We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model. The introduction of a clock-cycle for hidden units allows the model to learn short-term information from high-frequency update modules while retaining long-term memory from low-frequency update modules, thereby enhancing the model’s ability to capture data patterns. Experimental results demonstrate that the optimized clock-cycle RNN outperforms other network models in extracting the temporal and nonlinear features of network security situations, leading to improved prediction accuracy. Furthermore, our approach has low time complexity and excellent real-time performance, ideal for monitoring large-scale network traffic in sensor networks. The rapid development of cyberspace has brought about both convenience and challenges in terms of network security. Traditional network defense techniques relying on log analysis from a single detection device may result in a biased security situation analysis due to limitations in data acquisition channels and uncertainties in detection devices. To address these issues, the modern network security needs to shift from passive defense to active perception. Network Security Situation Awareness (NSSA) , particuIn recent practical applications, sensor networks have been increasingly utilized to perceive security events, intrusion behavior, and abnormal activities within networks, thus enhancing the accuracy and efficiency of network security prediction . HoweverIn this paper, we propose an optimized Grey Wolf Optimized Clockwork Recurrent Neural Network (CW-RNN) model to address the low accuracy of existing network security situation prediction methods. By analyzing network traffic attack data collected by sensor networks using the optimized neural network model, our research aims to improve the accuracy and real-time performance of network security predictions, effectively addressing network security challenges.Capturing the temporal characteristics of network security postures and optimization: CW-RNN combines the memory capabilities of Recurrent Neural Networks with a periodic update mechanism. This enables the model to better capture the temporal characteristics of network security postures, improving computational efficiency and real-time performance. Additionally, we employ the GWO algorithm to optimize the CW-RNN model’s hyperparameters, efficiently searching the hyperparameter space for an optimal combination. This optimization enhances the model’s performance and robustness in network security posture prediction.Comparative experiments with other algorithms: To demonstrate the superiority of the GWO algorithm, we conduct comparative experiments with other commonly used optimization algorithms such as Genetic Algorithm and Particle Swarm Optimization. By comparing the performance of different algorithms within the same hyperparameter search space, we validate the outstanding effectiveness of the GWO algorithm in optimizing the CW-RNN model.Model comparison experiments: To evaluate the performance of our proposed CW-RNN model, we conduct model comparison experiments. We select several widely used models, such as LSTM, GRU, and Transformer as benchmark models. By training and testing these models on the same dataset and comparing their performance metrics, we can demonstrate the superiority of the CW-RNN in network security posture prediction tasks.Our study proposes a Grey Wolf Optimization (GWO) algorithm optimized Clockwork Recurrent Neural Network (CW-RNN) network security situation prediction model. Our work makes the following contributions:The rest of the paper is organized as follows. First, we present the literature review in Probabilistic reasoning models such as attack graph models  and BayeWhen the dataset of situation data is small, researchers tend to use Support Vector Machines (SVM) . Sun WeiOn the other hand, Hu et al. proposed a MapReduce-based SVM state prediction algorithm, which solved the problem of long training time for SVM under a large number of samples and improved the prediction accuracy while reducing training time . HoweverZhang et al. proposed using BP neural networks to predict network security situations, utilizing the guided-head optimization algorithm to search for optimal weights and introducing the simulated annealing algorithm to enhance the global search capability of the algorithm . HoweverLi et al. proposed a model based on Recursive Neural Networks (RNN) and its variants, which effectively improved the accuracy of feature representation through recursive stacking network structures and feature separation methods based on the temporal characteristics of intrusion data . HoweverAnother related work  proposedAnother related work  proposedIn order to fully extract the temporal and nonlinear characteristics of network intrusion detection data, many scholars have started using Recurrent Neural Networks (RNN) and their variants. The Zhu Jiang team has made outstanding contributions to security situation prediction. Starting from the temporal features of network security situations, they proposed an improved Long Short-Term Memory (LSTM) situation awareness algorithm and accelerated the convergence speed of LSTM through the optimized NAdam algorithm. Experimental results showed that this algorithm had a small mean square error . SubsequAnother recent study  proposedIn addition, Zhao Dongmei proposed a network security situation prediction method based on Transformer, which reduced the difficulty of training required with Transformer by introducing GRU to reduce the dimensionality of sample features. This method showed good performance on two network security datasets . HoweverCompared to existing research on network security situation prediction, our optimized Clockwork Recurrent Neural Network model offers the following advantages :The Clockwork RNN (CW-RNN)  is a neuThe forward propagation calculation of CW-RNN is similar to that of the standard RNN. The hidden layer output Here, CW-RNN divides the weight parameters The connections between modules in the hidden layer of CW-RNN only exist from modules with larger clock periods to those with smaller clock periods. Therefore, the hidden layer weight The activation of modules in the hidden layer of CW-RNN is controlled by clocks. At time The differences between the hidden neurons of CW-RNN and standard RNN are as follows:By substituting Equation (5) into Equation (1) for calculation, the modules activated at time The backpropagation calculation of CW-RNN still uses a time-based backpropagation algorithm. However, similar to the forward propagation, the backpropagation only affects the activated modules at time The Grey Wolf Optimization Algorithm (GWO) , proposeDividing the wolf pack into levels: The highest-ranking leader wolf, the second-ranking wolf, the third-ranking wolf, and the common wolf.Tracking the prey: In nature, wolf packs track their prey before hunting. Then, they spread out according to the arrangement of the alpha wolf and prepare to attack the prey. This stage is called tracking the prey, and it is mapped to the GWO algorithm as the initialization of the wolf pack.Surrounding the prey: After entering the hunting phase, the wolf pack will chase the prey and try to surround it. The alpha wolf leads the chase, and the other wolves follow around it. This process is mathematically modeled in the GWO algorithm as:At this stage, The values in Prey attack: During the attack on prey, the wolf pack surrounds the prey to force it to stop, and then the leader wolf commands the pack to attack. Wolf Among them, Algorithm Selection and RationaleIn addition to the Grey Wolf Optimization (GWO), we included Particle Swarm Optimization (PSO)  and the As for the Genetic Algorithm (GA), it is a widely recognized optimization technique that employs principles inspired by biological evolution. GA operates on a population of potential solutions, utilizing genetic operators such as selection, crossover, and mutation to search for optimal solutions. Its ability to explore a diverse solution space and handle complex problems makes it a relevant choice for comparison in our study.In contrast, we did not include Ant Colony Optimization (ACO)  in our cWhile ACO may possess advantages in specific optimization problems, our research focus centers around a meaningful comparison between GWO, PSO, and GA. These three algorithms, GWO, PSO, and GA, are all swarm intelligence-based optimization methods that have been extensively researched and applied in the field of network security . TherefoIn this section, we present our novel approach for optimizing the CW-RNN network using the Grey Wolf Optimization (GWO) algorithm. While the CW-RNN network structure and concepts have been previously introduced, the optimization method using GWO is our original contribution. We propose to optimize the hyperparameter combination, including the number of hidden layer neurons Firstly, the number of neural units in the CW-RNN hidden layer refers to the quantity of neurons in that layer. A suitable number of hidden layer neural units can help the model capture the complex relationships and patterns within the data. By optimizing and finding the optimal number of neural units, the model’s expressive power and prediction accuracy can be enhanced.Secondly, the learning rate represents the step size of parameter updates in the model. A smaller learning rate can lead to a more stable convergence but may result in a slower convergence speed. On the other hand, a larger learning rate may cause oscillation or failure to converge during the optimization process. By optimizing and determining the most appropriate learning rate, a balance can be achieved between model stability and training speed.The Iteration times refer to the total number of parameter updates performed by the model during the training process. An appropriate number of iterations allows the model to learn the features and patterns within the data more comprehensively. Too few iterations may prevent the model from reaching its optimal performance, while excessive iterations may lead to overfitting. Therefore, through optimization, the appropriate number of iterations can be determined to improve the model’s generalization ability and prediction performance.Lastly, the batch size represents the number of samples inputted into the model during each iteration. A larger batch size can improve the training efficiency of the model but may cause it to get stuck in local optima or overfit. Conversely, a smaller batch size allows the model to better capture fine-grained features among the samples but may result in a slower training process. By optimizing and determining the optimal batch size, a balance can be achieved between training efficiency and model performance.Different combinations of hyperparameters have different effects on the final prediction accuracy. In order to avoid the arbitrariness of hyperparameter settings, GWO is used to optimize the hyperparameter combination # Iteratively search for the optimal solution6. for iteration in range(max_iterations):7.        fitness = evaluate_fitness(population) # Calculate the fitness value8.        