate method, respectively. Isolated phage was defined by transmission electron microscopy, one-step growth curve and optimal multiplicity of infection (MOI). Genomic sequencing, phylogenetic analysis, comparative genomic and proteomic analysis were performed. Results A Klebsiella quasipneumoniae phage was isolated from 32 isolates and named KL01. KL01 is morphologically identified as Caudoviricetes with an optimal MOI of 0.1, an incubation period of 10 min, and a lysis period of 60 min. The genome length of KL01 is about 45 kb, the GC content is 42.5%, and it contains 59 open reading frames. The highest average nucleotide similarity between KL01 and a known Klebsiella phage 6939 was 83.04%. Conclusions KL01 is a novel phage, belonging to the Autophagoviridae, which has strong lytic ability. This study indicates that there were not only some potential potentially pathogenic bacteria in the karst environment, but also phage resources for exploration and application.

BackgroundThe plant root system is critical for the absorption of water and nutrients, and have a direct influence on growth and yield. In cucumber, a globally consumed crop, the molecular mechanism of root development remains unclear, and this has implications for developing stress tolerant varieties. This study sought to determine the genetic patterns and related genes of cucumber root weight. A core cucumber germplasms population was used to do the GWAS analysis in three environments.ResultsHere, we investigated four root-weight related traits including root fresh weight (RFW), root dry weight (RDW), ratio of root dry weight to root fresh weight (RDFW) and the comprehensive evaluation index, D-value of root weight (DRW) deduced based on the above three traits for the core germplasm of the cucumber global repository. According to the D-value, we identified 21 and 16 accessions with light and heavy-root, respectively. We also found that the East Asian ecotype accessions had significantly heavier root than other three ecotypes. The genome-wide association study (GWAS) for these four traits reveals that 4 of 10 significant loci (gDRW3.1, gDRW3.2, gDRW4.1 and gDRW5.1) were repeatedly detected for at least two traits. Further haplotype and expression analysis for protein-coding genes positioned within these 4 loci between light and heavy-root accessions predicted five candidate genes (i.e., Csa3G132020 and Csa3G132520 both encoding F-box protein PP2-B1 for gDRW3.1, Csa3G629240 encoding a B-cell receptor-associated protein for gDRW3.2, Csa4G499330 encodes a GTP binding protein for gDRW4.1, and Csa5G286040 encodes a proteinase inhibitor for gDRW5.1).ConclusionsWe conducted a systematic analysis of the root genetic basis and characteristics of cucumber core germplasms population. We detected four novel loci, which regulate the root weight in cucumber. Our study provides valuable candidate genes and haplotypes for the improvement of root system in cucumber breeding.

Sensorineural hearing loss (SNHL) is a prevalent condition in otolaryngology. A key obstacle is finding effective strategies for regenerating damaged cochlear hair cells in adult animals. A practical and reliable approach has been developed to create a superior cell source for stem cell transplantation in the inner ear to treat SNHL. Atoh1 is involved in the differentiation of neurons, intestinal secretory cells, and mechanoreceptors including auditory hair cells, and thus plays an important role in neurogenesis. Lentivirus-mediated transfection of bone marrow mesenchymal stem cells (BMSCs) was utilized to achieve stable expression of the essential transcription factor Atoh1, which is crucial for developing auditory hair cells without compromising cell survival. By manipulating the induction conditions through altering the cell growth environment using anti-adherent culture, the synergistic impact of basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF) was effectively applied to significantly improve the differentiation efficiency of bone marrow-derived mesenchymal stem cells (BMSC) into neural stem cells (NSCs) following Atoh1 transfection, thereby reducing the induction time. The study indicated that the newly proposed transdifferentiation method effectively transformed BMSCs into NSCs in a controlled environment, presenting a potential approach for stem cell transplantation to promote hair cell regeneration.

