Rigid Formation Control on a Sphere: A Heterogeneous System Approach.

We consider a heterogeneous multiagent system for tracking multiple targets with a rigid formation on a unit sphere, where the targets and chasing agents are governed by single-and double-integrator models, respectively. To make asymptotic rendezvous between agents and their corresponding targets, we use an autonomous system consisting of attraction forces and velocity alignments. If the target's position, velocity, and acceleration information are available, we derive a multiagent system for complete rendezvous and obtain its exponential convergence result. If we have only the location and velocity information of the targets, we provide an autonomous system for practical rendezvous and the corresponding mathematical analysis. To prove the main results, we employ frame-rotation-structure decomposition for the double-integrator model and the geometric properties of a rigid formation on a sphere. We also provide numerical simulations to confirm our mathematical results and apply them to multiagent dynamics with a rigid formation that patrols the boundary line for a certain area on the sphere.

Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules.

The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigSMILES as an alternative method suitable for the representation of macromolecules. Nevertheless, research on BigSMILES remains limited due to its preprocessing requirements. Thus, this study proposes a conversion workflow of BigSMILES, focusing on its automated generation from SMILES representations of homopolymers. BigSMILES representations for 4,927,181 records are provided, thereby enabling its immediate use for various research and development applications. Our study presents detailed descriptions on a validation process to ensure the accuracy, interchangeability, and robustness of the conversion. Additionally, a systematic overview of utilized codes and functions that emphasizes their relevance in the context of BigSMILES generation are produced. This advancement is anticipated to significantly aid researchers and facilitate further studies in BigSMILES representation, including potential applications in deep learning and further extension to complex structures such as copolymers.

Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders.

Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.

Target Tracking Systems on a Sphere With Topographic Information.

We consider the target tracking problem on a sphere with topographic structure. For a given moving target on the unit sphere, we suggest a double-integrator autonomous system of multiple agents that track the given target under the topographic influence. Through this dynamic system, we can obtain a control design for target tracking on the sphere and the adapted topographic data provides an efficient agent trajectory. The topographic information, described as a form of friction in the double-integrator system, affects the velocity and acceleration of the target and agents. The target information required by the tracking agents consists of position, velocity, and acceleration. We can obtain practical rendezvous results when agents utilize only target position and velocity information. If the acceleration data of the target is accessible, we can get the complete rendezvous result using an additional control term in the form of the Coriolis force. We provide mathematically rigorous proofs for these results and present numerical experiments that can be visually confirmed.

Korean Validation of the Short Version of the TEMPS-A (Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire) in Patients with Mood Disorders.

BACKGROUND AND OBJECTIVES: The Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire (TEMPS-A) is designed to assess affective temperaments. The short version of the TEMPS-A (TEMPS-A-SV) has been translated into various languages for use in research and clinical settings. However, no research has been conducted to validate the Korean version of the TEMPS-A-SV in patients with mood disorders. The goal of this study is to evaluate the reliability and validity of the TEMPS-A-SV in Korean mood disorder patients. MATERIALS AND METHODS: In this cross-sectional retrospective study, a total of 715 patients (267 patients with major depressive disorder, 94 patients with bipolar disorder I, and 354 patients with bipolar disorder II) completed the Korean TEMPS-A-SV. Cronbach's alpha and McDonald's omega were used to assess the reliability. Exploratory factor analysis (EFA) was also performed. Spearman's correlation coefficient was used to examine associations between the five temperaments. The difference in five temperament scores between the gender or diagnosis groups was analyzed, and the correlation between five temperament scores and age was tested. RESULTS: The Korean TEMPS-A-SV displayed good internal consistency (α = 0.65-0.88, ω = 0.66-0.9) and significant correlations between the subscales except one (the correlation between hyperthymic and anxious). Using EFA, a two-factor structure was produced: Factor I (cyclothymic, depressive, irritable, and anxious) and Factor II (hyperthymic). The cyclothymic temperament score differed by gender and the anxious temperament score was significantly correlated with age. All the temperaments, except for irritable temperament, showed significant differences between diagnosis groups. CONCLUSIONS: Overall, the results show that the TEMPS-A-SV is a reliable and valid measurement that can be used for estimating Koreans' affective temperaments. However, more research is required on affective temperaments and associated characteristics in people with mood disorders.

Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary.