update_positions # Update wolf pack position9. best_solution = get_best_solution(population) # Get the optimal solution# Output the hyperparameters of the optimal solution10. best_hyperparameters = decode_parameters(best_solution)# Grey Wolf algorithm parameter initializationThe following are pseudocodes for our proposed gray wolf algorithm and CW-RNN network design (Algorithms 1 and 2):Algorithm 2: CW-RNN Network Pseudocodenum_hidden_units = …learning_rate = …num_iterations = …batch_size = …# CW-RNN network model construction5.model = build_cw_rnn_model# CW-RNN network training6.for iteration in range(num_iterations):# Get the current training batch data7.batch_data = get_next_batch(batch_size)# forward pass8.predictions = model.forward_pass(batch_data)# Compute the loss function9.loss = calculate_loss# Backpropagation10.gradients = model.backward_pass(loss)# Update network parameters11.model.update_parameters(gradients)# CW-RNN network prediction12.test_data = load_test_data13.predictions = model.predict(test_data)# CW-RNN network parameter initializationTo illustrate the process, we provide a flowchart in Step 1:Collect historical network security situation values.Step 2:Process the collected network security situation values using sliding window technology.Step 3:Divide the data set into training set and test set.Step 4:Check whether the data has been normalized. If it has been normalized, proceed to Step 6; otherwise, proceed to Step 5.Step 5:Normalize the historical network security situation values.Step 6:Input the training set.Step 7:Initialize the Grey Wolf algorithm parameters and wolf pack. The parameters of the Grey Wolf algorithm include the total number of wolves, the maximum number of evolution iterations, etc. Convert the number of hidden layer neurons Step 8:Initialize the parameters of the clock-cycle Recurrent Neural Network. The parameters of the neural network include the number of input neurons, the number of output neurons, and the clock-cycle of the network.Step 9:SCalculate the fitness value of each individual in the wolf pack. The fitness function is the mean square error between the predicted value and the true value.Step 10:Compare the fitness values of each grey wolf and select the top three wolves with the best fitness values as Step 11:Update the positions of other grey wolves D based on Step 12:Update the convergence factor Step 13:Check whether the maximum number of evolution iterations has been reached. If it has, proceed to Step 14; otherwise, increase the iteration number by one and proceed to Step 9.Step 14:Output the position coordinates of Step 15:Bring the optimal parameter combination (In order to optimize the CW-RNN network using the Grey Wolf Optimization (GWO) algorithm, we follow a series of design steps. The training phase consists of the following specific steps:Step 1:Input the test sample.Step 2:Predict using the optimized converged network security situation prediction model.Step 3:Output the prediction value.Step 4:Calculate the prediction result indicators, including the mean relative error, mean absolute error, and minimum mean square error.The specific steps of the prediction phase are as follows:In order to evaluate and validate the performance of the Clockwork Recurrent Neural Network (CW-RNN) in network security situation prediction, this study selected three commonly used network security datasets for experimentation, namely CICIDS2017, UNSW-NB15, and NSL-KDD. These datasets are widely applied and extensively validated and researched in the field of network intrusion detection. Specifically, these datasets provide a large number of network traffic records, including various types of normal traffic and different types of network intrusion behaviors, ensuring the comprehensiveness and reliability of the experiments to evaluate the performance and generalization ability of the proposed CW-RNN model on different datasets. Using these datasets, this paper aims to provide a comprehensive assessment of the application and performance of the CW-RNN model in real network security environments.The experiments in this study were conducted using a server provided by the university. The server was equipped with 48 NVIDIA GeForce RTX 2080 Ti graphics cards. The software used for the experiments was TensorFlow 2.5.0.The experimental data used in this study is from the CICIDS2017 dataset, which is a comprehensive and widely used dataset in network intrusion detection research. Collected by the Canadian Institute for Cybersecurity (CIC), the CICIDS2017 dataset provides an extensive collection of network traffic records that reflect real-world scenarios.The CICIDS2017 dataset encompasses a significant volume of network traffic data, with a size of approximately 8.3G. It comprises various network traffic features, including packet-level information, flow-level information, and application-level information. The dataset captures a diverse range of attack scenarios, such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), probing, infiltration, and botnet activities. These attack scenarios are based on different network protocols and exhibit distinct attack strategies and behaviors. Furthermore, the dataset incorporates a substantial amount of normal traffic data, which represents typical network activities. Each network traffic record in the CICIDS2017 dataset is labeled with its corresponding attack type or labeled as normal traffic. These labels provide ground truth information for evaluating the performance of intrusion detection models.By utilizing the CICIDS2017 dataset, we aim to demonstrate the effectiveness of our proposed optimization approach for the clock-cycle Recurrent Neural Network in network security situation prediction tasks. The dataset’s large size and diverse network traffic characteristics ensure the generalizability and robustness of the proposed optimization model, enabling us to evaluate its performance comprehensively. Overall, the CICIDS2017 dataset provides a realistic and challenging environment for evaluating network security prediction models, serving as a valuable resource for researchers and practitioners in the field of network intrusion detection and network security.Data PreprocessingBased on the situational evaluation, the data for network security situational prediction experiments were obtained using the situational evaluation method described in Chapter 3 to evaluate the situational values of the Friday dataset in the CICIDS-2017 traffic dataset. The situational values obtained from the situational evaluation were used to form historical and current situational values in chronological order. The data samples were divided according to Simulation Analysis of Optimal Parameter Combination for the Model−4, 100, 16], and the upper limit is . The fitness function is mean squared error.In the CW-RNN network, the selection of model parameters is mostly based on experience and manual settings, which has a certain degree of randomness and affects the performance of the model. To obtain a good model, multiple experiments are required to determine the appropriate parameters. In this paper, the GWO algorithm was used to optimize the CW-RNN parameters. The specific optimization parameters include the number of neural units in the CW-RNN hidden layer, learning rate, iteration times, and batch size. Other key parameter settings are as follows: 10 input neurons, 1 output neuron, initialization of 20 populations in GWO, a maximum of 15 iterations, and the lower limit of the optimization parameters is [8, 10Therefore, the best hyperparameters were obtained to correct the structure of the proposed situational prediction model in this chapter, and the optimal parameter combination of the model was obtained.Analysis of Network Situation Prediction ResultsHere, we present the experimental results and analysis of the proposed GWO-optimized CW-RNN, compared with the original CW-RNN and LSTM methods, on the test dataset. The prediction results of the three methods are illustrated in From Firstly, by examining the Mean Absolute Percentage Error (MAPE) metric, we can see that the GWO-CW-RNN method outperforms the others in network security situation prediction. Its MAPE value of 4.6153 is significantly lower than the values of 5.3840 for LSTM and 4.8902 for CW-RNN. This indicates that the GWO-CW-RNN method is capable of predicting network security situations more accurately with smaller relative errors.Furthermore, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics also support this conclusion. The GWO-CW-RNN method achieves an MAE of 0.03473, which is noticeably lower than the values of 0.03952 for LSTM and 0.03825 for CW-RNN. Similarly, GWO-CW-RNN has an MSE of 0.00615, smaller than LSTM’s 0.00741 and CW-RNN’s 0.00692. This suggests that the GWO-CW-RNN method exhibits smaller average errors and better fitting performance in predicting network security situations.In summary, the experimental results demonstrate that the GWO-CW-RNN method outperforms LSTM and CW-RNN in network security situation prediction. By optimizing the parameters of the Clockwork Recurrent Neural Network using the Grey Wolf Optimization algorithm, this method achieves more accurate predictions. These results serve as a foundation for further converting the model evaluation metrics to accuracy, precision, recall, and F1 score in a classification model, enabling a more comprehensive assessment of the performance of network security situation prediction methods.Comparative Experimental Results Analysis of Optimization AlgorithmsIn this study, we have transformed the evaluation metrics of the regression model into a classification model for comparison, aiming to better reflect the performance and effectiveness of the optimized model in practical applications. This transformation allows us to address the network security situation prediction problem as a decision problem with practical significance, rather than a purely numerical fitting problem. By categorizing the predicted results into different security levels and taking corresponding measures, we can more accurately address real-world situations.To achieve this transformation, we first set thresholds to categorize the network security situation values into different classes. Subsequently, we convert the real values and predicted values into corresponding class labels. Then, we calculate evaluation metrics such as accuracy, precision, recall, and F1-score based on the predicted labels and real labels. The results of these evaluation metrics are reported and compared with other models.Through this transformation, we can comprehensively assess the performance of the optimized model in classification problems, enabling a better judgment of its advantages in practical applications. Moreover, this approach allows us to compare the performance of different models using the same metrics, thereby determining whether the optimized model demonstrates a significant improvement. The transformation to a classification model provides us with a more comprehensive evaluation perspective and a better understanding of the practical decision-making capabilities of the optimized model.To provide a more intuitive and effective evaluation of the proposed network security situation prediction method in this chapter, we assess the