Accurate estimation of soil moisture (SM) at different depths is essential for orchard water management, yet the contribution of canopy physiological information to multi-depth SM retrieval remains insufficiently understood. In this study, we developed a multi-task learning (MTL) model that incorporates leaf water content (LWC) as an auxiliary physiological task to improve SM retrieval across varying depths (5, 10, 20 and 40 cm). LWC was significantly correlated with SM at 10, 20, and 40 cm, but not at 5 cm, suggesting that LWC may serve as a auxiliary task for deeper SM estimation. Evaluating Random Forest (RF), single-task learning (STL), and the proposed MTL models revealed distinct depth-wise performance differences. RF performed best at 5 cm (R2=0.685), whereas MTL achieved the highest testing accuracy at 10, 20 and 40 cm, with R2 of 0.691, 0.695, and 0.554, respectively. Overall estimation accuracy peaked at 20 cm, while the largest relative improvements of MTL over RF were observed at 10 and 40 cm (R2 increases of 0.181 and 0.234). Finally, SHapley Additive exPlanations (SHAP) analysis further showed that incorporating LWC as an auxiliary task was associated with a shift in feature contribution patterns, with increased importance assigned to vegetation condition, pigment-related, and thermal stress indicators. These results indicate that incorporating LWC as an auxiliary task can improve subsurface SM estimation and enhance model interpretability for precision orchard water management.

Understanding how soil moisture (SM) varies with depth is important for characterizing profile-scale water movement and storage behavior. However, the vertical propagation, persistence, and soil moisture–temperature (SM–ST) coupling of profile SM remain insufficiently quantified. Using multi-site observations at five depths (3, 5, 10, 20, and 50 cm) in the Shandian River Basin of northern China during the non-frozen periods from 2019 to 2022, this study quantified the temporal dynamics, vertical propagation, persistence, SM–ST coupling, and environmental controls of SM. Results showed a clear shift from a highly variable shallow layer to a delayed and persistent deep-soil regime. SM persistence increased by 164 % from 14 d at 3 cm to 37 d at 50 cm, accompanied by stronger spatial heterogeneity at depth. Vertical coupling between adjacent layers weakened downward by 18 %, with the median maximum correlation decreasing from 0.93 in the shallow intervals (3–5 and 5–10 cm) to 0.76 in the 20–50 cm interval, while the corresponding median lag increased from 0 to 5 d. For SM–ST coupling, shallow soil showed weak evaporation-related decoupling, whereas deeper soil showed positive hydrothermal co-variation; the median lag remained near zero but became spatially more constrained with depth. Environmental controls were predominantly nonlinear: clay content was more closely associated with deep-layer lag and persistence, whereas elevation showed a stronger influence on deep SM–ST coupling. These findings provide profile-scale observational evidence for representing depth-dependent soil moisture memory, vertical transmission, and SM–ST coupling in hydrological and land-surface studies.

Entity Alignment (EA) aims to identify equivalent entities across different knowledge graphs (KGs), a crucial task for knowledge fusion. Recent approaches treat EA as a representation learning challenge through graph embedding. However, they do not fully exploit the most reliable structural information to encode the KGs and leave considerable room for further exploration of decoding strategies. To address these issues, we propose Context-Aware and Multi-View Enhanced Model for Entity Alignment (CMEA), which concentrates solely on graph structure for both encoding and decoding in entity alignment. Specifically, we take a data augmentation strategy by adding reverse edges and constructing twin graphs. In the encoding phase, we design a Twin Graph Convolutional Network to model entities by leveraging the semantic context of neighboring entities and their associated relations. In the decoding phase, we utilize multi-view adjacency matrices to derive fine-grained entity representations without training, thereby improving EA performance. Comprehensive experiments conducted on four real-world datasets highlight the advantages of our method over previous approaches.

