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Abstract Diagnosing Sjogren’s syndrome requires considerable time and effort from physicians, primarily because it necessitates rigorously establishing the presence lymphatic infiltration in the pathological tissue of the labial gland. The aim of this study is to use deep learning techniques to overcome these limitations and improve diagnostic accuracy and efficiency in pathology. We develop an auxiliary diagnostic system for Sjogren’s syndrome. The system incorporates the state-of-the-art object detection neural network, YOLOv8, and enables the precise identification and flagging of suspicious lesions. We design the multi-dimensional attention module and S-MPDIoU loss function to improve the detection performance of YOLOv8. By extracting features from multiple dimensions of the feature map, the utilization of the multi-dimensional attention mechanism enhances the feature interaction across disparate positions, enabling the network to proficiently learn and retain salient cell features. S-MPDIoU introduces an angle penalty term that efficiently minimizes the diagonal distance between predicted and ground truth boxes. Additionally, it incorporates a flexible scale factor tailored to different size feature maps, which balances the issue of sudden gradient decrease during high overlap, thereby accelerating the overall convergence rate. To verify the effectiveness of our methods, we create a dataset of lymphocytes using labial gland biopsy pathology images collected from YanTaiShan hospital and trained the model with this dataset. The proposed model is assessed using standard metrics like precision, recall, mAP. The improved model achieves an increase in recall by 9.1%, mAP.5 by 3.2%, and mAP.95 by 2%. The study demonstrated deep learning’s potential to analysis pathology images, offering a reference framework for the application of deep learning technology in the medical domain.

Abstract Background Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. Methods In this cohort of 611 invasive breast cancer patients (ages 23–91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. Results We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08–1.51, ptrend =

We present a case of a 94-year-old female who was shown to have an incidental subclavian venous aneurysm on CT during stroke workup. Subclavian venous aneurysms are a rare finding with only 13 reported in our literature search. Patients can present clinically with pain or supraclavicular swelling, or not unusually with no symptoms at all. These aneurysms can be detected on multiple imaging modalities, with ultrasound providing information on presence of a thrombus. Recent advances in minimally invasive procedures provide a new avenue of management for these aneurysms, with multiple cases involving an endovascular approach.

Abstract Photovoltaic (PV) installations are a leading technology for generating green electricity and reducing carbon emissions. Roofing highways with solar panels offers a new opportunity for PV development, but its potential of global deployment and associated socio‐economic impacts have not been investigated. Here, we combine solar PV output modeling with the global highway distribution and levelized cost of electricity to estimate the potential and economic feasibility of deploying highway PV systems worldwide. We also quantify its co‐benefits of reducing CO2 equivalent emissions and traffic losses (road traffic deaths and socio‐economic burdens). Our analysis reveals a potential for generating 17.58 PWh yr−1 of electricity, of which nearly 56% can be realized at a cost below US$100 MWh−1. Achieving the full highway PV potential could offset 28.78% (28.21%–29.1%) of the global total carbon emissions in 2018, prevent approximately 0.15 million road traffic deaths, and reduce US$0.43 ± 0.16 trillion socio‐economic burdens per year. Highway PV projects could bring a net return of about US$14.42 ± 4.04 trillion over a 25‐year lifetime. To exploit the full potential of highway PV, countries with various income levels must strengthen cooperation and balance the multiple socio‐economic co‐benefits.

Abstract Rheumatoid arthritis (RA) is an autoimmune disorder characterized by chronic inflammation of the synovial joints and the dysfunction of regulatory T cells (Tregs) in the peripheral blood. Therefore, an optimal treatment strategy should aim to eliminate the inflammatory response in the joints and simultaneously restore the immune tolerance of Tregs in peripheral blood. Accordingly, we developed an efferocytosis‐mimicking nanovesicle that contains three functional factors for immunomodulating of efferocytosis, including “find me” and “eat me” signals for professional (macrophage) or non‐professional phagocytes (T lymphocyte), and “apoptotic metabolite” for metabolite digestion. We showed that efferocytosis‐mimicking nanovesicles targeted the inflamed joints and spleen of mice with collagen‐induced arthritis, further recruiting and selectively binding to macrophages and T lymphocytes to induce M2 macrophage polarization and Treg differentiation and T helper cell 17 (Th17) recession. Under systemic administration, the efferocytosis‐mimicking nanovesicles effectively maintained the pro‐inflammatory M1/anti‐inflammatory M2 macrophage balance in joints and the Treg/Th17 imbalance in peripheral blood to prevent RA progression. This study demonstrates the potential of efferocytosis‐mimicking nanovesicles for RA immunotherapy.

ObjectiveThis investigation sought to delineate the causal nexus between plasma glutamine concentrations and leukemia susceptibility utilizing bidirectional Mendelian Randomization (MR) analysis and to elucidate the metabolic ramifications of asparaginase therapy on glutamine dynamics in leukemia patients.MethodsA bidirectional two-sample MR framework was implemented, leveraging genetic variants as instrumental variables from extensive genome-wide association studies (GWAS) tailored to populations of European descent. Glutamine quantification was executed through a rigorously validated Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) protocol. Comparative analyses of glutamine levels were conducted across leukemia patients versus healthy controls, pre- and post-asparaginase administration. Statistical evaluations employed inverse variance weighted (IVW) models, MR-Egger regression, and sensitivity tests addressing pleiotropy and heterogeneity.ResultsThe MR findings underscored a significant inverse association between glutamine levels and leukemia risk (IVW p = 0.03558833), positing lower glutamine levels as a contributory factor to heightened leukemia susceptibility. Conversely, the analysis disclosed no substantive causal impact of leukemia on glutamine modulation (IVW p = 0.9694758). Notably, post-asparaginase treatment, a marked decrement in plasma glutamine concentrations was observed in patients (p = 0.0068), underlining the profound metabolic influence of the therapeutic regimen.ConclusionThis study corroborates the hypothesized inverse relationship between plasma glutamine levels and leukemia risk, enhancing our understanding of glutamine’s role in leukemia pathophysiology. The pronounced reduction in glutamine levels following asparaginase intervention highlights the critical need for meticulous metabolic monitoring to refine therapeutic efficacy and optimize patient management in clinical oncology. These insights pave the way for more tailored and efficacious treatment modalities in the realm of personalized medicine.

To address the burdensome and time-consuming nature of manual diagnosis of pathological sections, this study proposes an automated pathological image detection system. This system can directly detect pathological images and accurately locate lesion tissues, providing a reference for pathological diagnosis. We propose an improved MobileViT model for feature extraction in the system, which we have named CSMViT. Considering the complexity and multi-scale characteristics of pathological images, we made three significant modifications to the MobileViT model. First, the original MV2 module was replaced with an improved Ghost module to reduce the model’s parameter count, enhance detection accuracy, and accelerate inference speed. Second, we improved the backbone structure of the network to achieve multi-scale feature learning, which not only further reduces the parameter count but also allows for more effective capture of features at different scales. Lastly, we introduced a new CSA module that can simultaneously accept two feature maps of different sizes as input. Through internal attention mechanisms and feature fusion, this module achieves cross-scale feature learning. Experimental results indicate that the CSMViT model achieved accuracy, F1-score, and specificity of 99.42%, 99.4%, and 99.6%, respectively. Additionally, the detection accuracy of CSMViT for the entire pathological image is 84%, representing an 8% improvement over the original network. Notably, the FLOPs of CSMViT is 1.461G, which is a 72.19% reduction compared to the original network, significantly decreasing the model’s complexity. These results thoroughly demonstrate the effectiveness and substantial value of CSMViT in pathological image detection.