performance of the model using the following four metrics: accuracy, precision, recall, and F1-score. The detailed calculation process is shown in Equations (17)–(20).TP (True Positive): The number of instances correctly predicted as positive. It refers to the cases where the true value and the predicted value are both positive.TN (True Negative): The number of instances correctly predicted as negative. It refers to the cases where the true value and the predicted value are both negative.FP : The number of instances incorrectly predicted as positive. It refers to the cases where the true value is negative, but the predicted value is positive.FN : The number of instances incorrectly predicted as negative. It refers to the cases where the true value is positive, but the predicted value is negative.These parameters help us understand the performance of the model in terms of correctly identifying positive and negative instances, as well as the occurrences of false positives and false negatives.In the comparative experiments of optimization algorithms on the CICIDS2017 dataset, we employed Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the Clockwork Recurrent Neural Network (CW-RNN). The following table presents the performance of each algorithm in terms of Accuracy, Precision, Recall, and F1-score:The results shown in Additionally, we tested the performance of the unoptimized CW-RNN model, which exhibited relatively lower values in all metrics. It achieved an accuracy of 0.88, precision of 0.84762, recall of 0.86, and F1-score of 0.85375. This further validates the effectiveness of optimization algorithms in enhancing the performance of the CW-RNN model.In summary, through the comparative experiments of optimization algorithms, we observed that the GWO algorithm demonstrates excellent performance in optimizing the CW-RNN model, significantly improving its performance metrics, and providing an effective optimization solution for network security situational prediction. Next, we will further analyze the results of the model comparison experiments.Analysis of Model Comparison Experiment ResultsIn the comparative experiments of models on the CICIDS-2017 dataset, we tested the Grey Wolf Optimization (GWO) optimized Clockwork Recurrent Neural Network (CW-RNN), CW-RNN without optimization, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer model. The following table presents the performance of each model in terms of time per batch (s), accuracy, precision, recall, and F1-score:From the experimental results shown in The GWO-optimized CW-RNN exhibits the best performance. It has a significantly faster running time (0.087 s) compared to other models, along with high accuracy (0.95000), precision (0.91875), recall (0.93000), and F1-score (0.92435). This demonstrates the remarkable improvement achieved by the GWO-optimized CW-RNN in network security situational prediction.The unoptimized CW-RNN model has a slightly slower running time compared to the GWO-optimized model (0.127 s) but still demonstrates high performance. It has high accuracy (0.88000), precision (0.84762), recall (0.86000), and F1-score (0.85375). However, its performance is slightly lower compared to the GWO-optimized model.The Recurrent Neural Network (RNN) has a relatively slower running time (0.156 s) and lower accuracy (0.87500), precision (0.84211), recall (0.85500), and F1-score (0.84850).Both the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have slightly slower running times , but they exhibit a high accuracy, precision, recall, and F1-score.The Transformer model has the slowest running time (0.188 s) but still maintains high accuracy, precision, recall, and F1-score.In conclusion, the GWO-optimized CW-RNN demonstrates the best performance and faster running speed on the CICIDS-2017 dataset, showcasing its advantages in network security situational prediction. This confirms the effectiveness of our proposed model and supports the application value of GWO optimization in Clockwork Recurrent Neural Networks. Furthermore, our CW-RNN model holds potential for real-time monitoring of large-scale network traffic in sensor networks due to its fast execution speed, which contributes to reducing runtime.Comparison of the Time Complexity of the ModelsIn this section, we present a comparison of the time complexity of various models, which is an essential aspect to consider when selecting the most efficient model for practical applications. We have evaluated and calculated the time complexities of different models, including our proposed optimized CW-RNN model and several comparison models. The time complexities are presented in terms of big O notation, with simplified expressions by ignoring lower-order terms and constants. This allows us to focus on the dominant factors that impact the computational efficiency of the models. The table below provides an overview of the time complexities of each model.From The UNSW-NB15 dataset is a comprehensive network traffic dataset collected by the University of New South Wales (UNSW), specifically designed for network intrusion detection research. This dataset comprises a large-scale collection of network traffic records, consisting of approximately 2.5 million instances. The dataset provides an extensive range of network traffic features, including packet-level information, flow-level information, and application-level information. It offers a comprehensive view of network communication and facilitates in-depth analysis of network behaviors.The UNSW-NB15 dataset encompasses various attack scenarios, such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), probing, infiltration, and botnet activities. Each attack scenario exhibits distinct characteristics, reflecting different attack strategies and behaviors. In addition to the attack scenarios, the dataset also contains normal traffic data, representing typical network activities. This diversity enables researchers to evaluate intrusion detection models in a realistic and challenging environment. To facilitate model evaluation, the records in the UNSW-NB15 dataset are labeled, providing ground truth information for intrusion detection. These labels allow researchers to assess the accuracy and performance of their models in detecting and classifying network attacks.The UNSW-NB15 dataset, with its large scale and diverse network traffic records, serves as a valuable resource for developing and evaluating network security prediction models. It offers researchers the opportunity to explore innovative techniques and improve the detection and prediction capabilities of network security systems.Comparative Experimental Results Analysis of Optimization AlgorithmsIn the comparative experiments of optimization algorithms on the UNSW-NB15 dataset, we tested the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for optimizing the Clockwork Recurrent Neural Network (CW-RNN). The following table presents the performance of each model in terms of accuracy, precision, recall, and F1-score:The following conclusions can be drawn from the experimental results shown in The GWO-optimized network achieves the best performance in terms of accuracy, precision, recall, and F1-score. It has an accuracy of 0.94, precision of 0.91234, recall of 0.925, and F1-score of 0.91861. This indicates that the Grey Wolf Optimization algorithm performs well in optimizing the Clockwork Recurrent Neural Network, significantly improving the predictive performance of the network.The CW-RNN without any optimization shows relatively lower performance in all metrics, with an accuracy of 0.83, precision of 0.81225, recall of 0.815, and F1-score of 0.81362. Compared to the optimized models, its performance is lower, further confirming the importance of optimization algorithms in model performance.The PSO and GA-optimized networks exhibit moderate performance in all metrics, with accuracies of 0.86 and 0.89, respectively. These optimization algorithms achieve some performance improvements compared to the unoptimized model, but there is still a gap compared to the Grey Wolf Optimization algorithm.In summary, in the comparative experiments of optimization algorithms on the UNSW-NB15 dataset, the GWO-optimized Clockwork Recurrent Neural Network demonstrates the best performance with high accuracy, precision, recall, and F1-score. This indicates that the Grey Wolf Optimization algorithm has a good effect on optimizing the network for network security situational prediction tasks, enhancing the model’s generalization ability and robustness.Analysis of Model Comparison Experiment ResultsIn the comparative experiments on the UNSW-NB15 dataset, we evaluated the performance of different models for network security situational prediction. The following table presents the performance of each model in terms of time per batch (s), accuracy, precision, recall, and F1-score:From the experimental results shown in The GWO-CW-RNN model is the Clockwork Recurrent Neural Network model optimized using the Grey Wolf Optimization algorithm. This model exhibits the best performance and has a relatively short running time on the UNSW-NB15 dataset. Specifically, the GWO-CW-RNN model achieves an accuracy of 0.92000, a precision of 0.90575, a recall of 0.910, and an F1-score of 0.90786. Furthermore, this model has a time per batch of 0.091 s, making it faster compared to other models.In contrast, the unoptimized CW-RNN model shows relatively lower performance in terms of accuracy, precision, recall, and F1-score. It has a time per batch of 0.132 s, slightly longer than the GWO-CW-RNN model. Additionally, the traditional RNN, LSTM, GRU, and Transformer models perform between the GWO-CW-RNN model and CW-RNN model in terms of accuracy and other metrics, and they have relatively longer time per batch.In summary, the GWO-CW-RNN model, which is the Clockwork Recurrent Neural Network model optimized using the Grey Wolf Optimization algorithm, achieves higher accuracy and performance on the UNSW-NB15 dataset, while having a shorter running time. This indicates that the model has a significant advantage in network security situational prediction tasks.The NSL-KDD dataset is a widely used network intrusion detection dataset, derived from the original KDD Cup 1999 dataset. This dataset has undergone extensive preprocessing to address various limitations and enhance its usefulness for intrusion detection research.The NSL-KDD dataset encompasses a diverse set of network traffic features, providing a comprehensive representation of network communication. It includes information at the packet level, connection level, and application level, enabling researchers to analyze network behaviors from multiple perspectives. In terms of attack types, the NSL-KDD dataset covers a range of scenarios, including Denial-of-Service (DoS), probing, Remote-to-Local (R2L), and User-to-Root (U2R) attacks. Each attack type exhibits unique characteristics, reflecting different intrusion strategies and techniques. By incorporating multiple attack types, the dataset offers a realistic and challenging environment for evaluating intrusion detection models.Similar to the UNSW-NB15 dataset, the NSL-KDD dataset provides labeled records, allowing researchers to evaluate the performance