6091 Background: Neoadjuvant immunotherapy combined with chemotherapy is a promising strategy for resectable LAHNSCC. While most regimens employ single-target PD-1 inhibitors, dual-target agents (PD-1/CTLA-4 or PD-1/VEGF) have shown superior efficacy in recurrent/metastatic HNSCC. Building on our preliminary data (ASCO 2025) which suggested high pathological response rates, this ongoing randomized phase II trial aims to compare single- versus dual-target NAI regimens in an expanded cohort to identify the optimal treatment strategy. Methods: This is an ongoing randomized, open-label, phase II trial. Eligible pts were randomized (1:1:1) to receive 3 cycles of neoadjuvant therapy as follows: Cohort 1 will receive ivonescimab (PD-1/VEGF bispecific antibody, 10 mg/kg every 3 weeks), Cohort 2 will receive cadonilimab (PD-1/CTLA-4 bispecific antibody, 6 mg/kg every 3 weeks), and Cohort 3 will receive penpulimab (PD-1 antibody, 200 mg every 3 weeks), all in combination with cisplatin and nab-paclitaxel. After neoadjuvant treatment, Surgery was performed with the surgical margins based on pre-treatment (baseline) evaluation. Pts with pCR received adjuvant immunotherapy for up to 16 cycles. Pts without pCR received adjuvant radiotherapy or chemoradiotherapy, followed by 16 cycles of adjuvant immunotherapy. Results: Up to Dec. 2025, all 59 pts completed 3 cycles of neoadjuvant therapy and were evaluable, with a median follow-up of 12 months. The pCR rates were 60% (12/20) in Cohort 1, 42.1% (8/19) in Cohort 2, and 40% (8/20) in Cohort 3. The major pathologic response (MPR) rates were 75% (15/20), 57.9% (11/19), and 55% (11/20) in Cohort 1, 2, and 3, respectively. The ORR was 95% in Cohort 1 (CR: 8/20, PR: 11/20) and 84.2% in Cohort 2 (CR: 5/19, PR: 11/19), while Cohort 3 had an ORR of 80% (CR: 4/20, PR: 12/20). To date, pts have received a median of 6 cycles of adjuvant immunotherapy. The most common treatment-related adverse events (TRAEs) (>20%) were: leukopenia, anemia, neutropenia, thrombocytopenia, lymphocytopenia, hypothyroidism, hypertriglyceridemia, radiation dermatitis, stomatitis, vomiting, decreased appetite, and fatigue. Conclusions: Neoadjuvant dual-target (PD-1/VEGF) immunotherapy combined with chemotherapy demonstrated a higher pCR rate compared to dual-target (PD-1/CTLA-4) and single-target PD-1 regimens in resectable LAHNSCC. The treatment was well-tolerated, with all pts completing the intended neoadjuvant therapy and the observed most common TRAEs consistent with expected chemotherapy and radiotherapy profiles. Further analyses are ongoing with continued enrollment. Clinical trial information: NCT06444009 . RECIST and pathologic response. RECIST Pathologic Response CR PR SD PD pCR MPR pPR pNR (RVT=0) (0 （RVT:10-49%） (RVT:≥50%) Cohort 1 (n=20) 8 11 1 12 3 2 3 Cohort 2 (n=19) 5 11 3 8 3 2 6 Cohort 3 (n=20) 4 12 4 8 3 0 9

With the rapid development of Internet of Things (IoT) technology, edge devices such as smartphones and sensors generate large volumes of data. Although traditional synchronous federated learning frameworks can perform distributed model training while ensuring data privacy, they often face training bottlenecks and delays in IoT environments due to device heterogeneity and computational capacity differences. These issues significantly affect training efficiency and model performance. To address these challenges, we propose a two-layer asynchronous federated learning algorithm. The algorithm uses singular value decomposition for quantization and feature extraction of edge node data, and constructs a two-layer training architecture consisting of a central server, cluster leader nodes, and regular nodes through clustering methods. We design a two-stage asynchronous training process, where model parameters are first asynchronously submitted and aggregated within the cluster, and then the aggregation of the global model is improved by distinguishing the local convergence states of the nodes, thereby reducing communication overhead and mitigating model drift. Moreover, the algorithm implements inter-cluster synchronous training by quantifying the similarity of data features across clusters, improving the model’s generalization ability and accuracy. The experimental results on the Fashion-MNIST, CIFAR-10, Sentiment140, and Blue Gene/L datasets validate the effectiveness of our method. Compared with existing approaches, our algorithm demonstrates significant improvements in prediction accuracy while considerably reducing communication requirements.

