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Abstract Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups. The hybrid radiomics model (HMRadSum) was developed by extracting quantitative imaging features and deep learning features from multiparametric MRI using emerging attention mechanism. Tumor markers were subsequently predicted utilizing an XGBoost classifier. Model performance and interpretability were evaluated using standard classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanations (SHAP) techniques. For the MSI prediction task, the HMRadSum model achieved area-under-curve (AUC) value of 0.945 (95% CI 0.862-1.000) and accuracy of 0.889. For the Ki-67 prediction task, the AUC and accuracy of HMRadSum model was 0.888 (95% CI 0.743-1.000) and 0.810. This hybrid radiomics model effectively extracted features associated with EC gene expression, providing potential clinical implications for personalized diagnosis, treatment, and treatment strategy optimization.

Abstract Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source–related DEGs. Using the identified PE- and immune source–related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning–derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment.

Abstract The microenvironment of immunosuppression and low immunogenicity of tumor cells has led to unsatisfactory therapeutic effects of the currently developed nanoplatforms. Immunogenic cell death, such as pyroptosis and ferroptosis, can efficiently boost antitumor immunity. However, the exploration of nanoplatform for dual function inducers and combined immune activators that simultaneously trigger pyroptosis and ferroptosis remains limited. Herein, a multifunctional pH-responsive theranostic nanoplatform (M@P) is designed and constructed by self-assembly of aggregation-induced emission photosensitizer MTCN-3 and immunoadjuvant Poly(I: C), which are further encapsulated in amphiphilic polymers. This nanoplatform is found to have the characteristics of cancer cell targeting, pH response, near-infrared fluorescence imaging, and lysosome targeting. Therefore, after targeting lysosomes, M@P can cause lysosome dysfunction through the generation of reactive oxygen species and heat under light irradiation, triggering pyroptosis and ferroptosis of tumor cells, achieving immunogenic cell death, and further enhancing immunotherapy through the combined effect with the immunoadjuvant Poly(I: C). The anti-tumor immunotherapy effect of M@P has been further demonstrated in in vivo antitumor experiment of 4T1 tumor-bearing mouse model with poor immunogenicity. This research would provide an impetus as well as a novel strategy for dual function inducers and combined immune activators enhanced photoimmunotherapy.

Abstract Background Internal migrants in China have long been at a disadvantage in terms of access to publicly financed services, as well as the utilization of public health services. The aim of the study was to examine inequities in the use of basic public health services between internal migrants and the local population and estimate the factors that contributed to inequity in use. Methods The data for this study was derived from the 2017 wave of the China Migrants Dynamic Survey. Basic public health services utilization was measured by the establishment of health records, health education and chronic disease management. We performed multivariable logistic regressions to examine inequities in the utilization of basic public health services between locals and internal migrants, and Oaxaca-Blinder decomposition was used to explore possible explanations for such inequities between the two groups. Results A total of 27,998 cases were included in the analysis. We found that the utilization rates for establishment of health records, health education and chronic disease management among internal migrants were 71.3%, 49.2% and 65.7% lower than their local counterparts, respectively. The decomposition results indicated that the inequities in the establishment of health records between locals and internal migrants were mainly explained by whether people had heard of the National Basic Public Health Services Program (NBPHSP) (17.67%) and by health insurance (5.99%). The contributors to the inequities in health education between locals and internal migrants were community involvement (14.71%) and whether people had heard of the NBPHSP (13.89%). The main factors contributing to the difference in utilization of chronic disease management between the two groups were whether people had heard of the NBPHSP (14.49%) and community involvement (8.43%). Conclusions To reduce inequities in the utilization of basic public health services between locals and internal migrants, measures need to be taken to improve knowledge about the basic public health services and to help migrants integrate into the local community.

Abstract Diabetic foot ulcer (DFU) is a common but devastating complication of diabetes mellitus and might ultimately lead to amputation. Elucidating the regulatory mechanism of wound healing in DFU is quite important for developing DFU management strategies. Here, we show, mecenchymal stem cell (MSC)-derived exosomes promoted the proliferation, migration and angiogenesis of high glucose-treated endothelial cells and reduced cell apoptosis. These effects were further enhanced by MSC-derived exosomes carrying circMYO9B overexpression. Mechanistically, circMYO9B promoted the translocation of hnRNPU from nucleus to cytoplasm and consequently destabilized CBL, thereby reducing the ubiquitination and degradation of KDM1A to promote VEGFA expression in endothelial cells. MSC-derived exosomes carrying circMYO9B promotes angiogenesis and thus accelerates diabetic wound healing through regulating the hnRNPU/CBL/KDM1A/VEGFA axis, indicating potential therapeutic targets and strategies for DFU treatment.

Spiking Neural Network (SNN) simulation is very important for studying brain function and validating the hypotheses for neuroscience, and it can also be used in artificial intelligence. Recently, GPU-based simulators have been developed to support the real-time simulation of SNN. However, these simulators’ simulating performance and scale are severely limited, due to the random memory access pattern and the global communication between devices. Therefore, we propose an efficient distributed heterogeneous SNN simulator based on the Sunway accelerators (including SW26010 and SW26010pro), named SWsnn, which supports accurate simulation with small time step (1/16 ms), randomly delay sizes for synapses, and larger scale network computing. Compared with existing GPUs, the Local Dynamic Memory (LDM) (similar to cache) in Sunway is much bigger (4 MB or 16 MB in each core group). To improve the simulation performance, we redesign the network data storage structure and the synaptic plasticity flow to make most random accesses occur in LDM. SWsnn hides Message Passing Interface (MPI)-related operations to reduce communication costs by separating SNN general workflow. Besides, SWsnn relies on parallel Compute Processing Elements (CPEs) rather than serial Manage Processing Element (MPE) to control the communicating buffers, using Register-Level Communication (RLC) and Direct Memory Access (DMA). In addition, SWsnn is further optimized using vectorization and DMA hiding techniques. Experimental results show that SWsnn runs 1.4−2.2 times faster than state-of-the-art GPU-based SNN simulator GPU-enhanced Neuronal Networks (GeNN), and supports much larger scale real-time simulation.