of intrusion detection models accurately. These labels provide ground truth information for classifying network traffic instances as either normal or malicious, facilitating the assessment of model accuracy and effectiveness. With its comprehensive set of network traffic features, diverse attack types, and labeled records, the NSL-KDD dataset serves as an important benchmark for testing the performance of network security prediction models. Researchers can leverage this dataset to develop and evaluate innovative intrusion detection techniques, improving the detection and prediction capabilities of network security systems.In summary, the NSL-KDD dataset, derived from the original KDD Cup 1999 dataset, offers a collection of network traffic records with diverse features and attack types. Its labeled records provide valuable ground truth information for evaluating intrusion detection models and serve as a benchmark for assessing the performance of network security prediction models.Comparative Experimental Results Analysis of Optimization AlgorithmsIn the comparative experiments of optimization algorithms on the NSL-KDD dataset, the results are as follows:Based on the analysis of the experimental results shown in The GWO-CW-RNN model exhibits the best performance, with an accuracy of 0.89000, a precision of 0.88234, a recall of 0.885, and an F1-score of 0.88361. This indicates that the Grey Wolf Optimization algorithm performs well in optimizing Recurrent Neural Networks for the NSL-KDD dataset.The models optimized using the Particle Swarm Algorithm and the Genetic Algorithm also perform well in terms of accuracy, precision, recall, and F1-score. However, their performance is slightly inferior to the GWO-CW-RNN model.The unoptimized Clockwork Recurrent Neural Network shows mediocre performance on the NSL-KDD dataset, with an accuracy of 0.82000, a precision of 0.81225, a recall of 0.815, and an F1-score of 0.81362. Its performance is lower compared to the optimized models.In conclusion, the experimental results further demonstrate the effectiveness and superiority of the Grey Wolf Optimization algorithm in optimizing Clockwork Recurrent Neural Networks, particularly achieving good performance on the NSL-KDD dataset.Analysis of Model Comparison Experimental ResultsIn the comparative experiments of models on the NSL-KDD dataset, the results are as follows:Based on the analysis of the experimental results shown in The GWO-CW-RNN model, which is a Clockwork Recurrent Neural Network model optimized using the Grey Wolf Optimization algorithm, demonstrates the best performance and shorter running time on the NSL-KDD dataset. The model achieves an accuracy of 0.90000, a precision of 0.89275, a recall of 0.895, and an F1-score of 0.89386. Additionally, it has a faster running time of 0.089 s per batch compared to other models.In contrast, the unoptimized CW-RNN model shows relatively lower accuracy, precision, recall, and F1-score. Its running time per batch is 0.128 s. Traditional models such as RNN, LSTM, GRU, and Transformer perform between the GWO-CW-RNN model and the CW-RNN model in terms of performance metrics, and they have relatively longer running times per batch.In conclusion, the GWO-CW-RNN model, optimized using the Grey Wolf Optimization algorithm, exhibits higher accuracy and performance on the NSL-KDD dataset, along with a shorter running time. This further validates the advantages of this model in the task of network security situation prediction.In this study, we proposed a network security situation prediction method based on optimized Clockwork Recurrent Neural Networks to address the issue of low accuracy in existing methods when dealing with the temporal and nonlinear characteristics of network security situations. We designed Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the Clockwork Recurrent Neural Network, and through experiments, we selected GWO as the best optimization algorithm. Furthermore, we compared multiple models and demonstrated the advantages of the optimized Clockwork Recurrent Neural Network in terms of generalization capability. To validate the real-time performance of the model, we conducted experiments on multiple datasets and found that our network prediction model had the shortest execution time among all models, demonstrating its superior real-time performance. Therefore, our method is particularly suitable for monitoring large-scale network traffic in sensor networks.However, our experiments were conducted on specific datasets, namely CICIDS2017, UNSW-NB15, and NSL-KDD datasets. Although these datasets are widely used in network security research, further research and validation are needed to assess the generalization capability of our method on other network environments and datasets. Different network environments may exhibit distinct characteristics and data distributions, thus necessitating the expansion of our experimental scope to verify the applicability of our method.In practical applications, our optimized Clockwork RNN holds great potential for real-time monitoring and decision-making in network security. By accurately predicting the network security situation, appropriate measures can be taken to mitigate potential threats and ensure the security of network systems. For example, our model can be integrated into intrusion detection systems or security operation centers to provide timely alerts and facilitate proactive response strategies.To further enhance the practicality and applicability of our approach, future research can focus on integrating additional features, such as network traffic analysis or contextual information, into the prediction model. Moreover, efforts can be made to develop user-friendly interfaces and tools that allow security professionals to easily deploy and utilize our optimized model in real-world scenarios. In summary, this study successfully addressed the issues of temporal and nonlinear characteristics in network security situation prediction through the use of GWO-optimized Clockwork Recurrent Neural Networks, and its superiority was validated on multiple datasets. Our method not only improves the accuracy of network security situation prediction but also possesses real-time performance and is suitable for sensor network monitoring. Future research can further explore other optimization algorithms and model structures to further enhance the performance of network security situation prediction.


Several studies have reported on the feasibility and impact of e-monitoring using computers, or smartphones, in patients with mental disorders, including Bipolar Disorder (BD). Despite some promising early results, concerns have been raised about the motivation and ability of patients with BD to adhere to e-monitoring, in particular when they are depressed or manic. While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring of patients with BD.We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence.Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models and Multiple Component Analyses were fitted to compute the effects of predictors on GMM classes.Adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: (i) participants with good adherence with the protocol; (ii) participants with partial adherence; (iii) participants with poor adherence. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with good adherence.Participants with higher illness burden  have higher adherence rates to e-monitoring. This is important because our findings debunk myths around illness burden as an obstacle to adhere to e-monitoring studies. Participants might have seen e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.None Declared


Introduction: Gait, as a fundamental human movement, necessitates the coordination of muscles across swing and stance phases. Functional electrical stimulation (FES) of the tibialis anterior (TA) has been widely applied to foot drop correction for patients with post-stroke during the swing phase. Although the gastrocnemius (GAS) during the stance phase is also affected, the Functional electrical stimulation of the gastrocnemius received less attention.Methods: To address this limitation, a timing- and intensity-adaptive Functional electrical stimulation control strategy was developed for both the TA and GAS. Each channel incorporates a speed-adaptive (SA) module to control stimulation timing and an iterative learning control (ILC) module to regulate the stimulation intensity. These modules rely on real-time kinematic or kinetic data during the swing or stance phase, respectively. The orthotic effects of the system were evaluated on eight patients with post-stroke foot drop. Gait kinematics and kinetics were assessed under three conditions: no stimulation (NS), Functional electrical stimulation to the ankle dorsiflexor tibialis anterior (SA-ILC DS) and FES to the tibialis anterior and the ankle plantarflexor gastrocnemius (SA-ILC DPS).Results: The ankle plantarflexion angle, the knee flexion angle, and the anterior ground reaction force (AGRF) in the SA-ILC DPS condition were significantly larger than those in the NS and SA-ILC DS conditions (p < 0.05). The maximum dorsiflexion angle during the swing phase in the SA-ILC DPS condition was similar to that in the SA-ILC DS condition, with both being significantly larger than the angle observed in the NS condition (p < 0.05). Furthermore, the angle error and force error relative to the set targets were minimized in the SA-ILC DPS condition.Discussion: The observed improvements can be ascribed to the appropriate stimulation timing and intensity provided by the SA-ILC DPS strategy. This study demonstrates that the hybrid and adaptive control strategy of functional electrical stimulation system offers a significant orthotic effect, and has considerable potential for future clinical application. Stroke ranks as the second leading cause of death globally, and one of the primary causes of disability, affecting 15 million people annually. From 1990 to 2019, stroke incidence increased by 70%, and the burden of stroke grew substantially . Stroke Functional electrical stimulation (FES) is the most common intervention for correcting foot drop . A typicFurthermore, while traditional single-channel FES delivered to the TA increased ankle dorsiflexion angles during the swing phase, other gait deficits remained unaddressed. For instance, these deficits include decreased ankle plantarflexion, reduced swing-phase knee flexion, and diminished stance-phase propulsive force . These dThe objective of this study is to establish a hybrid and adaptive control strategy of FES system, aiming at correcting hemiplegic gait physiologically and effectively, addressing foot drop and inadequate forward propulsion. To assess the orthotic effect of this system, the kinematic and kinetic data of patients after stroke were compared under conditions of no FES, single-channel FES, and the FES with hybrid and adaptive control strategy.Eight post-stroke subjects , walking with single-channel FES delivered to the TA (SA-ILC DS), and walking with FES delivered to both the TA and the GAS (SA-ILC DPS). Subjects with post-stroke performed three trials for each condition in a random order, totaling nine trials per subject. During