BACKGROUND:Aortic angulation (AA), defined as the angle between the horizontal plane on the coronal plane and the plane of the aortic valve annulus, is an important anatomical factor in transcatheter aortic valve replacement (TAVR). Whether AA affects early clinical outcomes and complications in self-expanding (SE)-TAVR procedures is still controversial. METHOD:We conducted a retrospective cohort study of 519 consecutive patients who underwent SE-TAVR at our centre from January 2016 to January 2021. Preoperative AA, technique success, and one-year postoperative survival rates were primarily analysed. RESULTS:The AA of patients undergoing SE-TAVR ranged from 25° to 93°, with a mean value of 55.4°±9.7°. There was a statistically significant difference in technique success between AA≤55.5° and AA>55.5° groups (84.3% vs 75.1%, p=0.009), mainly driven by the higher proportion of second-valve implantation during TAVR (8.8% vs 19.6%, p<0.001). In valve-type subgroup analysis, larger AA demonstrated good predictive value for second-valve implantation (area under the curve 0.690; 95% confidence interval 0.617-0.763). However, AA showed limited predictive efficacy for technical success in patients with bicuspid aortic valve, and it was not associated with major complications or unplanned interventions in patients with tricuspid aortic valve. CONCLUSIONS:A larger AA is associated with a lower rate of technical success of SE-TAVR, mainly due to an increased frequency of second-valve implantation. The impact of AA is more evident in patients with tricuspid aortic valve rather than in those with bicuspid aortic valve.

Accurate and timely Leaf Area Index (LAI) estimation is critical for monitoring kiwifruit canopy growth and supporting precision orchard management. UAV remote sensing is widely used for LAI estimation, but traditional methods relying on single sensors or fixed band combinations perform poorly in estimating LAI of perennial fruit trees across different growth stages. To solve this problem, this study developed optimized spectral index (VI_MS), spectral-texture index (VI_MT), and thermal infrared-texture index (VI_TT) by leveraging UAV-acquired multispectral, thermal infrared data, and their derived texture features. Then, six machine learning regression (MLR) models were constructed for LAI estimation, including Support Vector Regression (SVR), Random Forest Regression (RFR), and four Boosting-based algorithms. Results showed that: (i) Optimized VI_MS, VI_MT, and VI_TT all strengthened the correlation with LAI across growth stages, and VI_MT performed best; (ii) Among all models, RFR had the highest prediction accuracy, followed by Boosting-based algorithms, while SVR performed worst; (iii) Combining VI_MT with RFR further improved kiwifruit LAI estimation accuracy. Notably, stagespecific LAI estimation was better than full growth cycle estimation, with mean R2 values of 0.916 f 0.036 and 0.893 f 0.083, and mean MAE values of 0.039 f 0.008 and 0.043 f 0.017, respectively. Overall, coupling optimized VI_MT with RFR or Boosting models improves kiwifruit LAI estimation under varying conditions. These results provide new insights for dynamic monitoring of orchard canopy growth in changing agroenvironments.