Abstract Objective This study aims to analyze factors associated with the missed diagnosis of high-grade squamous intraepithelial lesions (HSIL+) in patients initially diagnosed with low-grade squamous intraepithelial lesions (LSIL) through colposcopic biopsy and to develop a predictive model for assessing the risk of missed HSIL+. Methods We conducted a retrospective analysis of 505 patients who underwent loop electrical excision procedure (LEEP) following an LSIL diagnosis by colposcopic biopsy. Logistic regression was used to identify demographic and pathological parameters associated with missed diagnoses of HSIL+. Additionally, several machine learning methods were employed to construct and assess the performance of the risk prediction models. Results The overall rate of missed diagnoses for HSIL+ was 15.2%. Independent risk factors identified were HPV16/18 infection (OR 2.071; 95% CI 1.039–4.127; p = 0.039), TCT ≥ ASC-H (OR 4.147; 95% CI 1.392–12.355; p = 0.011), TZ3 (OR 1.966; 95% CI 1.003–3.853; p = 0.049) and Colposcopic impression G2 (OR 3.627; 95% CI 1.350–9.743; p = 0.011). Among the models tested, the Decision Tree algorithm demonstrated superior performance with an accuracy of 94.7%, sensitivity of 80.0%, specificity of 96.9%, and an area under the curve (AUC) of 0.936 in the validation set. Conclusion Key independent risk factors for the missed diagnosis of HSIL in patients with LSIL include HPV16/18 infection, TCT ≥ ASC-H, TZ3, and colposcopic impression G2. The Decision Tree model offers a cost-effective, reliable, and clinically valuable tool for accurately predicting the risk of missed diagnosis of HSIL+, facilitating early intervention and management.

Copper-silver alloy rod with high mechanical properties and stable conductivity are the key materials for the preparation of microfilaments used in endoscopy and other medical fields. In this paper, the microstructure of Cu–20Ag rod was regulated by adding trace rare earth Y in order to improve the comprehensive performance of copper-silver alloy further. and studies the distribution and existence of rare earth Y as well as the influence on the performance of copper-silver alloy rod billet. The results of research show that: rare earth Y mainly exists in the branching nodes of the eutectic organization, so that the alloy in the solidification of constitutional supercooling increases, thus refining the eutectic structure, and make it uniformly distributed. After addition of 0.01 wt%Y, Cu4Y was precipitated in the eutectic structure of the rod. After adding 0.03 wt%Y, the precipitates types increased to trace intermetallic compound AgY and nanoparticles Cu4Y. The increase of the interface between eutectic structure and Cu matrix decreases the electron scattering probability. This is the main factor to lead to the decrease of electrical conductivity. The precipitation hardening of precipitated phase and the increase of interface between eutectic structure and Cu matrix are the main factors for the increase of tensile strength of bar billet. The main factor of the decrease in the conductivity of rod billet is the increase of Ag phase dissolved in eutectic structure and the interface between eutectic structure and Cu matrix.

Abstract Glioblastoma (GBM) is the most lethal and common primary tumor of central nervous system with a poor prognosis. Glioma stem cells (GSCs) are particularly significant in GBM proliferation, invasion, self-renewal and recurrence. Circular RNAs (circRNAs) play important roles in various physiological and pathological processes, including regulating the biological behavior of GBM. Therefore, discovering novel circRNAs related to GSCs may contribute to a promising approach for treatment of GBM. Herein, we find out a novel circRNA termed circZEB1 with a high expression in glioma. Limb-bud and heart (LBH) is a transcription cofactor and promotes glioma stem cell tumorigenicity in our study. Mechanistically, circZEB1 can upregulate the expression of transcription cofactor LBH via sponging miR-128-3p in GSCs. LBH can facilitate the expression of tumor necrosis factor-α (TNF-α), thus activating the NF-κB signaling pathway to promote the glioma progression. Meanwhile, LBH can also upregulate the RNA binding protein Fused in Sarcoma (FUS) expression, which can bind to and maintain the stability of circZEB1. A positive feedback loop is formed among FUS, circZEB1, miR-128-3p and LBH in GSCs. Our study uncovers a critical role of circZEB1 and provides a novel biomarker for treating GBM.

Conventional carbon capture and storage technologies and carbon capture, utilization and storage technologies mainly target CO2 emissions from fixed sources, while direct air capture CO2 (DAC) technology, as an emerging negative carbon emission technology, can capture CO2 emissions from distributed sources and further reduce the global atmospheric CO2 concentration. In this paper, the development process of DAC's typical liquid absorption process, solid adsorption process and the construction of relevant demonstration projects are introduced, the technical characteristics of emerging DAC processes are analyzed, and the key equipment schemes and future development trends of existing DAC processes are discussed. The DAC liquid absorption process has the characteristics of low cost of absorbent raw materials and high selectivity, which can realize a large-scale continuous capture, but high energy consumption in the regeneration process. The DAC solid adsorption process has the characteristics of modularity, low investment cost, and relatively low energy consumption in the regeneration process, but requires a regular replacement of adsorption materials and maintenance of adsorption equipment, which is suitable for small-scale DAC application scenarios. Two typical DAC process absorption/adsorption materials are reviewed. In the DAC electric oscillation adsorption process, CO2 is chemically captured in the solid electrode, and CO2 desorption is achieved by changing the polarity of the solid electrode with applied electric field. This process has a higher efficiency than that of the heat or pressure-based separation process. The CO2 in the air is selected through the DAC separation membrane to achieve an efficient carbon capture. The DAC process achieves the CO2 absorption and desorption through the change of humidity, which breaks through the high energy consumption limit of conventional variable temperature/pressure swing adsorption. The DAC bio-absorption process absorbs and fixes CO2 through the photosynthesis of algae organisms. The DAC process based on bifunctional catalyst can capture and catalyze CO2 in one integrated process, saving the transportation and storage cost of CO2 capture. The key devices of DAC liquid absorption process are air contactor, particle reactor, calciner and curing device, among which the core of air contactor development is to improve gas-liquid contact efficiency, reduce water loss during spray process and reduce equipment corrosion, while the key of particle reactor and curing development is to improve the contact efficiency of solid-liquid two-phase materials and the solid-liquid separation efficiency after reaction. The DAC solid adsorption process is composed of a modular device consisting of an induced air module, an adsorption/regeneration module, an energy supply regeneration module and a CO2 compression module. The core of optimizing the adsorption module is to improve the gas-solid mass transfer rate, adjust the CO2 capture efficiency and reduce the pressure drop. It is very important to select suitable regeneration system or use clean energy based on the process requirements of different application scenarios, optimize DAC process and develop high-performance DAC core devices.