each trial, participants were instructed to walk at their most comfortable speed for a minimum of 2 min, with a 2-min rest interval between successive trials to avert muscle fatigue. The stimulation profile used in all conditions was biphasic waveform. The stimulation pulse width was set at 390 µs, and the frequency was 40 Hz.The hybrid timing- and intensity-adaptive control strategy of FES system was applied to facilitate proper ankle movement during walking. The control strategy comprised two independent components: the stimulation timing was regulated by linear models, and the stimulation intensity was controlled by iterative learning controllers. The hybrid and adaptive control strategy of FES system is shown in Linear models correlating walking speed with muscle activation phase parameters were established  due to tff event . The stime y3i . The uniThe rules for the iterative learning controllers to regulate the stimulation intensity for the GAS  on gait parameters. The significance level was set at 0.05, and when p < 0.05, it indicates a statistically significant difference. The statistical analyses were conducted using SPSS 19 .The Kolmogorov-Smirnov test was utilized to all data to ensure normal distribution. Subsequently, one-way analysis of the variance with repeated measures was conducted, followed by the Bonferroni In this experiment, the normalized ankle and knee angles are illustrated in p < 0.05). Nevertheless, there was no significant difference between the SA-ILC DS and SA-ILC DPS conditions. In the SA-ILC DS and SA-ILC DPS conditions, the maximum ankle dorsiflexion angles were closer to 4.9° relative to the NS condition.p < 0.05). No significant difference was observed between the NS and SA-ILC DS conditions.p < 0.05). There was no significant difference in the maximum knee flexion angle between the SA-ILC DS and NS conditions.p < 0.05). However, the ratio in the NS condition was similar to that in the SA-ILC DS condition, and there was no significant difference between them.In this experiment, the normalized anterior reaction force (AGRF) is displayed in p < 0.05).In this study, a hybrid and adaptive control strategy of FES system was developed for the ankle plantarflexor muscle (GAS) and the dorsiflexor muscle (TA), incorporating a linear model and an ILC controller to respectively modulate stimulation timing and intensity. The orthotic effect of the system for foot drop correction was evaluated by measuring the maximum ankle dorsiflexion angle in the swing phase, peak ankle plantarflexion angle, maximum knee flexion angle in the swing phase and peak AGRF in the stance phase under NS, SA-ILC DS, and SA-ILC DPS conditions.Triggering stimulation at fixed time intervals, rather than adjusting the stimulation timing according to the walking speed, led to adverse effects such as decreased ankle plantarflexion angle during the push-off phase , reducedThe implemented timing-adaptive linear models of muscle activation/termination time and walking speed were physiologically suitable for FES-assisted gait, which were consistent with the previous research , signifiUtilizing the ILC algorithms in the FES system enabled the provision of well-suited stimulation intensity, facilitated swift convergence towards the desired objectives  and prevIn previous studies, FES was usually applied only to the ankle dorsiflexor (TA) to correct foot drop during the swing phase . HoweverThe designed hybrid control strategy of FES system not only improved the angles of ankle and knee joints of patients after stroke, but also increased anterior ground reaction force. Significantly, larger ankle dorsiflexion, ankle plantarflexion and knee flexion angles were observed in the SA-ILC DPS condition compared to the NS condition, signifying an enhancement over the results of the dual-channel FES reported in the Kesar study . In compDespite the numerous advantages of the hybrid timing- and intensity-adaptive control strategy of FES system, the study presented some limitations. Firstly, further research will examine an expanded pool of stroke subjects in longitudinal clinical trials to validate the long-term rehabilitation effects of rehabilitation training. Additionally, only three joint angles and AGRF were utilized to evaluate FES-assisted walking performance. More comprehensive evaluation metrics will be encompassed to optimize control and improve other gait abnormalities, such as center of pressure, gait symmetry, and so on.In this study, a hybrid and adaptive control strategy of FES system targeting the tibialis anterior and the gastrocnemius was devised. The system integrated linear models of muscle activation time and walking speed with iterative learning controllers algorithm to respectively modulate stimulation timing and intensity. The proposed SA-ILC hybrid control strategy of FES demonstrated its capacity to enhance maximum ankle dorsiflexion angle, ankle plantarflexion angle and knee flexion angle during the swing phase, as well as anterior ground reaction force. These improvements yielded stable orthotic effects for patients afflicted by foot drop, suggesting promising clinical potential for this innovative approach.


This year’s GTI (“Groupe Transplantation and Infection”) annual meeting was held in Paris, France in February 2023. This meeting focused on new approaches to manage infectious complications in solid organ and stem cell transplant recipients.In this meeting report, we summarize the presentations and discussions from this annual meeting. Covered topics included new anti-infective agents and non-antibiotic approaches to manage infections due to multidrug-resistant Gram-negative bacteria, staphylococci, and fungal infections, as well as new approaches to manage symptomatic urinary tract infections and asymptomatic bacteriuria in kidney transplant recipients. Innovative approaches are needed to manage infectious complications in transplant recipients, who are at high risk of difficult-to-treat infections and side effects associated with the use of anti-infective agents.Solid-organ transplantation (SOT) is the treatment of choice for patients diagnosed with end-stage organ disease, and the median survival of both recipients and grafts has significantly increased in the last years . While tAmong these strategies, the exact benefits of fecal microbiota transplantation (FMT) remain unclear . A multiAltogether, FMT seems an interesting option for decolonization, but the safety profile and efficacy of the procedure must be determined more strongly to better assess the role of FMT in allo-HSCT recipients.One-promising way to protect the gut microbiota is to develop molecules to chelate or degrade the non-absorbed part of orally administered antibiotics and the fraction of oral and parenteral antibiotics excreted in the bile that reach the colon, induce dysbiosis and a decrease in richness and diversity of the microbiota. For example, ribaxamase  and DAV-132 which is a millimetric beads consisting of a core of a specific activated charcoal surrounded by a polymer coating that is insoluble during transit. The charcoal is activated in the ileum and adsorbs and thereby inactivates antibiotics in the caecum/colon –16. For Antibiotic-resistant Gram-negative bacterial infections are the leading cause of death attributable to antibiotic resistance in Europe and worldwide. This is linked to the epidemic success of 3rd generation cephalosporins (3GC)- resistant Enterobacteriaceae. The widespread use of carbapenems to treat 3GC-resistant strains has led to the emergence of carbapenem-resistant isolates, in particular those secreting carbapenemases, with very limited therapeutic options. New molecules have recently been developed to combat carbapenem-resistant bacteria. Victoire de Lastours summarized the updated antimicrobial management of carbapenem-resistant bacteria related infection.Klebsiella pneumoniae blood stream infections, ceftazidime-avibactam significantly increased the probability of 14 and 30 days clinical success, as compared to the best available therapy [These include ceftazidime-avibactam, a combination of a 3GC with a new betalactamase inhibitor, avibactam. This combination is effective on strains carrying OXA 48 or KPC, but not metallobetalactamases. This molecule was granted authorization in Europe and the USA following 3 phase 3 trials in complicated intra-abdominal infections versus meropenem, as well as two trials in complicated urinary tract infections yielding non-inferiority. In a retrospective cohort study of 210 SOT recipients with carbapenemase-producing  therapy .Pseudomonas aeruginosa, but not against carbapenem-resistant Acinetobacter baumanii (CRAB). The molecule has been approved in France only as a last resort for the treatment of patients with no other possible therapeutic alternative, and in particular if KPC-type carbapenemase are produced.A second compound, meropenem-varbobactam, is also active against class A betalactamases (KPC) and cephalosporinases, but inactive against metallobetalactamases and oxacillinases, which limits its interest in some European coutries such as France, where KPCs are rare. Non-inferiority has been demonstrated in several trials against optimized treatment. A third molecule, imipenem-relebactam, is also active against KPCs but not against oxacillinases or metallobetalactamases. Imipenem-relebactam is also effective against carbapenem-resistant strains of P. aeruginosa, ceftolozane-tazobactam is generally effective. Tolerance is generally good (as with beta-lactams), and these molecules are bactericidal. However, these molecules are not effective against metallobetalactamases nor against most CRAB, which poses major therapeutic problems. Its use was reported in a multicenter cohort study of 69 immunocompromised patients including 47 SOT, with multi-drug resistant P. aeruginosa infections, mostly respiratory and wound. Clinical cure was achieved in 68% and mortality was 19% [Altogether, several choices are now available to treat KPC and OXA-48 oxacillinases which are approved in France and Europe. For carbapenem-resistant  was 19% .A. baumanii infections, a result that remains unexplained. Cefiderocol has been marketed in Europe and the USA only as a last resort for infections caused by multi-resistant gram-negative bacteria, notably in cases of KPC and metallo-betalactamases.A recently approved molecule, cefiderocol, is a siderophore cephalosporin which uses the bacterial iron entry machinery to achieve high concentrations inside the bacteria. It is unaffected by betalactamases, even metallobetalactamases, and acts as a Trojan horse. In pivotal trials, cefiderocol showed non-inferiority to high-dose meropenem in the treatment of gram-negative nosocomial pneumonia, except for P. aeruginosa infections, but its use has broadened to other difficult-to-treat non-fermentative gram negative bacteria, especially S. maltophilia for which its complex virulence and resistance profile drastically limit available antibiotics. Updated clinical and safety outcome data are needed in highly susceptible immunocompromised settings.This molecule therefore represents an important therapeutic hope, although it appears to have a relatively significant inoculum effect, which needs