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Abstract Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low coverage, there is limited investigation into the later stages of high coverage, which could impact the accuracy of forecasting yield. High canopy coverage and severe occlusion in later stages pose significant challenges for plant detection and counting. Therefore, this study evaluated the performance of the Count Crops tool and a deep learning (DL) model derived from early-stage unmanned aerial vehicle (UAV) imagery in detecting and counting Konjac plants during the high-coverage growth stage. Additionally, the study proposed an approach that integrates the DL model with Konjac location information from both early-stage and high canopy coverage stage imagery to improve the accuracy of recognizing Konjac plants during the high canopy coverage stage. The results indicated that the Count Crops tool outperformed the DL model constructed solely from early-stage imagery in detecting and counting Konjac plants during the high-coverage period. However, given the single stem and erect growth characteristics of Konjac, incorporating the DL model with the location information of the Konjac plants achieved the highest accuracy (Precision = 98.7%, Recall = 86.7%, F1-score = 92.3%). Our findings indicate that combining DL detection results from the early growth stages of Konjac, along with plant positional information from both growth stages, not only significantly improved the accuracy of detecting and counting plants but also saved time on annotating and training DL samples in the later stages. This study introduces an innovative approach for detecting and counting Konjac plants during high-coverage periods, providing a new perspective for recognizing and counting other crop plants at high-overlapping growth stages.

Abstract Most state‐of‐the‐art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra‐dataset. However, they often show poor generalisation when facing cross‐datasets due to the overfitting problem. For this issue, numerous studies have shown that models do not generalise well in new scenarios due to the high correlation between the representations in CNNs. Furthermore, over‐parameterised CNNs have a large number of redundant representations. Therefore, we propose a novel Decorrelated Sparse Representation (DSR) regularisation. (1) It tries to minimise the correlation between feature maps to obtain decorrelated representations. (2) It forces the convolution kernels to extract meaningful features by allowing the sparse kernels to have additional optimisation. The DSR regularisation encourages diverse representations to reduce overfitting. Meanwhile, DSR can be applied to a wide range of vehicle recognition methods based on CNNs, and it does not require additional computation in the testing phase. In the experiments, DSR performs better than the original model on the intra‐dataset and cross‐dataset. Through ablation analysis, we find that DSR can drive the model to focus on the essential differences among all kinds of vehicles.

Abstract Background The alignment of the spine and pelvis significantly impacts overall body balance; therefore, alterations in hip and lumbar spine biomechanics following curved acetabular osteotomy (CPO) can help surgeons optimize acetabular correction. To achieve this goal, we conducted patient-specific finite element analyses to compare hip and lumbar disc contact pressure (CP) between patients with developmental dysplasia of the hip (DDH) and healthy individuals. Additionally, we examined the influence of CPO on the CP of both the hip and lumbar discs in patients with DDH. Methods We conducted finite element analyses of the hip and lumbar spine before and after CPO and compared them with those of a healthy human model. Subsequently, we simulated CPO on the preoperative model. Nonlinear contact analysis was employed to calculate the CP of the acetabular cartilage and lumbar discs during a single-leg stance. Results The maximum and average acetabular CP in patients with DDH were 5.4 MPa and 4.5 MPa, respectively. The average CP for the five lumbar discs were 3 MPa, 2.5 MPa, 2 MPa, 3.5 MPa, and 4.4 MPa. In contrast, the maximum and average acetabular CP in normal subjects were 3.7 MPa and 2.1 MPa, respectively, and the average CP of their lumbar discs were 1 MPa, 2 MPA, 1.88 MPa, 2.1 MPa, and 2.1 MPa, respectively. After CPO, the maximum and average CP of the hip decreased, as did the average CP of the lumbar discs. The maximum and average compressive stress of the acetabulum decreased to 3.79 MPa and 2.3 MPa, respectively, and the average compressive stress of the five intervertebral discs decreased to 1.96 MPa, 0.79 MPa, 0.78 MPa, 1.13 MPa, and 3.14 MPa, respectively. Conclusion Our finite element analysis indicated that CPO effectively normalizes hip contact pressure while reducing lumbar disc contact pressure. However, further investigation is required to elucidate the specific biomechanical mechanisms underlying these changes.