A verifiable random function (VRF) is a pseudorandom function that enables source verification. By providing a public verification key and accompanying proof with the output, all parties can verify the correctness of the output without interaction. VRF has gained widespread adoption in blockchain applications, including Algorand, Ouroboros, and ChainLink. This article introduces SM2VRF, the first VRF based on the Chinese standard SM2 cryptographic algorithm, and extends it to a batch construction called SM2VRF-B for efficient verification of multiple sources. We showcase the applicability of SM2VRF in an electronic random committee election scenario, where the blockchain is utilized for storing candidate parameters and votes. By employing the Hamming distance, our scheme eliminates the risk of election failure. We provide a security proof for the proposed scheme, followed by an evaluation of the performance of both SM2VRF and SM2VRF-B. We implement our committee election scheme with Ethereum to assess the feasibility and efficiency.

To address the limitations of traditional burdensome and time-consuming manual diagnosis of Sjogren’s syndrome, this study proposes and implements an improved version of YOLOv5s algorithm, named YOLOv5s-MSS. Using YOLOv5s-MSS, we are able to detect lymphocytic infiltrative lesions in pathological images and provide assistance for pathological diagnosis. Given the small size of lymphocytes and the difficulty in distinguishing them, we made four improvements to the YOLOv5s model. Firstly, we replace the original CIOU loss function with the Focal-SIOU loss function to accelerate model convergence and improve the detection accuracy. Additionally, we introduce the multi-head self-attention module into the backbone to enhance the model’s ability to capture long range dependencies and overcome the challenges posed by complex background. Furthermore, we introduce the Shuffle Attention module into the neck, which enhances the model’s ability to fuse features from both spatial and channel dimensions. Finally, we remove the 1/32 downsampling section in the neck and the corresponding large object detection head. This not only enhances accuracy but also reduces parameters and model complexity. Experimental results show that YOLOv5s-MSS achieves a mAP, Precision, and Recall of 93.2%, 87.2%, and 89%, representing increases of 2.9%, 2.6%, and 2.8% compared to the original YOLOv5s model. Additionally, YOLOv5s-MSS reduces the parameters by 28.2%. These results demonstrate the effectiveness and value of YOLOv5s-MSS for lymphocyte detection.

Abstract Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs), need to be considered in performing traffic forecasting tasks. However, the current research methods encounter difficulties in effectively incorporating these factors with traffic characteristics and efficiently updating them, which leads to a lack of dynamics and interpretability. Moreover, capturing temporal dependence and spatial dependence separately and sequentially can result in issues, such as information loss and model errors. To address these challenges, we present a Knowledge Representation learning-actuated spatial–temporal graph neural network (KR-STGNN) for traffic flow prediction. We combine the knowledge embedding with the traffic features via Gated Feature Fusion Module (GFFM), and dynamically update the traffic features adaptively according to the importance of external factors. To conduct the co-capture of spatial–temporal dependencies, we subsequently propose a spatial–temporal feature synchronous capture module (ST-FSCM) combining dilation causal convolution with GRU. Experimental results on a real-world traffic data set demonstrate that KR-STGNN has superior forecasting performances over diverse prediction horizons, especially for short-term prediction. The ablation and perturbation analysis experiments further validate the effectiveness and robustness of the designed method.

Novel strategies in intratumoral injection and emerging immunotherapies have heralded a new era of precise cancer treatments. The affinity of SARS-CoV-2 to ACE2 receptors, a feature which facilitates virulent human infection, is leveraged in this research. Colon cancer cells, with their high ACE2 expression, provide a potentially strategic target for using this SARS-CoV-2 feature. While the highly expression of ACE2 is observed in several cancer types, the idea of using the viral spike protein for targeting colon cancer cells offers a novel approach in cancer treatment. Intratumoral delivery of nucleic acid-based drugs is a promising alternative to overcoming the limitations of existing therapies. The increasing importance of nucleic acids in this realm, and the use of Lipid Nanoparticles (LNPs) for local delivery of nucleic acid therapeutics, are important breakthroughs. LNPs protect nucleic acid drugs from degradation and enhance cellular uptake, making them a rapidly evolving nano delivery system with high precision and adaptability. Our study leveraged a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) combined with a receptor-binding domain from the SARS-CoV-2 spike protein, encapsulated in LNPs, to target colon cancer cells. Our results indicated that the TRAIL fusion-mRNA induced apoptosis in vitro and in vivo. Collectively, our findings highlight LNP-encapsulated TRAIL fusion-mRNA as a potential colon cancer therapy.

Background: ARF family proteins are a kind of small GTPases, which are involved in regulating a variety of basic functions of cells. In recent years, the role and molecular regulatory mechanisms of ARFs in tumor progression have received increasing attention, and research reports on most of their family members are increasing. However, research on the clinical and pathological relevance of ARF5 in cancer, especially in hepatocellular carcinoma, still needs to be improved. Methods: RNA-seq data in the Cancer Genome Atlas (TCGA) and genome tissue expression (GTEx) databases were used to analyze the expression and pathological data of ARFs family in Pan-cancer. Kaplan-Meier and Cox regression were used for prognostic analysis of ARF5 and Pan-cancer. Combined with ImmuCellAI database and TIMER2 database, the relationship between ARF5 expression and immune cell tumor infiltration in hepatocellular carcinoma (HCC) was analyzed. WGCNA is used to construct the co-expression gene network related to ARF5 expression in HCC and screen important modules and central genes. GO and KEGG path enrichment analysis were carried out for the genes in the modules with clinical significance. GSEA analysis was performed to take into account the role of genes with small differences. Finally, ceRNA network analysis was used to explore the molecular mechanism of miRNAs and lncRNAs regulating ARF5 expression. Results: ARFs family (ARF1, ARF3, ARF4, ARF5, ARF6) are generally highly expressed in Pan-cancer. ARF5 is significantly highly expressed in 29 cancers, and the high expression of ARF5 in HCC patients is significantly negatively correlated with OS, DFI, PFI and DSS, which may lead to cancer deterioration by participating in tumor immune infiltration of HCC. Through WGCNA analysis, the expression of ARF5 in HCC may be involved in many cellular processes that consume a lot of energy, such as ribosome formation, RNA and protein synthesis and lipids, as well as COVID-19, nonalcoholic fatty liver, neurodegenerative diseases and other disease pathways. Conclusion: ARFs, especially ARF5, are overexpressed in many human tumors. This study shows for the first time that ARF5 is significantly correlated with the poor prognosis of HCC patients, which may play a role as an oncogene, suggesting that ARF5 has the potential as a biomarker for the diagnosis and treatment of HCC.

