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Abstract Background Metastasis, the leading cause of cancer-related death in patients diagnosed with ovarian cancer (OC), is a complex process that involves multiple biological effects. With the continuous development of sequencing technology, single-cell sequence has emerged as a promising strategy to understand the pathogenesis of ovarian cancer. Methods Through integrating 10 × single-cell data from 12 samples, we developed a single-cell map of primary and metastatic OC. By copy-number variations analysis, pseudotime analysis, enrichment analysis, and cell–cell communication analysis, we explored the heterogeneity among OC cells. We performed differential expression analysis and high dimensional weighted gene co-expression network analysis to identify the hub genes of C4. The effects of RAB13 on OC cell lines were validated in vitro. Results We discovered a cell subcluster, referred to as C4, that is closely associated with metastasis and poor prognosis in OC. This subcluster correlated with an epithelial–mesenchymal transition (EMT) and angiogenesis signature and RAB13 was identified as the key marker of it. Downregulation of RAB13 resulted in a reduction of OC cells migration and invasion. Additionally, we predicted several potential drugs that might inhibit RAB13. Conclusions Our study has identified a cell subcluster that is closely linked to metastasis in OC, and we have also identified RAB13 as its hub gene that has great potential to become a new therapeutic target for OC.

The Chinese Loess Plateau has undergone extensive revegetation to restore degraded land and enhance carbon sequestration. However, soil organic carbon (SOC) sequestrated in the soil profiles of deep-rooted plants has not been fully studied. Here, we investigated the SOC within a 0–23 m profile in farmlands and apple orchards converted from farmlands with different ages (A5, 20 years) and the controlling factors on three loess tablelands (Changwu, Qingyang, and Luochuan). The results show that SOC stocks among farmlands and orchards showed no significant difference (p = 0.88); however, SOC stocks showed a trend with tree ages, i.e., a decrease for A5 and A10 but an increase for A15 and A20. For the vertical variability, the SOC stock was the highest within 0–1 m, regardless of the standing age; however, the SOC stock in this layer only accounted for 8.8% of the total SOC stock (97.93 ± 9.18 kg m−2). Climate accounted for 82% of the variations and controlled the changes in SOC in the 0–1 m range, while soil texture dominated the SOC in the soil below 1 m, accounting for 57% of the SOC variations. The variations in SOC in the thick, unsaturated zones provide implications for future land use management and the sustainability of apple orchards in arid regions.

In order to obtain the quality information of Taishan black tea and provide data support for the establishment of assessment criteria for quality control of Taishan black tea, in this study, 45 samples of Taishan black tea were collected during the summer to investigate their quality characteristics. The results showed that the Taishan black tea typically exhibited a dark-brown, curly appearance and a uniform texture. The tea soup displayed a bright orange-red color. The tea was mainly described as having a fruity aroma, followed by a caramel flavor, sweet aroma, flowery flavor, and clean aroma, with a strong and enduring fragrance. The taste profile was mostly heavy and mellow or heavy and strong, with fewer samples exhibiting slight bitterness and astringency. The tea leaves were characterized by a reddish-auburn color, a soft texture, and an even appearance. The main biochemical components of the Taishan black tea fell within the following ranges: water extracts (38.65–43.35%), free amino acids (1.41–3.45%), tea polyphenols (9.80–15.05%), catechins (6.11–9.03%), and caffeine (1.65–3.05%). The phenolic acid/amino acid ratio was 2.31–6.65%. Catechins and tea polyphenols emerged as critical indicators influencing taste quality, followed by amino acids, water extracts, and caffeine. The aroma analysis identified common compounds, such as 3-methyl-butanal, 2-methyl-butanal, decanal, 2-methyl-propanal, 1-octen-3-ol, and β-ionone, in most samples. These compounds exhibited relatively high contents and high odor activity values, making them the primary contributors to the tea’s aroma. This investigation into the quality of Taishan black tea offers valuable scientific insights, providing a foundation for the standardization of Taishan black tea’s quality.

Abstract Visualization is crucial for analyzing the turbulent combustion simulation. Time-varying data allows us to investigate the evolution process of the turbulent flow field. To study the combustion effects, we calculated the enstrophy of the flow field since high enstrophy region can display valuable features, and extract components based on these features. We isolated large components to track their behaviors and characterized them using volume and spatial locations, which helps scientists to explore the dynamics and temporal changes of intense events individually. We analyzed the components’ structures and visualized them in contouring and statistical charts.

Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications.

Drought limits tea yield and can also negatively impact its quality. In this study, constant humidity and dry–wet alternating modes were compared to determine their impacts on soil nitrogen transformation and ammonia-oxidizing microorganisms. Drought was found to reduce the soil NH4+-N concentration under the constant humidity mode, and the NO3−-N concentration was highest in 60% water-holding capacity (WHC) soil. Soil NO3−-N content increased rapidly after rewatering, and increasing the frequency of dry–wet watering resulted in a higher accumulation of NO3−-N. In the constant humidity mode, drought reduced the abundance of ammonia-oxidizing archaea (AOA), whereas that of ammonite-oxidizing bacteria (AOB) increased. Increases in drought duration and the frequency of dry–wet watering inhibited the activity of AOA under the dry–wet alternating mode, whereas the relative activity of AOB increased after rehydration. The water supply mode did not change the community structure of AOA or AOB at the genus level but affected their relative abundance. In the constant humidity mode, the contribution rate of AOA to nitrification potential (PNR) was 42.75–49.72%, whereas that of AOB was 50.28–57.25%. In the dry–wet alternating mode, the contribution rate of AOA to PNR increased, and the contribution rate of AOB decreased. Taken together, these findings indicate that ammonia oxidation might be primarily driven by AOA and AOB in weakly acidic and neutral soil. This study reveals the effects of different water supply modes on soil nitrogen transformation and ammonia-oxidizing micro-organisms and provides a scientific basis for improving nitrogen use efficiency.

Network Function Virtualization (NFV) decouples network functions from hardware, improves the flexibility of resource allocation, and enhances network scalability. Any failures of software and hardware in an NFV environment will result in the interruption of Service Function Chains (SFCs). As one of the key technologies of 5G, NFV has more stringent delay and reliability requirements for services. This paper takes software and hardware failures into account, and proposes a reliability-aware service function chain backup protection (RABP) method to meet SFCs' high reliability and low latency demands. First, we formulate a mixed-integer linear programming model to maximize the revenue-to-cost ratio. Then, we propose a heuristic algorithm for suboptimal solutions. SFC deployment is divided into two stages: primary virtual network function (VNF) deployment and backup VNF deployment. The reliability enhancement and delay optimization deployment method is presented to deploy primary VNFs. A breadth-first search method is used to consolidate primary VNFs as much as possible to improve reliability of SFCs and reduce transmission delay. When deploying backup VNFs, a resource-efficient backup selection method is presented to reduce backup resource consumption while meeting reliability demands. Finally, experiment results show that the proposed RABP method not only has the best acceptance ratio and long-term average revenue to cost ratio, but also reduces backup resource consumption and transmission delay.

Summary: The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Various long non-coding RNAs (lncRNAs) are closely associated with lung adenocarcinoma (LUAD), playing oncogenic or anti-oncogenic roles in tumorigenesis and progression. Herein, we report a novel lncRNA—long intergenic non-protein coding RNA 1426 (LINC01426)—that has not yet been characterized in LUAD. We note that LINC01426 expression was markedly upregulated in LUAD tissues, and that functional assays verified that LINC01426 knockdown markedly inhibited cell proliferation, migration, and invasion in vitro. Xenografts derived from A549 cells knocked down of LINC01426 had evidently lower tumor weights and smaller tumor volumes. Our study also found that LINC01426 bound to hsa-miR-30b-3p as a competitive endogenous RNA in LUAD. Moreover, LINC01426 affected LUAD wound healing by interacting and combining with AZGP1, and LINC01426 expression was significantly associated with tumor-node-metastasis (TNM) staging and prognosis in patients with LUAD. To summarize, our study elucidates the oncogenic roles of LINC01426 in LUAD tumorigenesis and progression. We think that LINC01426 can serve as a potential diagnostic biomarker and therapeutic target in patients with LUAD.