The Yancheng "Green Energy Port" is an important project planned under the National Natural Gas Production, Supply, Storage, and Sales System Construction and Interconnectivity program, and it is also the largest liquefied natural gas (LNG) reserve base currently under construction in China. In the first phase of the "Green Energy Port" project, 10 large LNG storage tanks were built, including 4 tanks with a capacity of 220,000 m3 and 6 tanks with a capacity of 270,000 m3 , the latter of which are currently the largest storage tanks with a single-tank capacity in the world. This report provides a brief introduction to the project context, outlines the layout of the prestressed system in the LNG storage tanks and the construction phases, and summarizes the critical tests for the prestress system in cryogenic tanks.

This paper investigates the mechanism of radio-frequency (RF) heating that occurs when two adjacent orthopedic implants are present together under magnetic resonance imaging (MRI) at 1.5 Tesla and 3.0 Tesla. When a patient has multiple implants close to each other, interactions between the implants may increase RF heating. Typical generic interlocking plate and antibiotic nail implants are adopted as examples. To analyze the effect of adjacent implants, the amplitude and direction of incident and scattering vector electric fields at the hot spot position are calculated and extracted using numerical simulation based on Huygens principle. It is shown that a strong coupling effect occurs due to the existence of both the incident field and a strong scattering field. Huygens principle can be used to obtain the first and second order scattering fields generated between implants. If the first- and second-order electric field terms are summed within a certain region, the RF-induced heating of this dual-implant system increases.

Background: Surgical intervention, complemented by radiotherapy and chemotherapy with temozolomide, constitutes the conventional treatment protocol for patients with newly diagnosed grade 4 glioma. We have conducted a research to evaluate the efficacy and safety of an integrated treatment regimen that incorporates tumor-treating fields with concurrent chemoradiotherapy. Methods: This retrospective research analyzed the clinical data of 39 adults who were newly diagnosed with World Health Organization (WHO) grade 4 gliomas at the First Affiliated Hospital of Nanjing Medical University, between February 2022 and April 2023. Each participant received a concurrent treatment regimen consisting of temozolomide (75 mg/m 2 daily), tumor-treating fields (200 kHz), and brain irradiation (60 Gy delivered in 30 fractions). Maintenance treatment comprised ongoing temozolomide and tumor-treating fields. Adverse events were documented in accordance with the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE 5.0) and specific grading criteria for dermatological adverse events associated with tumor-treating fields. Results: Among the 39 enrolled patients, disease progression was observed in 22 individuals (56.4%), with a median progression-free survival (PFS) of 14.2 months (95% confidence interval [CI]: 13.1-14.3 months). The median overall survival (OS) was 18.2 months (95% CI: 17.3 months to not reached). Patients diagnosed with glioblastoma had a median PFS of 13.1 months (95% CI: 12.9-14.2 months) and a median OS of 18.2 months (95% CI: 17.3 months to not reached). In contrast, patients diagnosed with astrocytoma had a median PFS of 14.3 months (95% CI: 12.8 months to not reached) and a median OS of 17.0 months (95% CI: 10.6 months to not reached). Twenty-five patients (64.1%) experienced dermatological adverse events, and 30 (77.0%) experienced mild hematological adverse reactions related to chemoradiotherapy. Conclusion: The application of tumor-treating fields concurrent with post-surgery chemoradiotherapy is both safe and effective for treating patients with newly diagnosed WHO grade 4 gliomas, exhibiting only limited toxicity.

Rheumatoid arthritis (RA), a long-term autoinflammatory condition causing joint damage and deformities, involves a multifaceted pathogenesis with genetic, epigenetic, and immune factors, including early immune aging. However, its precise cause remains elusive. Cellular senescence, a hallmark of aging marked by a permanent halt in cell division due to damage and stress, is crucial in aging and related diseases. In our study, we analyzed RA microarray data from the Gene Expression Omnibus (GEO) and focused on cellular senescence genes from the CellAge database. We started by selecting five RA datasets from GEO. Next, we pinpointed 29 differentially expressed genes (DEGs) linked to cellular senescence in RA, aligning them with genes from CellAge. We explored the roles of these DEGs in cellular senescence through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We then pinpointed three key genes (DHX9, CYR61, and ITGB) using random forest and LASSO Cox regression machine learning techniques. An integrated diagnostic model was created using these genes. We also examined the variance in immune cell infiltration and immune checkpoint gene expression between RA and normal samples. Our methodology's predictive accuracy was confirmed in external validation cohorts. Subsequently, RA samples were classified into three distinct subgroups based on the cellular senescence-associated DEGs, and we compared their immune landscapes. Our findings reveal a significant impact of cellular senescence-related DEGs on immune cell infiltration in RA samples. Hence, a deeper understanding of cellular senescence in RA could offer new perspectives for diagnosis and treatment.

Microplastics (MPs) are present throughout the environment, and due to their nature, they are extremely difficult to decompose. Reportedly, microorganisms play an important role in degrading and decomposing MPs. Bacillus pasteurii can degrade various complex organic matter, including MPs, which are a class of polymeric organic compounds. This study investigated the degradation effect of B. pasteurii on polypropylene MPs (PP-MPs) in soil. B. pasteurii was extracted from gold mine tailings. Herein, three experimental groups were established—a blank control treatment group, a group with bacteria without Ca2+ added (T2 group), and a group with bacteria supplemented with Ca2+ (T3 group)—for a 30-day indoor simulation of MP degradation in MP-treated soil. The results showed that the total mass change rate of the PP-MPs in the T2 group was 20.95 %, and grooves and holes appeared on the PP-MP surfaces. The total mass change rate of the PP-MPs in the T3 group was 23.22 %, and abundant fissures and pits appeared on the PP-MP surfaces. Additionally, new dominant phyla, such as Bacteroidetes and Firmicutes, appeared after bacterial addition. The relative abundance of several common soil genera, such as Bacillus, Brevundimonas, Flavobacterium, and Arthrobacter, and genera capable of breaking down complex compounds increased after B. pasteurii addition. The soil microbial community diversity improved, with the distribution of each species being relatively uniform. These findings indicated that the B. pasteurii strain can be used to degrade PP-MPs. Additionally, the addition of Ca2+ generated microbially induced calcium carbonate precipitation, which further improved the degradation of MPs. This study provides theoretical support for studying the degradation mechanism of PP-MPs.