Vertebrate testosterone, an androgen present in the testes, is essential for male fertility. Vertebrate-type steroid hormones have been identified in insects, but their function remains unknown. Insect vitellogenin (Vg) is usually a female-specific protein involved in reproductive processes. However, males of some species, such as the green lacewing Chrysopa pallens, have Vg. Here, we demonstrated that the knockdown of C. pallens male Vg by RNAi significantly shortened the lifespan of males, suppressed the reproduction of post-mating females, and strikingly reduced the abundance of several immune-related compounds, including testosterone. LC-MS/MS revealed that C. pallens male testosterone had the same structure and molecular mass as vertebrate testosterone. Topical testosterone application partially restored the lifespan of Vg-deficient males and the reproduction of post-mating females. These results suggest that vertebrate-type testosterone maintains male longevity and female reproduction under the control of the male Vg in C. pallens.

Summary: Exploring cell-cell communication is pivotal for understanding biological processes in multicellular life forms. Here, we present a protocol that details the use of matrix decomposition to infer cell-cell communication (MDIC3) for unsupervised cell-cell communication inference. We describe steps for using the MDIC3 Python scripts to deduce cell-cell communication and identify key ligand-receptor pairs between a specific cell type pair from a single-cell gene expression dataset. This protocol has potential application in cell-cell communication inference on any species.For complete details on the use and execution of this protocol, please refer to Liu et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Background: Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success. However, few methods exist for generating full-body animations, and the portability of virtual character gestures and facial animations has not received sufficient attention. Methods: Therefore, we propose a deep-learning-based audio-to-animation-and-blendshape (Audio2AB) network that generates gesture animations andARK it’s 52 facial expression parameter blendshape weights based on audio, audio-corresponding text, emotion labels, and semantic relevance labels to generate parametric data for full- body animations. This parameterization method can be used to drive full-body animations of virtual characters and improve their portability. In the experiment, we first downsampled the gesture and facial data to achieve the same temporal resolution for the input, output, and facial data. The Audio2AB network then encoded the audio, audio- corresponding text, emotion labels, and semantic relevance labels, and then fused the text, emotion labels, and semantic relevance labels into the audio to obtain better audio features. Finally, we established links between the body, gestures, and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function. Results: By using audio, audio-corresponding text, and emotional and semantic relevance labels as input, the trained Audio2AB network could generate gesture animation data containing blendshape weights. Therefore, different 3D virtual character animations could be created through parameterization. Conclusions: The experimental results showed that the proposed method could generate significant gestures and facial animations.

Photonic neural networks are emerging as promising computing platforms for artificial intelligence (AI). Particularly, integrated photonic unitary neural networks (IPUNNs) are capable of mitigating gradient vanishing/explosion problems when deeper neural networks are constructed. Furthermore, their optical implementations are also much simpler compared to non-unitary counterparts. Meanwhile, real-valued datasets still dominate AI research and the encoding strategy is critical for IPUNNs' performances. However, there are few studies to compare different encoding strategies of IPUNNs to represent these real-valued datasets and their impacts on IPUNNs' performances. Here, in the scope of encoding strategies for real-valued features, we first compare different schemes, such as phase, amplitude and hybrid encoding using numerical simulations, with benchmarks of decision boundary and image recognition tasks. These encoding strategies of IPUNNs are also compared to non-unitary real-valued neural networks (RVNNs) with trainable biases for the same benchmarks. The results suggest that phase encoding outperforms amplitude and hybrid encoding, and exhibits comparable performances to non-unitary RVNNs. To verify the numerical results, a 10×10 IPUNN chip is designed and fabricated. The phase encoding is chosen to be implemented because of its superior performances in numerical studies. We reconfigure the IPUNN chip to perform decision boundary and image recognition tasks by on-chip in-situ training. The experimental results match the simulations well. Our work provides insights for implementing reconfigurable IPUNNs in AI computing.

