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Objective To evaluate the strengthens and feasibility of network-based teaching for clinical training in neonatology during COVID-19 pandemic. Methods Because of COVID-19 pandemic, the interns of Peking Union Medical College Hospital accepted the network-based teaching in the training of neonatology from February to May 2020. Compared with those who received traditional training from November 2019 to January 2020, the Systematic Evatuation of Teaching Quality(SETQ) questionnaire and Pediatric Voluntary Self-assessment Forms were conducted to evaluate the quality of teaching and of knowledge mastery. Results There were no significant differences in teaching quality between two groups. While network-based teaching has favored knowledge mastery for rare diseases, complicated clinical operation and the interpretation of auxiliary examinations (P＜0.05). Conclusions Network-based teaching and the traditional pathway have their strengthens respectively and can be complementary in the intern training of neonatology.

The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms.

DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems’ security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD’s ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM.

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

Summary: Autoimmunity in plants has been found in numerous hybrids as a form of hybrid necrosis and mutant panels. Uncontrolled cell death is a main cellular outcome of autoimmunity, which negatively impacts growth. Its occurrence highlights the vulnerable nature of the plant immune system. Genetic investigation of autoimmunity in hybrid plants revealed that extreme variation in the immune receptor repertoire is a major contributor, reflecting an evolutionary conundrum that plants face in nature. In this review, we discuss natural variation in the plant immune system and its contribution to fitness. The value of autoimmunity genetics lies in its ability to identify combinations of a natural immune receptor and its partner that are predisposed to triggering autoimmunity. The network of immune components for autoimmunity becomes instrumental in revealing mechanistic details of how immune receptors recognize cellular invasion and activate signaling. The list of autoimmunity‐risk variants also allows us to infer evolutionary processes contributing to their maintenance in the natural population. Our approach to autoimmunity, which integrates mechanistic understanding and evolutionary genetics, has the potential to serve as a prognosis tool to optimize immunity in crops. [ABSTRACT FROM AUTHOR]

Summary: Pattern recognition receptors (PRRs) sense microbial patterns and activate innate immunity against attempted microbial invasions. The leucine‐rich repeat receptor kinases (LRR‐RK) FLS2 and EFR, and the LRR receptor protein (LRR‐RP) receptors RLP23 and RLP42, respectively, represent prototypical members of these two prominent and closely related PRR families.We conducted a survey of Arabidopsis thaliana immune signaling mediated by these receptors to address the question of commonalities and differences between LRR‐RK and LRR‐RP signaling.Quantitative differences in timing and amplitude were observed for several early immune responses, with RP‐mediated responses typically being slower and more prolonged than those mediated by RKs. Activation of RLP23, but not FLS2, induced the production of camalexin. Transcriptomic analysis revealed that RLP23‐regulated genes represent only a fraction of those genes differentially expressed upon FLS2 activation. Several positive and negative regulators of FLS2‐signaling play similar roles in RLP23 signaling. Intriguingly, the cytoplasmic receptor kinase BIK1, a positive regulator of RK signaling, acts as a negative regulator of RP‐type immune receptors in a manner dependent on BIK1 kinase activity.Our study unveiled unexpected differences in two closely related receptor systems and reports a new negative role of BIK1 in plant immunity. [ABSTRACT FROM AUTHOR]