Network function virtualization technology makes the deployment and management of network service more flexible and elastic by decoupling network function from dedicated hardware. The service requests of network function virtualization are usually deployed in the form of a service function chain. In order to solve the problems of load imbalance, unreasonable utilization of substrate resources, and the high delay of the service function chain deployment in operator networks, a service function chain deployment method based on the network flow theory is proposed in this paper. First, on the basis of perceiving the substrate network resources and topology with a software-defined network controller in real time, a candidate node set is determined according to the resource constraints and the locations of ingress/egress switch nodes that service flow flows in/out. Second, the candidate node set, the ingress/egress switch nodes and the connection between them are used to form a directed network, and the service function chain deployment problem is transformed into an optimal path selection problem. Then, a node disassembling method is used to transform the directed network into a capacity-flow-cost network. Finally, a minimum-cost maximum-flow algorithm is used to find the optimal deployment path and complete the service function chain deployment. Experiments show that the method proposed in this paper can guarantee the load balance of operator networks, reduce the average transmission delay of service flow, and make the utilization of substrate resources more reasonable.

Tea brewed from leaves of Camellia sinensis (L.) is one of the most popular worldwide non-alcoholic beverages, and nitrogen is an essential nutrient that affects the growth and development of the tea plant. Inorganic nitrogen and organic nitrogen are major forms of nitrogen that are absorbed by the tea plant and also play an important role as a vital signaling molecule, which also affects the nitrogen use efficiency (NUE) of C. sinensis in its growth and development period. In addition, improving the NUE of C. sinensis is therefore important for the economic and environmental sustainability of the tea plantation industry. In this review, we discuss recently identified tea plant genes that have an essential role in the responses of different nitrogen forms in the short-term period and in the long-term nitrogen effect. We also focus in particular on how nitrogen regulates the growth and development of the tea plant, mainly on the aspects of root system architecture and leaf metabolism that are important parts of root development and leaf quality. Finally, this review concentrates recent research that has expanded the outline of the mechanisms underlying nitrogen responses and regulation in plants, focusing in particular on tea plants.

Network virtualization allows multiple independent and mutual non-interfering virtual networks (VNs) to run simultaneously on a shared substrate network (SN) providing a promising method for overcoming the network ossification. Substrate infrastructure failures occur frequently, most of which are substrate link failures. This has created a research hotspot in the field of survivable VN. In this paper, we add to the research by analyzing the substrate link failures and proposing a survivable VN link shared protection method based on the maximum spanning tree. First, an integer linear programming formulation for the survivable VN link shared protection is established to minimize the backup link bandwidth ratio. Second, an efficient VN mapping algorithm based on optimal matching is designed to make full use of the limited substrate resources. This mapping algorithm that fully considers the topological connections between nodes in the node mapping phase improves the acceptance ratio and the long-term average revenue to cost ratio. Finally, a maximum spanning tree link protection algorithm is proposed, and a reasonable sharing mechanism of backup link resources among different VNs is designed to ensure the VN survivability and reduce the SN resource consumption. The experimental results show that our method not only guarantees the VN survivability but also reduces SN resource consumption.

Background DNA-binding one zinc finger (Dof) proteins are plant-specific transcription factors important for seed development, hormone regulation, and defense against abiotic stress. Although drought stress is a key determinant of plant physiology and metabolic homeostasis, the role of Dof genes in different degrees of PEG6000-induced drought stress has received little attention. Methods Tea plants (Camellia sinensis) were exposed to mild, moderate and severe drought stress. The Tea Genome and Plant TFDB databases were used to identify Dof gene family members in the tea plant. Clustal W2.1, MEGA6.0, ScanProsite, SMART, ExPASy, GSDS, MEME and STRING were used to build a phylogenetic tree, predict the molecular masses and isoelectric points of the Dof proteins, and construct a predicted protein-protein interaction network between the CsDof TFs and proteins in the A. thaliana database. The expression patterns of Dof genes in different tissues were analyzed, and qRT-PCR was used to measure the expression of Dof genes under different degrees of drought stress in tea. Results We identified 16 Dof genes in tea (C. sinensis cv. Huangjinya) using whole-genome analysis. Through comparative analysis of tea and Arabidopsis thaliana, we divided the Dof genes into four families (A, B, C, and D). We identified 15 motifs in the amino acid sequences of the CsDof proteins. Gene sequences and motif structures were highly conserved among families, especially in the B1 and C2 subfamilies. The protein-protein interaction network indicated that multiple CsDof proteins may be involved in the response to drought stress. Real-time PCR was used to examine the tissue-specific expression patterns of the CsDof genes and to measure their responses to different levels of PEG6000-induced drought stress in mature leaves. Most CsDof genes responded to drought stress. These results provide information on the Dof gene family in tea, offer new insights into the function of CsDof genes in a perennial species, and lay the foundation for further analysis of their functions.