Objectives Identifying the specific biomarkers and molecular signatures of MM might provide novel evidence for MM prognosis and targeted therapy.Methods Bioinformatic analyses were performed through GEO and TCGA datasets. The differential expression of HIST1H2BH in MM sample was validated by the qRT-PCR. And the CCK-8 assay was performed to detect the proliferation activity of HIST1H2BH on MM cell lines.Results A total of 793 DEGs were identified between bone marrow plasma cells from newly diagnosed myeloma and normal donors in GSE6477. Among them, four vital genes (HIST1H2AC, HIST1H2BH, CCND1 and TCF7L2) modeling were constructed. The increased HIST1H2BH expression was correlated with worse survival of MM based on TCGA datasets. The transcriptional expression of HIST1H2BH was significantly up-regulated in primary MM patients. And knockdown HIST1H2BH decreased the proliferation of MM cell lines.Conclusions We have identified up-regulated HIST1H2BH in MM patients associated with poor prognosis using integrated bioinformatical methods.

Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions.

Abstract The lack of a simple design strategy to obtain ideal conjugated polymers (CPs) with high absorbance and fluorescence (FL) in the near-infrared-II (NIR-II; 1000–1700 nm) region still hampers the success of NIR-II light-triggered phototheranostics. Herein, novel phototheranostic nanoparticles (PPN-NO NPs) were successfully prepared by coloading a cationic NIR-II CPs (PBC-co-PBF-NMe3) and a NO donor (S-nitroso-N-acetylpenicillamine, SNAP) onto a 1:1 mixture of DSPE-PEG5000 and dimyristoylphosphatidylcholine (DMPC) for NIR-II FL and NIR-II photoacoustic (PA) imaging-guided low-temperature NIR-II photothermal therapy (PTT) and gas combination therapy for cancer treatment. A precise NIR-II FL dually enhanced design tactic was proposed herein by integrating flexible nonconjugated segments (C6) into the CPs backbone and incorporating quaternary ammonium salt cationic units into the CPs side chain, which considerably increased the radiative decay pathway, resulting in desirable NIR-II FL intensity and balanced NIR-II absorption and NIR PTT properties. The phototheranostic PPN-NO NPs exhibited distinguished NIR-II FL and PA imaging performance in tumor-bearing mice models. Furthermore, the low-temperature photothermal effect of PPN-NO NPs could initiate NO release upon 980 nm laser irradiation, efficiently suppressing tumor growth owing to the combination of low-temperature NIR-II PTT and NO gas therapy in vitro and in vivo. Graphical Abstract

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.

Abstract Objective Previous studies have shown a clear link between insulin resistance (IR) and an elevated risk of atrial fibrillation (AF). However, the relationship between the estimated glucose disposal rate (eGDR), which serves as a marker for IR, and the risk of AF recurrence after radiofrequency catheter ablation (RFCA) remains uncertain. Therefore, this study aimed to examine the potential association between the eGDR and the risk of AF recurrence following RFCA. Methods This retrospective study was conducted at Nanchang University Affiliated Second Hospital. The study enrolled 899 patients with AF who underwent RFCA between January 2015 and January 2022. The formula used to calculate the eGDR was as follows: 19.02 − (0.22 * body mass index) − (3.26 * hypertension) − (0.61 * HbA1c). Cox proportional hazard regression models and exposure–effect curves were used to explore the correlation between the baseline eGDR and AF recurrence. The ability of the eGDR to predict AF recurrence was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The study observed a median follow-up period of 11.63 months, during which 296 patients experienced AF recurrence. K‒M analyses revealed that the cumulative incidence AF recurrence rate was significantly greater in the group with the lowest eGDR (log-rank p

Cutaneous neurofibroma (cNF) is a prevalent clinical manifestation of neurofibromatosis type 1, significantly affecting the well-being and quality of life of the affected individuals. The adoption of reliable and reproducible volumetric measurement techniques is essential for precisely evaluating tumor burden and plays a critical role in the development of effective treatments for cNF. This study focuses on widely used volumetric measurement techniques, including vernier calipers, ultrasound, computed tomography, magnetic resonance imaging, and three-dimensional scanning imaging. It outlines the merits and drawbacks of each technique in assessing the cNF load, providing an overview of their current applications and ongoing research advancements in this domain.

In a study of DNA methylation changes in melatonin-deficient rice mutants, mutant plants showed premature leaf senescence during grain-filling and reduced grain yield. Melatonin deficiency led to transcriptional reprogramming, especially of genes involved in chlorophyll and carbon metabolism, redox regulation, and transcriptional regulation, during dark-induced leaf senescence. Hypomethylation of mCG and mCHG in the melatonin-deficient rice mutants was associated with the expression change of both protein-coding genes and transposable element-related genes. Changes in gene expression and DNA methylation in the melatonin-deficient mutants were compensated by exogenous application of melatonin. A decreased S-adenosyl-L-methionine level may have contributed to the DNA methylation variations in rice mutants of melatonin deficiency under dark conditions.

IntroductionGroundcover management plays a crucial role in improving water retention and soil nutrition in orchard systems, thereby preventing environmental constrains by non-point source pollution. However, effectiveness of groundcover management in citrus orchards developed on sloping farmland with eroded purple soil has not been studied in detail. In particular, information on the soil nutrient losses, e.g., nitrogen (N) and phosphorus (P), through interflow and its effects on growth and nutrition of citrus plants has not been reported.MethodsThe present study evaluated the effects of different cover crops, i.e., Lolium perenne L. (Lolium), Vicia villosa Roth (Vicia) and Orychophragmus violaceus (Ory), on nutrition and growth of citrus trees as well as water, soil and nutrient retention in an orchard developed in sloping farmland during two consecutive years.Results and discussionThe results show that the groundcover species Lolium and Vicia mediated nursing effects on nutrition and growth of citrus trees. These nursing effects included enhanced foliar levels of carbon(C), N and P as well as increased tree height, stem diameter, and crown width. Groundcover management generally reduced the annual surface runoff, interflow, soil loss, total N loss and total P loss. Among the cover crop species studied, Lolium and Vicia were overall more efficient than Ory in this context. Lolium reduced the average annual total loss of N and P by 42.53% and 49.23%, respectively, compared with clean tillage. The estimated annual reduction potentials of soil, N and P losses in Southwestern China were 16.3, 3.4 and 8.5 million tons yr-1, respectively. Obviously, Lolium and Vicia provide highly beneficial ground coverage on sloping farmland and, thus, can be used for future sustainable development of citrus orchards.