Cryogenic data transfer has numerous applications at 4.2 K for extracting data from supercomputers and quantum computers to address interface bottlenecks. The external modulation of data transmission across temperature zones is achieved through the use of electro-optic modulators. The performance of a thin-film lithium niobate (TFLN) Mach-Zehnder modulator was examined at cryogenic temperatures. Low-frequency measurements indicate that the Vπ (voltage needed for π phase shift) of the modulator increases as the temperature decreases from 298 K to 4.2 K. A comparison was conducted between the readout performance of the electrical wire link and the optical fiber link, spanning from 4.2 K to 298 K. Additionally, high-speed data transmission was successfully demonstrated in a 4.2 K environment without the need for cryogenic electric amplifiers, achieving signals of up to 30 Gbps. The proposed solution holds potential benefits for both classical and quantum computers in alleviating interface bottlenecks.

Urban tree cover plays a pivotal role in bolstering the sustainability, livability, and resilience of cities. However, the changes in urban tree cover and exposure under the backdrop of urbanization in China remain unclear. This study investigates the spatial distribution and temporal trends of urban tree cover expansion and exposure change from 1985 to 2023. Our findings highlight significant progress in enhancing urban tree cover, with the average tree cover in Chinese urban areas reaching 11.9% in 2023, representing a remarkable 116% increase from 5.5% in 1985. However, this positive trend contrasts with persistent deficiencies in forest resources, as reflected by the fact that urban forest cover in Chinese cities was only 4.1% in 2023, compared with 0.1% in 1985. Furthermore, our study revealed substantial heterogeneity in the spatiotemporal variations of urban tree cover. Only five cities exhibited a decreasing trend in tree cover during the study period, with coastal cities demonstrating significantly higher tree cover compared with inland cities. Additionally, we observed that urban tree cover and expansion rates decreased significantly with increasing latitude. Moreover, nationwide, there has been a sustained and accelerated improvement in urban tree exposure levels, with average tree exposure increasing from 0.66% in 1985 to 7.45% in 2020. The 69% of cities experienced positive changes in tree exposure levels, while only 1% exhibited negative changes. These findings offer valuable insights for policymakers, planners, and stakeholders to foster greener, more resilient, and sustainable urban environments in China.

Building height dataset is crucial in urban studies, holding significant importance in understanding the interactions of human activities and the built-up environment. However, high-resolution three-dimensional building datasets covering large areas are limited. A rapid and accurate method for revealing fine-scale urban morphology is required. In this study, we developed a method for estimating building heights at the building scale. First, we integrated multisource datasets (i.e., synthetic aperture radar, optical, terrain, social-economic, and vector-based datasets) and built the machine learning model for building height estimation. Second, we applied the model to 11 cities in the U.S. and assessed its performance. Our results were consistent with the reference data, indicating that the effectiveness of our method is applicable [i.e., the R2 was 0.82, and the root mean square error (RMSE) was 3.39 m]. The evaluated results in various cities, across different height intervals, and within distinct regions also show the good agreement with reference heights according to the correlation (R2: 0.51–0.86, RMSE: 2.57–5.97 m in cities) and similar height distribution. Moreover, our results also showed the superiority by comparing with other height datasets at different scales. Finally, we mapped the building-scale height to characterize the urban morphology. These results demonstrate our proposed method's usable accuracy and the vast application potential in estimating building heights. Our proposed method's refined building height information can significantly help socioeconomic and climatological urban studies.

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.

An on-chip, high-extinction-ratio transverse electric (TE) pass polarizer utilizing a silicon oxynitride (SiON) slab has been proposed and experimentally verified. The power confinement ratio of the mode field is manipulated by using a SiON slab, where most of the power of the transverse magnetic (TM) mode is transferred to the upper SiON slab and then attenuated through radiation, while the TE mode passes through with relatively low propagation loss. Experimental results show that our proposed device can achieve an extinction ratio that varies from 20.5 to 32.7 dB in the wavelength range of 790 to 870 nm, with an insertion loss of 0.6 to 1.7 dB. Potentially, this design has lower material refractive index contrast, larger minimum etching size, smaller lengths, and less stray light crosstalk, which is beneficial for systems applications such as gyroscopes.

