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To overcome the contradiction of the traditional grey first order accumulate with the new information priority principle, this paper establish the new grey fractional order accumulate, which is proposed to better reflect the priority of the new information. Based on this, This paper establish the fractional order grey direct model based on the principle of new information priority, which is proposed to improve the prediction precision. The results of practical numerical examples demonstrate that this new method has a good predication performance for small data set forecasting. This research also provides a new model for the less data forecasting.

A 26-year-old Chinese male consulted with the team regarding his alopecia areata and vitiligo for which previous treatment was ineffective. The patient, a homosexual man, denied having a history of drug abuse and of blood transfusion. No member of his family had vitiligo or alopecia. Laboratory studies revealed that the serum for anti-human immunodeficiency virus (HIV) antibody was positive. The patient′s CD4 lymphocyte count and CD4/CD8 ratio were both strikingly low (20 cells/mL and 0.04), but no other complaints or opportunistic infections were reported. One month after antiretroviral therapy, the patient′s alopecia areata dramatically improved, but no evident improvement in his vitiligo was found. This case is a very rare case of alopecia areata and vitiligo associated with HIV infection that might be attributed to the generation and maintenance of self-reactive CD8+ T-cells due to chronic immune activation with progressive immune exhaustion in HIV infection.

In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages. [ABSTRACT FROM AUTHOR]

Background: To investigate the screening value of a small‐molecule probe to assess the methylation of PAX1 in cervical cancer. Materials and Methods: The diagnostic threshold of the grayscale values for cervical lesions was assessed by plotting the Receiver Operating Characteristic (ROC) curve of subjects. Grayscale values were significantly different among the four groups (p < 0.05). Compared with the LSIL and cervicitis groups, a considerably higher grayscale value was found in the CA and high‐grade squamous intraepithelial lesion (HSIL) groups (both p < 0.05). Results: The differential ROC curves of the grayscale values showed that the diagnostic Area Under Curve of the probe for cervicitis and low‐grade squamous intraepithelial lesion (LSIL) was 0.8724 (95% CI = 0.7762–0.9685, p < 0.0001), for cervicitis and CA was 1.0000 (p < 0.0001), for the LSIL and HSIL was 0.5484 (95% CI = 0.3826–0.7142, p = 0.5755), and for the LSIL and CA was 0.7724 (95% CI = 0.6016–0.9432, p = 0.0138). Conclusion: The small molecular probe has certain application value in differentiating the type of cervical lesions and has better efficacy in distinguishing cervical inflammatory and precancerous lesions from carcinogenesis, but less efficacy in determining the type of precancerous lesions. [ABSTRACT FROM AUTHOR]

Background: Value analysis of a small‐molecule fluorescent probe for methylation detection in different cervical lesions. Materials and Methods: (1) The grayscale values of distinct lesion tissues were remarkably distinct among the four groups (p < 0.05). The comparison of the grayscale value between the two groups showed that the CA group noticeably exceeded the LSIL and cervicitis groups, and the HSIL group was apparently higher than the LSIL and cervicitis groups (p < 0.05); (2) The mean grayscale values of the enrolled subjects were calculated with 55.21 as the midline, with >55.21 as positive and ≤55.21 as negative. Results: The results showed that the positive rate of the cervicitis group was 0.00%, the LSIL group 67.74%, the HSIL group 83.33%, and the CA group 100.00%. The results among the four groups were notably distinct (p < 0.05); (3) The comparison among DAPI, probe, bright, and merged images of cervicitis, LSIL, HSIL, and CA indicated that different cervical lesions were with quite various stains. Conclusion: The grayscale value, positive rate, and stained picture of distinct cervical lesions were remarkably different. The small‐molecule fluorescent probe has a good value in differentiating cervical lesions and can be considered for popularization and application. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to systematically evaluate the efficacy of autologous platelet concentrate products in the preservation of the alveolar ridge after tooth extraction through meta‐analysis and provide a theoretical basis for the clinical application of autologous platelet concentrates to reduce alveolar bone resorption. Methods: This study conducted a meta‐analysis of clinical trials between 2013 and 2023, focusing on autologous platelet concentrate products (e.g., PRP, PRF, CGF, and PRCF) used for alveolar ridge preservation after tooth extraction. The analysis included 122 articles and 371 extraction sockets. All statistical analyses were performed using Review Manager version 5.4. Results: Results indicate that these platelet concentrates effectively reduced changes in horizontal width 1 mm below the alveolar crest and vertical socket height. They also promoted a higher percentage of new bone formation in extraction sockets compared with control groups. However, they did not significantly prevent horizontal bone resorption at 3 and 5 mm below the alveolar crest. Conclusion: In conclusion, autologous platelet concentrates are useful for alveolar ridge preservation, but larger clinical studies are needed to confirm these findings due to the relatively small sample size in this study. [ABSTRACT FROM AUTHOR]