This study investigates the effect of normal feeding (starvation for 0 days, control), starvation for 3 (S3), 9 (S9), 15 days (S15) and refeeding for 3 (S9R3), 9 (S9R9), 15 days (S15R15) on the expression of genes related to lipid and glycogen metabolism, the content of biochemical molecules, the activities of enzymes and the morphological structure of the hepatopancreas of C. sinensis. Results showed that the expression levels of cpt-I, acox, gdh, tre and pepck in hepatopancreas, cpt-I and acc in gonads, and tre and pepck in mantle initially increased and then decreased during starvation. the expression levels of gdh and pkm gradually increased with the duration of starvation. the mRNA level of cpt-I, dgat1 and acc reached a peak level in the S15R15 group, while the expression levels of gdh and pepck in mantle increased in the S9R3 and S9R9 groups. The hepatopancreatic LPS, LPL, and HK activity in the hepatopancreas and mantle of C. sinensis increased during starvation. Moreover, the TG content in the hepatopancreas and GN content in the mantle decreased with the prolongation of starvation. After refeeding, TG levels in the hepatopancreas increased slightly but remained significantly lower than in controls. The GN content in the hepatopancreas and mantle of the S9R9 and S15R15 groups was higher than those of the control group. The number of lipid droplets in the hepatopancreatic tubular decreased with prolonged starvation, but the content of lipid droplets in the hepatopancreatic tubular increased slightly after refeeding. However, with the increase in the duration of starvation, the wall of the hepatopancreatic tubular becomes thin and the number of vacuoles increases. These results indicate that lipid and glycogen metabolism are involved in this clam’s physiological regulation in response to starvation stress.

BackgroundIn recent years, climate change and environmental pollution have posed significant threats to public health. As environmental policies such as low-carbon city initiatives are progressively implemented, their role in enhancing public health has become a topic of growing interest. This study aimed to investigate the relationship between green low-carbon development and public health and to analyze the underlying mechanisms.MethodsWe utilized data from 271 prefecture-level cities in China spanning from 2007 to 2020, focusing on green low-carbon development, climate change, environmental pollution, and public health. Employing the quasi-natural experimental framework of China’s low-carbon city pilot projects, we constructed a multi-site difference-in-differences (DID) model for empirical analysis. Various robustness checks, including parallel trend tests, placebo tests, sample selection bias checks, and adjustments to the temporal and spatial scope of the samples, were conducted to ensure the reliability of the results. Additionally, we explored the positive effects of green low-carbon development on public health through dual mediation pathways involving climate change mitigation and pollution reduction. Finally, we examined the heterogeneity of the results across different city tiers, economic growth rates, levels of technological investment, and green finance development.ResultsThe findings indicate that green low-carbon development significantly enhances public health, a conclusion supported by robustness tests. Mechanism analysis reveals that the benefits of green low-carbon development on public health are realized through mitigating climate change and reducing environmental pollution. Further analysis reveals that the positive impact on public health is more pronounced in first-and second-tier cities, as well as in cities with faster economic growth, greater technological investment, and more developed green finance sectors.DiscussionThis study highlights the crucial role of urban green low-carbon development in improving environmental quality and public health. In addition to providing empirical evidence that supports the promotion of green low-carbon development in cities, the results point to policy recommendations for enhancing public health. Moreover, the findings contribute to the development of environmental policies and the implementation of the “Healthy China” strategy.

The collapse of immune homeostasis induces type 1 diabetes (T1D). In T1D, uncontrolled immune attacks against islet β cells reduce insulin secretion, resulting in hyperglycaemia and various complications. Type 1 regulatory (Tr1) cell therapy is a promising approach for the treatment of T1D. Tr1 cells are a subset of regulatory T (Treg) cells that are characterised by high interleukin-10 secretion and forkhead box protein P3 non-expression. Tr1 cells are reduced and have impaired function in patients with T1D. Immunotherapy is used to treat various diseases, and Treg cells have been applied to treat T1D in animal models and clinical trials. However, the safety and efficacy of Tr1 cells in treating diabetes and other diseases remain unclear. In this review, we aim to investigate the identification and biological function of Tr1 cells and related studies on immune diseases; additionally, we discuss the feasibility, limitations, and possible solutions of Tr1 cell therapy in T1D. This review shows that T1D is caused by an immune imbalance where defective Tr1 cells fail to control effector T cells, leading to the destruction of islet β cells. However, Tr1 cell therapy is safe and effective for other immune diseases, suggesting its potential for treating T1D.

Abstract Lithium‐ion secondary batteries (LIB) with high energy density have attracted much attention for electric vehicle (EV) applications. However, LIBs have a safety problem because these batteries contain a flammable organic electrolyte. As such, all‐solid secondary batteries that are not flammable have been extensively reported recently. In this study, we have focused on polymer electrolytes, which is flexible and is expected to address the safety problem. However, the conventional polymer electrolytes have low electrial conductivity at room temperature. Various attempts have been made to solve this problem, such as the addition of inorganic fillers and ionic liquids; however, these composite polymer electrolytes have not yet reached a practical level of lithium‐ion conductivity. In this study, high electrical conductivity and lithium dendrite formation‐free PEO based composite electrolytes are developed with both a filler of Li6,4La3Zr1.4Ta0.6O12 and liquid plasticizers of tetraethylene glycol dimethyl ether and 1,2 dimethoxyethane. The proposed flexible polymer electrolyte shows a high electrical conduciviy of 6.01×10−4 S cm−1 at 25 °C.

In magnetic resonance examination, the interaction between implants and the radio frequency (RF) fields induces heating in human tissue and may cause tissue damage. To assess the RF-induced heating of implants, three steps should be executed, including electromagnetic model construction, electromagnetic model validation, and virtual human body simulations. The crucial step of assessing RF-induced heating involves the construction of a test environment for electromagnetic model validation. In this study, a hardware environment, comprised of a RF generation system, electromagnetic field measurement system, and a robotic arm positioning system, was established. Furthermore, an automated control software environment was developed using a Python-based software development platform to enable the creation of a high-precision automated integrated test environment. The results indicate that the electric field generated in this test environment aligns well with the simulated electric field, making it suitable for assessing the RF-induced heating effects of implants.

During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849.

