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Abstract Direct cardiac reprogramming represents a novel therapeutic strategy to convert non-cardiac cells such as fibroblasts into cardiomyocytes (CMs). This process involves essential transcription factors, such as Mef2c, Gata4, Tbx5 (MGT), MESP1, and MYOCD (MGTMM). However, the small molecules responsible for inducing immature induced CMs (iCMs) and the signaling mechanisms driving their maturation remain elusive. Our study explored the effects of various small molecules on iCM induction and discovered that the combination of FGF4 and ascorbic acid (FA) enhances CM markers, exhibits organized sarcomere and T-tubule structures, and improves cardiac function. Transcriptome analysis emphasized the importance of ECM-integrin-focal adhesions and the upregulation of the JAK2–STAT3 and TGFB signaling pathways in FA-treated iCMs. Notably, JAK2–STAT3 knockdown affected TGFB signaling and the ECM and downregulated mature CM markers in FA-treated iCMs. Our findings underscore the critical role of the JAK2–STAT3 signaling pathway in activating TGFB signaling and ECM synthesis in directly reprogrammed CMs. Schematic showing FA enhances direct cardiac reprogramming and JAK–STAT3 signaling pathways underlying cardiomyocyte maturation.

Abstract The photolysis of disulfide bonds is implicated in denaturation of proteins exposed to ultraviolet light. Despite this biological relevance in stabilizing the structure of many proteins, the mechanisms of disulfide photolysis are still contested after decades of research. Herein, we report new insight into the photochemistry of L-cystine in aqueous solution by femtosecond X-ray absorption spectroscopy at the sulfur K-edge. We observe homolytic bond cleavage upon ultraviolet irradiation and the formation of thiyl radicals as the single primary photoproduct. Ultrafast thiyl decay due to geminate recombination proceeds at a quantum yield of >80 % within 20 ps. These dynamics coincide with the emergence of a secondary product, attributed to the generation of perthiyl radicals. From these findings, we suggest a mechanism of perthiyl radical generation from a vibrationally excited parent molecule that asymmetrically fragments along a carbon-sulfur bond. Our results point toward a dynamic photostability of the disulfide bridge in condensed-phase.

Abstract Debris flow hazards are often interpreted through back-calculated simulation analysis or empirical methods. The mobility of a debris flow is greatly influenced by mechanical and hydrological parameters. The strength parameters play important roles in the debris flow initiation and flow stages. In particular, the rheological parameters of yield strength and plastic viscosity directly affect the debris flow runout distance and velocity. One of the most important parameters to consider when evaluating debris flow hazards is the shear strength. This strength is called the residual shear strength in the failure stage and the yield strength in the post-failure stage. The residual shear strength obtained from ring shear tests can be related to the initiation of mass movements; the yield strength obtained from rheological tests can be related to the mobilization of debris flows. The residual shear stresses obtained from ring shear tests of weathered soils typically range between 10 and 100 kPa and strongly depend on the normal stress and shear velocity. When progressive slope failure (i.e., strain-softening behavior) occurs at a relatively shallow slope depth (e.g.,

Chronic dermatitis is a disease with large unmet need for pharmacological improvement. Dermatitis conditions are maintained and exacerbated by various cytokine actions in the context of inflammation. Interleukin 6 signal transducer (Il6st), also known as glycoprotein 130 (Gp130), is a key component for surface reception of a multitude of cytokines and transduction and amplification of their pro-inflammatory signals. We hypothesized accordingly that pharmacological inhibition of Il6st can alter dermatitis pathology. Treatment with SC-144 and bazedoxifene, two representative small molecule Il6st inhibitors with different binding modes led to moderate but significant improvement of skin conditions in a 1-chloro-2,4-dinitrobenzene animal model. Part of cytokine expressions indicating the dermatological index were normalized particularly when treated with SC-144. Pruritic behaviors were blunted, also possibly giving limited contribution to disease improvement. In psoriatic skin and itch of an imiquimod animal model, those two treatments appeared to be relatively moderate. Collectively, pharmacological inhibition of Il6st seems to lessen pathological irritation. Inversely, this experimental attempt newly implies that Il6st participates in pathological mechanisms. In conclusion, we suggest Il6st as a novel target for improving dermatitis, and that agents with suitable efficacy and safety for its modulation are translatable.

