Explore the vast range of titles available.

Natural killer (NK) cells, as fundamental components of innate immunity, can quickly react to abnormalities within the body. In-depth research has revealed that NK cells possess regulatory functions not only in innate immunity but also in adaptive immunity under various conditions. Multiple aspects of the adaptive immune process are regulated through NK cells. In our review, we have integrated multiple studies to illuminate the regulatory function of NK cells in regulating B cell and T cell responses during adaptive immune processes, focusing on aspects including viral infections and the tumor microenvironment (TME). These insights provide us with many new understandings on how NK cells regulate different phases of the adaptive immune response.

Accurately evaluating earthquake-induced slope displacement is a key factor for designing slopes that can effectively respond to seismic activity. This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. A dataset of 45 samples was used, with 70% allocated for training and 30% for testing. To improve model robustness, repeated 5-fold cross-validation was applied. Among the models, XGBoost demonstrated superior predictive accuracy, with an R 2 value of 0.99 on both the train and test data, outperforming ANN, SVM, and RF, which had R 2 values of 0.63 and 0.80, 0.87 and 0.86, 0.94 and 0.87 on the train and test data, respectively. Sensitivity analysis identified maximum horizontal acceleration (kmax = 0.714) as the most influential factor in slope displacement. The findings suggest that the XGBoost model developed in this study is highly effective in predicting earthquake-induced slope displacement, offering valuable insights for early warning systems and slope stability management.

Background Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. Methods In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. Results The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. Conclusion The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time–frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

The objective of this study was to assess the impact of immediate and delayed post- space preparation on the push-out bond strength (PBS) of fiber posts by employing two root canal obturation techniques: continuous wave of condensation (CWC) and single-cone (SC) obturation. Forty-eight human maxillary premolar teeth were instrumented, and the samples were divided into four groups according to the obturation technique and the time of post-space preparation. SC and CWC underwent immediate post-space preparation; and CWC and SC underwent delayed post-space preparation. The smear layer and dentine tubules from the apical, middle, and cervical regions of the samples were observed via scanning electron microscopy (SEM) and the %VoidsVol of the medium 4-mm fiber posts of each group was calculated via micro-computed tomography (CT). Each post space of the root was subsequently cut into slices, resulting in three 1-mm slices at 3 different depths (apical to the cervical region) and subjected to a push-out test. The failure mode was assessed. The data were analysed via the Shapiro-Wilk test, one-way analysis of variance and Bonferroni correction tests. In terms of depth from the apical to the cervical region, SC following immediate post-space preparation exhibited greater bond strength than did CWC following delayed post-space preparation. SEM images revealed that the smear layer was completely visible. In the SC with immediate post-space preparation group, the smear layer could be partially removed from the apical, middle and cervical regions of the samples and the outlines of the dentine tubules were visible. The percentage volume of the voids of the medium 4-mm fiber posts of the four groups and the samples in the three directions were not significantly different. No significant differences were observed in the CWC or SC obturation technique regardless of the time of post-space preparation or in immediate or delayed post-space preparation with different obturation techniques. SC followed by immediate post-space preparation provided better bond strength of fiber posts to intraradicular dentine than did CWC followed by delayed post-space preparation.

Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide evolution and the shortcomings of the traditional static prediction model, a dynamic prediction model of landslide displacement based on long short-term memory (LSTM) neural networks was proposed. Meanwhile, the Variational mode decomposition (VMD) theory was used to decompose the cumulative displacement and triggering factors, which not only give clear physical meaning to each displacement subsequence, but also closely connect the rock and soil conditions with the influence of external factors. Besides, the maximum information coefficient (MIC) was used to sort the redundant features. The LSTM is a dynamic model that can remember historical information and apply it to the current output. The hyperparameters of the LSTM model was optimized by the Grey wolf optimizer (GWO), and the dynamic one-step prediction was carried out for each displacement. All the predicted values were superimposed to complete the displacement prediction based on the time series model. The Tangjiao landslide in the Three Gorges Reservoir area (TGRA), China, was taken as a case study. The displacement data of monitoring sites GPS03 and GPS06 had step-like characteristics. Measured data from March 2007 to December 2016 were selected for analysis. The results indicate that the displacement prediction model based on MIC-GWO-LSTM model effectively improves the prediction accuracy and generalization ability, and is better than other prediction models. This model provides a new idea and exploration for the displacement prediction of step-like characteristics landslide in the Three Gorges Reservoir area.

The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R² of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field.

Adeno-associated virus (AAV), renowned for its exceptionally low pathogenicity and significant efficacy in clinical gene therapy, has emerged as a leading delivery vector in the field of gene therapy. AAV can achieve stable gene expression in various tissues, which has made it a promising treatment for genetic disorders. To date, eight AAV-based gene therapies have been approved by the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). This review summarizes clinical trials of AAV gene therapies for rare diseases, including ophthalmic diseases, nervous system disorders, hematological diseases, neuromuscular diseases, lysosomal storage diseases. We also explore potential side effects and toxicities associated with AAV therapies. Our objective is to provide valuable insights for researchers and clinicians working on AAV-based therapies, helping improve the safety and effectiveness of these treatments.

Data sources The Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Background To describe burden, and to explore cross-country inequalities according to socio-demographic index (SDI) for stroke and subtypes attributable to diet. Methods Death and years lived with disability (YLDs) data and corresponding estimated annual percentage changes (EAPCs) were estimated by year, age, gender, location and SDI. Pearson correlation analysis was performed to evaluate the connections between age-standardized rates (ASRs) of death, YLDs, their EAPCs and SDI. We used ARIMA model to predict the trend. Slope index of inequality (SII) and relative concentration index (RCI) were utilized to quantify the distributive inequalities in the burden of stroke. Results A total of 1.74 million deaths (56.17% male) and 5.52 million YLDs (55.27% female) attributable to diet were included in the analysis in 2019.Between 1990 and 2019, the number of global stroke deaths and YLDs related to poor diet increased by 25.96% and 74.76% while ASRs for death and YLDs decreased by 42.29% and 11.34% respectively. The disease burden generally increased with age. The trends varied among stroke subtypes, with ischemic stroke (IS) being the primary cause of YLDs and intracerebral hemorrhage (ICH) being the leading cause of death. Mortality is inversely proportional to SDI ( R  = -0.45, p  < 0.001). In terms of YLDs, countries with different SDIs exhibited no significant difference ( p  = 0.15), but the SII changed from 38.35 in 1990 to 45.18 in 2019 and the RCI showed 18.27 in 1990 and 24.98 in 2019 for stroke. The highest ASRs for death and YLDs appeared in Mongolia and Vanuatu while the lowest of them appeared in Israel and Belize, respectively. High sodium diets, high red meat consumption, and low fruit diets were the top three contributors to stroke YLDs in 2019. Discussion The burden of diet-related stroke and subtypes varied significantly concerning year, age, gender, location and SDI. Countries with higher SDIs exhibited a disproportionately greater burden of stroke and its subtypes in terms of YLDs, and these disparities were found to intensify over time. To reduce disease burden, it is critical to enforce improved dietary practices, with a special emphasis on mortality drop in lower SDI countries and incidence decline in higher SDI countries.

The monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.