Abstract Background Avian influenza viruses (AIV), particularly H5N6, have risen in infection frequency, prompting major concerns. Single-cell RNA sequencing (scRNA-seq) can illustrate the immune cell landscape present in the peripheral circulation of influenza H5N6-infected individuals at the single-cell level. This study attempted to employ scRNA-seq technology to map the potentially hidden single cell landscape of influenza H5N6. Methods High-quality transcriptomes were generated from scRNA-seq data of peripheral blood mononuclear cells (PBMCs), which were taken from a critically-ill child diagnosed with H5N6 avian influenza infection and one healthy control donor. Cluster analysis was then performed on the scRNA-seq data to identify the different cell types. The pathways, pseudotime developmental trajectories and gene regulatory networks involved in different cell subpopulations were also explored. Results In total, 3,248 single cell transcriptomes were captured by scRNA-seq from PBMC of the child infected with H5N6 avian influenza and the healthy control donor and further identified seven immune microenvironment cell types. In addition, a subsequent subpopulation analysis of innate lymphoid cells (ILC) and CD4+ T cells revealed that subpopulations of ILC and CD4+ T cells were involved in cytokine and inflammation-related pathways and had significant involvement in the biological processes of oxidative stress and cell death. Conclusion In conclusion, characterizing the overall immune cell composition of H5N6-infected individuals by assessing the immune cell landscape in the peripheral circulation of H5N6 avian influenza-infected and healthy control donors at single-cell resolution provides key information for understanding H5N6 pathogenesis.

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

BackgroundChronic rhinosinusitis (CRS) is a chronic inflammatory disease with an autoimmune background. Altered expression levels of T cell immunoglobulin and mucin-domain containing-3 (TIM-3), C-X-C chemokine receptor type 5 (CXCR5), and programmed cell death protein 1 (PD-1) are implicated in the progression of inflammatory and autoimmune diseases. Moreover, CXCR5+TIM-3-PD-1+ stem-like cytotoxic T cells function as memory stem cells during chronic disease processes and retain cytotoxicity-related gene networks.ObjectivesTo explore the expressions of CXCR5, TIM-3, and PD-1 on T cells and their correlation with clinical parameters in CRS.MethodsFlow cytometry was used to assess the expressions and co-expressions of CXCR5, TIM-3, and PD-1 on T cells in the tissues of the paranasal sinus and peripheral blood of patients with CRS as well as healthy controls. Immunofluorescence was used to assess the co-localization of TIM-3, CXCR5, and PD-1 with T cells. The disease severity of our patients with CRS was evaluated using the Lund-Mackay score. A complete blood count was also performed for the patients with CRS.ResultsExpression levels of CXCR5 and PD-1 on T cells were significantly increased in the nasal tissues of patients with CRS. Compared with those in healthy controls, patients with CRS had high percentages of CXCR5+TIM-3-PD-1+ CD8+ and CD4+ T cells in nasal tissues, while no significant difference was observed in peripheral blood levels. Patients with CRS had a higher density of nasal CXCR5+TIM-3-PD-1+ T cells than that in healthy controls. CXCR5+TIM-3-PD-1+ CD8+ T cell levels in the nasal polyps of patients with CRS were negatively correlated with the patients’ Lund-Mackay scores. The levels of CXCR5+TIM-3-PD-1+ T cells in nasal tissues were also negatively associated with disease duration and positively associated with the chronic inflammatory state of CRS.ConclusionsThe level of CXCR5+TIM-3-PD-1+ stem cell-like T cells, especially CXCR5+TIM-3-PD-1+ CD8+ T cells, is increased in CRS. Therefore, inducing CXCR5+TIM-3-PD-1+ T cell exhaustion may be an effective immunotherapy for CRS.

Background Cytomegalovirus (CMV) reactivation after unmanipulated haploidentical stem cell transplantation (SCT) frequently occurs, causing life-threatening morbidities and transplantation failure. Pre-emptive therapy upon the detection of CMV viremia using antiviral agents is currently the standard of care but it was associated with significant toxicity. The CMV antigen-specific cytotoxic T lymphocyte therapy was limited by the time-consuming manufacture process and relatively low success rate. More effective and safer approaches for the treatment of CMV reactivation after haploidentical SCT are in urgent need.Methods A single-arm, open-label, phase I clinical trial evaluating the safety and efficacy of CMV-targeting T cell receptor-engineered T (CMV-TCR-T) cell therapy as the first-line pre-emptive therapy for patients with CMV reactivation after haploidentical peripheral blood SCT (PBSCT) was conducted in the Chinese PLA General Hospital. Six patients with CMV reactivation after haploidentical SCT were adoptively transferred by one to three doses of SCT donors-derived CMV-TCR-T cells. This trial was a dose-escalation study with doses ranging from 1×103 CMV-TCR-T cells/kg body weight per dose to 5×105 CMV-TCR-T cells/kg per dose.Results Except for the grade 1 cytokine release syndrome observed in one patient and mild fever in two patients, no other adverse events were observed. Four patients had response within a month after CMV-TCR-T cell infusion without the administration of any antiviral agents. The other two patients who initially did not respond to CMV-TCR-T cell therapy had salvage ganciclovir and foscarnet administration and then had rapid CMV clearance. The CMV-TCR-T cells displayed overall robust expansion and persistence in the peripheral blood after infusion. The CMV-TCR-T cells were first detected in the peripheral blood of these patients 3–7 days after the first dose of CMV-TCR-T infusion, rapidly expanded and persisted for at least 1–4 months, providing long-term protection against CMV reactivation. In one patient, the CMV-TCR-T cells started to expand even when the anti-graft-versus-host disease reagents were still being used, further indicating the proliferation potential of CMV-TCR-T cells.Conclusions Our study first showed CMV-TCR-T cell as a highly feasible, safe and effective first-line pre-emptive treatment for CMV reactivation after haploidentical PBSCT.Trial registration number ClinicalTrials.gov Registry (NCT05140187).

Plants and insects are the most diverse and ecologically important organisms in the terrestrial biosphere. Their interactions are also among the richest biotic relationships, and offer significant insights into the evolution of terrestrial ecosystem complexity through the geological record. This investigation of the late Rhaetian Xujiahe and the earliest Jurassic Zhenzhuchong floral assemblages provides the first data on foliar herbivory generated by terrestrial arthropods across the Triassic–Jurassic transition in the eastern Tethys (East Asia) region. The damage types from two fossil assemblages are collectively attributed to seven functional feeding and egg-laying categories (i.e., hole feeding, margin feeding, surface feeding, skeletonization, piercing and sucking, oviposition, and galling). Most feeding strategies are spread across the major plant groups and persist through the Triassic–Jurassic boundary, with the exception of skeletonization (a category of external foliage feeding), which was restricted to the latest Triassic within dipteridacean ferns. The survey reveals that the respective frequency and diversity of interactions between plants and insects prior to and following the end-Triassic mass extinction event are almost the same, despite a substantial turnover of floral components. This suggest that insect herbivores were largely able to transfer to alternative (but commonly related) plant groups during the dramatic floristic turnover and environmental changes at the end of the Triassic. Sporadic occurrences of foliar modifications, such as marginal cusps on pinnules of Pterophyllum and prominent ridges on the rachises of some ferns and bennettites are interpreted as adaptations for defense against insect herbivores. A few differences in taxonomic composition and herbivory representation between the latest Triassic Xujiahe flora and the earliest Jurassic Zhenzhuchong flora are more likely to be related to collection and preservational biases rather than reflecting palaeoecological changes. We encourage further investigations exploring the distribution of insect damage in fossil floras from other palaeolatitudinal zones and spanning other major extinction events to develop a better understanding of terrestrial ecosystem responses to major crises in Earth’s history.

