scDMV: a zero-one inflated beta mixture model for DNA methylation variability with scBS-seq data.

MOTIVATION: The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. RESULTS: We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. AVAILABILITY AND IMPLEMENTATION: The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.

CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.

Curcumin blunts epithelial-mesenchymal transition to alleviate invasion and metastasis of prostate cancer through the JARID1D demethylation.

Prostate cancer (PCa) is one of the most common and prevalent cancers in men worldwide. The majority of PCa-related deaths result from metastasis rather than primary tumors. Several studies have focused on the relationship between male-specific genes encoded on the Y chromosome and PCa metastasis; however, the relationship between the male specific protein encoded on the Y chromosome and tumor suppression has not been fully clarified. Here, we report a male specific protein of this type, the histone H3 lysine 4 (H3K4) demethylase JARID1D, which has the ability to inhibit the gene expression program related to cell invasion, and can thus form a phenotype that inhibits the invasion of PCa cells. However, JARID1D exhibits low expression level in advanced PCa, and which is related to rapid invasion and metastasis in patients with PCa. Curcumin, as a multi-target drug, can enhance the expression and demethylation activity of JARID1D, affect the androgen receptor (AR) and epithelial-mesenchymal transition (EMT) signaling cascade, and inhibit the metastatic potential of castration resistant cancer (CRPC). These findings suggest that using curcumin to increase the expression and demethylation activity of JARID1D may be a feasible strategy to inhibit PCa metastasis by regulating EMT and AR.

Heterogeneity in PD-L1 expression between primary and metastatic lymph nodes: a predictor of EGFR-TKI therapy response in non-small cell lung cancer.

BACKGROUND: There is inconclusive evidence to suggest that the expression of programmed cell death ligand 1 (PD-L1) is a putative predictor of response to EGFR-TKI therapy in advanced EGFR-mutant non-small cell lung cancer (NSCLC). We evaluated the heterogeneity in PD-L1 expression in the primary lung site and metastatic lymph nodes to analyze the association between PD-L1 expression and response for patients treated with EGFR-TKI. METHODS: This study reviewed 184 advanced NSCLC patients with EGFR mutations who received first-generation EGFR-TKI as first-line treatment from 2020 to 2021 at Shanghai Chest Hospital. The patients were divided into the primary lung site group (n = 100) and the metastatic lymph nodes group (n = 84) according to the biopsy site. The patients in each group were divided into TPS < 1%, TPS 1-49%, and TPS ≥ 50% groups according to PD-L1 expression. RESULTS: The median PFS was 7 (95% CI: 5.7-8.3) months, and the median OS was 26 (95% CI: 23.5-28.5) months for all patients. No correlation existed between PFS or OS and PD-L1 expression. The median PFS in the primary lung site group was 11 months (95% CI: 9.6-12.4) in the TPS < 1% group, 8 months (95% CI: 6.6-9.4) in TPS 1-49% group, and 4 months (95% CI: 3.2-4.8) in TPS ≥ 50% group, with statistically significant differences (p = 0.000). The median OS of the TPS < 1% group and TPS ≥ 50% group showed a statistically significant difference (p = 0.008) in the primary lung site group. In contrast, PD-L1 expression in the lymph nodes of EGFR-mutant patients was unrelated to PFS or OS after EGFR-TKI therapy. CONCLUSION: PD-L1 expression from the primary lung site might predict clinical benefit from EGFR-TKI, whereas PD-L1 from metastatic lymph nodes did not. TRIAL REGISTRATION: This retrospective study was approved by the Ethics Committee of Shanghai Chest Hospital (ID: IS23060) and performed following the Helsinki Declaration of 1964 (revised 2008).

Thermally resistant nuclease in Serratia marcescens hinders PCR reactions and degrades PCR products.

