Search

Abstract Objective The Ureteral Access Sheath (UAS) has notable benefits but may fail to traverse the ureter in some cases. Our objective was to develop and validate a dynamic online nomogram for patients with ureteral stones who experienced UAS placement failure during retrograde intrarenal surgery (RIRS). Methods This study is a retrospective cohort analysis using medical records from the Second Hospital of Tianjin Medical University. We reviewed the records of patients with ureteral stones who underwent RIRS in 2022 to identify risk factors associated with UAS placement failure. Lasso combined logistic regression was utilized to identify independent risk factors associated with unsuccessful UAS placement in individuals with ureteral stones. Subsequently, a nomogram model was developed to predict the likelihood of failed UAS placement in this patient cohort. The model’s performance was assessed through Receiver Operating Characteristic Curve (ROC) analysis, calibration curve assessment, and Decision Curve Analysis (DCA). Results Significant independent risk factors for unsuccessful UAS placement in patients with ureteral stones included age (OR = 0.95, P

Abstract The study aims to explore the relationship among metacognition (MC), fear of disease of progression (FoP), psychological distress (PD), and quality of life (QoL), and verify whether FoP and PD have a chain mediating effect between MC and QoL. 231 hematologic tumor patients in a large tertiary hospital were investigated by using Meta-Cognitions Questionnaire-30, Fear of Progression Questionnaire-Short Form, Hospital Anxiety and Depression Scale, and Functional Assessment of Cancer Therapy scale. Data analyses were performed using IBM SPSS (version 25.0) and the PROCESS macro (version 4.1). The results showed that the direct impact of MC on QoL was not statistically significant. However, the indirect influence of MC on QoL manifest through the independent influences of PD and FoP, as well as the chain mediating effect of “PD → FoP.” In addition, all four dimensions of QoL (physical, social and family, emotional, and functional) satisfy the chain mediation model, except for the social and family domain. These insights advance our comprehension of the intricate interplay between MC and QoL, underscoring the importance of improving MC to alleviate patients' PD, mitigate FoP, and ultimately improve the QoL of hematologic tumor patients.

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream’s characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations.

Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy using a dual-margin dynamic threshold matrix is designed to enhance learning opportunities for minority class instances and those that are challenging to classify, and combined with a random strategy, it can estimate the current class imbalance distribution by accessing only a limited number of instance labels. Finally, an instance-adaptive sampling strategy is proposed, which comprehensively considers the imbalance ratio and classification difficulty of instances, and combined with a weighted ensemble strategy, improves the classification performance of the ensemble classifier in imbalanced data streams. Extensive experiments and analyses demonstrate that AdaAL-MID can handle various complex concept drifts and adapt to changes in class imbalance ratios, and it outperforms several state-of-the-art active learning algorithms.

The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings.

Abstract Background Surgical incision, endotracheal intubation, structural changes in the oral cavity, and other factors lead to a divergence in oral care between patients after oral surgery and ordinary inpatients. High-quality oral care can reduce the incidence of incision infection and ventilator-associated pneumonia. However, there is a lack of guidelines or expert consensus on oral care after oral cancer surgery. Therefore, the aim of this study was to assess the practicing situation of nurses in the intensive care unit (ICU) for postoperative patients with oral cancer and their need for training. Methods A multicenter cross-sectional study design was conducted in 19 ICUs of 11 tertiary hospitals from Henan province in China. Data were collected from 173 nurses and 19 head nurses online using a structured questionnaire. Mann–Whitney U and Kruskal–Wallis H tests were performed to analyze the data using SPSS (Version 25.0). Results Seven ICUs (36.8%) developed evaluation regulations for the oral care of postoperative patients with oral cancer, and eight ICUs (42.1%) described the operating standards. A total of 173 nurses completed the questionnaire, and the median score was 75 (68, 78). Almost all of the examined nurses (91.2%) assessed patients’ oral hygiene at a fixed time, while in 52.0% and 28.3% of nurses, the first oral care and frequency of oral care after surgery was determined based on the individual patient’s situation. More than half of the nurses (55.5%) spent approximately 5–10 min conducting oral care for patients. Physiological saline solution (82.7%), swabbing (91.9%), and oral care package with cotton ball (86.1%) were the most popular oral care mouthwash, method, and tool, respectively. Nurses sought help from senior nurses (87.3%) and doctors (83.8%), mostly to solve difficulties of oral care. Moreover, 76.9% of the nurses believed that the lack of knowledge and skills surrounding oral care was the main barrier for nurses to implement oral care. The majority of participants (69.4%) had never received continuing education or training in oral care for postoperative patients with oral cancer, and almost all (98.8%) of the respondents stated their preference to receive training in standardized oral care skills. Indications and contraindications (84.4%), tools (81.5%), and mouthwash (80.9%) of oral care were the items that the respondents were most eager to learn about. Approximately three quarters of nurses preferred scenario simulation practice as the training method. Conclusion Although the participants had high oral care scores for postoperative patients with oral cancer, there was great diversity in the practice. The lack of oral care knowledge was deemed the main barrier in delivering quality oral care, and the educational need was stated by almost all participants. We suggest that a standard protocol or clinical practice guidelines for oral care for postoperative patients with oral cancer should be developed, and nurses should be educated to equip them with professional knowledge and skills.