Ascites syndrome (AS) is a metabolic disease that seriously affects the growth and development of broiler chickens. Intestinal microbiota play a significant role in the growth of broiler chickens. Therefore, further research on the relationship between AS and intestinal microbiota will help to better understand the impact of AS on broiler growth. In this study, 0.2% sodium chloride was added to the drinking water, which induced AS in broiler chickens, and we detected the influence of AS on the growth performance and cecal microbiota of broiler chickens. The results showed that AS significantly reduced the cecal microbial diversity of broiler chickens and affected the cecal microbial composition at the phylum and genus levels (p = 0.05). Further, LEfSe analysis revealed that AS significantly increased the abundance of Bacteroidetes (p = 0.035) while simultaneously reducing the abundance of Actinobacteria (p = 0.031) in the cecum. Additionally, the differential metabolites associated with polycyclic aromatic hydrocarbon degradation were significantly diminished. The findings suggest that AS may further impact the growth rate of broiler chickens by altering cecal microorganisms.

Experimental approaches for the identification of dynamic parameters in synchronous machines mainly include two methods, a three-phase sudden short-circuit (TPSSC) test and a standstill frequency response (SSFR) test. However, the former has significant safety risks, while the latter has a complex implementation process, resulting in insufficient adaptability to large-scale units. To overcome the above obstacles, this paper proposes an improved DC-decay test method that can be performed at an arbitrary rotor position so that the rotor pre-positioning process in the conventional DC-decay test can be neglected. Meanwhile, combining the transient analysis theory and particle swarm optimization algorithm, a semi-analytical parameter identification method is proposed. Finally, the proposed method is applied using a 172 MVA large synchronous machine. Compared to the results obtained by the TPSSC test using the Prony algorithm and other conventional type tests, the error of the parameter calculation results obtained with the conventional method reached a maximum of 16.6%, while that of the proposed method was merely 8.6%, and the experimental period could be shortened from 5 days to half a day.

BackgroundThe reallocation of health resources, epidemic prevention and control measures during the COVID-19 pandemic triggered widespread restricted health service utilization, some residents and patients tried positive self-care behavior to maintain their health, yet the efficacy of this intervention remains unclear.ObjectBased on the reasoned action approach (RAA) theory, this study aimed to investigate the correlation between self-care behavior and restricted health service utilization among adults in China, trying to discover the vulnerable groups and external and intrinsic factors that affect self-care behavior among Chinese adults.MethodsData on demographics, socioeconomic, health status, and self-care behavior were collected in “The Early China COVID-19 Survey,” a cross-sectional anonymous online survey of the general population in China. Self-care behavior was measured by four indicators: weight control (WC), physical activity (PA), prevention behavior (PB), and online medical consultation (OMC). The multiple linear models and binary logistic regression were used to examine whether restricted health service utilization (RHSU) is associated with self-care behaviors; also, adjusted multivariate logistic regression was used to analyze subgroup heterogeneity.ResultsIn total, 8,428 adult participants completed the survey, the mean OMC score was 1.51 (SD 1.34), the mean PB score was 18.17 (SD 3.44), and the proportion of participants who engaged in WC and PA was 42.30 and 62.57%, respectively. According to the multiple regression model, the RHSU was significantly positively correlated with all four indicators of self-care (WC: OR = 1.34, p

Abstract Porcine epidemic diarrhoea virus (PEDV) infects pigs of all ages by invading small intestine, causing acute diarrhoea, vomiting, and dehydration with high morbidity and mortality among newborn piglets. However, current PEDV vaccines are not effective to protect the pigs from field epidemic strains because of poor mucosal immune response and strain variation. Therefore, it is indispensable to develop a novel oral vaccine based on epidemic strains. Bacillus subtilis spores are attractive delivery vehicles for oral vaccination on account of the safety, high stability, and low cost. In this study, a chimeric gene CotC‐Linker‐COE (CLE), comprising of the B. subtilis spore coat gene cotC fused to the core neutralizing epitope CO‐26 K equivalent (COE) of the epidemic strain PEDV‐AJ1102 spike protein gene, was constructed. Then recombinant B. subtilis displaying the CLE on the spore surface was developed by homologous recombination. Mice were immunized by oral route with B. subtilis 168‐CLE, B. subtilis 168, or phosphate‐buffered saline (PBS) as control. Results showed that the IgG antibodies and cytokine (IL‐4, IFN‐γ) levels in the B. subtilis 168‐CLE group were significantly higher than the control groups. This study demonstrates that B. subtilis 168‐CLE can generate specific systemic immune and mucosal immune responses and is a potential vaccine candidate against PEDV infection.

Fracture toughness is a fundamental material property that quantifies the critical loading stress necessary to overcome the resistance to mechanical fracture propagation. Herein, exposure of minerals to certain fluids can cause alterations in the fracture toughness, for example, during underground CO2 storage, influencing the integrity and sealing performance of the reservoirs. These changes would require an extensive examination of various properties of the layers, particularly those related to mechanical ones like fracture toughness. However, the role of water in changing the fracture toughness in different shale samples during the ScCO2 interactions is still unknown. In this study, we collected three samples with different lithologies and compared the fracture toughness under three different states (before the interactions, under the interactions without water, and the interactions with water) to further quantify the role of water in the changes of the fracture toughness. The Mann-Whitney U test shows that the interactions with ScCO2 could change the fracture toughness and the role of water involvement in the interactions could further change the fracture toughness. In addition, the role of water in the changes of the same sample parallel and perpendicular to the bedding lines is also different. In the directions parallel to the bedding, the fracture toughness of samples 2 (carbonate-rich felsic shale) and 3 (clayey carbonate-rich shale) increased by 39.2 % and 10.8 %, respectively. Conversely, the fracture toughness of sample 1 (clayey felsic shale) decreased by 21.3 %. In the directions perpendicular to the bedding, the fracture toughness of samples 1, 2 and 3 increased by 23 %, 55 %, and 8.58 %, respectively. Considering the samples that have been exposed to ScCO2 with water, the fracture toughness of Sample 1 decreased by 5.8 %, while the fracture toughness of Sample 2 and Sample 3 increased by 65.8 % and 11.4 % respectively. This study shows that when considering the storage site for the CO2, we should both consider the role of the lithologies and water in the interactions.