Citrus yield and fruit quality are severely limited by inefficient water and fertilizer management in Southwest China. Formulating appropriate deficit irrigation and fertilization (DIF) schedules is necessary for achieving sustainable production of citrus. Herein, three irrigation levels (CK, 100% control treatment; I80, 80% CK; and I60, 60% CK) and four fertilization levels (CK; F85, 85% CK; F70, 70% CK; and F55, 55% CK) were used at each growth stage of citrus. Multiple regression models efficiently predicted the effects of DIF on citrus water productivity (WPc), partial fertilizer productivity (PFP), fruit quality, and yield, with an R2= 0.71-0.98**. Specifically, the application of more fertilizer during stage III improved WPc, soluble sugar (SS) content, and vitamin C (Vc) content; these indices peaked when the irrigation amount was 280, 322, and 306 mm, respectively, and the fertilizer (N/P2O5/K2O) application was 483/480/762 kg/ha during stage III. SS and Vc contents increased as irrigation amount decreased and fertilization application rate increased during stage IV. During stage IV, a decrease in irrigation amount significantly reduced titratable acid content. Finally, different parameters were optimized using regression models and spatial projection methods. During stages I, II, III and IV, the optimal irrigation amounts were 28-36, 74-93, 304-334, 151-197 mm respectively, and the optimal fertilizer (N/P2O5/ K2O) application rates were 98/155/98-123/195/123, 213/187/307-271/239/392, 400/398/631-473/470/ 746, and 17/101/251-21/123/306 kg/ha, respectively. This study provides a quantitative basis for designing more effective irrigation and fertilization strategies for sustainable citrus cultivation in Southwest China.

Soil and Plant Analyzer Development (SPAD) value and leaf water content (LWC) are critical physiological parameters for agricultural irrigation and growth monitoring in late-maturing citrus. Accurate monitoring of citrus SPAD value and LWC is of great significance for guiding precision irrigation, improving water use efficiency, and enhancing yield. To rapidly and efficiently obtain the SPAD value and LWC of citrus orchards, this study extracted vegetation index (VI) and texture feature (TF) of late-maturing citrus at different growth stages based on UAV multi-spectral images. Feature variable selection methods (decision tree (DT) and least absolute shrinkage and selection operator (Lasso)) were combined with Support vector machine regression (SVR), AdaBoost (Ada), SVR-AdaBoost (SVR-Ada) and WOA-SVR-Ada. Models for estimating SPAD value and LWC in citrus orchards were constructed using VI, TF, and VI+TF as inputs. The results showed that the DT algorithm demonstrated superior capability in identifying feature variables compared to the Lasso. The integration of VI and TF can enhance the inversion accuracy of citrus SPAD value and LWC models. Compared to the SVR, Ada and SVR-Ada, the WOA-SVR-Ada model, constructed by combining the DT algorithm with VI+TF as inputs (WOA-SVR-AdaD3), exhibited the highest estimation accuracy for both SPAD value and LWC. Therefore, combining feature variable selection methods with ensemble learning algorithms, along with the fusion of multi-feature information from UAV multispectral, holds promise for providing precise and robust estimations of SPAD value and LWC for late-maturing citrus in the seasonal drought regions of Southwest China.

The optimal time for soil moisture content (SMC) estimation for different depths of kiwifruit root zone under irrigation can improve the efficiency of irrigation water utilization. Determining the optimal time and depth can provide strong support for accurately assessing orchard SMC post-irrigation and offer essential guidance for developing irrigation schemes. Multispectral (MS) images of the kiwifruit tree canopy combined with machine learning (ML) algorithms were used to estimate kiwifruit orchard SMC under irrigation in this study. Specifically, the Support Vector Machine (SVM) algorithm was used to extract kiwifruit canopy images obtained by remote sensing of UAV, and Random Forest Out-of-Bag (OOB) samples and Grey Relation Analysis (GRA) were used to calculate the correlation coefficient between vegetation indices (VIs) and the ground measured SMC. Three ML algorithms, Random Forest (RF), Back Propagation neural network (BP) and SVM were utilized to establish the SMC estimation models. Results showed that (1) RF models were superior to BP and SVM models for the SMC estimation, with R-2 values of OOB-RF and GRA-RF being 0.51 similar to 0.85 and 0.49 similar to 0.79, respectively. The NDVI, SAVI, EVI, and RDVI, including the red-edge VIs composed of re1, re2, and re3, all made a greater contribution to the model compared to the other VIs. (2) The highest accuracy of all the models for SMC estimates was obtained at 20 cm depth, with average R-2 and MAE values of 0.72 and 0.32, respectively. (3) The optimal time for acquiring kiwifruit canopy images and estimating SMC post-irrigation was determined to be 16:00 on the seventh day post-irrigation for the depth of 20 cm. The results of this study prove the feasibility of using MS images and red-edge VIs to estimate SMC after irrigation, which provides a new scheme for orchard irrigation management.