Objective: Sinus membrane perforation is a common complication of sinus lift surgery. This review aimed to examine if anatomical factors such as the presence of septa and lateral wall thickness influence the risk of membrane perforation. Methods: This study was registered on PROSPERO (CRD42023488259). PubMed, Embase, and Web of Science were searched for relevant studies published up to 26th June 2024. The outcome of interest was the risk of perforation based on presence of septa and lateral wall thickness. Random-effects meta-analysis was conducted with dichotomous data to obtain the odds ratio (OR) of perforation using Review Manager. Results: Ten studies with 1865 patients undergoing 2168 "lateral" sinus lift procedures were included. The total incidence of Schneiderian membrane perforations was 19% (405 cases). Schneiderian membrane perforation was present in 169/425 cases (39.76%) with sinus septa and 184/1492 cases (12.33%) without septa. Meta-analysis showed that septa were significantly associated with an increased risk of perforation (OR: 4.03 95% CI: 1.77, 9.19) with high heterogeneity (I2 = 87%). The certainty of the evidence was very low. Data on lateral wall thickness and risk of perforation was too heterogeneous for a meta-analysis. Studies reported mixed results on the risk of perforation based on lateral wall thickness. Conclusions: Our results show, with very low-quality evidence, that the presence of septa significantly increases the risk of perforations during maxillary sinus lift surgery. Evidence on the association between lateral wall thickness and a risk of perforations during sinus lift surgery is conflicting, and no clear conclusions can be derived at this stage. [ABSTRACT FROM AUTHOR]

Currently, data-driven deep learning models are widely applied in the field of wind power prediction. However, when historical data are insufficient, deep learning models struggle to exhibit satisfactory predictive performance. In order to overcome the issue of limited training data for new wind farms, this study proposes a novel transfer learning strategy to address the challenge of less-sample learning in short-term wind power prediction. The research is conducted in two stages. In the pre-training stage, the TimesNet-GRU prediction model is established using data from a source wind farm. Parallel TimesNet modules are employed to extract multi-period features from various input feature sequences, followed by the extraction of long- and short-term features from the time series through gate recurrent unit (GRU). In the transfer learning stage, an effective transfer strategy is designed to freeze and retrain certain parameters of the TimesNet-GRU, thereby constructing a prediction model for the target wind farm. To validate the effectiveness of this approach, the results from testing with actual data from five wind farms in northwest China demonstrate that the proposed method exhibits significant advantages over models without transfer learning as explored in this study. [ABSTRACT FROM AUTHOR]

Lodging is a common natural disaster during wheat growth. The accurate identification of wheat lodging is of great significance for early warnings and post-disaster assessment. With the widespread use of unmanned aerial vehicles (UAVs), large-scale wheat lodging monitoring has become very convenient. In particular, semantic segmentation is widely used in the recognition of high-resolution field scene images from UAVs, providing a new technical path for the accurate identification of wheat lodging. However, there are still problems, such as insufficient wheat lodging data, blurred image edge information, and the poor accuracy of small target feature extraction, which limit the recognition of wheat lodging. To this end, the collaborative wheat lodging segmentation semi-supervised learning model based on RSE-BiseNet is proposed in this study. Firstly, ResNet-18 was used in the context path of BiSeNet to replace the original backbone network and introduce squeeze-and-excitation (SE) attention, aiming to enhance the expression ability of wheat lodging characteristics. Secondly, the segmentation effects of the collaborative semi-supervised and fully supervised learning model based on RSE-BiSeNet were compared using the self-built wheat lodging dataset. Finally, the test results of the proposed RSE-BiSeNet model were compared with classic network models such as U-Net, BiseNet, and DeepLabv3+. The experimental results showed that the wheat lodging segmentation model based on RSE-BiSeNet collaborative semi-supervised learning has a good performance. The method proposed in this study can also provide references for remote sensing UAVs, other field crop disaster evaluations, and production assistance. [ABSTRACT FROM AUTHOR]