BACKGROUND: A computerized 12-lead electrocardiogram (ECG) can automatically generate diagnostic statements, which are helpful for clinical purposes. Standardization is required for big data analysis when using ECG data generated by different interpretation algorithms. The common data model (CDM) is a standard schema designed to overcome heterogeneity between medical data. Diagnostic statements usually contain multiple CDM concepts and also include non-essential noise information, which should be removed during CDM conversion. Existing CDM conversion tools have several limitations, such as the requirement for manual validation, inability to extract multiple CDM concepts, and inadequate noise removal. OBJECTIVES: We aim to develop a fully automated text data conversion algorithm that overcomes limitations of existing tools and manual conversion. METHODS: We used interpretations printed by 12-lead resting ECG tests from three different vendors: GE Medical Systems, Philips Medical Systems, and Nihon Kohden. For automatic mapping, we first constructed an ontology-lexicon of ECG interpretations. After clinical coding, an optimized tool for converting ECG interpretation to CDM terminology is developed using term-based text processing. RESULTS: Using the ontology-lexicon, the cosine similarity-based algorithm and rule-based hierarchical algorithm showed comparable conversion accuracy (97.8 and 99.6%, respectively), while an integrated algorithm based on a heuristic approach, ECG2CDM, demonstrated superior performance (99.9%) for datasets from three major vendors. CONCLUSION: We developed a user-friendly software that runs the ECG2CDM algorithm that is easy to use even if the user is not familiar with CDM or medical terminology. We propose that automated algorithms can be helpful for further big data analysis with an integrated and standardized ECG dataset.

Long-Term Effects of Ambient Particulate and Gaseous Pollutants on Serum High-Sensitivity C-Reactive Protein Levels: A Cross-Sectional Study Using KoGES-HEXA Data.

Ambient air pollutants reportedly increase inflammatory responses associated with multiple chronic diseases. We investigated the effects of long-term exposure to ambient air pollution on high-sensitivity C-reactive protein (hs-CRP) using data from 60,581 participants enrolled in the Korean Genome and Epidemiology Study-Health Examinees Study between 2012 and 2017. Community Multiscale Air Quality System with surface data assimilation was used to estimate the participants' exposure to criteria air pollutants based on geocoded residential addresses. Long-term exposure was defined as the 2-year moving average concentrations of PM10, PM2.5, SO2, NO2, and O3. Multivariable linear and logistic regression models were utilized to estimate the percent changes in hs-CRP and odds ratios of systemic low-grade inflammation (hs-CRP > 3 mg/L) per interquartile range increment in air pollutants. We identified positive associations between hs-CRP and PM10 (% changes: 3.75 [95% CI 2.68, 4.82]), PM2.5 (3.68, [2.57, 4.81]), SO2 (1.79, [1.10, 2.48]), and NO2 (3.31, [2.12, 4.52]), while negative association was demonstrated for O3 (-3.81, [-4.96, -2.65]). Elevated risks of low-grade inflammation were associated with PM10 (odds ratio: 1.07 [95% CI 1.01, 1.13]), PM2.5 (1.08 [1.02, 1.14]), and SO2 (1.05 [1.01, 1.08]). The odds ratios reported indicated that the exposures might be risk factors for inflammatory conditions; however, they did not reflect strong associations. Our findings suggest that exposure to air pollutants may play a role in the inflammation process.

An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks.

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.

Preparation and Characterization of Carbon Fibers from Lyocell Precursors Grafted with Polyacrylamide via Electron-Beam Irradiation.

Carbon fibers, which act as reinforcements in many applications, are often obtained from polyacrylonitrile (PAN). However, their production is expensive and results in waste problems. Therefore, we focused on producing carbon fibers from lyocell, a cellulose-based material, and analyzed the effects of the process parameters on their mechanical properties and carbon yields. Lyocell was initially grafted with polyacrylamide (PAM) via electron-beam irradiation (EBI) and was subsequently stabilized and carbonized. Thermal analysis showed that PAM grafting increased the carbon yields to 20% at 1000 °C when compared to that of raw lyocell, which degraded completely at about 600 °C. Stabilization further increased this yield to 55%. The morphology of the produced carbon fibers was highly dependent on PAM concentration, with fibers obtained at concentrations ≤0.5 wt.% exhibiting clear, rigid, and round cross-sections with smooth surfaces, whereas fibers obtained from 2 and 4 wt.% showed peeling surfaces and attachment between individual fibers due to high viscosity of PAM. These features affected the mechanical properties of the fibers. In this study, carbon fibers of the highest tensile strength (1.39 GPa) were produced with 0.5 wt.% PAM, thereby establishing the feasibility of using EBI-induced PAM grafting on lyocell fabrics to produce high-performance carbon fibers with good yields.