Background Farmland accounts for a relatively large fraction of the world’s vegetation cover, and the quantification of carbon fluxes over farmland is critical for understanding regional carbon budgets. The carbon cycle of farmland ecosystems has become a focus of global research in the field of carbon dynamics and cycling. The objectives of this study are to monitor the temporal variation in the net ecosystem exchange (NEE) and soil respiration in a spring maize (Zea mays L.) farmland ecosystem of the southern Loess Plateau of China. Methods A fully automated temperature-controlled flux chamber system was adopted in this study. The system contained nine chambers for CO2 flux measurements, and three treatments were conducted: with and without maize plants in the chamber, as well as a bare field. Observations were conducted from June to September 2011. This time period covers the seedling, jointing, heading, grain filling, and ripening stages of spring maize. Other factors, such as air temperature (Ta), soil temperature (Ts), soil water content (SWC), photosynthetically active radiation (PAR), and precipitation (P), were simultaneously monitored. Results There was observed diurnal variation in the NEE of the maize ecosystem (NEE-maize). A short “noon break” occurred when the PAR intensity was at its maximum, while soil respiration rates had curves with a single peak. During the overall maize growth season, the total NEE-maize was –68.61 g C m−2, and the soil respiration from the maize field (SR-maize) and bare field (SR-bare field) were 245.69 g C m−2 and 114.08 g C m−2, respectively. The temperature sensitivity of soil respiration in the maize field exceeded that in the bare field. Significant negative correlations were found between the NEE, PAR, and temperature (all p-values

Network virtualization is believed to be a promising way for the next generation Internet to overcome network ossification. It allows multiple heterogeneous virtual networks (VNs) to be embedded onto the shared substrate network (SN) to offer more flexibility and better manageability. With the increasing deployments of VNs in a variety of networks, how to protect the VNs against the single substrate link failure has become a key challenge. In this paper, we propose a survivable VN link protection method based on network coding and protection circuit. First, we provide an integer linear programming formulation for the survivable VN link protection to maximize the long-term average revenue to cost ratio. Second, a novel protection circuit technology is introduced to augment the VN at the VN level to reduce the backup resource consumption and provide more flexible VN management. Then, an efficient heuristic virtual network embedding algorithm is developed, which makes full use of the limited resources and transforms the single substrate link protection into the single virtual link protection in multiple protection circuits. Finally, the data units from different links are encoded using network coding, which not only provides 1 + N protection to virtual links but also reduces the recovery delay. The evaluation results show that our method not only has the best acceptance ratio and long-term average revenue to cost ratio, but it also greatly enhances the achievable backup sharing and shortens the average network recovery delay.

Therapeutic outcome for the treatment of glioma was often limited due to drug resistance and low permeability of drug across the multiple physiological barriers, including the blood-brain barrier (BBB), and the blood-tumor barrier (BTB). In order to overcome these hurdles, we designed T7 and DA7R dual peptides-modified liposomes (abbreviated as T7/DA7R-LS) to efficiently co-delivery doxorubicin (DOX) and vincristine (VCR) to glioma in this study. T7 is a seven-peptide ligand of transferrin receptors (TfR) capable of circumventing the BBB and then targeting glioma. DA7R is a d-peptide ligand of vascular endothelial growth factor receptor 2 (VEGFR 2) overexpressed on angiogenesis, presenting excellent glioma-homing property. By combining the dual-targeting delivery effect, the dual-modified liposomes displayed higher glioma localization than that of single ligand-modified liposomes or free drug. After loading with DOX and VCR, T7/DA7R-LS showed the most favorable antiglioma effect in vivo. In conclusion, this dual-targeting, co-delivery strategy provides a potential method for improving brain drug delivery and antiglioma treatment efficacy.