IntroductionTobacco root-knot nematode (RKN) is a highly destructive soil-borne disease worldwide. However, there is a lack of research on the relationship between RKN and tobacco root microbial community composition under large-scale geographical conditions in China.MethodsIn this study, we collected 65 samples from 28 main tobacco-growing areas across 10 provinces in China and conducted 16S rDNA sequencing to investigate the dynamic microbial changes in tobacco soil infected by RKN compared to healthy tobacco soil. Based on the analysis of rhizosphere soil bacterial communities, changes after RKN infection, and soil environmental factors.ResultsWe found the 28 tobacco-growing areas could be divided into two distinct groups with different microbial compositions and varying responses to RKN infection. In group1 of the provinces of Anhui, Henan, Shanxi, and Heilongjiang, Vicinamibacteria dominated the bacterial community, while Acidobacteriae was present in low abundance. In contrast, group2 of the other six provinces (Yunnan, Guizhou, Chongqing, Guangxi, Hubei, and Shandong) exhibited an opposite pattern. After infected by RKN, the genera Chitinophaga increased significant in group 1, while the genera Rhodococcus in group 2 exhibited a substantial increase. Alpha-diversity analysis revealed that RKN-infected tobacco exhibited a richer and more diverse rhizosphere soil bacterial community compared to healthy tobacco in most growing areas. A total of 12 kinds of soil environmental factors were measured in healthy and RKN-infected tobacco soil, and based on the co-occurrence and correlation analysis between environmental factors and microbial species, the pH level, calcium (Ca), magnesium (Mg), phosphorus (P), iron (Fe), and sodium (Na) were identified as key environmental factors influencing the population composition of rhizosphere microorganisms during RKN infection. We observed that RKN infection further increased the pH in weakly alkaline group 1 soil, while weakly acidic group 2 soil experienced a further decrease in pH. Furthermore, we identified three genera as potential biocontrol or plant growth-promoting bacteria for tobacco.DiscussionThese findings provide valuable reference data for managing RKN disease in different tobacco-growing areas and contribute to the exploration of new and effective biological control methods.

Normalized Difference Vegetation Index (NDVI) is widely used to represent the greenness indicator for the Ecological Environment Quality (EEQ) assessment based on the traditional Remote Sensing Ecological Index (RSEI). However, NDVI saturation issues are reported in agriculture and forest ecosystems at high greenness and biomass, creating a challenge when using NDVI to reflect the greenness components for forest EEQ assessment. In this paper, three-dimensional greenness (TDG) was obtained by the Forest Canopy Height (FCH) and Fractional Vegetation Cover (FVC) to quantify the forest greenness. The NDVI of RSEI was replaced with TDG to establish an improved remote sensing ecological index (TDRSEI) for the forest EEQ evaluation in the Central Yunnan, China. Moreover, we analyzed the difference between RSEI and TDRESI by qualitative and quantitative means and discussed deeply the optical saturation of NDVI by the quadratic function. The results shown that there were similar spatial distribution patterns and a strong correlation between TDG and FCH, Leaf Area Index (LAI), FVC, and NDVI, the TDG can be used to replace NDVI for reflecting forest greenness. The standard deviation of TDRSEI from the same FCH pixels was all less than that of RSEI, the absolute correlation coefficient between TDRSEI and ecological components were all greater than 0.63, and the mean values of TDRSEI (0.73, 0.84, and 0.90) from the relatively high FCH (20 m, 25 m, and 30 m) were greater than RSEI (0.71, 0.78, and 0.85), showing that the TDRSEI were more stable than RSEI for forest EEQ assessment and it can improve the EEQ in the high FCH. In Central Yunnan, China, the forest EEQ from TDRSEI and RSEI maps both increased with the growth of FCH, the distribution of RSEI had no significant difference between the low FCH and the high FCH, while the mean values of TDRSEI increased linearly with FCH growth. Besides, the ecological saturation points of TDRSEI and RSEI corresponding to the FCH were 19.50 m and 34.02 m, respectively. Therefore, TDRSEI method integrates the three-dimensional greenness to evaluate the forest EEQ objectively.

Objective: To investigate the chest computed tomography (CT) imaging features of novel coronavirus omicron strain infection in children. Methods: The chest CT imaging of 77 children diagnosed with coronavirus disease (COVID-19) using nucleic acid testing or antigen testing from December 10 to 31, 2022 at Shenzhen Children's Hospital were retrospectively analyzed, including 51 males and 26 females. Among them, 50 cases were positive for CT presentation, and their ages ranged from 1 month to 14 years, with a mean age of 5.5 years and a median age of 3.9 years, including 21 cases in the infant and toddler group (≤3 years), 14 in the preschool group (3~7 years), and 15 cases in the school-age group (≥7 years). Two radiologists independently identified the location of intrapulmonary lesions, CT manifestations of intrapulmonary lesions, concomitant CT manifestations of the lungs, extent of lesion involvement, and imaging staging. Results: 50 patients (64.9%) had positive lung CT presentation, with the most lesions in the lower lobes and subpleural areas of both lungs and the least lesions in the hilus of lung, and the number of lesions were single, multiple, and diffusely distributed, with diffuse distribution being the most common. Ground-glass opacity were seen in 16 cases (32%), solid nodules in 20 (40%), lamellar consolidation in 24 (48%), lamellar ground glass in 30 (60%), and solid and ground glass in 20 (40%), with lamellar ground glass being the most common; crazy-paving pattern in 4 cases (8%), thickening of lobular septa in 12 (24%), tree fog sign in 12 (24%), and vascular enlargement in 13 (26%). 7 cases (14%) were of subpleural curvilinear line, 6 (12%) of reversed halo sign, and 17 (34%) of air bronchogram, all without cavitation; 5 (10%) were of pleural effusion, 7 (14%) of pleural thickening, 1 (2%) of pericardial effusion, and 7 (14%) of lymphadenopathy. 29 patients (37.7%) were in the early stage of imaging, 15 (19.5%) were in the progressive stage, and 6 (7.8%) were in the severe stage. Conclusion: The chest CT imaging of novel coronavirus omicron strain infection in children was morphologically diverse. The presence of pleural effusion, pleural thickening, and pericardial effusion suggests a higher risk of serious prognosis. Chest CT in children with omicron strain infection can provide valuable information for diagnosis and determine the extent of the disease.

The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of multi-parameter models, which makes its practical application and the potential impact on predictive precision challenging due to the inclusion of non-essential or redundant variables. Therefore, this study systematically evaluated the performance of different parameter combinations derived from Sentinel-2 imagery, using variable optimization approaches with four machine learning algorithms (Random Forest Regression, RF; XGBoost Regression, XGBR; K Nearest Neighbor Regression, KNNR; and Support Vector Regression, SVR) for the estimation of the AGB of rubber plantations. The results indicate that RF achieved the best estimation accuracy (R2 = 0.86, RMSE = 15.77 Mg/ha) for predicting rubber plantation AGB when combined with Boruta-selected variables, outperforming other combinations (variable combinations obtained based on importance ranking, univariate combinations, and multivariate combinations). Our research findings suggest that the consideration of parameter-optimized remote sensing variables is advantageous for improving the estimation accuracy of forest biophysical parameters, when utilizing a large number of parameters for estimation.

The effect of the mainline slope on the ceiling temperature profile in a branched tunnel has not been clarified nor included in existing models. Thus, in this paper, the numerical code was employed to investigate the induced airflow velocity and gas temperature beneath the ceiling in a branch tunnel with a sloped upstream mainline. The mainline slope varied from 1% to 7%, with an interval of 1%. Five fire power of 3 MW, 5 MW, 10 MW, 15 MW, and 20 MW are employed on each slope. The airflow velocity and the longitudinal temperature in the mainline tunnel are measured and analyzed. Results show that the stack effect obviously occurred, which caused longitudinal velocity to prevent the smoke reverse flow in the mainline. The induced airflow velocity in the upstream inclined mainline is higher with increasing slope, and the dimensionless velocity is normalized well by the proposed expression. The maximum ceiling temperature is independent of the mainline slope and correlated well by Q*2/3, but the effect of the mainline slope on temperature longitudinal decay is worth considering. Finally, a normalized expression for longitudinal temperature decay in an inclined mainline is proposed by taking the fire power and mainline slope into account.

