A Review of Intrusion Detection Technology Based on Deep Rein-forcement Learning

With the rapid development of modern science and technology, all kinds of network attacks are updated constantly. Therefore, the traditional network security defense mechanism needs to be further improved. Through extensive investigation, this paper presents the latest work of network intrusion detection technology based on deep learning. Firstly, this paper introduces the related concepts of network intrusion detection technology. On this basis, we further evaluate the performance of three common deep learning models in intrusion detection, and conclude that DBN algorithm has some strong advantages. Afterwards, it also puts forward several improvement strategies of intrusion detection models

Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition

Automatic emotion recognition from speech, which is an important and challenging task in the field of affective computing, heavily relies on the effectiveness of the speech features for classification. Previous approaches to emotion recognition have mostly focused on the extraction of carefully hand-crafted features. How to model spatio-temporal dynamics for speech emotion recognition effectively is still under active investigation. In this paper, we propose a method to tackle the problem of emotional relevant feature extraction from speech by leveraging Attention-based Bidirectional Long Short-Term Memory Recurrent Neural Networks with fully convolutional networks in order to automatically learn the best spatio-temporal representations of speech signals. The learned high-level features are then fed into a deep neural network (DNN) to predict the final emotion. The experimental results on the Chinese Natural Audio-Visual Emotion Database (CHEAVD) and the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpora show that our method provides more accurate predictions compared with other existing emotion recognition algorithms

The importance of the Indo-Pacific humpback dolphin (<i>Sousa chinesis</i>) population of Sanniang bay, Guangxi Province, PR China: recommendations for habitat protection. Scientific Committee Document SC/58/SM18, International Whaling Commission, May-June 2006, St.Kitts

During the period June 2004 - January 2006, a research team from the Qinzhou Bay Chinese White Dolphins Research Center of Peking University, the Peoples Republic of China, conducted systematic and opportunistic boat surveys of Sanniang Bay, Guangxi Province, in which Indo-Pacific humpback dolphins Sousa chinensis were regularly seen. Ninety eight dolphins were photographically identified. The dolphins appear to inhabit a small, shallow area of core habitat within the greater Sanniang Bay area. They do not appear to travel up the two rivers which are located to each side of the bay. Of the five populations known from the coastal area of China, the one that resides in Sanniang Bay is determined as having the least impact from anthropogenic activities. The area itself has been designated as a nature tourism location and considerable effort and money has been spent on developing appropriate tourist facilities. The dolphin watching industry in the area is strictly monitored and controlled by one local authority. The largest estuary adjacent to Sanniang Bay has been allocated for industrial development and a paper pulp mill will be established there. Considering the investment already made in the nature tourism industry, the natural beauty of the bay and the surrounding area and the likelihood that this is the only population of Indo-Pacific humpback dolphins which remain in uncompromised and relatively pristine habitat in all of China, it is urged that all effort be made to maintain the natural integrity of the bay. It is recommended that all development and operational aspects of the paper pulp be thoroughly scrutinized and all efforts made to minimize impact upon the environment and that all current and future industries and activities in this area must not detrimentally impact the dolphin population or compromise the integrity of the bay ecosystem

The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology

In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for the domain of ncRNAs, thereby facilitating the discovery, curation, analysis, exchange, and reasoning of data about structures of ncRNAs, their molecular and cellular functions, and their impacts upon phenotypes. The goal of NCRO is to serve as a common resource for annotations of diverse research in a way that will significantly enhance integrative and comparative analysis of the myriad resources currently housed in disparate sources. It is our belief that the NCRO ontology can perform an important role in the comprehensive unification of ncRNA biology and, indeed, fill a critical gap in both the Open Biological and Biomedical Ontologies (OBO) Library and the National Center for Biomedical Ontology (NCBO) BioPortal. Our initial focus is on the ontological representation of small regulatory ncRNAs, which we see as the first step in providing a resource for the annotation of data about all forms of ncRNAs. The NCRO ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/ncro.owl

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p &lt; 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients

Research advances in the therapeutic potential of xanthine oxidoreductase inhibitors for periodontitis

Periodontitis is associated with abnormal purine metabolism, which is manifested by increased uric acid in host blood and increased expression of the purine-degrading enzyme, xanthine oxidoreductase (XOR), in periodontal tissues. Both XOR and uric acid are pro-oxidative and pro-inflammatory mediators under pathological conditions. Animal studies have found that injection of uric acid promotes the progression of periodontitis and that febuxostat (an XOR inhibitor) improves tissue destruction in periodontitis. Therefore, blocking the source of uric acid may be a therapeutic strategy to control the progression of periodontitis. In this article, the rationality of XOR inhibitors as potential therapeutic drugs for periodontitis is reviewed. The literature review results suggest that XOR inhibitors show antioxidative, anti-inflammatory, and anti-osteoclastic effects, and XOR inhibitors show clinical efficacy in the treatment of infectious, inflammatory and osteolytic diseases. Although there is no direct evidence to support the finding that XOR inhibitors can ameliorate periodontal microecological dysbiosis, these drugs can modulate intestinal microflora dysbiosis, and there is indirect evidence to support a beneficial effect of XOR inhibitors on periodontal microecological dysbiosis. In conclusion, XOR inhibitors may be used as immunomodulators for the adjuvant treatment of periodontitis by inhibiting inflammation, oxidative stress and anti-osteoclast effects

Prediction of five types of general surgical complications based on Logistic regression model

Objective To describe the occurrence of general surgical complications and establish prediction models for different types of complications based on a multicenter cohort study. Methods Based on Modern Surgery and Anesthesia Safety Management System Construction and Promotion(MSCP), patients who underwent general surgery in 4 hospitals from January to June 2015 and from January to June 2016 were selected as participants, and perioperative data of patients were collected. Logistic regression was used to identify risk factors and predict complications. Results Among 19 223 patients, 830(4.32%) had complications. Among participants who had complications, 371(44.70%) had incision complications, 190(22.89%) had fistula complications, 310(37.35%) had infection complications, 161(19.40%) had failure complications, and 104(12.5%) died. There were significant differences in the risk factors of different types of complications. The risk factors of incision,infection and failure covered the whole perioperative period, while fistula complications mainly focused on the difficulty of surgery and postoperative treatment, and for death outcomes, postoperative risk factors were more severe than preoperative risk factors. The areas under the curves of prediction were between 0.80-0.93. Conclusions In general surgery, different types of complications have different risk factors. The targeted prediction model can avoid the rough simplification of complications and fuzzy influence of each factor, and can provide reference for the prevention of complications