The contamination of tea with toxic pesticides is a major concern. Additionally, because of improved detection methods, importers are increasingly rejecting contaminated teas. Here, we describe an electronic nose technique for the rapid detection of pyrethroid pesticides (cyhalothrin, bifenthrin, and fenpropathrin) in tea. Using a PEN 3 electronic nose, the text screened a group of metal oxide sensors and determined that four of them (W5S, W1S, W1W, and W2W) are suitable for the detection of the same pyrethroid pesticide in different concentrations and five of them (W5S, W1S, W1W, W2W, and W2S) are suitable for the detection of pyrethroid pesticide. The models for the determination of cyhalothrin, bifenthrin, and fenpropathrin are established by PLS method. Next, using back propagation (BP) neural network technology, we developed a three-hidden-layer model and a two-hidden-layer model to differentiate among the three pesticides. The accuracy of the three models is 96%, 92%, and 88%, respectively. The recognition accuracies of the three-hidden-layer BP neural network pattern and two-hidden-layer BP neural network pattern are 98.75% and 97.08%, respectively. Our electronic nose system accurately detected and quantified pyrethroid pesticides in tea leaves. We propose that this tool is now ready for practical application in the tea industry.

Ralstonia solanacearum is an important soil-borne bacterial plant pathogen. In this study, an actinomycete strain named NEAU-HV9 that showed strong antibacterial activity against Ralstonia solanacearum was isolated from soil using an in vitro screening technique. Based on physiological and morphological characteristics and 98.90% of 16S rRNA gene sequence similarity with Streptomyces panaciradicis 1MR-8T, the strain was identified as a member of the genus Streptomyces. Tomato seedling and pot culture experiments showed that after pre-inoculation with the strain NEAU-HV9, the disease occurrence of tomato seedlings was effectively prevented for R. solanacearum. Then, a bioactivity-guided approach was employed to isolate and determine the chemical identity of bioactive constituents with antibacterial activity from strain NEAU-HV9. The structure of the antibacterial metabolite was determined as actinomycin D on the basis of extensive spectroscopic analysis. To our knowledge, this is the first report that actinomycin D has strong antibacterial activity against R. solanacearum with a MIC (minimum inhibitory concentration) of 0.6 mg L−1 (0.48 μmol L−1). The in vivo antibacterial activity experiment showed that actinomycin D possessed significant preventive efficacy against R. solanacearum in tomato seedlings. Thus, strain NEAU-HV9 could be used as BCA (biological control agent) against R. solanacearum, and actinomycin D might be a promising candidate for a new antibacterial agent against R. solanacearum.

Network virtualization can offer more flexibility and better manageability for next generation Internet. With the increasing deployments of virtual networks in military and commercial networks, a major challenge is to ensure virtual network survivability against hybrid multiple failures. In this paper, we study the problem of recovering virtual networks affected by hybrid multiple failures in substrate networks and provide an integer linear programming formulation to solve it. We propose a heuristic algorithm to tackle the complexity of the integer linear programming formulation, which includes a faulty virtual network reconfiguration ranking method based on weighted relative entropy, a hybrid multiple failures ranking algorithm, and a virtual node migration method based on weighted relative entropy. In the faulty virtual network reconfiguration ranking method based on weighted relative entropy and virtual node migration method based on weighted relative entropy, multiple ranking indicators are combined in a suitable way based on weighted relative entropy. In the hybrid multiple failures ranking algorithm, the virtual node and its connective virtual links are re-embedded, firstly. Evaluation results show that our heuristic method not only has the best acceptance ratio and normal operation ratio, but also achieves the highest long-term average revenue to cost ratio compared with other virtual network reconfiguration methods.

Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications. [ABSTRACT FROM AUTHOR]

Grape downy mildew caused by Plasmopara viticola is one of the most destructive diseases of grapes. All grape cultivars are susceptible to P. viticola. However, the resistance of grape plants could be induced in plant defense with some help of microbes. In this study, Bacillus megaterium BMJBN02 obtained from farmland soil was shown to regulate the resistance of grapevine against downy mildew. The salicylic acid (SA) content and the expression of pathogenesis-related (PR) genes of grapes under different treatments were examined using high-performance liquid chromatography-mass spectrometry (HPLC-MS) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and it was found that SA content and the expression of PR genes could play a role in regulating the resistance of grapevine against downy mildew. The five-year plot experiment showed that the resistance effectiveness of isolate BMJBN02 was approximately equal to that of 0.1 % nicotinyl morpholine (commercial fungicide). Therefore, this study provides a valuable candidate method that uses B. megaterium BMJBN02 by regulating the resistance of grape against downy mildew for quality and yield of grape in commercial productivity. [ABSTRACT FROM AUTHOR]