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Endoglin (ENG) is a causative gene of type 1 hereditary hemorrhagic telangiectasia (HHT1). HHT1 patients have a higher prevalence of brain arteriovenous malformation (AVM) than the general population and patients with other HHT subtypes. The pathogenesis of brain AVM in HHT1 patients is currently unknown and no specific medical therapy is available to treat patients. Proper animal models are crucial for identifying the underlying mechanisms for brain AVM development and for testing new therapies. However, creating HHT1 brain AVM models has been quite challenging because of difficulties related to deleting Eng-floxed sequence in Eng2fl/2fl mice. To create an HHT1 brain AVM mouse model, we used several Cre transgenic mouse lines to delete Eng in different cell-types in Eng2fl/2fl mice: R26CreER (all cell types after tamoxifen treatment), SM22α-Cre (smooth muscle and endothelial cell) and LysM-Cre (lysozyme M-positive macrophage). An adeno-associated viral vector expressing vascular endothelial growth factor (AAV-VEGF) was injected into the brain to induce focal angiogenesis. We found that SM22α-Cre-mediated Eng deletion in the embryo caused AVMs in the postnatal brain, spinal cord, and intestines. Induction of Eng deletion in adult mice using R26CreER plus local VEGF stimulation induced the brain AVM phenotype. In both models, Eng-null endothelial cells were detected in the brain AVM lesions, and formed mosaicism with wildtype endothelial cells. However, LysM-Cre-mediated Eng deletion in the embryo did not cause AVM in the postnatal brain even after VEGF stimulation. In this study, we report two novel HHT1 brain AVM models that mimic many phenotypes of human brain AVM and can thus be used for studying brain AVM pathogenesis and testing new therapies. Further, our data indicate that macrophage Eng deletion is insufficient and that endothelial Eng homozygous deletion is required for HHT1 brain AVM development.

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

BackgroundThe orbitofrontal cortex (OFC) has consistently been implicated in the pathology of both drug and behavioral addictions. However, no study to date has examined OFC thickness in internet addiction. In the current study, we investigated the existence of differences in cortical thickness of the OFC in adolescents with internet addiction. On the basis of recently proposed theoretical models of addiction, we predicted a reduction of thickness in the OFC of internet addicted individuals.FindingsParticipants were 15 male adolescents diagnosed as having internet addiction and 15 male healthy comparison subjects. Brain magnetic resonance images were acquired on a 3T MRI and group differences in cortical thickness were analyzed using FreeSurfer. Our results confirmed that male adolescents with internet addiction have significantly decreased cortical thickness in the right lateral OFC (p<0.05).ConclusionThis finding supports the view that the OFC alterations in adolescents with internet addiction reflect a shared neurobiological marker of addiction-related disorders in general.

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole‐brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2‐, T2*‐, T1‐, and steady‐state‐based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non‐MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin.Evidence Level: 5Technical Efficacy: 1J. MAGN. RESON. IMAGING 2021;53:360‐373.

Patients harboring brain arteriovenous malformation (bAVM) are at life-threatening risk of rupture and intracranial hemorrhage (ICH). The pathogenesis of bAVM has not been completely understood. Current treatment options are invasive and ≈ 20% of patients are not offered interventional therapy because of excessive treatment risk. There are no specific medical therapies to treat bAVMs. The lack of validated animal models has been an obstacle for testing hypotheses of bAVM pathogenesis and testing new therapies. In this review, we summarize bAVM model development; and bAVM pathogenesis and potential therapeutic targets that have been identified during model development.

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

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