Polymerase chain reaction (PCR) is an important tool for exogenous gene acquisition and recombinants identification. There exist two problems when using Serratia marcescens as a template for PCR amplification: amplified PCR products are rapidly degraded, and the results of PCR amplification are unstable. The aim of the present work was to elucidate the reasons for this. By mixing PCR products amplified from Escherichia coli DH5α with S. marcescens supernatant or pellet, we found that the DNA-degrading substance in S. marcescens is thermally resistant and present both intracellularly and extracellularly. We then determined that it is protein, and most likely S. marcescens nuclease, that degrades PCR products since the addition of SDS and EDTA can effectively inhibit or block the degradation of PCR products. By knocking out the S. marcescens nuclease encoding gene, nucA, we confirmed that the nuclease is responsible for the degradation of PCR products and the instability of PCR amplification. This work is the first to show that the S. marcescens nuclease is temporarily and partially inhibited by high temperatures during PCR and recovers rapidly at room temperature after PCR.

Multi-target angiogenesis inhibitor combined with PD-1 inhibitors may benefit advanced non-small cell lung cancer patients in late line after failure of EGFR-TKI therapy.

Treatments for NSCLC patients with EGFR-TKI resistance are limited. Given that immunotherapy and antiangiogenic agents may have synergistic antitumor effects, we aimed to analyze the effect of multi-target angiogenesis inhibitor anlotinib and immune checkpoint inhibitors (ICIs) combination therapy in NSCLC patients who failed EGFR-TKI. The medical records of lung adenocarcinoma (LUAD) patients with EGFR-TKI resistance were reviewed. After EGFR-TKI resistance, patients who simultaneously received anlotinib and ICIs were enrolled in the observation group, and those who received platinum-pemetrexed chemotherapy were included in the control group. A total of 80 LUAD patients were reviewed and allocated to the anlotinib and ICIs combination therapy (n = 38) and chemotherapy (n = 42) groups. A re-biopsy was performed in all patients in the observation group before the administration of anlotinib and ICIs. The median follow-up was 15.63 months (95% CI: 12.19-19.08). Combination therapy exhibited better PFS (median PFS: 4.33 months [95% CI: 2.62-6.05] vs 3.60 months [95% CI: 2.48-4.73], P = .005), and better OS (median OS: 14.17 months [95% CI: 10.17-18.17] vs 9.00 months [95% CI: 6.92-11.08], P = .029) than chemotherapy. Most patients (73.7%) received combination therapy as fourth and later lines of therapy, with a median PFS of 4.03 months (95% CI: 2.05-6.02) and a median OS of 13.80 months (95% CI: 8.25-19.36). The disease control rate was 92.1%. Four patients discontinued the combination therapy due to adverse events, but the other adverse reactions were manageable and reversible. The combination of anlotinib and PD-1 inhibitors is a promising regimen for the late-line treatment of LUAD patients with EGFR-TKI resistance.

Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data.

Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.

Monoamine oxidase A drives neuroendocrine differentiation in prostate cancer.

Neuroendocrine transdifferentiation (NED) of prostate cancer (PCa) is the main cause of failure of androgen receptor inhibitor treatment. However, the molecular mechanisms underlying the development of NEPC, especially treatment-induced NEPC, remain unclear. Emerging evidence indicates that elevated monoamine oxidase A (MAOA) contribute to the proliferation, cell stemness, and bone metastasis in PCa. Here, we generated an enzalutamide-induced NED cell model to assess the role of MAOA during NED. Overall, MAOA expression was significantly increased upon Enz long-term exposure and was required for neuroendocrine marker expression. In particular, Enz was found to induce NED via the MAOA/mTOR/HIF-1α signaling axis. Further analyses revealed that the MAOA inhibitor clorgyline(CLG) may bring multiple benefits to CRPC patients, including better therapeutic effect and delays NED. These findings suggest that MAOA may be an important target for the development of anti-NED therapies, thereby providing a novel strategy for the combined application of CLG and AR inhibitors in the clinic.

CrusTF: a comprehensive resource of transcriptomes for evolutionary and functional studies of crustacean transcription factors.