Hanwoo and Jeju Black cattle (Jeju Black) are native breeds of Korean cattle. Jeju Black cattle are recognized as natural monuments and are known to exhibit slower growth rates compared to Hanwoo. While several studies have analyzed the genetic characteristics of these cattle, there has been limited research on the differences in their microbiome. In this study, rumen fluid was obtained from three Hanwoo steers and three Jeju Black steers, and three different diets (total mixed rations [TMRs] for growing, early fattening, and late fattening periods) were used as substrates for in vitro fermentation. The in vitro incubation was conducted for 3 h and 24 h following a 2 × 3 factorial arrangement. After both incubation periods, fermentation characteristics were analyzed, and ruminal microbiome analysis was performed using 16S rRNA gene sequencing, employing both QIIME2 and PICRUSt2. The results revealed significant differences in the ruminal microbiota due to the inoculum effect. At the phylum level, Patescibacteria and Synergistota were found to be enriched in the Jeju Black inoculum-treated group. Additionally, using different inocula also affected the relative abundance of major taxa, including Ruminococcus, Pseudoramibacter, Ruminococcaceae CAG-352, and the [Eubacterium] ruminantium group. These microbial differences induced by the inoculum may have originated from varying levels of domestication between the two subspecies of donor animals, which mainly influenced the fermentation and microbiome features in the early incubation stages, although this was only partially offset afterward. Furthermore, predicted commission numbers of microbial enzymes, some of which are involved in the biosynthesis of secondary metabolites, fatty acids, and alpha amylase, differed based on the inoculum effect. However, these differences may account for only a small proportion of the overall metabolic pathway. Conversely, diets were found to affect protein biosynthesis and its related metabolism, which showed differential abundance in the growing diet and were potentially linked to the growth-promoting effects in beef cattle during the growing period. In conclusion, this study demonstrated that using different inocula significantly affected in vitro fermentation characteristics and microbiome features, mainly in the early stages of incubation, with some effects persisting up to 24 h of incubation.

Abstract Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link between balance impairments and joint kinematics during walking remains unclear. Clinical assessments and gait measurements using motion analysis system was conducted in 44 stroke patients. This study utilised principal component analysis to identify key joint kinematics characteristics of patients with stroke during walking using average joint angles of pelvis and bilateral lower limbs in every gait-cycle percentile related to balance impairments. Reconstructed kinematics showed the differences in joint kinematics in both paretic and nonparetic lower limbs that can be distinguished by balance impairment, particularly in the sagittal planes during swing phase. The impaired balance group exhibited greater joint variability in both the paretic and nonparetic limbs in the sagittal plane during entire gait phase and during terminal swing phase respectively compared with those with high balance scores. This study provides a more comprehensive understanding of stroke hemiparesis gait patterns and suggests considering both nonparetic and paretic limb function, as well as bilateral coordination in clinical practice. Principal component analysis can be a useful assessment tool to distinguish differences in balance impairment and dynamic symmetry during gait in patients with stroke.

Rumen cannulation is a surgical technique used to collect rumen contents from ruminants. However, rumen cannulation surgery may potentially impact the composition of the rumen microbiota. This study aimed to examine the longitudinal alterations in the rumen microbiota composition of Hanwoo steers after cannulation surgery. In this study, eight Hanwoo steers were used; four steers underwent rumen cannulation surgery (cannulation group), while the remaining four were left intact (control group). Rumen samples were collected from all eight steers using the stomach tubing method on the day before surgery (day 0) and on postoperative days 1, 4, 7, 10, 14, 17, 21, 24, and 28, resulting in 80 samples (10 timepoints × 8 animals). The microbiota of all 80 samples were analyzed using 16S rRNA gene amplicon sequencing with Quantitative Insights into Microbial Ecology version 2 (QIIME2). There were no significant differences (p > 0.05) in all major phyla and most major genera representing at least 0.5% of total sequences across all 80 samples between the control and cannulation groups on the preoperative and postoperative days. However, while the alpha diversity indices did not differ (p > 0.05) between the two groups on the preoperative day, they significantly differed (p < 0.05) between the two groups on the postoperative days. Further, the overall microbial distribution based on both unweighted and weighted principal coordinate analysis plots significantly differed (p < 0.05) between the two groups on both the preoperative and postoperative days. Orthogonal polynomial contrasts indicated that major genera and microbial diversity in the cannulation group decreased following surgery but returned to their initial states by postoperative day 28. In conclusion, this study demonstrates that rumen cannulation surgery affects some major taxa and microbial diversity, suggesting that the rumen cannulation method can alter the composition of rumen microbiota in Hanwoo steers.