To systematically appraise the quality of clinical practice guidelines (CPGs) regarding awake tracheal intubation (ATI) and to compare the consistency of common recommendations. Systematic review, critical appraisal and narrative synthesis of CPG recommendations for ATI. A systematic search of the PubMed, EMBASE, Cochrane, Web of Science, and Scopus databases was conducted up to July 1, 2024, to identify up-to-date CPGs. The AGREE II (Appraisal of Guidelines for Research and Evaluation) checklist was used to critically appraise the CPGs. Interrater agreement was determined via intraclass correlation coefficients (ICCs) with a two-way random effects model for each domain and overall rating score. All the suggestions extracted from the included guidelines were sorted and analyzed and summarized via the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. Our study resulted in 939 records and ultimately 7 CPGs were appraised. The content of these CPGs covered six themes of ATI: indications, airway local anesthesia, the intubation procedure, checking the tube position, management after ATI failure, and the extubation process. When the AGREE II tool was used to appraise CPGs, only 3 CPGs were rated as “high” quality. With the exception of domain 1, we observed good agreement in all five other domains (ICCs over 0.7). These CPGs provided relatively consistent recommendations and evidence on intubation procedures and checking tube position. In terms of indications and airway local anesthesia, there was controversy. Twenty-nine recommendations regarding ATI were summarized through the GRADE system, among which 16 were considered relatively reliable. Through the AGREE II tool and the GRADE system, the strengths and weaknesses of each CPG were comprehensively analyzed on the basis of its scientific validity and practicability. Moreover, the limitations of the current CPGs in terms of indications, airway local anesthesia and complex clinical situations are presented, and clinicians are encouraged to apply the guidelines more scientifically and to update and improve the guidelines. CRD4202458548 (PROSPERO).

Seasonal droughts and extreme weather events are threatening citrus production in south China. Investigating the effect of deficit irrigation (DI) on leaf physiology, fruit growth, yield and crop water productivity (WPc) is significantly important for the sustainable development of citrus industry. In this study, a full irrigation treatment (CK) and 16 DI treatments were designed including the low (LD, 85 %CK), mild (M1D, 70 %CK), moderate (M2D, 55 %CK) and severe (SD, 40 %CK) DI treatments at bud bust to flowering stage (I), young fruit stage (II), fruit expansion stage (III) and fruit maturation stage (IV), respectively. Compared with CK, DI treatments at stage I-IV raised the hydrogen peroxide content by 14.4 %-76.6 %, except for LD treatment. Meanwhile, the activities of superoxide dismutase, peroxidase, catalase and the content of proline also increased by 10.5 %-47.3 %, 24.9 %-77.4 %, 20.2-49.8 % and 10.1 %-39.0 %, respectively, which allowed crop to cope with DI-induced oxidative stress. When stomatal conductance (Gs) at stage I-IV reached 0.030-0.040, 0.074-0.096, 0.204-0.219, and 0.114-0.142 mmol center dot m- 2 center dot s- 1, respectively, leaf net photosynthesis rate (Pn) did not significantly change, but transpiration rate was limited, and hereby enhanced instantaneous water use efficiency. In addition, although DI treatments at all stages reduced Pn, they did not always have a negative impact on yield due to the obvious improvement of leaf photosynthesis and fruit growth after re-irrigation. Specifically, re-irrigation after IM1D, II-M1D and III-LD treatments increased the fruit growth rate at stages II, III and IV, respectively, which could further maintain or even enhance the yield, and improve WPcby 5.6 %-7.0 %, 5.7 %-8.6 % and 3.4 %4.7 %, respectively. IV-M2D treatment increased WPcby 13.7 %-14.5 %. In summary, DI treatment could regulate Gs and fruit compensatory growth after re-irrigation, respectively, so as to achieving water saving and high yield of citrus. I-M1D, II-M1D, III-LD and IV-M2D treatments was recommended as the suitable deficit drip irrigation pattern to ensure efficient citrus production.

