Modulation of Bax and mTOR for Cancer Therapeutics.

A rationale exists for pharmacologic manipulation of the serine (S)184 phosphorylation site of the proapoptotic Bcl2 family member Bax as an anticancer strategy. Here, we report the refinement of the Bax agonist SMBA1 to generate CYD-2-11, which has characteristics of a suitable clinical lead compound. CYD-2-11 targeted the structural pocket proximal to S184 in the C-terminal region of Bax, directly activating its proapoptotic activity by inducing a conformational change enabling formation of Bax homooligomers in mitochondrial membranes. In murine models of small-cell and non-small cell lung cancers, including patient-derived xenograft and the genetically engineered mutant KRAS-driven lung cancer models, CYD-2-11 suppressed malignant growth without evident significant toxicity to normal tissues. In lung cancer patients treated with mTOR inhibitor RAD001, we observed enhanced S184 Bax phosphorylation in lung cancer cells and tissues that inactivates the propaoptotic function of Bax, contributing to rapalog resistance. Combined treatment of CYD-2-11 and RAD001 in murine lung cancer models displayed strong synergistic activity and overcame rapalog resistanc

BGP Dataset-Based Malicious User Activity Detection Using Machine Learning

Recent advances in the Internet and digital technology have brought a wide variety of activities into cyberspace, but they have also brought a surge in cyberattacks, making it more important than ever to detect and prevent cyberattacks. In this study, a method is proposed to detect anomalies in cyberspace by consolidating BGP (Border Gateway Protocol) data into numerical data that can be trained by machine learning (ML) through a tokenizer. BGP data comprise a mix of numeric and textual data, making it challenging for ML models to learn. To convert the data into a numerical format, a tokenizer, a preprocessing technique from Natural Language Processing (NLP), was employed. This process goes beyond merely replacing letters with numbers; its objective is to preserve the patterns and characteristics of the data. The Synthetic Minority Over-sampling Technique (SMOTE) was subsequently applied to address the issue of imbalanced data. Anomaly detection experiments were conducted on the model using various ML algorithms such as One-Class Support Vector Machine (One-SVM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), Random Forest (RF), and Autoencoder (AE), and excellent performance in detection was demonstrated. In experiments, it performed best with the AE model, with an F1-Score of 0.99. In terms of the Area Under the Receiver Operating Characteristic (AUROC) curve, good performance was achieved by all ML models, with an average of over 90%. Improved cybersecurity is expected to be contributed by this research, as it enables the detection and monitoring of cyber anomalies from malicious users through BGP data

Targeting DNA Replication Stress for Cancer Therapy

The human cellular genome is under constant stress from extrinsic and intrinsic factors, which can lead to DNA damage and defective replication. In normal cells, DNA damage response (DDR) mediated by various checkpoints will either activate the DNA repair system or induce cellular apoptosis/senescence, therefore maintaining overall genomic integrity. Cancer cells, however, due to constitutive growth signaling and defective DDR, may exhibit “replication stress” —a phenomenon unique to cancer cells that is described as the perturbation of error-free DNA replication and slow-down of DNA synthesis. Although replication stress has been proven to induce genomic instability and tumorigenesis, recent studies have counterintuitively shown that enhancing replicative stress through further loosening of the remaining checkpoints in cancer cells to induce their catastrophic failure of proliferation may provide an alternative therapeutic approach. In this review, we discuss the rationale to enhance replicative stress in cancer cells, past approaches using traditional radiation and chemotherapy, and emerging approaches targeting the signaling cascades induced by DNA damage. We also summarize current clinical trials exploring these strategies and propose future research directions including the use of combination therapies, and the identification of potential new targets and biomarkers to track and predict treatment responses to targeting DNA replication stress

Research on Implementation of a PWM Generation Algorithm for Train Stationary Stopping Frequency