The automation of systems and the accelerated digital transformations across various industries have rendered the manual monitoring of systems difficult. Therefore, the automatic detection of system anomalies is essential in diverse industries. Various deep learning-based techniques have been developed for anomaly detection in multivariate time-series data with promising performance. However, there are several challenges: 1) difficulty in understanding the relationships among time-series data due to their complexity and high-dimensionality, 2) limitation in distinguishing anomalies from normal data that exhibit similar distributional patterns, and 3) lack of intuitive interpretation of anomaly detection results. To address these issues, we propose a novel approach referred to as the time-series to image-transformed adversarial autoencoder (T2IAE), which adopts image transformation techniques and convolutional neural network (CNN)-based adversarial learning. Image transformation techniques were used to effectively capture the local features of adjacent time points. Two CNN-based adversarial autoencoders competitively learned to distinguish between normal and abnormal data. We experimentally analyzed five real-world multivariate time-series datasets, wherein the proposed model achieved superior anomaly detection performance compared with state-of-the-art methods. Moreover, the proposed model enables humans to intuitively interpret the detection results, facilitating appropriate explanations of the results and enhancing the model’s usability.

This paper presents a novel localization technique—Multipath-radio tomographic imaging (RTI)—based on millimeter-wave (mm-wave) radio systems, such as 5G mobile and WiGig LAN systems. Because of their high delay and angle resolutions, mm-wave radio systems can realize highly accurate device-free localization (DFL) for smart home/smart building applications. They can thus easily separate the multipath components. The Multipath-RTI can drastically reduce the number of anchor nodes using virtual nodes created by multipaths. This paper established the signal processing procedure for RTI image generation and position estimation using Multipath-RTI. Then, a practical signal processing scheme, ray-tracing-assisted Multipath-RTI, is presented, and the DFL results obtained by multilink double-directional channel sounding measurements are shown. Here, the position estimation errors in the measurements were less than 0.5 m for all target positions. Furthermore, the empirical position estimation errors for a single target under various conditions obtained by simulations were less than 0.5 m in 96.2 % and 88.0 % of the areas in an L-shaped room and a room with an obstacle in the center, respectively. The position estimation accuracy for multiple targets was also evaluated, where, for example, the errors for three targets were less than 0.1 m at 50 % probability with failure rates of less than 20 %.

Microfluidics are crucial for managing small-volume analytical solutions for various applications, such as disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. The precise control of flow control devices can generate diverse flow patterns using pneumatic control with solenoid valves and a microcontroller. This system enables the active modulation of the pneumatic pressure through Arduino programming of the solenoid valves connected to the pressure source. Additionally, the incorporation of solenoid valve sets allows for multichannel control, enabling simultaneous creation and manipulation of various microflows at a low cost. The proposed microfluidic flow controller facilitates accurate flow regulation, especially through periodic flow modulation beneficial for droplet generation and continuous production of microdroplets of different sizes. Overall, we expect the proposed microfluidic flow controller to drive innovative advancements in technology and medicine owing to its engineering precision and versatility.

