Arctigenin from Forsythia viridissima Fruit Inhibits the Replication of Human Coronavirus.

Coronavirus can cause various diseases, from mild symptoms to the recent severe COVID-19. The coronavirus RNA genome is frequently mutated due to its RNA nature, resulting in many pathogenic and drug-resistant variants. Therefore, many medicines should be prepared to respond to the various coronavirus variants. In this report, we demonstrated that Forsythia viridissima fruit ethanol extract (FVFE) effectively reduces coronavirus replication. We attempted to identify the active compounds and found that actigenin from FVFE effectively reduces human coronavirus replication. Arctigenin treatment can reduce coronavirus protein expression and coronavirus-induced cytotoxicity. These results collectively suggest that arctigenin is a potent natural compound that prevents coronavirus replication.

Ultraefficient Electrocatalytic Hydrogen Evolution from Strain-Engineered, Multilayer MoS2.

This paper reports an approach to repurpose low-cost, bulk multilayer MoS2 for development of ultraefficient hydrogen evolution reaction (HER) catalysts over large areas (>cm2). We create working electrodes for use in HER by dry transfer of MoS2 nano- and microflakes to gold thin films deposited on prestrained thermoplastic substrates. By relieving the prestrain at a macroscopic scale, a tunable level of tensile strain is developed in the MoS2 and consequently results in a local phase transition as a result of spontaneously formed surface wrinkles. Using electrochemical impedance spectroscopy, we verified that electrochemical activation of the strained MoS2 lowered the charge transfer resistance within the materials system, achieving HER activity comparable to platinum (Pt). Raman and X-ray photoelectron spectroscopy show that desulfurization in the multilayer MoS2 was promoted by the phase transition; the combined effect of desulfurization and the lower charge resistance induced superior HER performance.

Graphene Buffer Layer on SiC as a Release Layer for High-Quality Freestanding Semiconductor Membranes.

Free-standing crystalline membranes are highly desirable owing to recent developments in heterogeneous integration of dissimilar materials. Van der Waals (vdW) epitaxy enables the release of crystalline membranes from their substrates. However, suppressed nucleation density due to low surface energy has been a challenge for crystallization; reactive materials synthesis environments can induce detrimental damage to vdW surfaces, often leading to failures in membrane release. This work demonstrates a novel platform based on graphitized SiC for fabricating high-quality free-standing membranes. After mechanically removing epitaxial graphene on a graphitized SiC wafer, the quasi-two-dimensional graphene buffer layer (GBL) surface remains intact for epitaxial growth. The reduced vdW gap between the epilayer and substrate enhances epitaxial interaction, promoting remote epitaxy. Significantly improved nucleation and convergent quality of GaN are achieved on the GBL, resulting in the best quality GaN ever grown on two-dimensional materials. The GBL surface exhibits excellent resistance to harsh growth environments, enabling substrate reuse by repeated growth and exfoliation.

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving.

LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles.

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either d y n a m i c or s t a t i c depending on their movement characteristics. A LiDAR point cloud is also segmented into d y n a m i c and s t a t i c points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into d y n a m i c and s t a t i c points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR's laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: d y n a m i c , s t a t i c , and u n k n o w n . Points are placed in the u n k n o w n class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.

Simultaneous Localization and Map Change Update for the High Definition Map-Based Autonomous Driving Car.

High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster⁻Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao⁻Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions.

Crowd-Sourced Mapping of New Feature Layer for High-Definition Map.

A High-Definition map (HD map) is a precise and detailed map composed of various landmark feature layers. The HD map is a core technology that facilitates the essential functions of intelligent vehicles. Recently, it has come to be required for the HD map to continuously add new feature layers in order to increase the performances of intelligent vehicles in more complicated environments. However, it is difficult to generate a new feature layer for the HD map, because the conventional method of generating the HD map based on several professional mapping cars has high costs in terms of time and money due to the need to re-drive on all of the public roads. In order to reduce these costs, we propose a crowd-sourced mapping process of the new feature layer for the HD map. This process is composed of two steps. First, new features in the environments are acquired from multiple intelligent vehicles. The acquired new features build each new feature layer in each intelligent vehicle using the HD map-based GraphSLAM approach, and these new feature layers are conveyed to a map cloud through a mobile network system. Next, the crowd-sourced new feature layers are integrated into a new feature layer in a map cloud. In the simulation, the performance of the crowd-sourced process is then analyzed and evaluated. Experiments in real driving environments confirm the results of the simulation.