BACKGROUND: Crustacea, the second largest subphylum of Arthropoda, includes species of major ecological and economic importance, such as crabs, lobsters, crayfishes, shrimps, and barnacles. With the rapid development of crustacean aquaculture and biodiversity loss, understanding the gene regulatory mechanisms of growth, reproduction, and development of crustaceans is crucial to both aquaculture development and biodiversity conservation of this group of organisms. In these biological processes, transcription factors (TFs) play a vital role in regulating gene expression. However, crustacean transcription factors are still largely unknown, because the lack of complete genome sequences of most crustacean species hampers the studies on their transcriptional regulation on a system-wide scale. Thus, the current TF databases derived from genome sequences contain TF information for only a few crustacean species and are insufficient to elucidate the transcriptional diversity of such a large animal group. RESULTS: Our database CrusTF ( http://qinlab.sls.cuhk.edu.hk/CrusTF ) provides comprehensive information for evolutionary and functional studies on the crustacean transcriptional regulatory system. CrusTF fills the knowledge gap of transcriptional regulation in crustaceans by exploring publicly available and newly sequenced transcriptomes of 170 crustacean species and identifying 131,941 TFs within 63 TF families. CrusTF features three categories of information: sequence, function, and evolution of crustacean TFs. The database enables searching, browsing and downloading of crustacean TF sequences. CrusTF infers DNA binding motifs of crustacean TFs, thus facilitating the users to predict potential downstream TF targets. The database also presents evolutionary analyses of crustacean TFs, which improve our understanding of the evolution of transcriptional regulatory systems in crustaceans. CONCLUSIONS: Given the importance of TF information in evolutionary and functional studies on transcriptional regulatory systems of crustaceans, this database will constitute a key resource for the research community of crustacean biology and evolutionary biology. Moreover, CrusTF serves as a model for the construction of TF database derived from transcriptome data. A similar approach could be applied to other groups of organisms, for which transcriptomes are more readily available than genomes.

Analysis of Temperature and Humidity Field in a New Bulk Tobacco Curing Barn Based on CFD.

A new structure bulk tobacco curing barn was presented. To study the temperature and humidity field in the new structure tobacco curing barn, a 3D transient computational fluid dynamics (CFD) model was developed using porous medium, species transport, κ-ε turbulence and discrete phase models. The CFD results demonstrated that (1) the temperature and relative humidity predictions were validated by the experimental results, and comparison of simulation results with experimental data showed a fairly close agreement; (2) the temperature of the bottom and inlet area was higher than the top and outlet area, and water vapor concentrated on the top and outlet area in the barn; (3) tobacco loading density and thickness of tobacco leaves had an explicit effect on the temperature distributions in the barn.

Exchange protein directly activated by cAMP plays a critical role in bacterial invasion during fatal rickettsioses.

Rickettsiae are responsible for some of the most devastating human infections. A high infectivity and severe illness after inhalation make some rickettsiae bioterrorism threats. We report that deletion of the exchange protein directly activated by cAMP (Epac) gene, Epac1, in mice protects them from an ordinarily lethal dose of rickettsiae. Inhibition of Epac1 suppresses bacterial adhesion and invasion. Most importantly, pharmacological inhibition of Epac1 in vivo using an Epac-specific small-molecule inhibitor, ESI-09, completely recapitulates the Epac1 knockout phenotype. ESI-09 treatment dramatically decreases the morbidity and mortality associated with fatal spotted fever rickettsiosis. Our results demonstrate that Epac1-mediated signaling represents a mechanism for host-pathogen interactions and that Epac1 is a potential target for the prevention and treatment of fatal rickettsioses.

[Detection of Late Blight Disease on Potato Leaves Using Hyperspectral Imaging Technique].