Flexible amorphous metal-organic frameworks with π Lewis acidic pore surface for selective adsorptive separations.

Selective separation of light hydrocarbons (LHs) and adsorption of volatile organic compounds (VOCs) remain expensive and complex scientific challenges in the petrochemical industry. Shape-selective adsorbent materials can cost-effectively face these demands. Two new porous, dynamic and amorphous metal-organic frameworks (MOFs), NEU-3 [= Zn(PMDA)(Py)2] and NEU-4 [= Fe(PMDA)(Py)2] are disclosed. These MOFs along with NEU-1c [= Zn(BPDI)(Py)2] and NEU-2 [= Fe(BPDI)(Py)2] display an electron-deficient pore surface due to predesigned π-electron-deficient ligands. They are unique smart guest-responsive materials owing to their π Lewis acidic pore surface and presumably their framework flexibility. A variety of effective adsorptions and adsorptive separations is achieved by using beds of NEU-1c, NEU-2, NEU-3 and NEU-4. Promising for further investigations into the petrochemical industry, NEU-4 shows ultrahigh benzene adsorption, recognition capability, selectivity for benzene over its analogues, and high stability and regenerability.

Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.

Yeungnam University type drive-through (YU-Thru) coronavirus disease 2019 (COVID-19) screening system: a rapid and safe screening system.

Active and prompt scale-up screening tests are essential to efficiently control the coronavirus disease 2019 (COVID-19) outbreak. The goal of this work was to identify shortcomings in the conventional screening system (CSS) implemented in the beginning of the outbreak. To overcome these shortcomings, we then introduced a novel, independently developed system called the Yeungnam University type drive-through (YU-Thru), and distributed it nationwide in Korea. This system is similar to the drive-throughs utilized by fast food restaurants. YU-Thru system has shortened the time taken to test a single person to 2-4 minutes, by completely eliminating the time required to clean and ventilate the specimen collection room. This time requirement was a major drawback of the CSS. YU-Thru system also reduced the risk of subjects and medical staff infecting one another by using a separate and closed examination system. On average, 50 to 60 tests were conducted per day when using the CSS, while now up to 350 tests per day are conducted with the YU-Thru system. We believe that the YU-Thru system has made an important contribution to the rapid detection of COVID-19 in Daegu, South Korea. Here, we will describe the YU-Thru system in detail so that other countries experiencing COVID-19 outbreaks can take advantage of this system.

Rational Synthesis of a Hierarchical Supramolecular Porous Material Created via Self-Assembly of Metal-Organic Framework Nanosheets.

Hierarchically porous materials with high stability and tailorable pore characters have potential for mass transfer applications, including bulky molecule capture and separation, heterogeneous catalysis, and drug delivery. The scope of functionalities can be notably broadened by employing metal-organic framework (MOF) sheets with tunable thickness as giant molecular building blocks for self-assembly into hierarchical supramolecular porous coordination materials. However, synthesizing MOF sheets with controllable bulkiness has proved challenging for scientists. We present a rational yet unprecedented bottom-up strategy to prepare a novel two-dimensional MOF sheet [Zn(BPDI)(Py)2] (BPDI = N,N'-bis(glycinyl)pyromellitic diimide; Py = pyridine) with unusual and highly desired tunable thickness. These sheets self-organize into a unique three-dimensional supramolecular coordination material (NEU-1) with tailorable porosity. To assess its technological relevance, NEU-1c is tested as a support of amine sorbent for CO2 capture. Multichannel porous NEU-1c solves the conventional trade-off suffered by supported amine carbon dioxide adsorbents between increasing amine content and decreasing access to amine sites. Our synthesis process opens the door to novel MOF nanosheets and unique hierarchical supramolecular porous materials with tailorable porosity.

Clinical Implication of Concordant or Discordant Genomic Profiling between Primary and Matched Metastatic Tissues in Patients with Colorectal Cancer.