Pain management remains a major challenge in medicine, highlighting the need for the development of new therapeutic agents. The transient receptor potential ankyrin 1 (TRPA1) and vanilloid 1 (TRPV1) are ion channels that play key roles in pain perception. Targeting both TRPA1 and TRPV1 simultaneously with dual antagonists offers a promising approach to pain relief. In this study, we investigated a series of hybrid analogs of TRPA1 and TRPV1 antagonists to discover novel therapeutic agents for pain. Among these compounds synthesized by a condensation reaction forming 1,2,4-oxadiazole between the A- and C-regions, compound 50 exhibited substantial dual-acting antagonism to TRPA1 and TRPV1 with IC50 values of 1.42, 2.84, 2.13, and 5.02 μM for hTRPA1, mTRPA1, hTRPV1, and rTRPV1, respectively. In the formalin test, compound 50 demonstrated dose-dependent analgesic activity with an ED50 of 85.9 mg/kg in phase 1 and 21.6 mg/kg in phase 2, respectively, and was able to inhibit pain behavior completely at a dose of 100 mg/kg. This study presents the discovery and characterization of a novel dual TRPA1/TRPV1 antagonist, highlighting its potential as a therapeutic agent for pain management.

The function of peripheral nociceptors, the neurons that relay pain signals to the brain, are frequently tuned by local and systemic modulator substances. In this context, neurohormonal effects are emerging as an important modulatory mechanism, but many aspects remain to be elucidated. Here we report that gonadotropin-releasing hormone (GnRH), a brain-specific neurohormone, can aggravate pain by acting on nociceptors in mice. GnRH and GnRHR, the receptor for GnRH, are expressed in a nociceptor subpopulation. Administration of GnRH and its analogue, localized for selectively affecting the peripheral neurons, deteriorated mechanical pain, which was reproducible in neuropathic conditions. Nociceptor function was promoted by GnRH treatment in vitro, which appears to involve specific sensory transient receptor potential ion channels. These data suggest that peripheral GnRH can positively modulate nociceptor activities in its receptor-specific manner, contributing to pain exacerbation. Our study indicates that GnRH plays an important role in neurohormonal pain modulation via a peripheral mechanism.

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX’23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.1 We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers and musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.

Abstract Background Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. Results We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. Conclusions Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

This study evaluates how different feeding systems impact ruminal fermentation, methane production, and microbiota of Hanwoo steers native to Korea. In a replicated 2 × 2 crossover design over 29 days per period, eight Hanwoo steers (507.1 ± 67.4 kg) were fed twice daily using a separate feeding (SF) system comprising separate concentrate mix and forage or total mixed rations (TMR) in a 15:85 ratio. The TMR-feeding group exhibited a considerable neutral detergent fiber digestibility increase than the SF group. However, ruminal fermentation parameters and methane production did not differ between two feeding strategies. In addition, TMR-fed steers expressed elevated Prevotellaceae family, Christensenellaceae R-7 group, and an unidentified Veillonellaceae family genus abundance in their rumen, whereas SF-fed steers were rich in the Rikenellaceae RC9 gut group, Erysipelotrichaceae UCG-004, and Succinivibrio. Through linear regression modeling, positive correlations were observed between the Shannon Diversity Index and the SF group’s dry matter intake and methane production. Although feeding systems do not affect methane production, they can alter ruminal microbes. These results may guide future feeding system investigations or ruminal microbiota manipulations as a methane-mitigation practice examining different feed ingredients.

This work investigated the viral infection in jujube plants in Korea. A total of 61 samples with the symptoms of putative viral infection were collected from experimental fields and orchards. Thereafter, the samples were subjected to metatranscriptome analysis, Reverse transcription polymerase chain reaction analysis, and nucleotide sequence analysis. These analyses identified the presence of two DNA viruses, jujube-associated badnavirus (JuBV), jujube mosaic-associated virus (JuMaV), and one RNA virus, jujube yellow mottle-associ-ated virus (JYMaV). All samples collected were confirmed to be infected by at least one of the three viruses, with most showed multiple infections. The detection rates of JuBV, JYMaV, and JuMaV were 100%, 90.2%, and 8.2%, respectively. Only three combinations of viral infections were found: 9.8% of samples showed single infection of JuBV, 82.0% showed double infection of JuBV+JYMaV, and 8.2% showed triple infection of JuBV+JYMaV+JuMaV. Sequence analysis of the three viruses showed very high homology with respective virus isolates reported in China. This study is predicted to provide fundamental data to produce virus-free jujube seedlings and represents the first report of JuBV and JuMaV infection in Korea.