Hyperspectral imaging feature on potato leaves stressed by late blight was studied in the present paper. The experiment used 60 potato leaves. Among those 60 potato leaves, 48 leaves were vitro inoculated with pathogen of potato late blight, the rest 12 leaves were used as control samples. The leaves were observed for 7 continuous days before and after inoculated and samples including healthy and infested were acquired. Hyperspectral data of healthy and infected potato samples of different disease severity were obtained by the hyperspectral imaging system from 374 to 1,018 nm and then extract spectral data of region of interest (ROI) from those hyperspectral data by the ENVI software. In order to improve the signal-to-noise ratio, the spectral data were preprocessed using different pretreatment methods such as moving average smoothing, normalization, derivative, baseline etc. The least squares-support vector machine(LS-SVM) models were developed based on the raw and those preprocessed data. Among the nine models, the model that used the raw data and the data after the spectroscopic transformation performed best with the discrimination of 94.87%. It was demonstrated that it is realized to determine the potato late blight disease of different disease severity using hyperspectral imaging technique.

Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods.

Inferring gene regulatory networks from gene expression data at whole genome level is still an arduous challenge, especially in higher organisms where the number of genes is large but the number of experimental samples is small. It is reported that the accuracy of current methods at genome scale significantly drops from Escherichia coli to Saccharomyces cerevisiae due to the increase in number of genes. This limits the applicability of current methods to more complex genomes, like human and mouse. Least absolute shrinkage and selection operator (LASSO) is widely used for gene regulatory network inference from gene expression profiles. However, the accuracy of LASSO on large genomes is not satisfactory. In this study, we apply two extended models of LASSO, L0 and L1/2 regularization models to infer gene regulatory network from both high-throughput gene expression data and transcription factor binding data in mouse embryonic stem cells (mESCs). We find that both the L0 and L1/2 regularization models significantly outperform LASSO in network inference. Incorporating interactions between transcription factors and their targets remarkably improved the prediction accuracy. Current study demonstrates the efficiency and applicability of these two models for gene regulatory network inference from integrative omics data in large genomes. The applications of the two models will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.

Marine compound catunaregin inhibits angiogenesis through the modulation of phosphorylation of akt and eNOS in vivo and in vitro.

Angiogenesis is the formation of blood vessels from pre-existing vasculature. Excessive or uncontrolled angiogenesis is a major contributor to many pathological conditions whereas inhibition of aberrant angiogenesis is beneficial to patients with pathological angiogenesis. Catunaregin is a core of novel marine compound isolated from mangrove associate. The potential anti-angiogenesis of catunaregin was investigated in human umbilical vein endothelial cells (HUVECs) and zebrafish. HUVECs were treated with different concentrations of catunaregin in the presence or absence of VEGF. The angiogenic phenotypes including cell invasion cell migration and tube formation were evaluated following catunaregin treatment in HUVECs. The possible involvement of AKT, eNOS and ERK1/2 in catunaregin-induced anti-angiogenesis was explored using Western blotting. The anti-angiogenesis of catunaregin was further tested in the zebrafish embryo neovascularization and caudal fin regeneration assays. We found that catunaregin dose-dependently inhibited angiogenesis in both HUVECs and zebrafish embryo neovascularization and zebrafish caudal fin regeneration assays. In addition, catunaregin significantly decreased the phosphorylation of Akt and eNOS, but not the phosphorylation of ERK1/2. The present work demonstrates that catunaregin exerts the anti-angiogenic activity at least in part through the regulation of the Akt and eNOS signaling pathways.

[Kinetic models for determination of yeast in fresh jujube using near infrared spectroscopy].

