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BACKGROUND: Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent app

Tenofovir alafenamide (TAF) is a novel prodrug that reduces tenofovir plasma levels by 90% compared to tenofovir disoproxil fumarate (TDF), resulting in decreased bone mineral density (BMD) loss and renal toxicity. We aimed to study changes in BMD and markers of renal function of chronic hepatitis B (CHB) patients previously treated with TDF who were switched to TAF in as early as 12 weeks. This was a prospective single-arm open-label study of 75 CHB patients treated with TDF 300 mg daily who we

To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion attack. Po

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research

BACKGROUND & AIMS: HBV nucleos(t)ide reverse transcriptase inhibitors (NrtIs) do not completely suppress HBV replication. Previous reports indicate persistent viremia during NrtI treatment despite HBV DNA being undetectable. HBV core inhibitors may enhance viral suppression when combined with NrtIs. This phase II trial (NCT03576066) evaluated the efficacy and safety of the investigational core inhibitor, vebicorvir (VBR), in virologically- suppressed patients on NrtIs. METHODS: Non-cirrhotic, Nr

Deep neural networks (DNNs) have achieved impressive results in several image classification tasks. However, these architectures are unstable for adversarial examples (AEs) such as inputs crafted by a hardly perceptible perturbation with the intent of causing neural networks to make errors. AEs must be considered to prevent accidents in areas such as unmanned car driving using visual object detection in Internet of Things (IoT) networks. Gaussian noise with label smoothing or logit squeezing can

Abstract Background and Aim Tenofovir disoproxil fumarate (TDF) has been efficacious in treating chronic hepatitis B (CHB), but long‐term use is accompanied by a decline in renal function and bone mineral density (BMD). Tenofovir alefanamide (TAF) is a prodrug of tenofovir, with similar efficacy in CHB but with fewer side effects than TDF. Recent studies on patients who underwent the switch from TDF to TAF have shown improved bone and renal profiles from 24 to 48 weeks of follow‐up. Methods This

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various me

Instantaneous drowsiness (i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject's drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial examples</i> . Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography becaus