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping.

Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment.

Molecular and morphological data reveal hybrid origin of wild Prunus yedoensis (Rosaceae) from Jeju Island, Korea: implications for the origin of the flowering cherry.

PREMISE OF THE STUDY: The subgenus Cerasus of the genus Prunus includes several popular ornamental flowering cherries. Of the hundreds of cultivars, P. ×yedoensis ('Somei-yoshino') is the most popular and familiar cultivar in Korea and Japan and is considered to be of hybrid origin. However, the hybrid origin of P. ×yedoensis and its relationship to wild P. yedoensis, naturally occurring on Jeju Island, Korea, are highly controversial. METHODS: We extensively sampled wild P. yedoensis, cultivated P. ×yedoensis, and numerous individuals from other species belonging to subgenus Cerasus on Jeju Island. Samples from 71 accessions, representing 13 species and one cultivar (P. ×yedoensis), were sequenced for nrDNA ITS/ETS (952 characters) and seven noncoding cpDNA regions (5421 characters) and subjected to maximum parsimony and maximum likelihood analysis. Additive polymorphisms in the ITS/ETS regions were confirmed by cloning amplicons from representative species. KEY RESULTS: The nuclear (ITS/ETS and G3pdh) and cpDNA data, along with several morphological characteristics, provide the first convincing evidence for the hybrid origin of wild P. yedoensis. The maternal parent was determined to be P. spachiana f. ascendens, while the paternal parent was unresolved from the taxonomically complex P. serrulata/P. sargentii clade. The presence of two kinds of ribotypes was confirmed by cloning, and the possible origin of cultivated P. ×yedoensis from wild populations on Jeju Island was also suggested. CONCLUSIONS: Bidirectional and multiple hybridization events were responsible for the origin of wild P. yedoensis. Extensive gene flow was documented in this study, suggesting an important role of reticulate evolution in subgenus Cerasus.

Estimation of the parameters of Burr type III distribution based on dual generalized order statistics.

The estimation of the parameters of Burr type III distribution based on dual generalized order statistics is considered by using the maximum likelihood (ML) approach as well as the Bayesian approach. The exact expression of the expected Fisher information matrix of the parameters in the distribution is obtained. Also, an approximation based on Lindley is used to obtain the Bayes estimator. To compare the maximum likelihood estimator and the Bayes estimator of the parameters, Monte Carlo simulation study is performed.

3D E-textile for Exercise Physiology and Clinical Maternal Health Monitoring.

Electronic textiles (E-textiles) offer great wearing comfort and unobtrusiveness, thus holding potential for next-generation health monitoring wearables. However, the practical implementation is hampered by challenges associated with poor signal quality, substantial motion artifacts, durability for long-term usage, and non-ideal user experience. Here, we report a cost-effective E-textile system that features 3D microfiber-based electrodes for greatly increasing the surface area. The soft and fluffy conductive microfibers disperse freely and securely adhere to the skin, achieving a low impedance at the electrode-skin interface even in the absence of gel. A superhydrophobic fluorinated self-assembled monolayer was deposited on the E-textile surface to render it waterproof while retaining the electrical conductivity. Equipped with a custom-designed motion-artifact canceling wireless data recording circuit, the E-textile system could be integrated into a variety of smart garments for exercise physiology and health monitoring applications. Real-time multimodal electrophysiological signal monitoring, including electrocardiogram (ECG) and electromyography (EMG), was successfully carried out during strenuous cycling and even underwater swimming activities. Furthermore, a multi-channel E-textile was developed and implemented in clinical patient studies for simultaneous real-time monitoring of maternal ECG and uterine EMG signals, incorporating spatial-temporal potential mapping capabilities.

Compound C inhibits the replication of feline coronavirus.