European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) is a state-of-the-art reanalysis dataset and has been widely used in climate change analysis and land surface process simulations. However, few assessments have been conducted across the entire ocean for this dataset till now. The motivation of this study is to utilize the in-situ observations and remotely sensed sea surface temperature (SST) products to propose a new approach to evaluate the precision of ERA5 air temperature over the whole ocean. First, a stacked multilayer perceptron regressor model was employed to map global marine air temperature (MAT) by incorporating in-situ records into moderate-resolution imaging spectroradiometer (MODIS) SST products. Second, a monthly MAT dataset with a spatial resolution of 0.05° × 0.05° was developed and validated for the assessment of ERA5 air temperature particularly in the regions where only satellite observations are available. It demonstrates that the quality of estimated MAT (RMSE = 1.62 °C, Bias = 0.03 °C) is evidently high in 80.3% of the observation stations, which is much better than that of averaged SST (RMSE = 2.29 °C, Bias = 1.14 °C). Thus, the estimated MAT is credible enough to be employed to assess ERA5 air temperature both at the grid and regional scale. Finally, the ERA5 air temperature was compared and validated with the estimated MAT temporally and spatially. The further study suggested that estimated MAT and ERA5 air temperature basically maintain consistency in expressing persistent warming, particularly in the western Pacific, western Atlantic, and northern Indian Ocean. In addition, the trend of ERA5 air temperature during 50°S–50°N reaches 0.016 ± 0.003°C/yr, which is similar to that of estimated MAT (0.017 ± 0.002 °C/yr) during 2003–2021.

The growing integration of distributed energy resources into distribution systems poses challenges for voltage regulation. Dynamic VAR Compensators (DVCs) are a new generation of power electronics-based Volt/VAR compensation devices designed to address voltage issues in distribution systems with a high penetration of renewable generation resources. Currently, the IEEE Std. 1547-based Volt/VAR Curve (VV-C) is widely used as the local control scheme for controlling a DVC. However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC. In this paper, we propose an optimal dispatch and control mechanism to enhance the conventional VV-C based localized DVC control. First, we establish a multi-objective optimization framework to identify the optimal dispatch strategy and suitable placement for the DVC. Next, we introduce two supervisory control strategies to determine the appropriate instances for adjusting the VV-C when the operating condition changes. The outlined scheme comprises two primary stages: time segmentation and VV-C fitting. Within this framework, each time segment aims to produce optimized Q-V trajectories. The proposed method is tested on a modified IEEE 123-bus test system using OpenDSS for a wide range of operating scenarios, including sunny and cloudy days. Simulation results demonstrate that the proposed scheme effectively reduces voltage variations compared to the standard VV-C specified in IEEE Std. 1547.

The driver’s mental workload is closely related to driving safety. However, how to analyze the driver’s mental workload in a reasonable and correct manner remains an open question. As an important factor to evaluate mental workload, changes in physiology encounter two clear problems: (1) Physiological factor contains multi-characteristic indicators, there is a lack of reasonable means for synchronizing multi-dimensional tabular data, and the limits of tabular data processing in the evaluation of mental workload have a significant impact on the evaluation results. (2) The physiological data obtained during the driving process are of the time-series variety. The correlation of numerous indicators must be considered in time-series data correlation analysis. Mental workload should be the result of multiple indicators interacting over time, rather than a single instant. In this regard, we propose a model, that is the long time sequences and multiple physiological factors(LTS-MPF), for classifying and predicting multiple physiological changes in the time series. In contrast to previous methods of processing data in a single instant, LTS-MPF can directly analyze all time-series factors that may affect the driver’s mental workload during a time interval, such as Heart rate growth, Heart rate variability, and Electrodermal activity, and so on. Furthermore, LTS-MPF can predict the driver’s mental workload in the next 1s as well as classify the current sequence’s results. Specifically, we collect physiological data from drivers via sensors. These collected data are processed and transformed into tabular data. The table’s columns represent features, while the rows represent all feature data at one moment in time. The row order also indicates the forward and backward order of the different moments. We convert each row in this table into an embedding feature and feed the entire table into our proposed LTS-MPF based on the Transformer model. The LTS-MPF achieves time series correlation while eliminating column feature series irrelevance. The experiment results reveal that LTS-MPF exceeds earlier techniques in forecasting the driver’s mental workload, with an accuracy of up to 94.3%. And its accuracy in predicting mental workload in the future for one second can reach 93.5%. These findings suggest that LTS-MPF can be utilized to not only better evaluate a driver’s mental workload in the present, but also in the future, providing solid data for early warning of dangerous driving behaviors and enhancing driving safety.

The polarization of distinct scaffold-signaling hubs at opposite cell poles constitutes the basis of asymmetric cell division. Here, the authors show that phase separation serves as a general mechanism to regulate the assembly and dynamics of a new-pole scaffold-signaling hub.

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world. The pro-viral integration site for Moloney murine leukemia virus 1 (PIM1) is a proto-oncogene and belongs to the serine/threonine kinase family, which are involved in cell proliferation, migration, and apoptosis. Fibroblast growth factor receptor 1 (FGFR1) is a tyrosine kinase that has been implicated in cell proliferation, differentiation and migration. Small molecule HCI-48 is a derivative of chalcone, a class of compounds known to possess anti-tumor, anti-inflammatory and antibacterial effects. However, the underlying mechanism of chalcones against colorectal cancer remains unclear. This study reports that HCI-48 mainly targets PIM1 and FGFR1 kinases, thereby eliciting antitumor effects on colorectal cancer growth in vitro and in vivo. HCI-48 inhibited the activity of both PIM1 and FGFR1 kinases in an ATP-dependent manner, as revealed by computational docking models. Cell-based assays showed that HCI-48 inhibited cell proliferation in CRC cells (HCT-15, DLD1, HCT-116 and SW620), and induced cell cycle arrest in the G2/M phase through modulation of cyclin A2. HCI-48 also induced cellular apoptosis, as evidenced by an increase in the expression of apoptosis biomarkers such as cleaved PARP, cleaved caspase 3 and cleaved caspase 7. Moreover, HCI-48 attenuated the activation of downstream components of the PIM1 and FGFR1 signaling pathways. Using patient-derived xenograft (PDX) murine tumor models, we found that treatment with HCI-48 diminished the PDX tumor growth of implanted CRC tissue expressing high protein levels of PIM1 and FGFR1. This study suggests that the inhibitory effect of HCI-48 on colorectal tumor growth is mainly mediated through the dual-targeting of PIM1 and FGFR1 kinases. This work provides a theoretical basis for the future application of HCI-48 in the treatment of clinical CRC.