Wheat is one of the widely cultivated crops. Accurate and efficient high-throughput ear counting is important for wheat production, yield evaluation, and seed breeding. The traditional wheat ear counting method is inefficient due to the small scope of investigation. Especially in the wheat field scene, the images obtained from different platforms, including ground systems and unmanned aerial vehicles (UAVs), have differences in density, scale, and wheat ear distribution, which makes the wheat ear counting task still face some challenges. To this end, a density map counting network (LWDNet) model was constructed for cross-platform wheat ear statistics. Firstly, CA-MobileNetV3 was constructed by introducing a collaborative attention mechanism (CA) to optimize the lightweight neural network MobileNetV3, which was used as the front end of the feature extraction network, aiming to solve the problem of occlusion and adhesion of wheat ears in the field. Secondly, to enhance the model's ability to learn the detailed features of wheat ears, the CARAFE upsampling module was introduced in the feature fusion layer to better restore the characteristics of wheat ears and improve the counting accuracy of the model for wheat ears. Finally, density map regression was used to achieve high-density, small-target ear counting, and the model was tested on datasets from different platforms. The results showed that our method can efficiently count wheat ears of different spatial scales, achieving good accuracy while maintaining a competitive number of parameters (2.38 million with a size of 9.24 MB), which will benefit wheat breeding and screening analysis to provide technical support. [ABSTRACT FROM AUTHOR]

DNA methyltransferase 1 (DNMT1) is one of the most essential proteins for the development and maintenance of DNA methylation patterns. Developing a simple and accurate method to assess DNMT1 levels is crucial because its overexpression is associated with abnormalities in DNA methylation and disease development. In our previous work, a series of RG108-fluorescein conjugates were constructed as DNMT1 detectors and confirmed their membrane permeability, binding site, and detection ability in cells through a confocal laser-scanning microscopy assay. In the present study, the fluorescence microplate reader was applied to further validate DNMT1 detection ability of probe 1a in cells due to its accuracy for fluorescence detection. The results show that the fluorescent intensity of probe 1a in HeLa cells correlated with both the probe concentration and cell number. By introducing the concept of relative fluorescent unit ratio (RFU ratio), the detection capability of probe 1a was comparable with anti-DNMT1 Ab in a series of cell lines, suggesting the considerable potential of probe 1a as a replacement for anti-DNMT1 Ab with better cell membrane permeability. Furthermore, probe 1a was confirmed to be applicable for DNMT1 quantification in different types of cell lines, including normal cells, cancer cells, and DNMT1 down-regulated HeLa cells, with the ability being verified by western blotting or quantitative real-time polymerase chain reaction (qRT-PCR). [ABSTRACT FROM AUTHOR]

Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount of total network factors of production that can be moved is built based on the social capital theory in order to realize the cross-border collaborative control of regional air pollution. The issue of an intergovernmental cooperation framework for collaborative prevention and control of large-scale air pollution is addressed from the standpoint of the multi-stakeholder "benefit–cost" drive. Additionally, the major variables influencing the behavioral approach selection for intergovernmental cooperation are considered. The initial sensitivity of the evolution path of the local government behavioral strategy is also analyzed. The results of this study are: (1) The primary elements impacting intergovernmental cooperation on joint prevention and control of air pollution are fixed costs and fixed benefits, and reducing the fixed costs of such cooperation in an appropriate manner without compromising local governments' pollution control can do so. (2) Under the assumption that local governments have fixed expenses, the total amount of network factors of production and total network factors of production that can be moved factors of production have a direct impact on intergovernmental cooperation. When local governments' fixed costs are constant, they are more likely to choose the cooperative behavior option if the sum of their network production factors and total network factors of production that can be moved is higher. (3) The initial probability of cooperation among the three parties and the total amount of production factors have an impact on the system's ESS when local governments in the area have equal total production factors. The study's findings can offer theoretical justification for the "profit-driven" intergovernmental coordination of joint prevention and management of air pollution. [ABSTRACT FROM AUTHOR]

Reliable short-term wind-power forecasting is crucial for the safe operation of power systems that accommodate large amount of wind power. With the development of large-scale wind power bases, wind power spatiotemporal correlations of multiple wind farms (WFs) can be used to improve short-term wind power prediction accuracy and efficiency. However, the time-varying and coupling characteristics of wind power spatiotemporal correlations present significant challenges for the accurate prediction of multi-WF power. In this paper, we propose an adaptive spatiotemporal fusion graph neural network for short-term power forecasting of multiple wind farms. Based on regional weather forecasts and historical wind power data, an adaptive directed graph is generated to model the dynamic spatial correlations between the WFs and the impact of regional weather factors on the wind power of different WFs. Graph convolution modules and temporal recursive modules are then alternately arranged to fuse the spatial and temporal features of wind power and to output the wind power prediction results for multiple WFs and time points. The case study results show that the proposed model outperforms the investigated baseline models in terms of prediction accuracy and that each model component has a unique contribution. [ABSTRACT FROM AUTHOR]