To achieve evacuation-optimized design at the architectural planning phase, this study analyzed the effect of floor openings in multipurpose commercial buildings on their evacuation efficiency. Herein, the IFC Mall in Yeouido, Seoul, Korea was considered the study site, because all the factors influencing the evacuation efficiency can be controlled owing to the underground location of all floors. Therefore, all evacuees followed the same evacuation direction. Comprehensively, a spatial analysis methodology(Cellular ECEM) was employed to derive the evacuation costs through the sum of visibility cost and distance cost. Subsequently, the evacuation costs were compared in all cases to review the hindrances in evacuation according to the type and area ratio of the planned floor opening, and several causes of evacuation cost deviations were discussed. Overall, increasing the area of the floor opening deteriorates the evacuation efficiency. The evacuation efficiency increased if the area of the floor opening is appropriately separated and a bridge is constructed in the middle to connect a path. Furthermore, following the longer path around the floor opening to use the main vertical means of circulation, owing to the connection between the atrium and floor opening, is more vulnerable toward evacuation efficiency than other floor opening types.

Abstract The phase transition between water and ice is ubiquitous and one of the most important phenomena in nature. Here, we performed time-resolved x-ray scattering experiments capturing the melting and recrystallization dynamics of ice. The ultrafast heating of ice I is induced by an IR laser pulse and probed with an intense x-ray pulse which provided us with direct structural information on different length scales. From the wide-angle x-ray scattering (WAXS) patterns, the molten fraction, as well as the corresponding temperature at each delay, were determined. The small-angle x-ray scattering (SAXS) patterns, together with the information extracted from the WAXS analysis, provided the time-dependent change of the size and the number of liquid domains. The results show partial melting (~13%) and superheating of ice occurring at around 20 ns. After 100 ns, the average size of the liquid domains grows from about 2.5 nm to 4.5 nm by the coalescence of approximately six adjacent domains. Subsequently, we capture the recrystallization of the liquid domains, which occurs on microsecond timescales due to the cooling by heat dissipation and results to a decrease of the average liquid domain size.

Abstract Objective Although chronic exposure to air pollution is associated with an increased risk of dementia in normal elderlies, the effect of chronic exposure to air pollution on the rates of cognitive decline in Alzheimer's disease (AD) has not been elucidated. Methods In this longitudinal study, a total of 269 patients with mild cognitive impairment or early dementia due to AD with the evidence of brain β‐amyloid deposition were followed‐up for a mean period of 4 years. Five‐year normalized hourly cumulative exposure value of each air pollutant, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10), was computed based on nationwide air pollution database. The effects of chronic exposure to air pollution on longitudinal cognitive decline rate were evaluated using linear mixed models. Results Higher chronic exposure to SO2 was associated with a faster decline in memory score, whereas chronic exposure to CO, NO2, and PM10 were not associated with the rate of cognitive decline. Higher chronic exposure to PM2.5 was associated with a faster decline in visuospatial score in apolipoprotein E ε4 carriers. These effects remained significant even after adjusting for potential confounders. Interpretation Our findings suggest that chronic exposure to SO2 and PM2.5 is associated with faster clinical progression in AD.

The lateral habenula (LHb) is a well-established brain region involved in depressive disorders. Synaptic transmission of the LHb neurons is known to be enhanced by stress exposure; however, little is known about genetic modulators within the LHb that respond to stress. Using recently developed molecular profiling methods by phosphorylated ribosome capture, we obtained transcriptome profiles of stress responsive LHb neurons during acute physical stress. Among such genes, we found that KCNB1 (Kv2.1 channel), a delayed rectifier and voltage-gated potassium channel, exhibited increased expression following acute stress exposure. To determine the roles of KCNB1 on LHb neurons during stress, we injected short hairpin RNA (shRNA) against the kcnb1 gene to block its expression prior to stress exposure. We observed that the knockdown of KCNB1 altered the basal firing pattern of LHb neurons. Although KCNB1 blockade did not rescue despair-like behaviors in acute learned helplessness (aLH) animals, we found that KCNB1 knockdown prevented the enhancement of synaptic strength in LHb neuron after stress exposure. This study suggests that KCNB1 may contribute to shape stress responses by regulating basal firing patterns and neurotransmission intensity of LHb neurons.