Abstract Background Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease; cellular glutamine metabolism in fibroblast-like synoviocytes (FLSs) of RA was known to be essential for RA pathogenesis and progression. NEAT1, a long non-coding RNA, functions as an oncogene in diverse cancers. The exact roles and molecular mechanisms of NEAT1 in fibroblast-like synoviocytes (FLSs) of RA patients are unknown. Methods Expression of NEAT1 and miR-338-3p was measured by qRT-PCR. lncRNA-miRNA and miRNA-mRNA interactions were predicted from starBase and validated by RNA pull-down and luciferase assay. The glutamine metabolism of FLSs was evaluated by glutamine uptake and glutaminase activity. Cell death in FLSs in response to H2O2 was assessed by MTT and Annexin V assays. Results NEAT1 was significantly upregulated, and miR-338-3p was significantly downregulated in FLSs from RA patients compared to normal FLSs. Silencing of NEAT1 and overexpression of miR-338-3p suppressed glutamine metabolism in FLSs-RA and promoted H2O2-induced apoptosis. Bioinformatics analysis showed that NEAT1 sponges miR-338-3p to form competing endogenous RNA (ceRNAs), which was verified by RNA pull-down assay and luciferase assay FLSs-RA had an increased rate of glutamine metabolism compared to normal FLSs increased compared to normal FLSs. The results confirmed that GLS (Glutaminase), a key enzyme in glutamine metabolism, is a direct target of miR-338-3p in FLSs-RA. miR-338-3p inhibition of glutamine metabolism was verified by rescue experiments verified. Finally, restoration of miR-338-3p in FLSs-RA expressing NEAT1 overcomes NEAT1-promoted glutamine metabolism and resistance to apoptosis. Conclusions This study reveals the essential role and molecular targets of NEAT1-regulated glutamine metabolism and FLSs-RA dysfunction in fibroblast-like synoviocytes of RA and indicates that blocking the molecular pathway via non-coding RNAs may be beneficial for RA patients.

Objective Ki-67 is associated with breast cancer subtypes, but the optimal cutoff point of Ki-67 has not been established in our center. We evaluated the cutoff point of Ki-67 in breast cancer and analyzed the associations among Ki-67, clinicopathological features, and prognosis. Methods The clinicopathological data and prognostic information of patients with breast cancer treated in our center were retrospectively collected, and the optimal cutoff point of Ki-67 was determined by univariate and multivariate survival risk analyses. The cutoff point was used to group the patients, and the differences in the clinicopathological features and prognosis were analyzed between the two groups. Results In total, 609 patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative primary breast cancer were enrolled. The mean Ki-67 value was 22.3% ± 15.4%, the median was 20%, and a cutoff point of 30% was an independent factor influencing recurrence-free survival. When 30% was used as the cutoff point, patients with a Ki-67 value of ≤30% had a better prognosis and lower tumor malignancy. Conclusion The optimal cutoff point of Ki-67 in breast cancer in our center is 30%.

We propose a newly optimized nonlinear point-coupling parameterized interaction, PC-L3R, for the relativistic Hartree-Bogoliubov framework with a further optimized separable pairing force by fitting to observables, i.e., the binding energies of 91 spherical nuclei, charge radii of 63 nuclei, and 12 sets of mean pairing gaps consisting of 54 nuclei in total. The separable pairing force strengths of proton and neutron are optimized together with the point-coupling constants, and are justified in satisfactory reproducing the empirical pairing gaps. The comparison of experimental binding energies compiled in AME2020 for 91 nuclei with the ones generated from the present and other commonly used point-coupling interactions indicates that the implementation of PC-L3R in relativistic Hartree-Bogoliubov yields the lowest root-mean-square deviation. The charge radii satisfactory agree with experiment. Meanwhile, PC-L3R is capable of estimating the saturation properties of the symmetric nuclear matter and of appropriately predicting the isospin and mass dependence of binding energy. The experimental odd-even staggering of single nucleon separation energies is well reproduced. The comparison of the estimated binding energies for 7,373 nuclei based on the PC-L3R and other point-coupling interactions is also presented.

ABSTRACT Cell polarity development is the prerequisite for cell differentiation and generating biodiversity. In the model bacterium Caulobacter crescentus, the polarization of the scaffold protein PopZ during the predivisional cell stage plays a central role in asymmetric cell division. However, our understanding of the spatiotemporal regulation of PopZ localization remains incomplete. In the current study, a direct interaction between PopZ and the new pole scaffold PodJ is revealed, which plays a primary role in triggering the new pole accumulation of PopZ. The coiled-coil 4-6 domain in PodJ is responsible for interacting with PopZ in vitro and promoting PopZ transition from monopolar to bipolar in vivo. Elimination of the PodJ-PopZ interaction impairs the PopZ-mediated chromosome segregation by affecting both the positioning and partitioning of the ParB-parS centromere. Further analyses of PodJ and PopZ from other bacterial species indicate this scaffold-scaffold interaction may represent a widespread strategy for spatiotemporal regulation of cell polarity in bacteria. IMPORTANCE Caulobacter crescentus is a well-established bacterial model to study asymmetric cell division for decades. During cell development, the polarization of scaffold protein PopZ from monopolar to bipolar plays a central role in C. crescentus asymmetric cell division. Nevertheless, the spatiotemporal regulation of PopZ has remained unclear. Here, we demonstrate that the new pole scaffold PodJ functions as a regulator in triggering PopZ bipolarization. The primary regulatory role of PodJ was demonstrated in parallel by comparing it with other known PopZ regulators, such as ZitP and TipN. Physical interaction between PopZ and PodJ ensures the timely accumulation of PopZ at the new cell pole and the inheritance of the polarity axis. Disruption of the PodJ-PopZ interaction impaired PopZ-mediated chromosome segregation and may lead to a decoupling of DNA replication from cell division during the cell cycle. Together, the scaffold-scaffold interaction may provide an underlying infrastructure for cell polarity development and asymmetric cell division.

In the study, tartary buckwheat sprouts of Chuanqiao 1 cultivar were investigated, and the effects of microwave-assisted L-phenylalanine (L-Phe) treatment on the flavonoids of tartary buckwheat during germination were studied. The content of flavonoids of tartary buckwheat sprouts was used as the standard, the single factor test and CCD response surface design methods were used to optimize the process of flavonoid enrichment by microwave-assisted L-Phe treatment of tartary buckwheat during germination. High performance liquid chromatography (HPLC) was used to analyze the flavonoid in tartary buckwheat sprouts. The results showed that the tartary buckwheat seeds were disinfected and germinated, then the seeds were treated by microwave with the power of 250 W for 90 s, the microwave temperature was (40±2)℃. Then, the tartary buckwheat seeds were cultured in the dark for 7 days at the condition of the temperature of 25 ℃ and the humidity of 75%±5% RH, and 2.90 mmol/L L-Phe solution was put into the germination tray as a nutrient solution. The result by detection showed the flavonoid content of tartary buckwheat sprouts on the 7th day was 6.26 g/100 g. Simultaneously, three flavonoids, chlorogenic acid, vitexin and rutin, were separated and determined by HPLC, and the content was 3.2764, 0.5644 and 31.8962 mg/g, respectively.