PURPOSE: The purpose of this study was to identify the concordant or discordant genomic profiling between primary and matched metastatic tumors in patients with colorectal cancer (CRC) and to explore the clinical implication. MATERIALS AND METHODS: Surgical samples of primary and matched metastatic tissues from 158 patients (335 samples) with CRC at Korea University Anam Hospital were evaluated using the Ion AmpliSeq Cancer Hotspot Panel. We compared genetic variants and classified them as concordant, primary-specific, and metastasis-specific variants. We used a combination of principal components analysis and clustering to find genomic groups. Kaplan-Meier curves were used to appraise survival between genomic groups. We used machine learning to confirm the correlation between genetic variants and metastatic sites. RESULTS: A total of 282 types of deleterious non-synonymous variants were selected for analysis. Of a total of 897 variants, an average of 40% was discordant. Three genomic groups were yielded based on the genomic discrepancy patterns. Overall survival differed significantly between the genomic groups. The poorest group had the highest proportion of concordant KRAS G12V and additional metastasis-specific SMAD4. Correlation analysis between genetic variants and metastatic sites suggested that concordant KRAS mutations would have more disseminated metastases. CONCLUSION: Driver gene mutations were mostly concordant; however, discordant or metastasis-specific mutations were present. Clinically, the concordant driver genetic changes with additional metastasis-specific variants can predict poor prognosis for patients with CRC.

Implementation of Combinatorial Genetic and Microenvironmental Engineering to Microbial-Based Field-Deployable Microbead Biosensors for Highly Sensitive and Remote Chemical Detection.

Bioreporters, microbial species genetically engineered to provide measurable signals in response to specific chemicals, have been widely investigated as sensors for biomedical and environmental monitoring. More specifically, the bioreporter encapsulated within a biocompatible material, such as a hydrogel that can provide a suitable microenvironment for its prolonged activity as well as efficient scalable production, has been viewed as a more broadly applicable mode of biosensors. In this study, alginate-based microbeads encapsulated with the bacterial bioreporter capable of expressing green fluorescence protein in response to nitro compounds (e.g., trinitrotoluene and dinitrotoluene) are developed as biosensors. To significantly enhance the sensitivity of the microbial-based microbead biosensors, "multifaceted" modification strategies are simultaneously employed: (1) multiple genetic modifications of the bioreporter, (2) tuning the physicomechanical properties of the encapsulating microbeads, (3) controlling the initial cell density within the microbeads, and (4) enrichment of nitro compounds inside microbeads via functional nanomaterials. These microbial and microenvironmental engineering approaches combine to significantly enhance the sensing capability, even allowing highly sensitive remote detection under a low-vapor phase. Thus, the strategy developed herein is expected to contribute to various cell-based biosensors.

Integrated transcriptomes throughout swine oestrous cycle reveal dynamic changes in reproductive tissues interacting networks.

Female fertility is a highly regulated process involving the synchronized activities of multiple tissues. The underlying genomic regulation of the tissue synchronization is poorly understood. To understand this better we investigated the transcriptomes of the porcine ovary, endometrium, and oviduct at days 0, 3, 6, 9, 12, 15, or 18 of the oestrous cycle. We analysed the transcriptome profiles of the individual tissues and focus on the bridging genes shared by two or more tissues. The three tissue-networks were connected forming a triangular shape. We identified 65 bridging genes with a high level of connectivity to all other genes in the network. The expression levels showed negative correlations between the ovary and the other two tissues, and low correlations between endometrium and oviduct. The main functional annotations involved biosynthesis of steroid hormones, cell-to-cell adhesion, and cell apoptosis, suggesting that regulation of steroid hormone synthesis and tissue viability are major regulatory mechanisms.

Expression and regulation of interleukin 6 and its receptor at the maternal-conceptus interface during pregnancy in pigs.

It has been well established that interleukin 6 (IL6), a pleiotropic cytokine with multiple functional roles, is widely expressed in the female reproductive tract and mediates blastocyst implantation and placental development in many species. Uterine expression of IL6 during early pregnancy has been studied in pigs, but expression and function of IL6 at the maternal-placental interface throughout pregnancy have not been determined. Thus, we examined expression of IL6 and its receptors, IL6 receptor (IL6R) and GP130, in the uterine endometrium on Days 12 and 15 of the estrous cycle, and Days 12, 15, 30, 60, 90, and 114 of pregnancy, conceptus on Days 12 and 15, and chorioallantoic tissues on Days 30, 60, 90, and 114 of pregnancy in pigs. The expression of IL6, IL6R, and GP130 mRNA in the endometrial tissues increased dramatically during mid-to late-pregnancy and decreased at term. IL6, IL6R, and GP130 mRNAs were also expressed in conceptus and chorioallantoic tissues. Expression of IL6 mRNA was mainly localized to endometrial epithelial and stromal cells and chorioallantoic tissues, while IL6R and GP130 mRNAs were localized to glandular epithelial cells during pregnancy. The expression of IL6 mRNA was decreased by estrogen and progesterone treatment, whereas increasing doses of IL1ß induced the expression of IL6 mRNA, but not IL6R and GP130 mRNAs, in endometrial tissue explants. These results indicate that expression of IL6 and its receptors at the feto-maternal interface is regulated in a stage- and cell-type-specific manner during pregnancy, suggesting that IL6 and its receptor signaling system may play an important role in the maintenance of pregnancy in pigs.