The objectives of this study were: (1) to optimize a near-infrared (NIR) spectroscopy model for fresh jujube stored at room temperature to predict the quality change (yeast growth), (2) to establish a kinetic model of yeast growth for fresh jujubes at room temperature according to NIR spectroscopy data and storage time, and (3) to predict the shelf life of fresh jujube at room temperature. The Lizao samples of fresh jujubes were adopted as the research object in the study. The NIR spectral data were achieved before yeast infection level measured. In order to optimize the NIR model, the pretreatment techniques such as Savitzky-Golay smoothing (S-G smoothing), multiplicative scatter correction (MSC), first derivative (1-Der) and second derivative (2-Der) were compared with the raw spectra by using a statistical software package (Unscrambler 9.8), and the regression coefficient (RC) method was used to choose the characteristic wavenumber. Multiple linear regression (MLR) was applied as NIR modeling method. According to the predicted yeast infection level using NIR model, the chemical kinetic models of spectral data and storage time at room temperature with zero-order and first-order reaction were established by using a statistical software package (SPSS 18). The shelf life could be predicted based on the chemical kinetic model. The results showed that the characteristic wave numbers of 10 300, 8 330, 6 900, 5 666, 5 150 and 4 060 cm(-1) in the whole near-infrared range with MSC technique could be chosen to model the quality deterioration of fresh jujube at room temperature. The NIR model that produced the best prediction had the form of B = 320.027 - 233.920(chi1) - 206.663(chi2) - 61.584(chi3) - 14.847(chi4) - 2.680(chi5) - 9.131(chi6), where B is yeast value (lg/cfu x g(-1)), chi1-chi6 are absorbance value of characteristic wavenumber. The correlation coefficient of calibration (R(c)) was 0.950, the root mean square error of calibration (RMSEC) was 2. 560, the correlation coefficient of prediction (R(p)) was 0.863, and the root mean square error of prediction (RMSEP) was 2.447. The zero-order reaction kinetic model performed better than the first-order model. The zero-order reaction kinetic model of yeast growth with storage time was predicted by B(t) = 171.395-124.445(chi1) - 109.945(chi2) - 32.763(chi3) - 7.899(chi4) - 1.426(chi5) - 4.857(chi6) + 0.045t with a correlation coefficient of 0.996. Based on the linear correlation between the NIR measurement and storage time, the shelf life of fresh jujube at room temperature was predicted to be 8 days for the yeast infection level less than 10 cfu x g(-1). The study showed that the NIR when combed with kinetic models could be used as a non-destructive, rapid method to detect the yeast growth in fresh jujube, and to predict the shelf life and ensure the quality and safety of fresh jujube.

Comprehensive review on the novel immunotherapy target: Leucine-rich repeat-containing 8A/volume-regulated anion channel.

Leucine-rich repeat-containing 8A (LRRC8A) is a key component of the volume-regulated anion channel (VRAC) that influences essential homeostatic processes in various immune cells. These processes include the regulation of cell volume and membrane potential and the facilitation of the transport of organic agents used as anticancer drugs and immune-stimulating factors. Therefore, understanding the structure-function relationship of LRRC8A, exploring its physiological role in immunity, assessing its efficacy in treating diseases, and advancing the development of compounds that regulate its activity are important research frontiers. This review emphasized the emerging field of LRRC8A, outlined its structure and function, and summarized its role in immune cell development and immune cell-mediated antiviral and antitumor effects. Additionally, it explored the potential of LRRC8A as an immunotherapeutic target, offering insights into resolving persistent challenges and future research directions.

Adaptive Internal Model Backstepping Control for a Class of Second-Order Electromagnetic Micromirror with Output Performance Constraints and Anomaly Control.

This paper investigates the asymptotic tracking problem for a class of second-order electromagnetic micromirror model with output performance constraints and anomaly control, which is subject to model parameter uncertainties and external disturbances. Specifically, this paper formulates the trajectory tracking control problem of an electromagnetic micromirror as a closed-loop control trajectory tracking problem based on the general solution framework of output regulation. Moreover, the extended internal model is introduced to reformulate the closed-loop control problem into a state stabilization problem of the augmented system. Based on the augmented system, an internal model backstepping controller is proposed by integrating the barrier Lyapunov Functions (BLF) and the Nussbaum gain function with the backstepping structure.This controller not only satisfies the output performance constraints of the micromirror, but also maintains the control performance in anomalous control situations. The final performance simulation demonstrates the efficacy of the proposed controller.

Selection of dialysis methods for end-stage kidney disease patients with diabetes.

