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Gene silencing is a natural antiviral defense mechanism in plants. For effective infection, plant viruses encode viral silencing suppressors to counter this plant antiviral response. The geminivirus-encoded C4 protein has been identified as a gene silencing suppressor, but the underlying mechanism of action has not been characterized. Here, we report that Cotton Leaf Curl Multan virus (CLCuMuV) C4 protein interacts with S-adenosyl methionine synthetase (SAMS), a core enzyme in the methyl cycle, and inhibits SAMS enzymatic activity. By contrast, an R13A mutation in C4 abolished its capacity to interact with SAMS and to suppress SAMS enzymatic activity. Overexpression of wild-type C4, but not mutant C4R13A, suppresses both transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS). Plants infected with CLCuMuV carrying C4R13A show decreased levels of symptoms and viral DNA accumulation associated with enhanced viral DNA methylation. Furthermore, silencing of NbSAMS2 reduces both TGS and PTGS, but enhanced plant susceptibility to two geminiviruses CLCuMuV and Tomato yellow leaf curl China virus. These data suggest that CLCuMuV C4 suppresses both TGS and PTGS by inhibiting SAMS activity to enhance CLCuMuV infection in plants.

•Unsupervised MR harmonization without traveling subjects.•Unified latent space for MR contrast synthesis.•A novel framework for disentangling contrast and anatomy in MR images.•Downstream segmentation consistency shows significant improvements after harmonization. In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

SARS-CoV-2 inactivated vaccines have shown remarkable efficacy in clinical trials, especially in reducing severe illness and casualty. However, the waning of humoral immunity over time has raised concern over the durability of immune memory following vaccination. Thus, we conducted a nonrandomized trial among the healthcare workers (HCWs) to investigate the long-term sustainability of SARS-CoV-2-specific B cells and T cells stimulated by inactivated vaccines and the potential need for a third booster dose. Although neutralizing antibodies elicited by the standard two-dose vaccination schedule dropped from a peak of 29.3 arbitrary units (AU)/mL to 8.8 AU/mL 5 months after the second vaccination, spike-specific memory B and T cells were still detectable, forming the basis for a quick recall response. As expected, the faded humoral immune response was vigorously elevated to 63.6 AU/mL by 7.2 folds 1 week after the third dose along with abundant spike-specific circulating follicular helper T cells in parallel. Meanwhile, spike-specific CD4+ and CD8+ T cells were also robustly elevated by 5.9 and 2.7 folds respectively. Robust expansion of memory pools by the third dose potentiated greater durability of protective immune responses. Another key finding in this trial was that HCWs with low serological response to two doses were not truly “non-responders” but fully equipped with immune memory that could be quickly recalled by a third dose even 5 months after the second vaccination. Collectively, these data provide insights into the generation of long-term immunological memory by the inactivated vaccine, which could be rapidly recalled and further boosted by a third dose.

Background LncRNA-PACERR plays critical role in the polarization of tissue-associated macrophages (TAMs). In this study, we found the function and molecular mechanism of PACERR in TAMs to regulate pancreatic ductal adenocarcinoma (PDAC) progression. Methods We used qPCR to analyse the expression of PACERR in TAMs and M1-tissue-resident macrophages (M1-NTRMs) which were isolated from 46 PDAC tissues. The function of PACERR on macrophages polarization and PDAC proliferation, migration and invasion were confirmed through in vivo and in vitro assays. The molecular mechanism of PACERR was discussed via fluorescence in situ hybridization (FISH), RNA pull-down, ChIP-qPCR, RIP-qPCR and luciferase assays. Results LncRNA-PACERR was high expression in TAMs and associated with poor prognosis in PDAC patients. Our finding validated that LncRNA-PACERR increased the number of M2-polarized cells and facilized cell proliferation, invasion and migration in vitro and in vivo. Mechanistically, LncRNA-PACERR activate KLF12/p-AKT/c-myc pathway by binding to miR-671-3p. And LncRNA-PACERR which bound to IGF2BP2 acts as an m6A-dependent manner to enhance the stability of KLF12 and c-myc in cytoplasm. In addition, the promoter of LncRNA-PACERR was a target of KLF12 and LncRNA-PACERR recruited EP300 to increase the acetylation of histone by interacting with KLF12 in nucleus. Conclusions This study found that LncRNA-PACERR functions as key regulator of TAMs in PDAC microenvironment and revealed the novel mechanisms in cytoplasm and in nucleus.

To address the problem present in current subgrade settlement detection methods, this paper proposes a nondestructive intelligent and dynamic detection method for subgrade settlement based on vehicle-mounted binocular stereo vision technology. This method aims to achieve all season, the whole road, long-term detection of subgrade settlement for Road of ART (Autonomous rail Rapid Transit) in coastal tidal flat areas. Firstly, improved Schneider encoding is adopted as the marker for subgrade settlement monitoring points. Binocular camera calibration and stereo rectification are performed using Zhang’s method and the Bouguet algorithm before acquiring the marker images at the monitoring points, followed by efficient capture of Schneider ring coding images by the vehicle-mounted binocular stereo vision system. Thirdly, OpenCV is employed to preprocess the images, which improve image quality, eliminate noise, and enhance the features of the ring coding markers. On this basis, an improved SGBM algorithm is utilized for binocular stereo matching. Finally, according to the principle of triangulation, the three-dimensional coordinates of the monitoring points are obtained, and the corresponding settlement values of each monitoring point are determined through decoding and matching. Experimental results indicate that, for a true settlement value of 60 mm, the proposed detection method achieves an average settlement value of 58.897 mm, with a relative error rate of 1.84%. In the same experimental environment, the relative error rate of using a monocular camera detection method is 10.3%. The vehicle-mounted binocular camera method, with lower relative error than the monocular camera, offers a more efficient and accurate solution for nondestructive subgrade settlement detection, enhancing its intelligence.

Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi‐omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi‐omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

Thyroid cancer is the most common endocrine cancer, of which the incidence has dramatically increased worldwide in the past few decades. The reasons for the observed rapid increase still are not fully understood, but evidence suggests that overdiagnosis, with the advancement in detection methods and screening policies, is not the sole driver of the substantial increase of the incidence. However, the effect of environmental/lifestyle factors remains speculative other than that of radiation exposure at a young age. This review tries to give a balanced view of debated factors leading to the thyroid cancer epidemic, to offer some alternatives in understanding the controversies, and to suggest potential directions in the search of modifiable risk factors to help reduce thyroid cancer.

Background The symbiotic interaction between arbuscular mycorrhizal fungi (AMF) and roots can change root traits, soil carbon - nitrogen processes and crop yield. However, the precise mechanisms by which AMF affect soil carbon economic strategies and crop yield remain unclear. A two - factor pot experiment was done with cotton. Factor 1 was nitrogen application (1.5, 1.0, 0.5 g·kg −1 ), Factor 2 was AMF treatment (colonization and non - colonization) to study relationships between AMF and root traits, nutrient strategies, yield. Results The analysis of the root economic spectrum reveals that after inhibiting AMF colonization in roots, root nitrogen content (RNC), root intersection count (RIC), specific root length (SRL), root branching intensity (RBI), specific root area (SRA), and root tip count (RTC) adopting an acquisitive strategy, whereas AMF colonization and root diameter (RD) showed a conservative strategy. When AMF normally colonizes roots, AMF colonization, RNC, RBI and RTC exhibit conservative strategy, whereas SRA, RD, SOC and leaf nitrogen content (LNC) display an acquisitive strategy. Additionally, there is a non - linear relationship between root traits and seed - cotton yield. Notably, AMF colonization leads to variability in the relationships between SRA and yield, and between RTC and yield. Conclusions Under nitrogen reduction conditions, AMF colonization can enhance nitrogen acquisition by optimizing root characteristics (SRA and RBI), coordinating nitrogen metabolism between leaves and roots, and adjusting the soil carbon economic strategy. In addition, AMF hyphae will adopt a strategy of slowly acquiring nitrogen as a reward for plants, which is one of the key factors contributing to the observed differences between the trends in root morphology and seed - cotton yield.

Accurate segmentation of key tobacco structures is essential for enabling automated harvesting. However, complex backgrounds, variable lighting conditions, and blurred boundaries between the stem and petiole significantly hinder segmentation accuracy in field environments. To overcome these challenges, we propose an enhanced instance segmentation approach based on YOLOv8-seg, incorporating depth-based background filtering and architectural improvements. Specifically, depth information from RGB-D images is employed to spatially filter non-target background regions, thereby enhancing foreground clarity. In addition, a Hybrid Dilated Residual Attention Block (HDRAB) is integrated into the YOLOv8-seg backbone to improve boundary discrimination between petioles and stems, while a Lightweight Shared Detail-Enhanced Convolution Detection Head (LSDECD) is designed to efficiently capture fine-grained texture features. Experimental results demonstrate that depth filtering increases mAP50 bb and mAP50 seg by 7.9% and 6.3%, respectively, while the architectural enhancements further raise them to 89.5% and 91.1%, surpassing the YOLOv8-seg baseline by 5.2% and 10.0%. Compared with mainstream models such as Mask R-CNN and SOLOv2, the proposed method achieves superior segmentation accuracy with low computational cost, highlighting its potential for practical deployment in automated tobacco harvesting

ObjectiveSurgical site infections (SSI) after hysterectomy constitute significant postoperative complications, affecting patient recovery and healthcare costs. We conducted a systematic review of risk factors for SSI in patients undergoing hysterectomy.DesignThe current study conducted a systematic review with meta-analysis to identify and summarise risk factors for SSI following hysterectomy.Data sourcesPubMed, Medline, Embase, Web of Science and Cochrane Central Register of Controlled Trials were searched through 1 November 2023.Eligibility criteriaThe inclusion criteria were (1) population: female participants who had post-hysterectomy SSI; (2) intervention: hysterectomy surgeries; (3) comparators: the number of participants who had or had not post-hysterectomy SSI; (4) outcomes: the number of participants exposed and not exposed to the risk factors of SSI. The exclusion criteria were (1) non-English studies and (2) studies that provided insufficient data.Data extraction and synthesisTwo reviewers conducted the screening process independently. Articles that did not meet the inclusion criteria were excluded. For those that met the criteria, full-text papers were procured. Any discrepancies between the reviewers were resolved through discussion. The meta-analysis synthesised risk factors reported in ≥4 datasets via random-effects models, assessing heterogeneity, sensitivity (leave-one-out), publication bias (Egger’s test/funnel plots) and subgroup analyses (incision types).ResultsBlood transfusion emerged as the largest risk factor (OR=2.55, 95% CI (1.84, 3.56)), followed by tumour presence (OR=2.23, 95% CI (1.86, 2.66)), obesity (OR=1.79, 95% CI (1.43, 2.23)), diabetes (OR=1.70, 95% CI (1.26, 2.29)) and tobacco use (OR=1.43, 95% CI (1.26, 1.63)). The ORs varied by incision type.ConclusionsThe study establishes blood transfusion, tumour presence, obesity, diabetes and tobacco use as significant risk factors for SSI after hysterectomy, with variations in risk evident across different incision types. The findings also suggest vaginal and laparoscopic hysterectomies as preferable alternatives to abdominal hysterectomy in mitigating SSI risk. Future research should aim for more granular data to untangle the interplay between comorbidities and further elucidate the differential risk across SSI types.