ObjectiveThis study aimed to examine the relationship between lipoprotein (a) (Lp[a]) and other blood lipid indexes and carotid artery atherosclerosis in patients with acute ischemic stroke (AIS).MethodsA total of 2,018 patients were selected from the hospital “acute stroke intervention and secondary prevention registration database” by identifying blood fat indexes (cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and Lp[a]). Based on the results of carotid artery ultrasound examinations, the patients were divided into a “no plaque” group, comprising 400 patients, a “plaque and no stenosis” group, comprising 1,122 patients and a “carotid stenosis” group, comprising 496 patients. The relationship between Lp(a) and blood lipid indexes and carotid artery atherosclerosis was then investigated using multi-factor logistics regression analysis.ResultsThere were 400 patients (19.8%) with no carotid plaque, 1,122 patients (55.6%) with plaque and no carotid stenosis and 496 patients (24.6%) with carotid stenosis. As the degree of carotid artery atherosclerosis increased, the Lp(a) level gradually increased; Lp(a) and cholesterol were identified as independent risk factors for carotid atherosclerosis.ConclusionLipoprotein (a) and cholesterol are independent risk factors for patients with AIS with carotid atherosclerosis, and their levels increase with the degree of carotid artery atherosclerosis; therefore, attention should focus on levels of cholesterol and Lp(a) in acute stroke patients to control atherosclerosis effectively.

Abstract Alternative biofuels have the potential to reduce greenhouse gas emissions and particulate matter due to free of aromatics compared to traditional petroleum-based aviation fuel. The potential mitigating emission of hydrothermal-condensation-hydrotreating jet biofuel (HCHJ) derived from agriculture residue was investigated. The effects of aviation biofuel components, blend ratio and equivalent ratio on emission characteristics were conducted by Premixed Pre-evaporated Bunsen burner (PPBB) for laminar combustion and ZF850 jet engine for turbulent combustion. In compositions, HCHJ had a higher concentration of cycloparaffins (mostly in C8–C10) while petroleum-based aviation fuel (RP-3) had a higher concentration of alkylbenzenes (mostly in C8–C11). In laminar combustion, HCHJ and both 50% blend HCHJ appear no unburned hydrocarbon (UHC) due to low aromatics content and no sulfur in the biofuel. Moreover, there were no significant differences in NO and NO2 concentration for HCHJ and HCHJ blends. In turbulent combustion, HCHJ blends and RP-3 were compared engine emissions at various state points. Considering all complex effects of fuel and combustion environment, HCHJ blend had a noticeable reduction in PM2.5 emissions in comparison with RP-3 due to their lower aromatics and sulfur content. As HCHJ is similar to RP-3 in C/H ratio, density and heat value and the different aromatics contents have different tendencies to generate PM2.5 at different condition, PM2.5 emission is not only related with the total aromatic content and individual aromatic structure but also the combustion environment at thrust setting and coexisting pollutants including NOx and UHC emissions. CO and NOx emission indicated that both of turbulent state and fuel type influence emissions. HCHJ blend can be benefit for PM2.5 reduction and combustion efficiency growth. PM2.5 reduction can be obtained 77.5% at 10% HCHJ blend and 9.5% at 5% HCHJ blend while combustion efficiency can be obtained 0.05% at 5% HCHJ blend and 0.36% at 10% HCHJ blend through all thrust output.

Background: Melanocytic nevus is mainly treated by complete or partial removal. However, predicting the risk of malignant transformation of melanocytic nevi and which treatment patients should receive, surgical or non-surgical management, to gain the best results and aesthetic outcomes is controversial. Methods: Global literature on melanocytic nevus treatment, published between 1997 and 2022, was scanned using the Web of Science Core Collection database. Microsoft Office Excel, CiteSpace V, VOSviewer, Scimago Graphica, Bibliometrix, and Biblioshiny packages in R were used for the bibliometric analysis to summarize the leading countries, institutions, professors, and research trends in this field. Results: This study included 1 723 articles. Publications and citations exhibited positive trends over the past 20 years. The United States had the most productive organizations and publications in the comprehensive worldwide cooperation network, and China was recently one of the most active major participants. Professor Giovanni Pellacani, whose H-index, G-index, and M-index ranked first in this field, founded a virtual biopsy using reflectance confocal microscopy. In addition, Krengel and Kinsler contributed significantly to diagnosing and treating melanocytic nevi. The top 25 keywords in recent years were mostly about the mechanisms and risk factors for the malignant transformation of nevi. Conclusion: The future trend for melanocytic nevi treatment is to specify genotype-phenotype and genotype-outcome correlations, choose proper therapy to reduce the risk of malignant transformation, and simultaneously achieve the best aesthetic outcomes.

The capabilities of AUV mutual perception and localization are crucial for the development of AUV swarm systems. We propose the AUV6D model, a synthetic image-based approach to enhance inter-AUV perception through 6D pose estimation. Due to the challenge of acquiring accurate 6D pose data, a dataset of simulated underwater images with precise pose labels was generated using Unity3D. Mask-CycleGAN technology was introduced to transform these simulated images into realistic synthetic images, addressing the scarcity of available underwater data. Furthermore, the Color Intermediate Domain Mapping strategy is proposed to ensure alignment across different image styles at pixel and feature levels, enhancing the adaptability of the pose estimation model. Additionally, the Salient Keypoint Vector Voting Mechanism was developed to improve the accuracy and robustness of underwater pose estimation, enabling precise localization even in the presence of occlusions. The experimental results demonstrated that our AUV6D model achieved millimeter-level localization precision and pose estimation errors within five degrees, showing exceptional performance in complex underwater environments. Navigation experiments with two AUVs further verified the model’s reliability for mutual 6D pose estimation. This research provides substantial technical support for more complex and precise collaborative operations for AUV swarms in the future.

Abstract Objective To assess the feasibility and safety of three-dimensional (3D) laparoscopic nephrectomy in combination with bench surgery and autotransplantation for treating highly complex renal tumors. Materials and methods The clinical data of six patients with highly complex renal cell carcinoma were collected. All patients underwent 3D laparoscopic nephrectomy in combination with bench surgery and autotransplantation by the same surgeons, two of them had previously undergone laparoscopic partial nephrectomy for contralateral renal cancer. Results The total operative time was 366 ± 65 min, the warm ischemia time (WIT) was 1.3 ± 0.4 min, and the cold ischemia time was 121 ± 26 min. While one patient received a diluted autologous blood transfusion, the intraoperative blood loss was 217 ± 194 ml. No increase in the serum creatinine (SCr) level was observed at postoperative day 30 compared with the preoperative time, and none of the patients received dialysis either during the hospital stay or to date. Although one patient underwent nephrectomy due to tumor recurrence in the transplanted kidney, the others reported no tumor recurrence or distant metastases on imaging to date. Conclusion 3D laparoscopic nephrectomy, when combined with bench surgery and autotransplantation, can become a feasible option for treating highly complex renal cell carcinoma cases when expecting to preserve renal function maximally.