ABSTRACT: Background: Gastric adenocarcinoma (GA) is a heterogeneous tumor, and the accurate classification of GA is important. Previous classifications are based on molecular analysis and have not focused on GA with the primitive enterocyte phenotype (GAPEP), a unique subtype with a poor prognosis and frequent liver metastases. New substituted molecular (SM) classifications based on immunohistochemistry (IHC) are needed. Methods: According to the IHC staining results, we divided 582 cases into six types: mismatch repair deficient (dMMR), Epstein-Barr virus associated (EBVa), the primitive enterocyte phenotype (PEP), the epithelial mesenchymal transition (EMT) phenotype, not otherwise specified/P53 mutated (NOS/P53m) and not otherwise specified/P53 wild-type (NOS/P53w). We analyzed the clinicopathological features, the immune microenvironment (PD-L1, CD8) and expression of HER2 and VEGFR2 of those types. Results: There were 31 (5.3%) cases of the dMMR type, 13 (2.2%) cases of the EBVa type, 44 (7.6%) cases of the PEP type, 122 (21.0%) cases of the EMT type, 127 (21.8%) cases of the NOS/P53m type and 245 (42.1%) cases of the NOS/P53w type. Patients with the dMMR type had the best survival (P < 0.001). Patients with the EBVa type were younger (P < 0.001) and had higher PD-L1 and CD8 expression (P < 0.001) than other patients. Patients with the EMT type exhibited poor differentiation and a higher rate of abdominal metastasis. Patients with the NOS/P53m and PEP types had the worst survival rates and the highest PD-L1/HER2/VEGFR2 expression levels among all patients (P < 0.001). Conclusion: Different SM classifications have different clinicopathological features and expression patterns, which indicate the probable clinical treatment strategies for these subtypes.

Airborne geophysical surveys serve as an effective tool for litho-structural mapping, providing extensive and high-resolution underground information. They offer vital data support for the interpretation and analysis of lithologies and structures, complementing surface geological mapping. In the study area of the Nanpanjiang-Youjiang metallogenic belt in southeast China, we obtained high-resolution aeromagnetic and gamma-ray spectrometry data covering an area of approximately 27,000 km2, which facilitated the conduct of litho-structural mapping. The total magnetic intensity, reduction to the pole, and directional derivative maps generated from the aeromagnetic data, efficiently identified concealed rocks and faults. Additionally, the total count, potassium, thorium to potassium ratio, and ternary maps generated from the airborne gamma-ray spectrometry data demonstrated advantages in distinguishing carbonate rocks from clastic rocks. They also provided more comprehensive geological information, refining the structural strike and location interpreted by the aeromagnetic data. The litho-structural map produced in this study significantly contributes to our understanding of the structures in the Nanpanjiang area and offers valuable guidance for successful mineral exploration endeavors.

Quantification of three-dimensional green volume (3DGV) plays a crucial role in assessing environmental benefits to urban green space (UGS) at a regional level. However, precisely estimating regional 3DGV based on satellite images remains challenging. In this study, we developed a parametric estimation model to retrieve 3DGV in UGS through combining Sentinel-1 and Sentinel-2 images. Firstly, UAV images were used to calculate the referenced 3DGV based on mean of neighboring pixels (MNP) algorithm. Secondly, we applied the canopy height model (CHM) and Leaf Area Index (LAI) derived from Sentinel-1 and Sentinel-2 images to construct estimation models of 3DGV. Then, we compared the accuracy of estimation models to select the optimal model. Finally, the estimated 3DGV maps were generated using the optimal model, and the referenced 3DGV was employed to evaluate the accuracy of maps. Results indicated that the optimal model was the combination of LAI power model and CHM linear model (3DGV = 37.13·LAI−0.3·CHM + 38.62·LAI1.8 + 13.8, R2 = 0.78, MPE = 8.71%). We validated the optimal model at the study sites and achieved an overall accuracy (OA) of 75.15%; then, this model was used to map 3DGV distribution at the 10 m resolution in Kunming city. These results demonstrated the potential of combining Sentinel-1 and Sentinel-2 images to construct an estimation model for 3DGV retrieval in UGS.

Accurate information on the location, shape, and size of photovoltaic (PV) arrays is essential for optimal power system planning and energy system development. In this study, we explore the potential of deep convolutional neural networks (DCNNs) for extracting PV arrays from high spatial resolution remote sensing (HSRRS) images. While previous research has mainly focused on the application of DCNNs, little attention has been paid to investigating the influence of different DCNN structures on the accuracy of PV array extraction. To address this gap, we compare the performance of seven popular DCNNs—AlexNet, VGG16, ResNet50, ResNeXt50, Xception, DenseNet121, and EfficientNetB6—based on a PV array dataset containing 2072 images of 1024 × 1024 size. We evaluate their intersection over union (IoU) values and highlight four DCNNs (EfficientNetB6, Xception, ResNeXt50, and VGG16) that consistently achieve IoU values above 94%. Furthermore, through analyzing the difference in the structure and features of these four DCNNs, we identify structural factors that contribute to the extraction of low-level spatial features (LFs) and high-level semantic features (HFs) of PV arrays. We find that the first feature extraction block without downsampling enhances the LFs’ extraction capability of the DCNNs, resulting in an increase in IoU values of approximately 0.25%. In addition, the use of separable convolution and attention mechanisms plays a crucial role in improving the HFs’ extraction, resulting in a 0.7% and 0.4% increase in IoU values, respectively. Overall, our study provides valuable insights into the impact of DCNN structures on the extraction of PV arrays from HSRRS images. These findings have significant implications for the selection of appropriate DCNNs and the design of robust DCNNs tailored for the accurate and efficient extraction of PV arrays.

Reliable and accurate full-field deformation measurements at elevated temperatures using digital image correlation (DIC) require stable and high-contrast high-temperature speckle patterns to be prepared on the sample surface. However, conventional high-temperature speckle patterns fabricated by the existing methods possess several limitations, e.g., easily fail to preserve original pattern features due to the harsh environment and heavily dependent on the operator’s experience. In this study, we propose a reliable and reproducible high-temperature speckle fabrication method based on air plasma spraying (APS) and flexible speckle templates. This method involves covering the sample surface with pre-designed speckle templates and then spraying the melted speckle powders onto the specimen surface using an air plasma spray technique to obtain customized speckle patterns. The validity of the proposed method was verified by the speckle fabrication on both planar and curved samples and heating tests with these samples. Experimental results demonstrate that the speckle patterns made by the proposed method adhere well to the sample surface, remain stable during the heating process, and exhibit excellent agreement with the reference values in terms of the thermal expansion coefficients. The proposed method provides a reliable and efficient way to create customized and stable speckle patterns for accurate high-temperature DIC measurements.

This paper presents a novel dispatch and evaluation framework for battery energy storage systems (BESSs) to minimize a load servicing entity’s coincident demand during system peak hours. The framework consists of i) a two-step BESS dispatch process that accounts for uncertainties in forecasting system peak and using limited battery cycle life, and ii) procedures to design control parameters, determine BESS duration, and estimate the corresponding net benefits. In the proposed dispatch, a rule-based triggering mechanism is executed to determine whether to dispatch a BESS on an operating day by comparing the peak-day probability with a predetermined threshold. Once the dispatch is triggered, a model predictive control is carried out to maximize the expected reduction in peak demand. By exercising this two-step dispatch method with different thresholds, one can explore the trade-off between peak demand reduction effectiveness and loss of battery life, and thereby identify the optimal thresholds to maximize cumulative economic benefits. Case studies are conducted using the data provided by utilities in North Carolina. Simulation results are presented to demonstrate the effectiveness of the proposed method.

With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.

