Quantitative Analysis of the Effect of an Ectopic Beat on the Heart Rate Variability in the Resting Condition

The purpose of this study is to quantitatively analyze the effect of an ectopic beat on heart rate variability (HRV) in the time domain, frequency domain, and in a non-linear analysis. A quantitative analysis was carried out by generating artificial ectopic beats that probabilistically contained a missed beat or a false-detected beat, and the statistical significance was evaluated though a comparison with an ectopic-free HRV by increasing the ratio of the ectopic beat in 0.1% increments from 0 to 50%. The effect of the interpolation on the ectopic HRV was also investigated by applying nearest-neighbor interpolation, linear interpolation, and cubic spline interpolation. The results confirmed a statistically significant difference (P < 0.05) even in the less-than-1% ectopic HRV in every domain. When interpolation was applied, there were differences according to the interpolation method used, but statistical significance was secured for an ectopic beat ratio from 1 to 2% to several tens of a percent. In the effect, linear interpolation, and spline interpolation were confirmed to have a higher effect on the high-frequency related HRV variables, and nearest-neighbor interpolation had a higher effect on low-frequency related variables

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/

Cause of microfibers found in the domestic washing process of clothing; focusing on the manufacturing, wearing, and washing processes

To prepare measures for washing synthetic fibers, which cause proliferation of microplastics in the marine ecosystem, a fundamental analysis is required. Therefore, this study established an efficient method for quantitatively analyzing microfibers using artificial neural networks, comparing the amounts of microfibers generated in the manufacturing, wearing, and washing processes of clothing. The proportion of microfiber emitted during the manufacturing process was the largest (49%), followed by that emitted during the washing (28%) and wearing (23%) processes. This suggests that minimizing the amount of microfiber emitted during the manufacturing process is key to solving microfiber issues in the fashion industry. Additionally, during the wearing process, the amount of waterborne microfiber detected in washing was slightly larger than the amount of airborne microfiber. In the washing process, the washing temperature did not significantly affect microfiber emissions. However, when reducing the amount of water used or increasing the number of washings, microfiber emissions increased noticeably due to the greater friction applied to clothes. A common result of all experiments was that the largest proportion of microfibers was released during the first five washing cycles. Therefore, before wearing new items, consumers can minimize microfiber release by pre-washing using a laundry bag that filters microfibers. Furthermore, the most effective way to minimize microfibers is to eliminate them from the manufacturing process before they are distributed to consumers.This study was conducted with the support of LG Electronics (No. 2-2020-1139-001-1)

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

Smartphone-Based Bioelectrical Impedance Analysis Devices for Daily Obesity Management

Current bioelectric impedance analysis (BIA) systems are often large, cumbersome devices which require strict electrode placement on the user, thus inhibiting mobile capabilities. In this work, we developed a handheld BIA device that measures impedance from multiple frequencies (5 kHz~200 kHz) with four contact electrodes and evaluated the BIA device against standard body composition analysis systems: a dual-energy X-ray absorptiometry (DXA) system (GE Lunar Prodigy, GE Healthcare, Buckinghamshire, UK) and a whole-body BIA system (InBody S10, InBody, Co. Ltd, Seoul, Korea). In the study, 568 healthy participants, varying widely in body mass index, age, and gender, were recruited at two research centers: the Samsung Medical Center (SMC) in South Korea and the Pennington Biomedical Research Center (PBRC) in the United States. From the measured impedance data, we analyzed individual body fat and skeletal muscle mass by applying linear regression analysis against target reference data. Results indicated strong correlations of impedance measurements between the prototype pathways and corresponding InBody S10 electrical pathways (R = 0.93, p < 0.0001). Additionally, body fat estimates from DXA did not yield significant differences (p > 0.728 (paired t-test), DXA mean body fat 29.45 ± 10.77 kg, estimated body fat 29.52 ± 12.53 kg). Thus, this portable BIA system shows a promising ability to estimate an individual’s body composition that is comparable to large stationary BIA systems