Abstract The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape.

IntroductionField wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field.MethodsThere are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios.ResultsExperiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information.DiscussionThe findings demonstrate the practical potential of our framework for intelligent agriculture applications.

ObjectivesThe diagnosis and treatment of brain tumors have greatly benefited from extensive research in traditional radiomics, leading to improved efficiency for clinicians. With the rapid development of cutting-edge technologies, especially deep learning, further improvements in accuracy and automation are expected. In this study, we explored a hybrid deep learning scheme that integrates several advanced techniques to achieve reliable diagnosis of primary brain tumors with enhanced classification performance and interpretability.MethodsThis study retrospectively included 230 patients with primary brain tumors, including 97 meningiomas, 66 gliomas and 67 pituitary tumors, from the First Affiliated Hospital of Yangtze University. The effectiveness of the proposed scheme was validated by the included data and a commonly used data. Based on super-resolution reconstruction and dynamic learning rate annealing strategies, we compared the classification results of several deep learning models. The multi-classification performance was further improved by combining feature transfer and machine learning. Classification performance metrics included accuracy (ACC), area under the curve (AUC), sensitivity (SEN), and specificity (SPE).ResultsIn the deep learning tests conducted on two datasets, the DenseNet121 model achieved the highest classification performance, with five-test accuracies of 0.989 ± 0.006 and 0.967 ± 0.013, and AUCs of 0.999 ± 0.001 and 0.994 ± 0.005, respectively. In the hybrid deep learning tests, LightGBM, a promising classifier, achieved accuracies of 0.989 and 0.984, which were improved from the original deep learning scheme of 0.987 and 0.965. Sensitivities for both datasets were 0.985, specificities were 0.988 and 0.984, respectively, and relatively desirable receiver operating characteristic (ROC) curves were obtained. In addition, model visualization studies further verified the reliability and interpretability of the results.ConclusionsThese results illustrated that deep learning models combining several advanced technologies can reliably improve the performance, automation, and interpretability of primary brain tumor diagnosis, which is crucial for further brain tumor diagnostic research and individualized treatment.

In order to study the detection performance of the Advanced Direction Time Lightning Detection System (ADTD) over mainland China, the relationship between the detection efficiency (DE) and distance for ADTD sensor is analyzed using lightning location data by ADTD during 2014–2020, which fits well with the probability density function of the Gamma distribution. The DE and location accuracy of ADTD across China are hereafter evaluated according to the constructed probability density function. The results show that the DE of a single sensor first increases rapidly and then decreases slowly with the increase of the detection distance, and the model constructed based on the probability density function of Gamma distribution can explain this relationship well. In the regions with dense sensors such as central and eastern China, the DE of ADTD can reach 95%, with the majority of regions having lower location errors (less than 1,000 m). While, in the areas where sensors are sparse such as the Qinghai-Tibet Plateau and at the edge of ADTD, the DE of ADTD is less than 90% (in some areas it is even lower than 50%), and the location error greater than 1,000 m. In general, ADTD can accurately detect cloud-to-ground lightning events in most areas of China, but its accuracy is limited in some areas due to sparse sensors and instrument parameter settings.

We experimentally, numerically, and theoretically investigate the dynamics of cavitation bubbles in viscous liquids in a tube during a transient process. In experiments, cavitation bubbles are generated by a modified tube-arrest setup, and the bubble evolution is captured with high-speed imaging. Numerical simulations using OpenFOAM are employed to validate our quasi-one-dimensional theoretical model, which effectively characterizes the bubble dynamics. We find that cavitation onset is minimally affected by the liquid viscosity. However, once cavitation occurs, various aspects of bubble dynamics, such as the maximum bubble length, bubble lifetime, collapse time, and collapse speed, are closely related to the liquid viscosity. We further establish that normalized bubble dynamics are solely determined by the combination of the Reynolds number and the Euler number. Moreover, we also propose a new dimensionless number, Ca2, to predict the maximum bubble length, a critical factor in determining the occurrence of liquid column separation.

As the core equipment of pumped storage power stations, reversible pump-turbines will frequently switch between different working modes during operation, and it is easy to appear hump region under pump condition, which will greatly affect the performance of the pumped storage unit. Therefore, in order to explore the causes of the hump region, this paper takes the model reversible pump-turbine as the research object. First, the unit speed–unit flow characteristics are compared with the model test results under different working conditions. Then, based on the omega vortex identification method, the vortex distribution in the flow channel of the unit is analyzed. By analyzing the flow characteristics of the flow components of the unit under different flow rates in the hump region, the internal flow law of the hump region is revealed. It is found that when the flow rate decreases, the rising head cannot offset the head lost by the hydraulic loss, thus forming the hump region, and the unstable flow gradually appears in the guide vane and runner area. There are unstable phenomena, such as flow separation, in the guide vane area. There is a sudden change of radial pressure in the vaneless area between the guide vane and the runner; that is, the unstable vortex almost occupies the whole flow channel due to the change of pressure in this area, resulting in the deterioration of the instability of the unit operation. When the flow rate is large, the radial pressure mutation zone mainly exists between the guide vane cascades, making it easy to form an unstable vortex. The research results can provide a theoretical reference for improving the stability of reversible pump-turbines.

The spine is one of the most important structures in the human body, serving to support the body, organs, protect nerves, etc. Medical image segmentation for the spine can help doctors in their clinical practice for rapid decision making, surgery planning, skeletal health diagnosis, etc. The current difficulty is mainly the poor segmentation accuracy of skeletal Magnetic Resonance Imaging (MRI) images. To address the problem, we propose a spine MRI image segmentation method, Atrous Spatial Pyramid Pooling (ASPP)-U-shaped network (UNet), which combines an ASPP structure with a U-Net network. This approach improved the network feature extraction by introducing an ASPP structure into the U-Net network down-sampling structure. The medical image segmentation models are trained and tested on publicly available datasets and obtained the Dice coefficient and Mean Intersection over Union coefficients with 0.866 and 0.755, respectively. The experimental results show that ASPP-UNet has higher accuracy for spine MRI image segmentation compared with other mainstream networks.