In industrial electronic equipment or communication equipment, a reference clock should be generated for stable operation of the equipment, which requires precise and stable reference frequency generation. As a method for generating this reference frequency, an analog method called PLL (phase locked-loop) has been devised and widely used. However, in order to make a more precise and stable reference frequency simple and economical, a DDS (direct digital synthesizer) has been developed. In this paper, we propose a stable and accurate method to generate a low frequency of the PWM method via pure logic circuit configuration without a microprocessor for digital reference frequency generation. Depending on the electronic communication equipment, the required reference frequency varies from a low frequency to a very high frequency. The reference frequency synthesis required in these frequency bands has been studied in various ways, but in industries such as railways, the low-frequency band based on the DDS method is used. In particular, it is very important to operate without a single operating error or failure in order to obtain information for stopping the train. Therefore, it is necessary to design a pure logic method that excludes a stored program type processor that minimizes the possibility of temporary interruption due to disturbance such as surge or high voltage. Therefore, through this study, the algorithm is implemented so that the duty ratio is output at 50:50, the circuit is configured so that two target frequencies are generated at the same time, and the performance is verified by generating the low-frequency band used for stopping the railway train. It was confirmed that the accuracy and stability were improved compared to the analog method used for stopping the railway train, and it was verified that the frequency resolution was superior to the similar results obtained in the digital frequency synthesis field so far

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

With advances in cyber threats and increased intelligence, incidents continue to occur related to new ways of using new technologies. In addition, as intelligent and advanced cyberattack technologies gradually increase, the limit of inefficient malicious code detection and analysis has been reached, and inaccurate detection rates for unknown malicious codes are increasing. Thus, this study used a machine learning algorithm to achieve a malicious file detection accuracy of more than 99%, along with a method for visualizing data for the detection of malicious files using the dynamic-analysis-based MITRE ATT&CK framework. The PE malware dataset was classified into Random Forest, Adaboost, and Gradient Boosting models. These models achieved accuracies of 99.3%, 98.4%, and 98.8%, respectively, and malicious file analysis results were derived through visualization by applying the MITRE ATT&CK matrix

Design and Implementation of Multi-Cyber Range for Cyber Training and Testing

It is essential to build a practical environment of the training/test site for cyber training and weapon system test evaluation. In a military environment, cyber training sites should be continuously developed according to the characteristics of the military. Weapons with cyber security capabilities should be deployed through cyber security certification. Recently, each military has been building its own cyber range that simulates its battlefield environment. However, since the actual battlefield is an integrated operation environment, the cyber range built does not reflect the integrated battlefield environment that is interconnected. This paper proposes a configuration plan and operation function to construct a multi-cyber range reflecting the characteristics of each military to overcome this situation. In order to test the multi-cyber range, which has scenario authoring and operation functions, and can faithfully reflect reality, the impact of DDoS attacks is tested. It is a key to real-world mission-based test evaluation to ensure interoperability between military systems. As a result of the experiment, it was concluded that if a DDoS attack occurs due to the infiltration of malicious code into the military network, it may have a serious impact on securing message interoperability between systems in the military network. Cyber range construction technology is being developed not only in the military, but also in school education and businesses. The proposed technology can also be applied to the construction of cyber ranges in industries where cyber-physical systems are emphasized. In addition, it is a field that is continuously developing with the development of technology, such as being applied as an experimental site for learning machine learning systems

Design and Implementation of Multi-Cyber Range for Cyber Training and Testing

It is essential to build a practical environment of the training/test site for cyber training and weapon system test evaluation. In a military environment, cyber training sites should be continuously developed according to the characteristics of the military. Weapons with cyber security capabilities should be deployed through cyber security certification. Recently, each military has been building its own cyber range that simulates its battlefield environment. However, since the actual battlefield is an integrated operation environment, the cyber range built does not reflect the integrated battlefield environment that is interconnected. This paper proposes a configuration plan and operation function to construct a multi-cyber range reflecting the characteristics of each military to overcome this situation. In order to test the multi-cyber range, which has scenario authoring and operation functions, and can faithfully reflect reality, the impact of DDoS attacks is tested. It is a key to real-world mission-based test evaluation to ensure interoperability between military systems. As a result of the experiment, it was concluded that if a DDoS attack occurs due to the infiltration of malicious code into the military network, it may have a serious impact on securing message interoperability between systems in the military network. Cyber range construction technology is being developed not only in the military, but also in school education and businesses. The proposed technology can also be applied to the construction of cyber ranges in industries where cyber-physical systems are emphasized. In addition, it is a field that is continuously developing with the development of technology, such as being applied as an experimental site for learning machine learning systems