Efficient irrigation strategies are crucial for improving crop yield, fruit quality, and water productivity (WP), particularly under water scarcity conditions. This study developed dated crop water production functions (DCWPF) to simulate kiwifruit yield and quality responses under deficit irrigation, and integrated them with multi-objective optimization (MOO) algorithms to identify optimal irrigation strategies under varying total available water (TAW) conditions. The Jensen model exhibited robust performance in simulating yield, while the Q-Rao model effectively captured the nonlinear response of fruit quality to water stress. Water deficit sensitivity indexes revealed that fruit expansion stage (III) was the most critical for yield, while moderate deficit during fruit maturation stage (IV) could improve quality traits. The multi-objective particle swarm optimization (MOPSO) exhibited superior performance in both computational efficiency and solution quality, highlighting its suitability for optimizing kiwifruit irrigation strategies. Under adequate TAW condition, the optimal relative evapotranspiration allocations across the four growth stages of kiwifruit were 0.53, 1.00, 1.00, and 0.90. At this strategy, a 7.2 % reduction in yield was traded off for a 9.1 % improvement in fruit quality and a 2.2 % enhancement in WP. Under limited TAW, the recommended strategies prioritized irrigation during stages III and II (flowering to fruit set stage). The findings not only provide theoretical support for irrigation water management in kiwifruit cultivation, but also demonstrate the effectiveness of coupling DCWPF with MOO algorithms for optimizing irrigation strategies, offering valuable insights into the application of multi-objective optimization in agriculture practices.

Traffic flow prediction plays an important role in smart cities. Although many neural network models already existed that can predict traffic flow, in the face of complex spatio-temporal data, these models still have some shortcomings. Firstly, they although take into account local spatio-temporal relations, ignore global information, leading to inability to capture global trend. Secondly, most models although construct spatio-temporal graphs for convolution, ignore the dynamic characteristics of spatio-temporal graphs, leading to the inability to capture local fluctuation. Finally, the current popular models need to take a lot of training time to obtain better prediction results, resulting in higher computing cost. To this end, we propose a new model: Multi-Step Trend Aware Graph Neural Network (MSTAGNN), which considers the influence of global spatio-temporal information and captures the dynamic characteristics of spatio-temporal graph. It can not only accurately capture local fluctuation, but also extract global trend and dramatically reduce computing cost. The experimental results showed that our proposed model achieved optimal results compared to baseline. Among them, mean absolute error (MAE) was reduced by 6.25% and the total training time was reduced by 79% on the PEMSD8 dataset. The source codes are available at: https://github.com/Vitalitypi/MSTAGNN.

