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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]