Accurate change detection using remote sensing data is crucial for understanding surface dynamics. Despite the impressive success of current convolutional neural network (CNN)-based techniques, their feature extraction and representation capabilities are limited, leading to pseudochanges and omissions. To address this issue, we propose a multiscale fusion CNN-transformer network (MSFCTNet), which incorporates the strengths of CNN and transformer to improve change detection capability. The network utilizes a Siamese CNN to extract features from the bi-temporal image pairs and then combines multiscale features using a CNN-transformer hybrid structure to extract global and local features. In the decode stage, a gated attention module is used to filter the extracted features layer by layer. Moreover, before outputting the prediction results, a feature refinement head is employed to further refine the features, suppress background noise, and improve detection capability. LEVIR-CD, SVCD, and SYSU-CD datasets are used to evaluate the proposed network. Experimental results indicate that MSFCTNet outperforms other state-of-the-art change detection methods, proving the potential of MSFCTNet for change detection tasks.

Mobile fans, as flexible and convenient new longitudinal ventilation and smoke extraction equipment for tunnels, demonstrate more significant effectiveness in an emergency response to tunnel fires compared to traditional smoke extraction methods. This study employs computational fluid dynamics simulation methods, selecting two fire scenarios to investigate the effects of fan inclined angles and fan airflow volumes on the longitudinal temperature distribution and smoke back-layering length in tunnels. The results indicate that when using mobile fans for longitudinal ventilation in tunnels, at a lower fan airflow volume, the temperature distribution along the longitudinal axis is nearly symmetrical. The fire source and the fan installed in the upstream are within a certain range, and it is more effective to use the horizontal angle for longitudinal ventilation. As the fan airflow volume increases, the back-layering length significantly decreases (210,000 m3/h < V < 270,000 m3/h). However, as the fan flow volume continues to increase (270,000 m3/h < V < 300,000 m3/h), the reduction in the back-layering length becomes less pronounced, the smoke spread distance of the latter is only 11% of that of the former. Therefore, selecting appropriate fan airflow volumes and fan inclined angles them can effectively enhance the performance of tunnel smoke extraction systems. Moreover, by comparing with traditional fans, we find that mobile fans provide an alternative effective strategy during firefighting by allowing adjustments in distance from the fire source and fan inclination angles, enhancing fire suppression effectiveness while reducing energy losses. The research findings can serve as a reference for tunnel fire prevention design.

The multiway valve is the core control element of the hydraulic system in construction machinery, such as excavators. Its complex internal structure, especially the flow channels, significantly impacts the machine’s efficiency and reliability. This study focuses on the boom flow channel of excavator multiway valves and establishes a multi-physical field coupling simulation model. We propose six key flow channel structural parameters and analyze changes in the valve’s flow field, temperature field, and structural field using orthogonal test simulation data. The range analysis method identifies the primary and secondary influences of structural parameters on pressure loss, temperature, stress, and strain. A multi-objective optimization model was developed using a neural network and the Non-dominated Sorting Genetic Algorithm II(NSGA-II), with pressure loss and maximum stress as the optimization objectives. The Pareto front solution set for key flow channel parameters was calculated. The optimization results showed a 9.0% reduction in pressure loss and a 40.7% reduction in maximum stress. A test bench verified the simulation model, achieving prediction accuracies of 94.8% for pressure loss in the inlet area and 92.3% in the return area. This method can provide a reference for the optimal design of the dynamic characteristics of high-pressure multiway valves.

