An optimized LSTM-based deep learning model for anomaly network intrusion detection.

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations.

Discussing Weight in Real World GP Consultations: A Video Recording Analysis Study.

Objective: GPs have a complex role in obesity management due to patients' individualized experience of living with obesity, coupled with the challenge to deliver healthcare messages in non-stigmatizing ways. This study aimed to explore who initiates the topic of weight and how weight was discussed in real-world GP-patient consultations.

Method: A multi-disciplinary team, including obesity lived experience experts, undertook a secondary data analysis of 43 Australian video recorded consultations and patient surveys from The Digital Library using descriptive content analysis.

Quantifying VMAT2 target occupancy at effective valbenazine doses and comparing to a novel VMAT2 inhibitor: a translational PET study.

Positron emission tomography (PET) is frequently used to obtain target occupancy (%TO) of central nervous system (CNS) drug candidates during clinical development. Obtaining %TO with PET can be particularly powerful when the %TO associated with efficacy is known for a protein target. Using the radiotracer [F]AV-133, the relationship between plasma concentration (PK) and %TO of NBI-750142, an experimental inhibitor of the vesicular monoamine transporter type 2 (VMAT2) was obtained in both nonhuman primate (NHP) and human.