Abstract In total, 16 yeast were isolated from Chinese strong flavour Daqu samples and underwent RAPD analysis and identification. Totally, 11 different species were identified among these isolates including Saccharomyces cerevisiae , Hanseniaspora vineae, Pichia kluyveri, Trichosporon asahii, Wickerhamomyces anomalus, Kluyveromyces lactis, Yarrowia lipolytica, Wickerhamomyces mori, Galactomyces geotrichum, Dabaryomyces hansenii, and Saccharomyces kudriavzevii. To understand the impact of these yeast strains on the quality and flavour of Daqu, we then assessed volatile compounds associated with Daqu samples fermented with corresponding strains. These analyses revealed strain YE006 exhibited the most robust ability to produce ethanol via fermentation but yielded relatively low quantities of volatile compounds, whereas strain YE010 exhibited relatively poor fermentation efficiency but produced the greatest quantity of volatile compounds. These two yeast strains were then utilized in a mixed culture to produce fortified Daqu, with the optimal inoculum size being assessed experimentally. These analyses revealed that maximal fermentation, saccharifying, liquefying, and esterifying power as well as high levels of volatile compounds were achieved when using a 2% inoculum composed of YE006/YE010 at a 1:2 (v/v) ratio. When the liquor prepared using this optimized fortified Daqu was compared to unfortified control Daqu, the former was found to exhibit significantly higher levels of flavour compounds and better sensory scores. Overall, our findings may provide a reliable approach to ensuring Daqu quality and improving the consistency and flavour of Chinese strong-flavour liquor through bioaugmentation.

Nowadays, the output of light crude oil is approaching its peak, which has shifted the attentions of petroleum industry to a large quantity of heavy oil and bitumen in order to offset the growing demand of energy and fuels. The heavy oil and bitumen are usually characterized by high viscosity, high density/low API gravity, low yield of low boiling point fuel fraction, and high heteroatom content in comparison with the light oil, requiring in situ upgrading to improve the mobility of heavy oil. before recovery it. One of the methods for upgrading is to add catalysts to the underground reservoir.This paper firstly reviewed various kinds of catalysts, including mineral, water-soluble, oil-soluble, solid acid, ionic liquid (IL) catalysts, and dispersed and super-dispersed catalysts that reported in domestic and foreign literatures, and secondly described their effectiveness in underground catalytic cracking and upgrading of heavy oil. This critical review suggests that it is necessary to develop the catalytic cracking catalysts of low-cost, high activity, high selectivity, and wide adaptability in order to accommodate a wide range of heavy oil with different properties, especially the ultra-dispersed nano-catalysts. The in situ upgrading technology by injecting super-dispersed nano-catalysts is a promising method to improve the quality of the lowest grade heavy oil components and enhance the recovery of heavy oil reservoirs. Moreover, further research is needed to explore the optimal process of catalyst synthesis, catalytic working conditions and the actual mechanisms of heavy oil with catalysts.

Abstract Background Both gastric adenocarcinoma with primitive enterocyte phenotype (GAPEP) (including hepatoid adenocarcinoma) and alpha-fetoprotein (AFP)-producing gastric adenocarcinoma have poor prognoses. However, the value of the serum AFP test and AFP/glypican-3 (GPC3)/spalt-like transcription factor 4 (SALL4) immunohistochemistry is still not clear, and these two methods have not yet been thoroughly compared. Methods We collected 421 consecutive non-neoadjuvant surgically or endoscopically resected gastric adenocarcinoma patients with serum AFP results before surgery (group A). We divided these cases into serum AFP-high (sAFP-H) and serum AFP-normal (sAFP-N) by serum AFP levels, and into GAPEP (expressing AFP, GPC3, or SALL4) and non-GAPEP (nGAPEP) by AFP/GPC3/SALL4 immunohistochemistry results. We also collected 12 non-resected gastric adenocarcinoma patients with serum AFP ≥ 7 ng/mL before treatment (group B). We analyzed these patients’ clinicopathological characteristics and prognoses. Results Seventeen (4.04%) patients in group A were sAFP-H. These patients were younger and mainly had tubular adenocarcinoma with later pT (P = 0.014) and pN (P = 0.047) categories and more lymphovascular invasion (P 1000 ng/mL) had simultaneous metastases, mainly liver metastases. Both the serological method and immunohistochemical method were useful for predicting prognosis (AUC sAFP = 0.625, AUC A/G/S-IHC = 0.723, z statistic = 1.726, P = 0.084). The serum AFP level (especially > 1000 ng/mL) is more specific (100%), and immunohistochemistry is more sensitive (50%). Conclusion Both the serum AFP level and immunohistochemical expression of AFP/GPC3/SALL4 can be used to indicate a poor prognosis for gastric adenocarcinoma.

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.

Abstract Soil–water retention curve (SWRC) describes the relationship between soil matric potential and soil water content, which is recognized as the most important constitutive function for unsaturated soil that governs many hydrological and mechanical properties, such as fluid flow, hydraulic conductivity, chemical transport, and soil freezing and thawing, etc. Soil–water isotherm (SWI) measured from vapor adsorption technique captures the SWRC at medium to high matric potential range, where three physical mechanisms of soil–water retention (SWR) with distinct free energy levels are involved: adsorption on external particle surface, adsorption in interlamellar space, and capillarity. So far, there is not a single SWI model that can take all three mechanisms into full consideration in full matric potential range. Here a closed‐form SWI model is formulated as the mathematical sum of three independent SWIs, which are external sorptive SWI, internal sorptive SWI, and capillary SWI; each corresponds to one of the aforementioned SWR processes. The generality and effectiveness of the proposed model are experimentally validated through a suite of 21 soils, covering all common soil types with plasticity index (PI) of 4–132. The proposed SWI model shows excellent performance as it nearly perfectly (R2 > 0.99) fits both the adsorption isotherm and the desorption isotherm for each soil, validating the generality of the model. The external specific surface area (SSA) obtained from the external sorptive SWI correlates well with that determined from the classic Brunauer–Emmett–Teller equation, while the internal SSA obtained from the internal sorptive SWI also coincides with that measured independently; both further confirm the capability of the proposed SWI model to distinguish between external surface adsorption and interlamellar adsorption.

Aboveground biomass (AGB), as a crucial indicator of forest growth and quality, plays an important role in monitoring the global carbon cycle and forest health. Rapid, accurate, and non-destructive assessment of AGB in rubber plantations is beneficial not only for predicting rubber yield but also for understanding the carbon storage potential in tropical areas. Previous studies have employed spectral information and texture features derived from unmanned aerial vehicle data to estimate the AGB of mangroves. However, few studies systematically assessed the effects of grey level co-occurrence matrix parameters for extracting texture features on AGB estimation in rubber plantations. Whether the combination of spectral information and texture features with suitable grey level co-occurrence matrix parameters selection derived from a low-cost unmanned aerial vehicle system can improve the AGB estimation accuracy remains unclear. To this end, this study evaluated the performance of spectral information and texture features derived from UAV-based high-resolution RGB imagery with different textural parameter settings. Three types of machine learning algorithms (support vector regression; random forest; extreme gradient boosting regressor) and stepwise multiple linear regression were used to compare and analyze their performance for AGB estimation of rubber plantations. The results indicated that appropriate textural parameter selection significantly improved the AGB estimation accuracy when using texture features alone. Among four regression techniques, stepwise multiple linear regression exhibited poor performance, while support vector regression performed the best. The best estimation accuracy (R2 = 0.752, RMSE = 28.72 t/ha) was obtained by support vector regression when using the combination of spectral information and texture features with the textural parameters of the orientation of 135°, displacement of 2 pixels, and moving window size parameter of 7 × 7 pixels. The findings suggested that the AGB estimation accuracy can be further improved by the integration of spectral information and texture features when considering appropriate textural parameters.

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.