BackgroundRhabdomyosarcoma(RMS) is the most common soft tissue sarcoma in children and single nucleotide polymorphisms(SNPs) in certain genes influence risk of RMS. Although FOXO3 had been reported in multiple cancers including RMS, the role of FOXO3 polymorphisms in RMS remains unclear. In this case-control study, we evaluated the association of FOXO3 SNPs with RMS risk and prognosis in children.MethodsFour FOXO3 SNPs(rs17069665 A>G, rs4946936 T>C, rs4945816 C>T and rs9400241 C>A) were genotyped in 110 RMS cases and 359 controls. The associations between FOXO3 polymorphisms and RMS risk were determined by odds ratios(ORs) with 95% confidence intervals(CIs). The associations of rs17069665 and rs4946936 with overall survival in RMS children were estimated using the Kaplan-Meier method and log-rank test. Functional analysis in silico was performed to estimate the probability that rs17069665 and rs4946936 might influence the regulation of FOXO3.ResultsWe found that rs17069665 (GG vs. AA+AG, adjusted OR=2.96; 95%CI [1.10-3.32]; P=0.010) and rs4946936 (TC+CC vs. TT, adjusted OR=0.48; 95%CI [0.25-0.90]; P=0.023) were related to the increased and decreased RMS risk, respectively. Besides, rs17069665(P

BackgroundBased on pharmacoeconomics, drug availability and actual treatment, optimal treatment regimens for Chinese non-small-cell lung carcinoma (NSCLC) patients over 70 years old are needed.MethodsThis multicenter, single-arm pilot trial enrolled patients with advanced non-squamous NSCLC who refused systemic chemotherapy. Eligible patients received anlotinib (12 mg/day, d1-14, Q3W) until disease progression, intolerant toxicities, or withdrawal from the study. The primary endpoint was progression-free survival (PFS).ResultsForty-nine patients were screened between January 2019 and September 2021, of whom 40 patients were eligible. The median age was 76 years. With a median follow-up period of 16.20 (95% CI: 8.77, 25.10) months, the median PFS was 5.45 months (95% CI: 3.52-9.23) and the median overall survival was 10.32 months (95% CI: 6.44-12.78). Three patients achieved a partial response and 34 had stable disease, with an objective response rate of 7.5% and a disease control rate of 92.5%. Thirty-three (82.5%; 33/40) patients reported treatment-related adverse events (TRAEs) of any grade, and the incidence rate of grade ≥3 TRAEs was 35% (14/40). The most common grade ≥3 TRAEs were hypertension (4/40; 10.0%), hand-foot syndrome (3/40; 7.5%), and proteinuria (2/40; 5.0%).ConclusionAnlotinib treatment was feasible and safe in Chinese elderly patients with advanced non-squamous NSCLC who did not receive any systemic chemotherapy.

The hydrodynamics of the flow around piers affects the motion of ships navigating near these structures, while the motion of the ships, in turn, affects the distribution of the flow field near the piers. This study investigates the forces exerted on a ship in various ship–pier transverse distances using commercial computational fluid dynamics (CFD) software, Fluent 13.0, based on the RNG k-ε model, complemented by experiments with a physical model. The interaction between the ship’s motion and the flow field near the piers was considered. The results indicate that during the encounter between the ship and the pier, the boundary of the approaching ship affects the flow field near the pier, thereby affecting the generation and detachment of vortices behind the pier. The yaw moment of the ship demonstrates a marked “positive peak–negative peak–positive peak” pattern. Moreover, as the ship–pier transverse distance increases, the impact of the pier on the ship’s motion decreases, and it becomes negligible when the distance reaches 0.9 times the diameter of the pier (D), suggesting that the pier has a minimal impact on ship navigation if the ship–pier transverse distance exceeds this threshold.

Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (

Based on the observed biological activity of 1,2,4-triazin-5-one derivatives and their cyclic analogues, a novel series of 7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives that contain ester moiety compounds 3a-3g, carboxylic acid moiety compounds 4a-4g and piperazine amide moiety compounds 5a-5k at position-3 of the thiazolotriazinone scaffold were synthesized. The intermolecular cyclization occurred regioselectively at N2-position of 1,2,4-triazine ring was characterized by X-ray single-crystal diffraction analysis. The in vitro biological activities of the target compounds were assayed against some bacterial strains. Compared with ciprofloxacin, compounds 3g and 4g exhibited more excellent antibacterial activity, especially the activity against Staphylococcus aureus and Escherichia coli, showing that the fluorine at the para position of the benzyl group would be the best choice. In addition, compounds 4e-4g with carboxylic acid moiety can enhance the antibacterial activity. Compounds 5g-5k containing bulky 1-(substituted phenyl)piperazine moiety were found with slightly less biological activity. Similar to ciprofloxacin, the docking result of target compounds with DNA topoisomerase II indicates the carboxyl group of the target compounds with carboxylic acid moiety has a crucial salt bridge interaction with Mg2+ in the protein.