Aiming at the problem of category-level object pose estimation, a method was proposed to accurately estimate the pose of the target object by only taking the point cloud scanned by the depth camera as the input, with knowing the category of input point cloud only.The method did not reply on a huge amount of labeled dataset, but used virtual data produced by simulation instead, which achieved better accuracy on real-world dataset.This method first filtered the background noise of the input point cloud.Then standardized the point cloud through the well-designed center prediction module.After that, the normalized object coordinate space would be estimated through a shape template deformation module.Finally, the pose would be obtained from least squares.Experiments on real-world dataset demonstrates that the method achieve higher accuracy and better generalization ability.

Abstract Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions.

Objective: The purpose of this study was to investigate the effect of dapagliflozin (DAPA), a sodium-glucose cotransporter 2 inhibitor, on relieving cardiac hypertrophy and its potential molecular mechanism.Methods: Cardiac hypertrophy induced by abdominal aortic constriction (AAC) in mice, dapagliflozin were administered in the drinking water at a dose of 25 mg/kg/d for 12 weeks was observed. Echocardiography was used to detect the changes of cardiac function, including LVEF, LVFS, LVEDd, LVEDs, HR and LV mass. Histological morphological changes were evaluated by Masson trichrome staining and wheat germ agglutinin (WGA) staining. The enrichment of differential genes and signal pathways after treatment was analyzed by gene microarray cardiomyocyte hypertrophy was induced by AngII (2 μM) and the protective effect of dapagliflozin (1 μM) was observed in vitro. The morphological changes of myocardial cells were detected by cTnI immunofluorescence staining. ELISA and qRT-PCR assays were performed to detect the expressions levels of cardiac hypertrophy related molecules.Results: After 12 weeks of treatment, DAPA significantly ameliorated cardiac function and inhibited cardiac hypertrophy in AAC-induced mice. In vitro, DAPA significantly inhibited abnormal hypertrophy in AngII-induced cardiacmyocytes. Both in vivo and in vitro experiments have confirmed that DAPA could mediate the Plin5/PPARα signaling axis to play a protective role in inhibiting cardiac hypertrophy.Conclusion: Dapagliflozin activated the Plin5/PPARα signaling axis and exerts a protective effect against cardiac hypertrophy.

Depth estimation is a classical problem in computer vision, which typically relies on either a depth sensor or stereo matching alone. The depth sensor provides real-time estimates in repetitive and textureless regions where stereo matching is not effective. However, stereo matching can obtain more accurate results in rich texture regions and object boundaries where the depth sensor often fails. We fuse stereo matching and the depth sensor using their complementary characteristics to improve the depth estimation. Here, texture information is incorporated as a constraint to restrict the pixel’s scope of potential disparities and to reduce noise in repetitive and textureless regions. Furthermore, a novel pseudo-two-layer model is used to represent the relationship between disparities in different pixels and segments. It is more robust to luminance variation by treating information obtained from a depth sensor as prior knowledge. Segmentation is viewed as a soft constraint to reduce ambiguities caused by under- or over-segmentation. Compared to the average error rate 3.27% of the previous state-of-the-art methods, our method provides an average error rate of 2.61% on the Middlebury datasets, which shows that our method performs almost 20% better than other “fused” algorithms in the aspect of precision.

In order to avoid the unsafe operation and raise efficiency of yokeless and segmented armature axial flux motors at high speed, the control current of air gap flux is expected to be as small as possible with the same field-weakening effect. To reduce the control complexity, a new structure of module poles with a combination of permanent magnet and soft magnetic material is proposed, which has the characteristics of lower d-axis reluctance and a higher performance of yokeless and segmented armature axial flux motor with surface mounted permanent magnet. According to finite element analysis (FEA), the flux distributions of a rotor pole in no-load and demagnetization condition are contrasted, and under this new configuration, the derivative analytical models of back electromotive-force (EMF), electromagnetic torque, and air gap flux are validated, moreover, the influence of soft magnetic material of rotor poles on controlling the air gap flux is investigated in different load. Based on a particular objective function, the combination of permanent magnet and soft magnetic material is optimized. The results show that optimal solution of field-weakening performance of yokeless and segmented armature axial flux motors can be improved effectively and legitimately.

BACKGROUND: The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction. RESULTS: Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 x 10-2 g N kg-1 and RPD value of 6.721 for TVB-N content predictiin. CONCLUSION: The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. [ABSTRACT FROM AUTHOR]