IntroductionTriple-negative breast cancer (TNBC) is characterized by its aggressive nature and absence of specific therapeutic targets, necessitating the reliance on chemotherapy as the primary treatment modality. However, the drug resistance poses a significant challenge in the management of TNBC. In this study, we investigated the role of DDX58 (DExD/H-box helicase 58), also known as RIG-I, in TNBC chemoresistance.MethodsThe relationship between DDX58 expression and breast cancer prognosis was investigated by online clinical databases and confirmed by immunohistochemistry analysis. DDX58 was knockout by CRISPR-Cas9 system (DDX58-KO), knockdown by DDX58-siRNA (DDX58-KD), and stably over expressed (DDX58-OE) by lentivirus. Western blotting, immunofluorescence and qPCR were used for related molecules detection. Apoptosis was analyzed through flow cytometry (Annexin V/7AAD apoptosis assay) and Caspase 3/7 activity assay.ResultsPatients with lower expression of DDX58 led to lower rate of pathological complete response (pCR) and worse prognosis by online databases and hospital clinical data. DDX58-KD cells showed multiple chemo-drugs resistance (paclitaxel, doxorubicin, 5-fluorouracil) in TNBC cell lines. Similarly, DDX58-KO cells also showed multiple chemo-drugs resistance in a dosage-dependent manner. In the CDX model, tumours in the DDX58-KO group had a 25% reduction in the tumour growth inhibition rate (IR) compared to wild-type (WT) group after doxorubicin (Dox) treatment. The depletion of DDX58 inhibited proliferation and promoted the migration and invasion in MDA-MB-231 cells. The findings of our research indicated that DDX58-KO cells exhibit a reduction in Dox-induced apoptosis both in vivo and in vitro. Mechanistically, Dox treatment leads to a significant increase in the expression of double-stranded RNAs (dsRNAs) and activates the DDX58-Type I interferon (IFN) signaling pathway, ultimately promoting apoptosis in TNBC cells.DiscussionIn the process of TNBC chemotherapy, the deficiency of DDX58 can inhibit Dox-induced apoptosis, revealing a new pathway of chemotherapy resistance, and providing a possibility for developing personalized treatment strategies based on DDX58 expression levels.

Cardiac myxoma is a commonly encountered tumor within the heart that has the potential to be life-threatening. However, the cellular composition of this condition is still not well understood. To fill this gap, we analyzed 75,641 cells from cardiac myxoma tissues based on single-cell sequencing. We defined a population of myxoma cells, which exhibited a resemblance to fibroblasts, yet they were distinguished by an increased expression of phosphodiesterases and genes associated with cell proliferation, differentiation, and adhesion. The clinical relevance of the cell populations indicated a higher proportion of myxoma cells and M2-like macrophage infiltration, along with their enhanced spatial interaction, were found to significantly contribute to the occurrence of embolism. The immune cells surrounding the myxoma exhibit inhibitory characteristics, with impaired function of T cells characterized by the expression of GZMK and TOX, along with a substantial infiltration of tumor-promoting macrophages expressed growth factors such as PDGFC. Furthermore, in vitro co-culture experiments showed that macrophages promoted the growth of myxoma cells significantly. In summary, this study presents a comprehensive single-cell atlas of cardiac myxoma, highlighting the heterogeneity of myxoma cells and their collaborative impact on immune cells. These findings shed light on the complex pathobiology of cardiac myxoma and present potential targets for intervention.

Accurate and real -time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0-10 cm [SMC10], 0-20 cm [SMC20], 0-30 cm [SMC30], 0-40 cm [SMC40], 0-50 cm [SMC50], 0-60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R 2 value of 0.963 +/- 0.030 and low RMSE values of 0.238 +/- 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R 2 and RMSE values of 0.963 +/- 0.024 and 0.117 +/- 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R 2 and RMSE of 0.782 +/- 0.090 and 0.037 +/- 0.011) compared to SMC10 and SMC20 (with R 2 and RMSE of 0.765 +/- 0.097 and 0.056 +/- 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