Objective: To optimize the preparation process of polysaccharides from Lycium ruthenicum, analyze the monosaccharide composition and evaluate its pancreatic lipase inhibitory activity in vitro. Methods: Ultrasound-assisted aqueous two-phase extraction method was employed. Taking the overall evaluation value of polysaccharide recovery and extraction rate as the evaluation index, on the basis of single factor experiment, response surface analysis was conducted to optimize the extraction process. The composition of monosaccharides was studied by high performance liquid chromatography with pre-column derivatization. The pancreatic lipase inhibitory activity of Lycium ruthenicum polysaccharides was evaluated by p-nitrophenyl palmitate method. Results: The optimum extraction conditions: Ethanol-ammonium sulfate system, ethanol concentration 30.89%, ammonium sulfate concentration 17.00%, extraction temperature 49.05 ℃, extraction time 81.85 min, liquid to material ratio 29.80 g/g. At this time, the predicted total evaluation value was 0.9242, and the extraction and recovery rates were 104.76 mg/g and 96.96%, respectively. Lycium ruthenicum polysaccharides were composed of monosaccharides such as mannose, ribose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose and arabinose, with the relative molar ratios of 4.92:0.29:4.80:0.40:9.69:44.79:17.04:18.06, respectively. The IC50 value of Lycium ruthenicum polysaccharides inhibiting pancreatic lipase was 1.51 mg/mL. Conclusion: The preparation process of Lycium ruthenicum polysaccharides optimized by response surface methodology was stable. The detection method of monosaccharide composition was accurate and reliable. Lycium ruthenicum polysaccharides had good pancreatic lipase inhibitory activity in vitro. The results would provide a basis for the further development and utilization of Lycium ruthenicum polysaccharides.

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.

With the arrival of the 5G era, wireless communication technologies and services are rapidly exhausting the limited spectrum resources. Spectrum auctions came into being, which can effectively utilize spectrum resources. Because of the complexity of the electronic spectrum auction network environment, the security of spectrum auction can not be guaranteed. Most scholars focus on researching the security of the single-sided auctions, while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and buyers. Researchers begin to design the secure double spectrum auction mechanisms, in which two semi-honest agents are introduced to finish the spectrum auction rules. But these two agents may collude with each other or be bribed by buyers and sellers, which may create security risks, therefore, a secure double spectrum auction is proposed in this paper. Unlike traditional secure double spectrum auctions, the spectrum auction server with Software Guard Extensions (SGX) component is used in this paper, which is an Ethereum blockchain platform that performs spectrum auctions. A secure double spectrum protocol is also designed, using SGX technology and cryptographic tools such as Paillier cryptosystem, stealth address technology and one-time ring signatures to well protect the private information of spectrum auctions. In addition, the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification, and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum auctions. Finally, security analysis and performance evaluation of our protocol are discussed.

BackgroundUncontrolled severe eosinophilic chronic rhinosinusitis (eCRS) is associated with elevated levels of Th2 cells and raised immunoglobulin concentrations in nasal polyp tissue. eCRS is characterized by high eosinophilic infiltration and type 2 inflammation. Gαi1/3 proteins participate in allergic inflammation by regulating immune cells. Whether Gαi1/3 proteins have a role in the development of eCRS remains unknown.ObjectivesTo investigate the association between Gαi1/3 expression levels and CRS and the underlying mechanismsMethodsWestern blotting and immunohistology were used to detect Gαi1/3 expression. Correlations between Gαi1/3 and immune cells and clinical parameters were analyzed. Signaling pathway activation in IL-5-induced Gαi1/3-knockout or knockdown mouse embryonic fibroblasts (MEFs) and eosinophils (EoL-1 cells) was detected by western blotting. EdU/DAPI was used to evaluate the proliferation of EoL-1 cells. A CRS model was established using Gαi1/3-knockout mice, and histological analysis and inflammatory cytokine measurements were performed.ResultsCompared with the non-eCRS subset, the eCRS subset showed significantly increased Gαi1/3 expression levels. High nasal tissue Gαi1/3 levels were linked to high tissue eosinophil infiltration, and high disease severity and allergic conditions in CRS patients. Gαi1/3 were required for IL-5-induced Akt-mTOR and Erk activation in MEFs. In EoL-1 cells, Gαi1/3 was associated with IL-5-activated IL-5Rα, promoting IL-5Rα endocytosis and transducing downstream signaling. IL-5-induced EoL-1 cell proliferation and degranulation were suppressed after Gαi1/3 silencing. In a CRS murine model, immune cell infiltration and type 2 inflammation were largely impaired in Gαi1/3-double-knockout mice.ConclusionIncreased Gαi1/3 expression levels in nasal tissue are linked to eosinophil infiltration and increased disease severity in CRS patients. Gαi1/3 contributes to eosinophil activation and participates in regulating allergic inflammation in CRS patients.