The content of tea polyphenols (TP) is one of the important indicators for judging the quality of tea. Accurate and non-destructive estimation technology for tea polyphenol content has attracted more and more attention, which has become a key technology for tea production, quality identification, grading and so on. Hyperspectral imaging technology is a fusion of spectral analysis and image processing technology, which has been proven to be an efficient technology for predicting tea polyphenol content. To make full use of spectral and spatial features, a prediction model of tea polyphenols based on spectral-spatial deep features extracted using convolutional neural network (CNN) was proposed, which not only broke the limitations of traditional shallow features, but also innovated the technical path of integrated deep learning in non-destructive detection for tea. Firstly, one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN) models were constructed to extract the spectral deep features and spatial deep features of tea hyperspectral images, respectively. Secondly, spectral deep features, spatial deep features, and spectral-spatial deep features are used as input variables of machine learning models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF). Finally, the training, testing and evaluation were realized using the self-built hyperspectral dataset of green tea from different grades and different manufacturers. The results showed that the model based on spectral-spatial deep features had the best prediction performance among the three machine learning models (R2 = 0.949, MAE = 0.533 for training sets, R2 = 0.938, MAE = 0.799 for test sets). Moreover, the visualization of estimation results of tea polyphenol content further demonstrated that the model proposed in this study had strong estimation ability. Therefore, the deep features extracted using CNN can provide new ideas for estimation of the main components of tea, which will provide technical support for the estimation tea quality estimation. [ABSTRACT FROM AUTHOR]

Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform can simultaneously extract time domain and frequency domain features, which is a powerful tool in the field of image signal processing. To address this gap, a method for tea recognition based on a lightweight convolutional neural network and support vector machine (L-CNN-SVM) was proposed, aiming to realize tea recognition using wavelet feature figures generated by wavelet time-frequency signal decomposition and reconstruction. Firstly, the redundant discrete wavelet transform was used to decompose the wavelet components of the hyperspectral images of the three teas (black tea, green tea, and yellow tea), which were used to construct the datasets. Secondly, improve the lightweight CNN model to generate a tea recognition model. Finally, compare and evaluate the recognition results of different models. The results demonstrated that the results of tea recognition based on the L-CNN-SVM method outperformed MobileNet v2+RF, MobileNet v2+KNN, MobileNet v2+AdaBoost, AlexNet, and MobileNet v2. For the recognition results of the three teas using reconstruction of wavelet components LL + HL + LH, the overall accuracy rate reached 98.7%, which was 4.7%, 3.4%, 1.4%, and 2.0% higher than that of LH + HL + HH, LL + HH + HH, LL + LL + HH, and LL + LL + LL. This research can provide new inspiration and technical support for grade and quality assessment of cross-category tea. [ABSTRACT FROM AUTHOR]

The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R2 = 0.896, RMSE = 0.188 for calibration, R2 = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring. [ABSTRACT FROM AUTHOR]

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness. [ABSTRACT FROM AUTHOR]

Aim: To assess the role of consolidation therapy after cessation of nucleoside/nucleotide analogs (NAs) for off‐treatment outcome and to explore the optimal consolidation period in hepatitis B e‐antigen (HBeAg) positive chronic hepatitis B (CHB) patients. Methods: We undertook a prospective, observational cohort study including HBeAg positive CHB patients discontinuing NAs. Virological relapse was the primary endpoint of the current study and was defined as a serum hepatitis B virus DNA more than 104 copies/ml (another assay was carried out for confirmation at least 2 weeks later). Propensity score matching analysis was used to balance the baseline characteristics between HBeAg positive CHB patients with different consolidation periods. Results: A total of 190 HBeAg positive CHB patients were included. Fifty‐four patients relapsed during follow‐up and the cumulative relapse rates at years 1, 3, 5, and 10 were 19.5%, 24.1%, 27.2%, and 32.1%, respectively. Most patients (61.1%) relapsed in the first 6 months. After propensity score matching, significantly lower cumulative relapse rates were observed in patients with consolidation period ≥36 months compared with patients with consolidation period more than 36 months (29.3% vs. 52.8% at year 10; p = 0.012). Conclusions: Cessation of NAs after HBeAg seroconversion and long‐term consolidation is generally safe and feasible in HBeAg positive CHB patients. Three years of consolidation should be preferred in clinical practice. [ABSTRACT FROM AUTHOR]