Introduction: Mild stenosis [degree of stenosis (DS) < 50%] is commonly labeled as nonobstructive lesion. Some lesions remain stable for several years, while others precipitate acute coronary syndromes (ACS) rapidly. The causes of ACS and the factors leading to diverse clinical outcomes remain unclear.Method: This study aimed to investigate the hemodynamic influence of mild stenosis morphologies in different coronary arteries. The stenoses were modeled with different morphologies based on a healthy individual data. Computational fluid dynamics analysis was used to obtain hemodynamic characteristics, including flow waveforms, fractional flow reserve (FFR), flow streamlines, time-average wall shear stress (TAWSS), and oscillatory shear index (OSI).Results: Numerical simulation indicated significant hemodynamic differences among different DS and locations. In the 20%–30% range, significant large, low-velocity vortexes resulted in low TAWSS (<4 dyne/cm2) around stenoses. In the 30%–50% range, high flow velocity due to lumen area reduction resulted in high TAWSS (>40 dyne/cm2), rapidly expanding the high TAWSS area (averagely increased by 0.46 cm2) in left main artery and left anterior descending artery (LAD), where high OSI areas remained extensive (>0.19 cm2).Discussion: While mild stenosis does not pose any immediate ischemic risk due to a FFR > 0.95, 20%–50% stenosis requires attention and further subdivision based on location is essential. Rapid progression is a danger for lesions with 20%–30% DS near the stenoses and in the proximal LAD, while lesions with 30%–50% DS can cause plaque injury and rupture. These findings support clinical practice in early assessment, monitoring, and preventive treatment.

The triglyceride glucose (TyG) index, as a reliable marker of insulin resistance, is associated with the incidence and poor prognosis of various cardiovascular diseases. However, the relationship between the TyG index and clinical outcomes in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR) remains unclear. This study consecutively enrolled 1569 patients with AS underwent TAVR at West China Hospital of Sichuan University between April 2014 and August 2023. The outcomes of interest included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Multivariate adjusted Cox regression and restricted cubic splines (RCS) regression analyses were used to assess the associations between the TyG index and the clinical outcomes. The incremental prognostic value of the TyG index was further assessed by the time-dependent Harrell’s C-index, integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). During a median follow-up of 1.09 years, there were 146, 70, and 196 patients experienced all-cause death, cardiovascular death, and MACE, respectively. After fully adjusting for confounders, a per-unit increase of TyG index was associated with a 441

Water deficit drip irrigation (WDDI) in citrus orchards was found has a positive effect on fruit quality, particularly enhancing the soluble sugar content during the late period of fruit growth. However, the underlying molecular mechanism remains unclear. In this study, four deficit irrigation levels of D-60%, D-45%, D-30%, and D-15% were set at fruit expansion stage (stage III) and maturation stage (stage IV), respectively, and a fully irrigated treatment was set as the control (CK). Combined analysis of metabolomics and transcriptomics was used to reveal the molecular mechanism of sugar accumulation changes under different water deficit (WD) treatments. The results showed that sugar metabolism was growth stage dependent: stage III is a key period of sucrose metabolism and synthesis, while for stage IV, the accumulation of glucose and fructose increased significantly. Importantly, III-D15% and IV-D30% treatments were identified as optimal strategies for enhancing sugar content of citrus fruits. WDDI during the late growth stages up-regulated the expression of sugar metabolism-related differential expression genes (DEGs), consequently increasing the content of sugar-related differential expression metabolites (DEMs), and ultimately enhancing sugar accumulation in citrus fruits. For III-D15% treatment, DEMs related to sugar metabolism were mainly enriched in ‘carbon metabolism’, with significant increases in Sucrose, ɑ-D-Glucose-6 P and ß-D-Fructose-6 P content observed. SPS showed significant up-regulation, wihle BGLU, GN, TPS11, TPPF and TPPJ were down-regulated. For IV-D30% treatment, most DEMs were significantly enriched in ‘Glycolysis/Gluconeogenesis’. Key DEMs (Sucrose, D-Glucose-6 P, ß-D-Fructose-6 P) and DEGs (SUS, SPS, BGLU, HXK) involved in the metabolism and synthesis of sucrose, fructose and glucose were significantly increased. This study sheds light on the molecular mechanisms of sugar accumulation in citrus fruit under appropriate WDDI, providing valuable guidance for high-quality fruit production.