Feline Coronavirus (FCoV) is a viral pathogen of cats and a highly contagious virus. Cats in a cattery can be infected by up to 100%, and even household cats are infected by 20-60%. Some strains of FCoV are known to induce a fatal disease in cats named Feline Infectious Peritonitis (FIP). However, no effective treatments are available. We demonstrated that compound C (dorsomorphin) can potentially inhibit feline coronavirus replication. Compound C treatment decreased the FCoV-induced plaque formation and cytopathic effect in FCoV-infected cells. Compound C treatment also significantly reduced the amount of viral RNA and viral protein in the cells in a dose-dependent manner. Our findings suggest that compound C is potentially useful for feline coronavirus-related diseases.

Amentoflavone from Selaginella tamariscina inhibits SARS-CoV-2 RNA-dependent RNA polymerase.

The SARS-CoV-2 pandemic caused millions of deaths due to its prominent infectivity and mortality. Although the vaccines and medicines for SARS-CoV-2 are on the market, new coronavirus variants like influenza are expected to reemerge continuously. Therefore, effective and inexpensive medicines will be required to respond to SARS-CoV-2 variants. Here, we used herbal plant extracts to search for effective compounds that can interfere with SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and found that Selaginella tamariscina extract (STE) can reduce SARS-CoV-2 RdRp activity. The HCoV-OC43 beta coronavirus model was used to examine whether STE treatment could inhibit coronavirus replication and reduce coronavirus-induced cytotoxicity. Next, we searched the active compound of STE and found that amentoflavone is the main active compound of STE. Finally, we demonstrated that amentoflavone inhibits SARS-CoV-2 RdRp and coronavirus replication. Our results collectively indicate that amentoflavone from STE is possibly beneficial in responding to coronavirus-related diseases, including SARS-CoV-2.

High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

Effect of the carrier gas flow rate on the microstructure evolution and the generation of the charged nanoparticles during silicon chemical vapor deposition.

The generation of charged nanoparticles in the gas phase has been continually reported in many chemical vapor deposition processes. Charged silicon nanoparticles in the gas phase were measured using a differential mobility analyzer connected to an atmospheric-pressure chemical vapor deposition reactor at various nitrogen carrier gas flow rates (300-1000 standard cubic centimeter per minute) under typical conditions for silicon deposition at the reactor temperature of 900 degrees C. The carrier gas flow rate affected not only the growth behavior of nanostructures but also the number concentration and size distribution of both negatively and positively charged nanoparticles. As the carrier gas flow rate decreased, the growth behavior changed from films to nanowires, which grew without catalytic metal nanoparticles on a quartz substrate.

Formation of tetrapod-shaped nanowires in the gas phase during the synthesis of ZnO nanostructures by carbothermal reduction.

We experimentally confirmed that charged ZnO nanoparticles were generated abundantly in the gas phase using a differential mobility analyzer (DMA). When they were size-selected by the DMA and captured on a grid for transmission electron microscopy, particles of 10 nm had a rod or tetrahedron shape, while particles larger than 10 nm tended to have a tetrapod shape. The tetrahedral particles seemed to be a seed for the growth of tetrapod nanowires. Although the growth of a tetrahedron shape enclosed by a close-packed plane (0002) of the hexagonal close-packed ZnO can be explained by the classical crystal growth theory, the growth of tetrapod nanowires can be better explained by the building block of charged nanoparticles generated in the gas phase.

Controlled spatial characteristics of ligands on nanoparticles: Determinant of cellular functions.

Interactions of various ligands and receptors have been extensively investigated because they regulate a series of signal transduction leading to various functional cellular outcomes. The receptors on cell membrane recognize their specific ligands, resulting in specific binding between ligands and receptors. Accumulating evidence reveals that the receptors recognize the difference on the spatial characteristics of ligands as well as the types of ligands. Thus, control on spatial characteristics of multiple ligands presented on therapeutic nanoparticles is believed to impact the cellular functions. Specifically, the localized and multivalent distribution of ligands on nanoparticles can induce receptor oligomerization and receptor clustering, controlling intensity or direction of signal transduction cascades. Here, we will introduce recent studies on the use of material-based nanotechnology to control spatial characteristics of ligands and their effect on cellular functions. These therapeutic nanoparticles with controlled spatial characteristics of ligands may be a promising strategy for maximized therapeutic outcome.