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 =

Background: Platelet-rich plasma (PRP) helps promote wound healing, but it is unclear whether it stimulates breast cancer cell proliferation, which restricts its application in breast cancer patients. This article explored the effect of PRP on breast cancer cell proliferation through preclinical experiments. Method: We cultivated MDA-MB-231 breast cancer cells with PRP in vitro. Subsequently, we employed Cell Counting Kit-8 (CCK-8) assays to assess their proliferation ability, wound healing assays to evaluate their migration ability, and Transwell assays to detect their invasion ability. Mouse breast cancer 4T1 cells were subcutaneously inoculated into nude mice. After tumor formation, PRP was injected around each tumor. Tumor size was measured. After 15 days, the tumors were surgically removed. Immunohistochemistry was used to detect key proteins involved in proliferation and apoptosis. Results: PRP inhibited the proliferation, migration, and invasion of MDA-MB-231 in vitro. After PRP was injected around tumors in nude mice, tumor growth slowed, Ki67 and phospho-histone H3 (pHH3) expression decreased, and Caspase 3 and Poly (adenosine diphosphate-ribose) polymerase 1 (PARP1) expression increased. Conclusion: The PRP inhibits the proliferation of breast cancer MDA-MB-231 and 4T1 cells.

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.

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolution of 10 m, 5-day revisit period and rich spectral information of Sentinel-2 imagery. This study integrated Sentinel-2 images and uncrewed aerial vehicle (UAV) RGB imagery to develop six regression models at the field scale, which were employed for the estimation of seasonal and annual fresh tea yields of the Yunlong Tea Cooperatives in Yixiang Town, Pu’er City, China. Firstly, we gathered fresh tea production data from 133 farmers in the cooperative over the past five years and obtained UAV RGB and Sentinel-2 imagery. Secondly, 23 spectral features were extracted from Sentinel-2 images. Based on the UAV images, the parcel of each farmer was positioned and three topographic features of slope, aspect, and elevation were extracted. Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. Thirdly, we applied six different regression algorithms to establish fresh tea yield models for each season and evaluated their estimation accuracy. The results showed that random forest regression models were the optimal choice for estimating spring and summer yields, with the spring model achieving an R2 value of 0.45, an RMSE of 40.38 kg/acre, and an rRMSE of 40.79%. Similarly, the summer model achieved an R2 value of 0.5, an RMSE of 78.46 kg/acre, and an rRMSE of 39.81%. For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R2 value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R2 value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. This study provides a promising new method for estimating fresh tea yield in different seasons at the field scale.

The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural details and heterogeneity. However, most previous studies primarily focused on developing biomass estimation models for rubber using machine learning (ML) algorithms in conjunction with feature selection methods based on UAV-acquired multispectral imagery. The reliance on feature selection methods limits the model’s generalizability, robustness, and predictive accuracy. In contrast, deep learning (DL) exhibits considerable promise in extracting features from high-resolution UAV-based multispectral imagery without the need for manual selection. Nonetheless, it remains unclear whether DL can surpass traditional ML methods in improving the AGB estimation accuracy in rubber plantations. To address this, our study evaluated the performance of three ML algorithms (random forest regression, RFR; XGBoost regression, XGBR; categorical boosting regression, CatBoost) combined with feature selection techniques and a deep convolutional neural network (DCNN) using multispectral imagery obtained from UAV for the AGB estimation of rubber plantations. The results indicate that the RFR combined with a principal component analysis (PCA) for feature selection yielded the best performance (R2 = 0.81, RMSE = 11.63 t/ha, MAE = 9.27 t/ha) between the three ML algorithms. Meanwhile, the DCNN model derived from the G, R, and NIR spectral bands achieved the highest estimation accuracy (R2 = 0.89, RMSE = 6.44 t/ha, MAE = 5.72 t/ha), where it outperformed the other ML methods. Our study highlights the great potential of combining UAV-based multispectral imagery with DL techniques to improve AGB estimation in rubber plantations, offering a new perspective for estimating the physiological and biochemical growth parameters of forests.

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.

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.

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.

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.

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 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.

The Dongzhongla deposit is a skarn-type lead–zinc ore deposit located in the eastern segment of the Gangdese metallogenic belt, situated in the Xizang province, China. The high-altitude mountainous terrain of the region poses significant challenges to ground-based exploration. To facilitate more accurate mineral exploration in the deposit and its surrounding area, a high-resolution airborne magnetic survey was conducted over the mining area and its periphery. The airborne magnetic data were processed using derivative and Euler deconvolution methods, yielding results that reflect the geological structural features of the study area. By integrating the geological characteristics of the ore deposit, we inferred that the areas of magnetic anomaly extensions and the peripheries of other magnetic anomalies are favorable zones for mineralization, providing positive leads for further mineral exploration.

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.

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.

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.

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.

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.

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).

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 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 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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.