A Discrete Velocity Kinetic Model with Food Metric: Chemotaxis Traveling Waves.

We introduce a mesoscopic scale chemotaxis model for traveling wave phenomena which is induced by food metric. The organisms of this simplified kinetic model have two discrete velocity modes, [Formula: see text] and a constant tumbling rate. The main feature of the model is that the speed of organisms is constant [Formula: see text] with respect to the food metric, not the Euclidean metric. The uniqueness and the existence of the traveling wave solution of the model are obtained. Unlike the classical logarithmic model case there exist traveling waves under super-linear consumption rates and infinite population pulse-type traveling waves are obtained. Numerical simulations are also provided.

Functionalization of Metal-Organic Frameworks for Enhanced Stability under Humid Carbon Dioxide Capture Conditions.

Metal-organic frameworks (MOFs) have been highlighted recently as promising materials for CO2 capture. However, in practical CO2 capture processes, such as capture from flue gas or ambient air, the adsorption properties of MOFs tend to be harmed by the presence of moisture possibly because of the hydrophilic nature of the coordinatively unsaturated sites (CUSs) within their framework. In this work, the CUSs of the MOF framework are functionalized with amine-containing molecules to prevent structural degradation in a humid environment. Specifically, the framework of the magnesium dioxybenzenedicarboxylate (Mg/DOBDC) MOF was functionalized with ethylenediamine (ED) molecules to make the overall structure less hydrophilic. Structural analysis after exposure to high-temperature steam showed that the ED-functionalized Mg/DOBDC (ED-Mg/DOBDC) is more stable under humid conditions, than Mg/DOBDC, which underwent drastic structural changes. ED-Mg/DOBDC recovered its CO2 adsorption capacity and initial adsorption rate quite well as opposed to the original Mg/DOBDC, which revealed a significant reduction in its capture capacity and kinetics. These results suggest that the amine-functionalization of the CUSs is an effective way to enhance the structural stability of MOFs as well as their capture of humid CO2 .

Amine-Oxide Hybrid Materials for CO2 Capture from Ambient Air.

Oxide supports functionalized with amine moieties have been used for decades as catalysts and chromatographic media. Owing to the recognized impact of atmospheric CO2 on global climate change, the study of the use of amine-oxide hybrid materials as CO2 sorbents has exploded in the past decade. While the majority of the work has concerned separation of CO2 from dilute mixtures such as flue gas from coal-fired power plants, it has been recognized by us and others that such supported amine materials are also perhaps uniquely suited to extract CO2 from ultradilute gas mixtures, such as ambient air. As unique, low temperature chemisorbents, they can operate under ambient conditions, spontaneously extracting CO2 from ambient air, while being regenerated under mild conditions using heat or the combination of heat and vacuum. This Account describes the evolution of our activities on the design of amine-functionalized silica materials for catalysis to the design, characterization, and utilization of these materials in CO2 separations. New materials developed in our laboratory, such as hyperbranched aminosilica materials, and previously known amine-oxide hybrid compositions, have been extensively studied for CO2 extraction from simulated ambient air (400 ppm of CO2). The role of amine type and structure (molecular, polymeric), support type and structure, the stability of the various compositions under simulated operating conditions, and the nature of the adsorbed CO2 have been investigated in detail. The requirements for an effective, practical air capture process have been outlined and the ability of amine-oxide hybrid materials to meet these needs has been discussed. Ultimately, the practicality of such a "direct air capture" process is predicated not only on the physicochemical properties of the sorbent, but also how the sorbent operates in a practical process that offers a scalable gas-solid contacting strategy. In this regard, the utility of low pressure drop monolith contactors is suggested to offer a practical mode of amine sorbent/air contacting for direct air capture.