The increasing prevalence of diabetes has led to a growing population of end-stage kidney disease (ESKD) patients with diabetes. Currently, kidney transplantation is the best treatment option for ESKD patients; however, it is limited by the lack of donors. Therefore, dialysis has become the standard treatment for ESKD patients. However, the optimal dialysis method for diabetic ESKD patients remains controversial. ESKD patients with diabetes often present with complex conditions and numerous complications. Furthermore, these patients face a high risk of infection and technical failure, are more susceptible to malnutrition, have difficulty establishing vascular access, and experience more frequent blood sugar fluctuations than the general population. Therefore, this article reviews nine critical aspects: Survival rate, glucose metabolism disorder, infectious complications, cardiovascular events, residual renal function, quality of life, economic benefits, malnutrition, and volume load. This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis, thereby improving patients' quality of life and survival rates.

MLG-YOLO: A Model for Real-Time Accurate Detection and Localization of Winter Jujube in Complex Structured Orchard Environments.

Our research focuses on winter jujube trees and is conducted in a greenhouse environment in a structured orchard to effectively control various growth conditions. The development of a robotic system for winter jujube harvesting is crucial for achieving mechanized harvesting. Harvesting winter jujubes efficiently requires accurate detection and location. To address this issue, we proposed a winter jujube detection and localization method based on the MobileVit-Large selective kernel-GSConv-YOLO (MLG-YOLO) model. First, a winter jujube dataset is constructed to comprise various scenarios of lighting conditions and leaf obstructions to train the model. Subsequently, the MLG-YOLO model based on YOLOv8n is proposed, with improvements including the incorporation of MobileViT to reconstruct the backbone and keep the model more lightweight. The neck is enhanced with LSKblock to capture broader contextual information, and the lightweight convolutional technology GSConv is introduced to further improve the detection accuracy. Finally, a 3-dimensional localization method combining MLG-YOLO with RGB-D cameras is proposed. Through ablation studies, comparative experiments, 3-dimensional localization error tests, and full-scale tree detection tests in laboratory environments and structured orchard environments, the effectiveness of the MLG-YOLO model in detecting and locating winter jujubes is confirmed. With MLG-YOLO, the mAP increases by 3.50%, while the number of parameters is reduced by 61.03% in comparison with the baseline YOLOv8n model. Compared with mainstream object detection models, MLG-YOLO excels in both detection accuracy and model size, with a mAP of 92.70%, a precision of 86.80%, a recall of 84.50%, and a model size of only 2.52 MB. The average detection accuracy in the laboratory environmental testing of winter jujube reached 100%, and the structured orchard environmental accuracy reached 92.82%. The absolute positioning errors in the X, Y, and Z directions are 4.20, 4.70, and 3.90 mm, respectively. This method enables accurate detection and localization of winter jujubes, providing technical support for winter jujube harvesting robots.

Interaction between the EPHB2 receptor and EFNB1 ligand drives gastric cancer invasion and metastasis via the Wnt/ß-catenin/FAK pathway.

The survival benefit for patients with gastric cancer (GC) is modest due to its high transfer potential. Targeted therapy for metastasis-related genes in GC may be a viable approach, however, inhibitors specifically targeting GC are limited. In this study, GC patient-derived xenografts (PDX) with metastatic burden were established via orthotopic transplantation. PCR-Array analysis of primary and metastatic tumors revealed EPH receptor B2 (EPHB2) as the most significantly upregulated gene. The interaction between the EPHB2 receptor and its cognate-specific EFNB1 ligands was high in GC and correlated with a poor prognosis. Fc-EFNB1 treatment increased the invasion and migration abilities of GC cells and induced a high EPHB2 expression. EPHB2 knockdown in GC cells completely abolished the ephrin ligand-induced effects on invasion and migration abilities. Signal transduction analysis revealed Wnt/ß-catenin and FAK as downstream signaling mediators potentially inducing the EPHB2 phenotype. In conclusion, the observed deregulation of EPHB2/EFNB1 expression in GC enhances the invasive phenotype, suggesting a potential role of EPHB2/EFNB1 compound in local tumor cell invasion and the formation of metastasis.