to be better studied. Finally, some cefiderocol-resistant strains have been described, combining several resistance mechanisms. To date, very few data are available in specific immunocompromised settings including solid organ transplantation , hematolAnother interesting combination in this context is ceftazidime-avibactam + aztreonam for strains carrying metallo-betalactamases. Several studies have demonstrated the efficacy of the avibactam + aztreonam combination, which is currently being developed by the manufacturer. An inoculum effect could also have an impact on the efficacy of this combination. This combination proved effective and safe in a serie of 4 SOT recipients with metallo-β-lactamase carbapenemase-producing Enterobacteriaceae .Lastly, plazomicin, an aminoglycoside developed for the treatment of carbapenem-resistant Enterobacteriaceae infections, had shown interesting results in the United States, but was not developed in Europe due to its low commercial potential.Treatment recommendations for carbapenem-resistant infections are summarized in the 2022 ESCMID guidelines . SeveralFinally, in the face of this type of infection, optimizing the use of available molecules is a crucial point, including rapid diagnosis of resistance, determination of MICs  for the different molecules and combinations available, and optimization of dosages with the use of high doses and prolonged infusions. Last but not least, multidisciplinary discussions between microbiologists and clinicians and the reduction of bacterial inoculum through drainage are essential. A summary of antibiotics efficiency regarding resistance mutation has been made in The management of urinary tract infections (UTIs) in kidney transplant recipients represents a major opportunity for antimicrobial stewardship because kidney transplantation is the most common type of organ transplant worldwide, and because UTI is the most common infection in this population , 24. JulAsymptomatic bacteriuria, which is generally defined as significant bacteriuria (≥100.000 CFU/mL) without signs or symptoms of UTI , is relatively common after kidney transplantation .Recent randomized trials have shown that the historical practice of screening for and treating asymptomatic bacteriuria is not beneficial in stable kidney transplant recipients –28. A liEscherichia coli are also promising [E. coli.Besides, innovative non-antibiotic-based approaches are needed to better prevent symptomatic UTIs, which remain prevalent and detrimental after kidney transplantation. Julien Coussement discussed the potential benefits, harms and applicability of emerging approaches, including anti-adhesion therapies  , intraveromising . Many chAurélien Dinh reminded the drawbacks of vancomycin and daptomycin, before presenting new antibiotics targeting gram-positive cocci.occi are now available.Vancomycin is a relatively old and difficult-to-manage glycopeptide. Several new antibiotics with activity against methicillin-resistant StaphylocStaphylococcus aureus and vancomycin for methicillin-resistant S. aureus, according to a randomized controlled trial (RCT) on bloodstream infections (BSI) [in vivo exposure to endogenous cationic peptides [Daptomycin is bactericidal and as effective as penicillin M against methicillin-susceptible ns (BSI) . Neverthpeptides . In livepeptides . In kidnpeptides , 44.S. aureus and resistant coagulase-negative staphylococci are low. One retrospective cohort compared dalbavancin versus standard of care in patients with S. aureus bacteremia and found no significant difference [S. aureus bacteremia [Dalbavancin is a new long acting glycolipopeptide, with a half-life of 14 days. MIC of dalbavancin against fference . Two RCTcteremia , 47. Dalcteremia .versus daptomycin in S. aureus bacteremia showed non-inferiority [Ceftaroline and ceftobiprole are new generation cephalosporins with excellent activity against methicillin-resistant staphylococci according to bacterial killing curves . Clinicaeriority .So far, to our knowledge, no data exist regarding the use of dalvabancin, ceftaroline and ceftobiprole in SOT recipients.Finally, oritavancin is a recently available lipopeptide, with a semi long-life activity (7 days) and important intra-cellular activity, which could be of interest for device-associated infection with biofilm .These new antibiotics may allow new management and innovative approaches to treat patients with infections due to resistant Staphylococci.Because of the toxicities of the available drugs and the emergence of resistance caused by an increased use of antifungal agents in the growing population at risk of invasive fungal diseases and in agriculture, there is a pressing need for more antifungal drug options. Recently, several new antifungal drugs have reached late-stage clinical development and obtained a temporary use authorization, as depicted by Alexandra Serris.in vitro against azole-resistant Aspergillus, Scedosporium, Lomentospora, Rasamsonia, dimorphic fungi (notably Histoplasma), dermatophytes, but has no activity against yeasts, Mucorales and Alternaria alternata [Olorofim is the only member of a novel class named orotomide. It inhibits fungal growth through inhibition of the fungal dihydroorotate dehydrogenase enzyme involved in pyrimidine synthesis. It has a good tissue distribution, notably in the kidney, liver, lung, and the brain  . It is mlternata , 56.ClinicalTrials.gov identifier: NCT03583164) and one phase III, randomized study to evaluate the efficacy and safety of olorofim versus liposomal amphotericin B in patients with invasive aspergillosis . Published experience is currently limited to case reports (abstracts).Olorofim is currently evaluated in two clinical studies: one open-label, single-arm study including patients with invasive fungal infections due to Lomentospora prolificans, Scedosporium spp., Aspergillus spp., and other resistant fungi with limited treatment options . It is fungicidal against most wild-type, echinocandin or azole-resistant llus spp . Based ollus spp .An interim analysis of the phase III FURI study evaluating the efficacy and safety of ibrexafungerp in patients with severe mucocutaneous candidiasis, invasive candidiasis, chronic or invasive aspergillosis reported complete or partial response in 58% of the patients . Inclusiin vitro activity against most Candida spp., including C. auris, and common dermatophytes [Rezafungin is the first member of second-generation echinocandins with enhanced pharmacokinetic/pharmacodynamic parameters, allowing for a weekly administration and potential less hepatic toxicity . It has tophytes .in vivo by eradicating both the cyst and trophic forms of the fungus [Moreover, rezafungin has shown promising results as prophylactic and curative treatment of pneumocystis e fungus , 62. A ce fungus  and rezae fungus .These antifungal treatments offer significant improvement in terms of spectrum of activity, tolerability, drug interactions and/or route of administration. Further clinical studies will be needed to evaluate their optimal place in the therapeutic arsenal in the solid organ transplant recipient population, taking into account the emergence of drug-resistant fungi and the problem of drug-drug interactions with immunosuppressants. Infection and Transplantation Group” day, the major advances in the field of new anti-infective therapies in transplantation were presented and discussed. New direct and indirect anti-infective approaches in transplantation are devoted to several improvements:- decrease antibiotics pressure in our high risk multidrug resistant bacteria population with a better use of already known antibiotics and new original non-antibiotic approaches that have promising usages.- improve efficacy of bacterial and fungal treatment with antibiotics or antifungal therapy that have a good inoculum effect and a good broadcast- improve the tolerance of antimicrobial drugs in our polymedicated population with high risk of drugs interactions.During the well-attended “Altogether, those new approaches are likely to feature alternative anti-infective therapies that promise to change patient management.


Early childhood health development is positively associated with income, but the strength of this relationship with ethnoracial background remains unclear. This study examined the extent of health development inequities among California kindergarteners based on ethnoracial backgrounds and neighborhood-level income.n = 106,574) were collected through teacher report between 2010–2020 across 52 school districts and 964 schools. Student addresses were geocoded and linked to American Community Survey neighborhood income levels. Health development was measured using the Early Development Instrument, a population-level measure which includes physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge domains. Outcomes included being “on-track” in each domain as well as overall health development.This cross-sectional study assessed health development inequities by analyzing neighborhood-level income, ethnoracial background, and health development data for California kindergarteners. Student-level data (Using a Generalized Estimation Equation with a log-link function, while accounting for interactions between ethnoracial background, income, and income-squared, we found significant health development inequities by ethnoracial background and neighborhood-level income. Regarding overall health development, as well as the physical, social and emotional domains, Black students had a lower likelihood of being on-track compared to the weighted average across income levels, whereas Asian students surpassed the weighted average. White students exhibited the steepest slope, and at the lowest income levels, their health development scores were akin to their Black and Hispanic/Latino/a low-income counterparts but resembled their Asian counterparts at higher income levels. For the general knowledge and communication domain, white students consistently had the highest likelihood of being on-track, while Hispanic/Latino/a students had the lowest likelihood across all income levels.This study examines health development inequities among California kindergarteners in diverse communities. Our analysis shows that the relationship between neighborhood-level income and kindergartners’ health development varies by domain and is weaker for students of color. Given the scarcity of population-level data on health development outcomes, these analyses offer valuable insights for identifying ecosystems necessitating support in promoting equitable early childhood health development.The online version contains supplementary material available at 10.1186/s12889-023-17246-7. Lifelong health inequities originate early in life –3, and eWithout intervention, children from minoritized ethnoracial backgrounds and children from low-income neighborhoods are more likely to experience social adversities and inequities with impacts across a range of health development outcomes . For insUnlike many other nations, the United States has not yet adopted a universal, population-level measure of kindergarten readiness and health development administered when children start school. Canada and Australia both utilize the Early Development