<i>Macromonas nakdongensis</i> sp. nov., Isolated from Freshwater and Characterization of Bacteriophage BK-30P—The First Phage That Infects Genus <i>Macromonas</i>

A Gram-stain-negative, non-motile, non-pigmented, rod-shaped bacterium was isolated from a freshwater sample of Nakdong River in South Korea and designated as strain BK-30T. An analysis of the 16S rRNA gene sequence of strain BK-30T revealed its closest phylogenetic neighbors were members of the genus Macromonas. Specifically, the strain formed a robust clade with Macromonas bipunctata DSM 12705T, sharing 98.4% similarity in their 16S rRNA gene sequences. The average nucleotide identity value between strain BK-30T and M. bipunctata DSM 12705T was 79.8%, and the genome-to-genome distance averaged 21.3%, indicating the representation of a novel genomic species. Strain BK-30T exhibited optimum growth at 30 °C and pH 7.0, in the absence of NaCl. The major respiratory isoprenoid quinone identified was ubiquinone-8 (Q-8). The principal fatty acids detected were C16:1 ω7c and/or C16:1 ω6c (49.6%), C16:0 (27.5%), and C18:1 ω7c and/or C18:1 ω6c (9.2%). The DNA G+C content of the strain was determined to be 67.3 mol%. Based on these data, we propose a novel species within the genus Macromonas, named Macromonas nakdongensis sp. nov., to accommodate the bacterial isolate. Strain BK-30T is designated as the type strain (=KCTC 52161T = JCM 31376T = FBCC-B1). Additionally, we present the isolation and complete genome sequence of a lytic phage infecting strain BK-30T, named BK-30P. This bacteriophage is the first reported to infect Macromonas, leading us to propose the name “Macromonasphage”

Longitudinal Healthcare Data Management Platform of Healthcare IoT Devices for Personalized Services

Recently, many studies have been conducted on how to manage and analyze various types of health data such as clinical data, genomic data, and wirelessly collected multiple sensory data. In this paper, we propose a web-based healthcare data integration and management platform that collects heterogeneous types of health-related medical record as well as real-time lifelogging data. This platform provides flexible architecture to different types of data exchanges. The platform manages real-time data such as heart rate, blood pressure, and activity information extracted from various healthcare devices and provides functions to transmit them to the server. Then it analyses the risk based on a domain knowledge and individual differences by applying machine learning tools, then visualizes the result to the patient and doctor dynamically based on information simplification method. It also controls the data access authority concerning the level of expertise and role. For evaluation of integrated data analysis, we apply open database and evaluate the proposed risk analyser result. The proposed platform could be utilized for future healthcare service to share accumulated healthcare data in various situations

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism

Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak&ndash;end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively

MULTIPLE-CRITERIA DECISION-MAKING BASED ON PROBABILISTIC ESTIMATION WITH CONTEXTUAL INFORMATION FOR PHYSIOLOGICAL SIGNAL MONITORING

We propose a multiple-criteria decision-making (MCDM) method based on Maximum A Posteriori (MAP) estimation to analyze users' physiological status either normal or abnormal. The decision-making problem is formulated using MAP estimation and is turned out to be MCDM problem given the assumption that all probability density functions (pdfs) follow exponential forms, especially Gaussian. It indicates that this MCDM equation is decomposed into direct sum of group's physiological status distribution. Group distribution is estimated by probabilistic approach using population from the same age or same sex. For verification, we applied the proposed method to public heart rate database. According to experimental results, the proposed method considering group context reduced overall classification errors by 20.42% compared to typical decision-making (TDM) method. This method is applicable to various personalized health monitoring applications, which estimates user's physiological status by referring other group distribution without prior knowledge about previous health records.Multiple-criteria decision-making, probabilistic decision-making, group context, physiological signal monitoring