LAnoBERT : System Log Anomaly Detection based on BERT Masked Language Model

The system log generated in a computer system refers to large-scale data that are collected simultaneously and used as the basic data for determining simple errors and detecting external adversarial intrusion or the abnormal behaviors of insiders. The aim of system log anomaly detection is to promptly identify anomalies while minimizing human intervention, which is a critical problem in the industry. Previous studies performed anomaly detection through algorithms after converting various forms of log data into a standardized template using a parser. These methods involved generating a template for refining the log key. Particularly, a template corresponding to a specific event should be defined in advance for all the log data using which the information within the log key may get lost.In this study, we propose LAnoBERT, a parser free system log anomaly detection method that uses the BERT model, exhibiting excellent natural language processing performance. The proposed method, LAnoBERT, learns the model through masked language modeling, which is a BERT-based pre-training method, and proceeds with unsupervised learning-based anomaly detection using the masked language modeling loss function per log key word during the inference process. LAnoBERT achieved better performance compared to previous methodology in an experiment conducted using benchmark log datasets, HDFS, and BGL, and also compared to certain supervised learning-based models

Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews

As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.Comment: WASSA at ACL 202

DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training

Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past research efforts focused on enhancing DST performance through alterations to the model structure or integrating additional features like graph relations, they often require additional pre-training with external dialogue corpora. In this study, we propose DSTEA, improving Dialogue State Tracking via Entity Adaptive pre-training, which can enhance the encoder through by intensively training key entities in dialogue utterances. DSTEA identifies these pivotal entities from input dialogues utilizing four different methods: ontology information, named-entity recognition, the spaCy, and the flair library. Subsequently, it employs selective knowledge masking to train the model effectively. Remarkably, DSTEA only requires pre-training without the direct infusion of extra knowledge into the DST model. This approach resulted in substantial performance improvements of four robust DST models on MultiWOZ 2.0, 2.1, and 2.2, with joint goal accuracy witnessing an increase of up to 2.69% (from 52.41% to 55.10%). Further validation of DSTEA's efficacy was provided through comparative experiments considering various entity types and different entity adaptive pre-training configurations such as masking strategy and masking rate

Genome-scale CRISPR screening identifies cell cycle and protein ubiquitination processes as druggable targets for erlotinib-resistant lung cancer.

Erlotinib is highly effective in lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, despite initial favorable responses, most patients rapidly develop resistance to erlotinib soon after the initial treatment. This study aims to identify new genes and pathways associated with erlotinib resistance mechanisms in order to develop novel therapeutic strategies. Here, we induced knockout (KO) mutations in erlotinib-resistant human lung cancer cells (NCI-H820) using a genome-scale CRISPR-Cas9 sgRNA library to screen for genes involved in erlotinib susceptibility. The spectrum of sgRNAs incorporated among erlotinib-treated cells was substantially different to that of the untreated cells. Gene set analyses showed a significant depletion of \u27cell cycle process\u27 and \u27protein ubiquitination pathway\u27 genes among erlotinib-treated cells. Chemical inhibitors targeting genes in these two pathways, such as nutlin-3 and carfilzomib, increased cancer cell death when combined with erlotinib in both in vitro cell line and in vivo patient-derived xenograft experiments. Therefore, we propose that targeting cell cycle processes or protein ubiquitination pathways are promising treatment strategies for overcoming resistance to EGFR inhibitors in lung cancer

iCSDB: an integrated database of CRISPR screens.

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/

Pharmacokinetic Comparison of 2 Fixed-Dose Combination Tablets of Amlodipine and Valsartan in Healthy Male Korean Volunteers: A Randomized, Open-Label, 2-Period, Single-Dose, Crossover Study

AbstractBackgroundAmlodipine and valsartan have different mechanisms of action, and it is known that the combination therapy with the 2 drugs increases treatment effects compared with the monotherapy with each drug. A fixed-dose combination (FDC) drug is a formulation including fixed amounts of active drug ingredients combined in a single dosage form that is expected to improve medication compliance.ObjectiveThe goal of this study was to compare the pharmacokinetic profiles of single administration of a newly developed FDC tablet containing amlodipine orotate 10 mg and valsartan 160 mg (test formulation) with the conventional FDC tablet of amlodipine besylate 10 mg and valsartan 160 mg (reference formulation) in healthy male Korean volunteers.MethodsThis was a randomized, open-label, single-dose, 2-way crossover study. Eligible subjects were between the ages of 20 and 50 years and within 20% of their ideal weight. Each subject received a single dose of the reference and the test formulations, with a 14-day washout period between formulations. Blood samples were collected up to 144 hours after the dose, and pharmacokinetic parameters were determined for amlodipine and valsartan. Adverse events were evaluated based on subject interviews and physical examinations.ResultsForty-eight of the 50 enrolled subjects completed the study. For both amlodipine and valsartan, the primary pharmacokinetic parameters were included in the range for assumed bioequivalence, yielding 90% CI ratios of 0.9277 to 0.9903 for AUC0–last and 0.9357 to 1.0068 for Cmax in amlodipine, and 0.9784 to 1.1817 for AUC0–last and 0.9738 to 1.2145 for Cmax in valsartan. Dizziness was the most frequently noted adverse event, occurring in 4 subjects with the test formulation, followed by oropharyngeal pain occurring in 1 subject with the test formulation and 3 subjects with the reference formulation. All other adverse events occurred in <3 subjects.ConclusionsThese findings suggest that the pharmacokinetics of the newly developed FDC tablet of amlodipine and valsartan did not differ significantly from the conventional FDC tablet in these healthy Korean male subjects. Both formulations were well tolerated, with no serious adverse events observed. ClinicalTrials.gov identifier: NCT01823913

Prebiotic potential of green banana flour: impact on gut microbiota modulation and microbial metabolic activity in a murine model

IntroductionGreen banana flour can be used as a prebiotic due to its ability to promote gut health and provide several health benefits. In this study, we investigated whether feeding mice green banana flour at different doses would alter intestinal microbiota composition.MethodsWe fed C57BL/6N mice either a Low-dose (500 mg/kg/day) or High-dose (2000 mg/kg/day) of green banana flour daily for 3 weeks, and fecal samples were collected on days 0, 14, and 21 for microbiota analysis.ResultsOur results showed that the composition of intestinal microbiota was significantly altered by day 21, regardless of the dose. Notably, the consumption of green banana flour increased the presence of beneficial bacteria, including Coriobacteriaceae_UCG-002, Turicibacter, Parasutterella, Gastranaerophilales_ge, and RF39_ge. These changes in the intestinal microorganisms were accompanied by increased biological processes such as amino acid biosynthesis and secondary metabolite biosynthesis. Conversely, the consumption of green banana flour resulted in a decrease in biological processes related to carbohydrate degradation, glycerol degradation, and similar functions.DiscussionThese results emphasize the potential of green banana flour as a prebiotic that can benefit the gut microbiome