Considering cell volume in dopant screening for improving Li-ion mobility in an amorphous LiPON solid-state electrolyte: an ab initio study.

Engineering of solid electrolytes of Li-ion batteries is carried out for achieving high levels of ionic conductivity and preserving low levels of electrical conductivity. Doping metallic elements into solid electrolyte materials composed of Li, P, and O is quite challenging due to instances of possible decomposition and secondary phase formation. To accelerate the development of high-performance solid electrolytes, predictions of thermodynamic phase stabilities and conductivities are necessary, as they would avoid the need to carry out exhaustive trial-and-error experiments. In this study, we demonstrated theoretical approach to increase the ionic conductivity of amorphous solid electrolyte by doping: cell volume-ionic conductivity relation. Using density functional theory (DFT) calculations, we examined the validity of the hypothetical principle in predicting improvements in stability and ionic conductivity with 6 candidate doping elements (Si, Ti, Sn, Zr, Ce, Ge) in a quaternary Li-P-O-N solid electrolyte system (LiPON) both in crystalline and amorphous phases. The doping of Si into LiPON (Si-LiPON) was indicated to stabilize the system and enhance ionic conductivity based on our calculated doping formation energy and cell volume change. The proposed doping strategies provide crucial guidelines for the development of solid-state electrolytes with enhanced electrochemical performances.

COVID-19 Cases and Deaths among Healthcare Personnel with the Progression of the Pandemic in Korea from March 2020 to February 2022.

Healthcare personnel (HCP) are vulnerable to COVID-19 infection due to their higher risk of contact with infected persons. The numbers of cases and deaths among HCP in Korea were divided into four periods associated with different major variants of SARS-CoV-2: GH clade, Alpha, Delta, and Omicron. To evaluate the implication of HCP infection in Korea, we overviewed the pandemic status in Korea and in other countries: the cases, deaths, excess mortality, and vaccination rates in Germany, Israel, Italy, Japan, the United Kingdom, and the United States. In about two years, there were 10,670 HCP cases among all COVID-19 cases (1.15% of 925,975 cases). HCP cases had a lower death rate (%) compared to that for all cases (0.14 versus 0.75). Nurses were the most infected (55.3%), followed by HCP of other categories (28.8%) and doctors (15.9%), while deaths were mostly reported among doctors (9 out of 15, 60%). Cases among HCP gradually increased, but the death rate decreased as the pandemic progressed. Compared to five of the other countries examined, Korea had a higher incidence of cases but a lower mortality, lower excess mortality, and a higher vaccination rate.

Predicting response to anti-EGFR antibody, cetuximab, therapy by monitoring receptor internalization and degradation.

Anti-epidermal growth factor receptor (EGFR) antibody, cetuximab, therapy has significantly improved the clinical outcomes of patients with colorectal cancer, but the response to cetuximab can vary widely among individuals. We thus need strategies for predicting the response to this therapy. However, the current methods are unsatisfactory in their predictive power. Cetuximab can promote the internalization and degradation of EGFR, and its therapeutic efficacy is significantly correlated with the degree of EGFR degradation. Here, we present a new approach to predict the response to anti-EGFR therapy, cetuximab by evaluating the degree of EGFR internalization and degradation of colorectal cancer cells in vitro and in vivo. Our newly developed fluorogenic cetuximab-conjugated probe (Cetux-probe) was confirmed to undergo EGFR binding, internalization, and lysosomal degradation to yield fluorescence activation; it thus shares the action mechanism by which cetuximab exerts its anti-tumor effects. Cetux-probe-activated fluorescence could be used to gauge EGFR degradation and showed a strong linear correlation with the cytotoxicity of cetuximab in colorectal cancer cells and tumor-bearing mice. The predictive ability of Cetux-probe-activated fluorescence was much higher than those of EGFR expression or KRAS mutation status. The Cetux-probes may become useful tools for predicting the response to cetuximab therapy by assessing EGFR degradation.