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.

Mitochondrial fission promotes glioma progression. The function and regulation mechanisms of lncRNAs in glioma mitochondrial fission are unclear. The expression of LINC00475 and its correlation with clinical parameters in glioma were analyzed using bioinformatics. Then, in vitro and in vivo assays were performed to explore the function of spliced variant LINC00475 (LINC00475-S) in gliomas. To explore the mechanisms, RNA-seq, MeRIP, RIP, pulldown-IP, dCas9-ALKBH5 editing system, LC/MS, and Western blotting were utilized. LINC00475 was confirmed to be overexpressed and with higher frequencies of AS events in gliomas compared to normal brain tissue and was associated with worse prognosis. In vitro and animal tumor formation experiments demonstrated that the effect of LINC00475-S on proliferation, metastasis, autophagy, and mitochondrial fission of glioma cells was significantly stronger than that of LINC00475. Mechanistically, METTL3 induced the generation of LINC00475-S by splicing LINC00475 through m6A modification and subsequently promotes mitochondrial fission in glioma cells by inhibiting the expression of MIF. Pull-down combined LC/MS and RIP assays identified that the m6A recognition protein HNRNPH1 bound to LINC00475 within GYR and GY domains and promoted LINC00475 splicing. METTL3 facilitated HNRNPH1 binding to LINC00475 in an m6A-dependent manner, thereby inducing generation of LINC00475-S. METTL3 facilitated HNRNPH1-mediated AS of LINC00475, which promoted glioma progression by inducing mitochondrial fission. Targeting AS of LINC00475 and m6A editing could serve as a therapeutic strategy against gliomas.

Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.

Inferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some influencing factors are not directly observed. To tackle this problem, we’ve developed a new algorithm called Regression Loss-increased with Causal Intensity (RLCI). This approach uses regression and entropy analysis to uncover hidden variables. Through tests on various real-world datasets, RLCI has been proven to be effective. It can help spot hidden factors that may affect the relationship between variables and determine the direction of causal relationships.

Membrane fouling presents a significant challenge in the treatment of wastewater. Several detection methods have been used to interpret membrane fouling processes. Compared with other analysis and detection methods, atomic force microscopy (AFM) is widely used because of its advantages in liquid-phase in situ 3D imaging, ability to measure interactive forces, and mild testing conditions. Although AFM has been widely used in the study of membrane fouling, the current literature has not fully explored its potential. This review aims to uncover and provide a new perspective on the application of AFM technology in future studies on membrane fouling. Initially, a rigorous review was conducted on the morphology, roughness, and interaction forces of AFM in situ characterization of membranes and foulants. Then, the application of AFM in the process of changing membrane fouling factors was reviewed based on its in situ measurement capability, and it was found that changes in ionic conditions, pH, voltage, and even time can cause changes in membrane fouling morphology and forces. Existing membrane fouling models are then discussed, and the role of AFM in predicting and testing these models is presented. Finally, the potential of the improved AFM techniques to be applied in the field of membrane fouling has been underestimated. In this paper, we have fully elucidated the potentials of the improved AFM techniques to be applied in the process of membrane fouling, and we have presented the current challenges and the directions for the future development in an attempt to provide new insights into this field.