Instrument (EDI), which comprehensively measures the health development of kindergartners, including physical, social, emotional, language, and cognitive development . DespiteThe current study utilizes EDI data collected in selected geographic regions across California, one of the most diverse states in the nation, with approximately 40.2 percent of residents identifying as Hispanic/Latino/a, 15.9 percent as Asian, and 6.5 percent as Black . Addition = 106,574) face health development inequities by ethnoracial background and neighborhood-level income.We have used the EDI because it represents the most extensive dataset on holistic health development for young children in California, providing a snapshot of kindergartners’ physical health, social-emotional skills, and language and cognitive development. Using 11 years of population-level data on early childhood health development (as measured by the EDI from 2010–2020) we sought to determine the extent to which California kindergarteners , and non-geocoded records. The final study sample includes 106,574 kindergarten students living in 2,472 census tracts and attending 964 schools in 52 school districts throughout California, with data collected between 2010 and 2020. See Additional file To partially account for varying levels of data completeness at the school district level, the study used the National Center for Education Statistics Common Core of Data enrollment figures to compare total number of kindergarten students enrolled in a district in a given year and the total EDI records collected in the same district and year. A probability weight was constructed from this proportion, aiming to increase internal validity by upweighting district-years with higher levels of completeness of EDI data collection and thus lower likelihood of introducing bias into the sample. The probability weight is not intended to address generalizability to the broader California population.Many studies in the US, Canada, and Australia have employed the EDI to measure early childhood health development, with substantial psychometric investigation. The EDI has undergone multilevel validity, internal consistency, factor structure, and differential item functioning validation , 22, 28.th percentile of that domain based on the normative US database distribution from the 2008–2009 school year convenience sample . This study examines whether children are on track for each domain separately and on all five domains in total, with dichotomous variables .The EDI includes five domains: 1. Physical health and well-being (13 items); 2. Social competence (26 items); 3. Emotional maturity (30 items); 4. Language and cognitive development (26 items); and 5. Communication skills and general knowledge (8 items). To be “on-track” on a specific domain, a child must be above the 25Child demographics include ethnoracial background and neighborhood-level ethnoracial-group-specific median household income, since the EDI does not include household-level income. Ethnoracial background was divided into five categories: 1. African American/Black, 2. Asian/Native Hawaiian/Pacific Islander, 3. Hispanic/Latino/a, 4. White, and 5. Other. The ‘other’ ethnoracial category consists of children who are identified by their districts as multiracial, American Indian/Alaskan Native, other, or unknown race/ethnicity). US Census American Community Survey 5-year estimates from the year of the student’s EDI data collection were used to assign ethnoracial-group-specific median household income by census tract.n = 106,574) and cluster size (135 district-years), there are minimal differences in coefficients and interpretation between GEE-exchangeable and GEE-independent. These GEE Models include interaction terms for ethnoracial category by neighborhood-level income as well as by neighborhood-level income-squared to account for the potential for nonlinearity. There are separate models for each domain of the EDI as well as being on-track in all five domains.First, this study analyzed the data descriptively, presenting ethnoracial-group-specific sample size, household median neighborhood-level income, and percent on-track overall and by domain for the EDI without probability weights. To investigate the extent to which ethnoracial background and neighborhood-level income predict health development, the study utilized a Generalized Estimating Equation (GEE) with the exchangeable correlation structure, clustering at the district-year level, and a log link function to account for dichotomous outcome variables , representing all coefficients as odds ratios, with a probability weight at the district-year level. GEE-exchangeable is more efficient when calculating within-cluster covariates compared to GEE-independent. Due to the large sample (Weighted effect coding (WEC) was employed for ethnoracial background rather than dummy variables so that there are no category-specific reference groups. Instead, each ethnoracial group is compared to the weighted mean , 30. WECMarginal effects were calculated post-estimation to interpret the combination of interaction terms and quadratic terms and the between-group differences. Marginal estimates were calculated for each ethnoracial group from neighborhood-level incomes of $10,000 to $140,000 in $10,000 increments. Graphs represent this data visually.There were 340 missing values for ethnoracial background (from the EDI dataset) and 4,955 missing values for neighborhood-level income (from the census-tract-level American Community Survey dataset), making up less than five percent of the total sample. Due to the small amount of missing data, complete case deletion was used to account for missing values.Table p < 0.05 level in each of the models.Table p < 0.05) at each neighborhood income level. For emotional maturity, the trend for Black students is not significant. For language and cognition, Hispanic/Latino/a students have the lowest on-track likelihood across neighborhood income levels. The trends for Asian and Hispanic/Latino/a students are statistically significant at all neighborhood income levels (p < 0.05). For general knowledge, white students have the highest likelihood of being on-track after $30,000 neighborhood-level median income, and Hispanic/Latino/a students have the lowest likelihood. The trend for Hispanic/Latino/a students is significant at all neighborhood income levels, and that of white students is significant from $20,000 on.The models in Table n= 106,574) health development across domains, stratified by ethnoracial background and neighborhood-level income. While income has long been established as a significant predictor of health outcomes across the lifespan [This study addresses an important gap in the literature by examining population-level kindergartners’ . To partially address this issue post-hoc, we employed a probability weight based on total enrollment of kindergartners, upweighting districts with higher levels of completeness. Second, while we have data ranging from 2010–2020, the number of years of data per school district varies widely, so it was not possible to investigate the data longitudinally. Thus, we collapsed all years of data into one cross-sectional sample. Third, due to the unavailability of individual family income data, we had to rely on median household income from the census tract. While these estimates offer a population-level perspective at the neighborhood level, they may provide a better measure of the local resource context. To better approach an estimate of household income, we used neighborhood income estimates specific to the child’s ethnoracial background. Fourth, the study used five broad categories for ethnoracial background. The use of these categories enhances comparability and interpretability of our findings, given their resemblance to widely used classifications. The ‘other’ ethnoracial category does not represent a specific or cohesive population, so we cannot draw meaningful conclusions for this category. Future research should aim to further disaggregate ethnoracial groups, including understudied populations such as Native Americans. Finally, due to data limitations, we were unable to assess potential confounding factors such as parental mental health, education, marital status, employment, and other positive and negative influences. Consequently, further research is necessary to address these gaps.Life course health development studies clearly indicate that early inequities have long-term consequences, leading to exacerbated and severe health inequities in later stages of life. It is thus imperative to identify investments that can effectively and efficiently address health inequities as soon as possible. California’s recent early childhood investments present an extraordinary opportunity for the state to take bold action in addressing these glaring health development inequities. An important initial step is to utilize a sufficiently sensitive population-level measure such as the EDI to serve as a monitoring and accountability system that can illuminate the landscape of inequities and progress across California. This data system can then inform the design, implementation, and evaluation of place-based child health initiatives, allowing local leaders to tailor interventions based on the specific needs of their communities.A valid life course population measurement also provides a critical advancement by illuminating the health or vulnerability of ecosystems. Since government investments often prioritize a service sector framework, communities face the challenge of effectively braiding and bridging siloed funding streams and services. Synthesizing life course theory with population measurement allows stakeholders  to focus on the holistic well-being of a dynamic and adaptive ecosystem. By employing such an approach, data systems present new opportunities to embolden and mobilize stakeholders, enabling them to recognize the foundational and historical patterns of resilience within an ecosystem, thereby fostering widespread success and nurturing human flourishing.Adopting a life course population health measurement also serves as an added benefit of bringing together various stakeholders, including local governments, philanthropy organizations, local education agencies, and schools. By prioritizing targeted, place-based interventions instead of simply offering a universal, one-size-fits-all approach, this strategy enables all communities to collaborate towards a shared goal, without perpetuating advantages for privileged groups . Aiming Targeted universal programs for healthy development require data systems that can be used to set universal goals for health promotion. Traditionally, California’s state health surveillance systems have primarily focused on traditional public health indicators over multidimensional measures of health and well-being. While the state’s educational data has made significant improvements , these data have been primarily reported as a school measure and not as a robust ecosystem measure . The recAdditional file 1: Appendix A: Sample Flowchart.Additional file 2: Appendix B: Marginal Post-Estimation Graphs Representing the Confidence Intervals Across Income Groups by Ethnoracial Group Compared to the Weighted Average.


Circular RNAs (circRNAs) have recently been established as a new and large class of noncoding RNAs, conserved in all eukaryotes investigated. However, functional and mechanistic investi-gations lag behind the rapid progress in circRNA expression studies, and no uniform function has emerged so far. Here, we focus on circRNA–protein (circRNP) complexes (circRNPs), since proteins associated with natural circRNAs likely guide us in the search for physiological functions of individual circRNAs. First, approaches for how to systematically search for circRNA-interacting proteins in natural circRNPs will be summarized, including some experimental considerations important for good scientific practice. Second, building on natural protein-sponging circRNAs, we introduce the concept of designer circRNAs functioning as specific protein sponges, using the heterogeneous nuclear ribonucleoprotein L (hnRNP L) and IMP3 proteins as two examples of classical RNA-binding proteins. Third, we discuss the general implications of this concept, in particular how it can be applied as a new way to intervene with RNA-based gene-regulatory networks. In sum, designer circRNAs sponging specific proteins open up exciting new perspectives in molecular medicine and on therapeutic options to treat human disease.CircRNAs were first discovered in 1976 as important plant–pathogenic viroid RNAs, revealed in their circular configuration by electron microscopy EM; . SimilarFunctionally, however, circRNAs have remained largely enigmatic until today , with feFirst, one of the best-characterized circRNAs, ciRS-7 (or cdr1as) is abundantly expressed in the brain, conserved, and carries > 70 miR-7-binding sites per circRNA in humans, clearly functioning in miRNA-specific sponging . SimilarDrosophila derived from the second exon of the muscleblind (MBL) gene expressing the MBL splicing factor  by circRNAs has been documented in a few cases, first for circMBL, a circRNA in g factor . Linear-in-vitro-evolving circRNAs with predicted and optimized functions to be used for modulation of gene expression  separates macromolecular complexes by size and therefore allows the direct comparison of free and protein-bound RNAs. We have used this approach for identifying circRNPs present in cellular extracts and evaluating the shift in gradient mobility of specific circRNAs and their circRNPs. The extent of the shift yields initial information on the size of the circRNPs and the total molecular mass of the associated proteins .Second, as a protein-based approach, circRNPs can be selected by co-immunoprecipitation, which in combination with RNA-sequencing (RNA-seq) identifies subgroups of circRNPs associated with a protein of special interest. For example, we have revealed a group of circRNAs with the IMP3 protein bound as a stable component . In addiThird, a circRNA-based approach has been applied in a few instances, using affinity selection of circRNPs, using biotin-labelled circRNAs and streptavidin agarose, or biotin selection with antisense oligonucleotides . In thesbona fide miRNA sponges, each of which contains oligomeric binding sites for a single specific miRNA (see above). This principle can be directly transferred to protein-sponging circRNAs: stoichiometry of circRNA-binding sites on the circRNA and cellular concentrations and localization of the target protein, as well as the affinity of circRNA–protein interaction, are most important parameters for an efficient circRNA-based protein sponge , or another classical RBP, IMP3, recognizes RNA through two RRMs and four KH domains . Therefoin-vitro-generated circRNAs are usually purified and can be functionally studied initially by in vitro systems in extracts, or following transfection in cell culture, and ultimately in animal models. An obvious advantage thereby is that circRNA amounts and concentrations can be titrated, and dosis-dependent effects help in the functional analysis of gene expression. On the other hand, the circ-RNA expression by the Tornado system in cell culture, which involves a ribozymatic excision and an RtcB tRNA-ligase step, allows dramatic overexpression of both small and large circRNAs, up to millimolar levels in the cell, corresponding to >106 copies per cell , instead of comparing enrichment factors (fold-changes) relative to an unspecific control ; in contrast, in the negative control sequence with mutated elements, IMP3-binding affinities were strongly reduced . Although the binding affinities of this particular sponge for IMP3 protein are very similar for circRNA vs. corresponding linear isoform, this may differ in either direction for other examples. Note that the effectiveness of sponge circRNAs for in vivo applications is determined not only by protein-binding affinity but also by several other factors, such as stability, cellular localization, and protein-binding specificity.First, the domain structure of the multifunctional IMP3 protein consists of two N-terminal RRM domains, followed by four KH domains; how these multiple domains collaborate in RNA recognition has recently been studied by SELEX and structural approaches, revealing a cluster of five distinct RNA elements spread over ∼100 nucleotides . This vaurements . We founN40-degenerate sequence, which revealed distinct spacing preferences between individual short CA-rich elements  inclusion increased after expression of the L-12/10/12 sponge, whereas CC2D2A exon 6 (NM_001378617) inclusion decreased (compare L-12/10/12 ◯ and mut ◯). Comparable effects were observed for another short circRNA sponge (CA)20 (as a positive control), as previously described by 20 circRNA.To assess the functionality of the new, optimized L-12/10/12 circRNA sponge in splicing modulation, the circRNA  was transfected in HeLa cells, followed by RT–PCR assays of alternative splicing of two known hnRNP L target genes, ive exon : BPTF exThe general principle of designer circ-RNAs acting as protein sponges  has beenHow common this principle of circRNA-based protein sponging exists in nature remains still unknown. The autoregulation of MBL expression provides an example (see above), where alternative splicing generates linear and circular isoforms, although the exact circRNA–protein stoichiometry had not been investigated in that case. The classical multidomain-RBP IMP3 may be similarly integrated in such circRNA networks, since a subclass of natural circ-RNAs has been identified that specifically binds to IMP3 , and at in vivo, the cell-type specificity of such effects, and the impact of RNA modifications  have been developed as a new principle of interfering agents. Further investigations on the development and use of circRNAs for disease therapies have to consider the potential immunogenicity of newly added RNA agents Supplementary material is available at Journal of Molecular Cell Biology online. We thank Tim Schneider and all other lab members for helpful discussions. Funding is acknowledged from the Deutsche Forschungsgemeinschaft  and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska–Curie grant agreement No 721890 . A.B. filed a patent application on the use of designer circRNAs as protein sponges . S.S.N. and C.J.U. designed and carried out experiments; A.B., S.S.N., and C.J.U. wrote the manuscript.][mjad006_Supplemental_FileClick here for additional data file.


Digital technologies may address known physical and psychological barriers to recovery experienced by intensive care survivors following hospital discharge and provide solutions to care fragmentation and unmet needs. The review highlights recent examples of digital technologies designed to support recovery of survivors of critically illness.Despite proliferation of digital technologies supporting health in the community, there are relatively few examples for intensive care survivors. Those we identified included web-based, app-based or telemedicine-informed recovery clinics or pathways offering services, including informational resources, care planning and navigation support, medication reconciliation, and recovery goal setting. Digital interventions supporting psychological recovery included apps providing adaptive coping skills training, mindfulness, and cognitive behavioural therapy. Efficacy data are limited, although feasibility and acceptability have been established for some. Challenges include difficulties identifying participants most likely to benefit and delivery in a format easily accessible to all, with digital exclusion a resultant risk.Digital interventions supporting recovery comprise web or app-based recovery clinics or pathways and digital delivery of psychological interventions. Understanding of efficacy is relatively nascent, although several studies demonstrate feasibility and acceptability. Future research is needed but should be mindful of the risk of digital exclusion. Furthermore, fragmentation of healthcare services during recovery from critical illness is a common phenomenon .The USA-based STAR program for sepsis survivors  uses a c13https://www.kingshealthpartners.org/our-work/lifelines/information-for-families/information-on-the-digital-icu-recovery-pathway) designed and implemented an ICU digital recovery pathway into a large ICU recovery service in a tertiary centre in the UK. The pathway is hosted in the e-platform aTouchAway  and comprises e-forms for the assessment of baseline status and barriers to recovery; setting of individualized recovery goals; patient self-reported e-monitoring of goal achievement with automated reminders; provision of e-resources tailored to recovery barriers; patient recovery e-diary; tailored activity reminders; note function enabling the recovery coordinator to document patient encounters with optional electronic medical record upload; and (8) two-way digitally secure text, audio, and video communication between the patient, nominated family members, and the recovery coordinator (Fig. During the pandemic, our group Life Lines (tor Fig. . Selectiet al. [Telemedicine and telerehabilitation programmes also have been introduced for COVID-19 survivors, many of whom have experienced an ICU admission. In an Italian study, Bernocchi et al.  reportedet al. [et al. [Capin et al.  develope [et al. ▪ have usThe stresses of critical illness promote psychological distress symptoms, including depression , anxietyAs yet, there are few examples of effective interventions that alleviate ICU survivor distress. Rigorously conducted trials demonstrate no effect on PTSD, depression, or anxiety symptoms using either ICU-based interventions (e.g., music therapy , nurse-lAlthough there are numerous more generalized digital interventions promoting self-management of psychological distress symptoms, there are few examples designed specifically for ICU survivors following hospital discharge Fig. . One exaAnother example of a digital intervention to address psychological distress of ICU survivors is the month-long, self-directed mobile app-based mindfulness Lift intervention. In a pilot RCT  with 80 et al. [et al. [Other recent trials of digital technology-based interventions to promote psychological recovery include virtual reality and internet-based cognitive behavioural therapy. Vlake et al. ▪ found n [et al.  conducteSurvivors of an ICU admission experience numerous physical, psychological, and cognitive sequalae for which digital technologies may offer alternate interventions and delivery methods to traditional in-person recovery clinics. These in-person clinics in themselves are not offered consistently across regions or countries, if at all. Digital interventions described in the evidence base primarily comprise web or app-based recovery clinics or pathways addressing a range of ICU survivor needs; digital delivery of psychological interventions such as coping, mindfulness, or cognitive behavioural therapy; and use of virtual reality. Evidence in relation to the efficacy of such interventions is relatively nascent although several studies demonstrate feasibility and acceptability. Future research is needed to establish efficacy of digital technologies in supporting ICU survivor recovery but should also be mindful of the associated risk of digital exclusion.None.None.L.R. is a co-founder of Life Lines, a philanthropic COVID-19 rapid response project that received charitable donations to enable provision of over 1400 4G enabled Android tablets and a bespoke virtual visiting solution to ICUs across the UK. Major philanthropic contributors to Life Lines include Google, True Colours, and the Gatsby Trust. British Telecom contributed in-kind time and resources to facilitate the supply of 4G-enabled tablets to UK ICUs.Life Lines also received funding from the Guy's and St Thomas Hospital Foundation Trust for the development and implementation of the ICU digital recovery pathway described in this paper. L.R. has no financial or commercial interests in Life Lines or the ICU digital recovery pathway.C.C. received grant funding from the National Institutes of Health. He has no other financial or commercial interests.