Steel plates are widely used in various industries and are often processed into important equipment components. During production and use, these plates can develop U-shaped defects due to factors such as impact, vibration and bending. These defects can pose safety hazards under the influence of stress and fatigue load. To address this issue, this research paper proposes the design of an orthogonally distributed eddy current probe for detecting U-shaped defects in steel plates. The study uses COMSOL simulation and eddy current testing to obtain relevant data, which is then used to fit the relationship between the length, width, maximum depth and peak voltage of the U-shaped defects. A fitting formula is generated to provide a calculation method for subsequent detection. The results indicate that this type of eddy current inspection probe can effectively detect U-shaped defects with small dimensions. [ABSTRACT FROM AUTHOR]

In this article, under the circumstance of dead zones input and unknown control direction, the adaptive practical fixed‐time control strategy is presented for a general class of multi‐input and multi‐output (MIMO) nonlinear systems. The inherent explosion of computational complexity difficulty is eliminated by adopting a command filter technique and the universal approximation properties of radial basis function neural networks (RBFNNs) are applied to model the unknown nonlinear functions. The difficulties of the dynamic surface method and unknown directions can be handled by invoking error compensation mechanism and Nussbaum‐type functions, respectively. The uniqueness of the presented control scheme is that the tracking system can achieve the fixed‐time stability without relying on the boundedness of dead‐zone parameters. The fixed‐time convergence of the output tracking error and the semiglobally fixed‐time stable of closed‐loop system are assured via the developed adaptive fixed‐time command filtered controller. Finally, a practical example is supplied to further validate the availability of the presented theoretic result. [ABSTRACT FROM AUTHOR]

With the rapid expansion of urbanization, it is imperative to monitor builtup 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 FROM AUTHOR]

Abstract The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

The biomass raw material magnolol with a biphenyl structure was used to synthesize bio-based epoxy resin, and flexible polyetheramine curing agents(D230, D400, D2000 and T403) were used to cure the bio-based epoxy anti-erosion coating. The effects of polyetheramine molecular weight and functionality on the mechanical properties, corrosion resistance and anti-erosion protection performance of magnolol epoxy resin were explored, and the anti-erosion mechanism of magnolol epoxy coatings was proposed. Results showed that the magnolol epoxy coating cured with D230 maintained a high impedance value(1.25×1011 Ω·cm2) after 15 d of immersion in 3.5%(mass fraction) NaCl solution. The coating cured with T403 possessed a high tensile strength(26.59 MPa), low friction coefficient(0.373) and wear rate [0.018 3 mm3/(N·m)], while the magnolol epoxy coating cured with D2000 exhibited lower mass loss(244.74 mg) and volume loss(157 mm3) in the erosion test. Under the coupled erosion of solid/liquid/gas three-phase flow, the longer flexible segment of D2000 endowed the magnolol epoxy coating with better elasticity and flexibility, and therefore reduced the impact of the erosion medium, thus showing excellent anti-erosion performance.

For improving the anti-corrosion performance and durability of waterborne epoxy coatings in high-salt， high-humidity marine environments， polyaniline/boron nitride nanoparticles (PBN) were prepared by the in-situ polymerization of polyaniline (PANI) on the surface of hydroxylated hexagonal boron nitride (BN).In addition， PBN were used as anti-corrosion filler to enhance the anti-corrosion performance of waterborne epoxy coatings.The structure and micro-morphology of nano-particles were characterized by Fourier transform infrared spectroscopy(FT-IR) and scanning electron microscopy (SEM).Moreover， electrochemical tests and neutral salt spray tests (NSS) were used to explore the effects of BN， PANI modifications and PBN addition amount on the anti-corrosion performance of waterborne epoxy coatings.Results showed that PANI grew in situ on the surface of BN nanosheets to form a coral-like structure， and the bonding strength of waterborne epoxy coating reached 6.05 MPa after adding 0.50%(mass fraction，the same below) PBN.The corrosion current density (1.99×10-9 A/cm2) of the composite coating immersed in 3.5%NaCl solution after 120 h was reduced by about 3 orders of magnitude compared to that of pure epoxy coating (1.01×10-6 A/cm2).This was attributed to the fact that the addition of 0.50%PBN could evenly disperse in the epoxy resin， fill small gaps in the coating and enhance the barrier effect against corrosive medium.Meanwhile， PANI had a corrosion inhibition effect， which could synergistically improve the anti-corrosion performance of pure epoxy coatings.