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network–long short-term memory (CNN-LSTM) and CNN–gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance.

PurposeWe aimed to retrospectively analyzed the feasibility of fast four-dimensional computed tomography (4DCT)-based O-ring LINAC treatment for patients with an average respiratory amplitude was< 0.5 cm and who cannot endure long treatment times due to poor performance status in lung 4D-stereotactic body radiotherapy (SBRT).MethodsThis study included data of 38 patients who received lung 4D-SBRT and had average respiratory amplitude< 0.5 cm in the full phase. C-arm LINAC plans were based on 4DCT data obtained at phase values ranging from 20–70% using a C-arm LINAC. O-ring LINAC plans were retrospectively established based on 4DCT data obtained at phase values of 0–90% using an O-ring LINAC. The conformity index (CI), homogeneity index (HI), and gradient measurement of the planning target volumes (PTV) were analyzed to compare dosimetric data between C-arm LINAC and O-ring LINAC plans. Organs at risk were analyzed in accordance with the Radiation Therapy Oncology Group 0915 protocol. Treatment delivery time and total monitor units were analyzed to compare the efficiency of treatment delivery. Statistical comparisons were performed using the Wilcoxon signed-rank test (P< 0.05).ResultsFor the PTV, there was no significant difference in the CI or HI between C-arm LINAC and O-ring LINAC plans. For organs-at-risk, all plans met the criteria for dose constraint. There was a significant difference between C-arm LINAC and O-ring LINAC plans except in the spinal cord. Treatment delivery time was 92% longer for C-arm LINAC plans than for O-ring LINAC plans. The total MU value for C-arm LINAC plans was 9.6% higher than that for O-ring LINAC plans.ConclusionWe verified the feasibility of fast 4DCT-based O-ring LINAC treatment for patients with average respiratory amplitude< 0.5 cm and who cannot endure long treatment times due to poor performance status in lung 4D-SBRT.

Sustainable water management is essential to guaranteeing access to safe water and addressing the challenges posed by climate change, urbanization, and population growth. In a typical household, greywater, which includes everything but toilet waste, constitutes 50–80% of daily wastewater generation and is characterized by low organic strength and high volume. This can be an issue for large urban wastewater treatment plants designed for high-strength operations. Segregation of greywater at the source for decentralized wastewater treatment is therefore necessary for its proper management using separate treatment strategies. Greywater reuse may thus lead to increased resilience and adaptability of local water systems, reduction in transport costs, and achievement of fit-for-purpose reuse. After covering greywater characteristics, we present an overview of existing and upcoming technologies for greywater treatment. Biological treatment technologies, such as nature-based technologies, biofilm technologies, and membrane bioreactors (MBR), conjugate with physicochemical treatment methods, such as membrane filtration, sorption and ion exchange technologies, and ultraviolet (UV) disinfection, may be able to produce treated water within the allowable parameters for reuse. We also provide a novel way to tackle challenges like the demographic variance of greywater quality, lack of a legal framework for greywater management, monitoring and control systems, and the consumer perspective on greywater reuse. Finally, benefits, such as the potential water and energy savings and sustainable future of greywater reuse in an urban context, are discussed.

The gastrointestinal (GI) tract of ruminants contains diverse microbes that ferment various feeds ingested by animals to produce various fermentation products, such as volatile fatty acids. Fermentation products can affect animal performance, health, and well-being. Within the GI microbes, the ruminal microbes are highly diverse, greatly contribute to fermentation, and are the most important in ruminant nutrition. Although traditional cultivation methods provided knowledge of the metabolism of GI microbes, most of the GI microbes could not be cultured on standard culture media. By contrast, amplicon sequencing of 16S rRNA genes can be used to detect unculturable microbes. Using this approach, ruminant nutritionists and microbiologists have conducted a plethora of nutritional studies, many including dietary interventions, to improve fermentation efficiency and nutrient utilization, which has greatly expanded knowledge of the GI microbiota. This review addresses the GI content sampling method, 16S rRNA gene amplicon sequencing, and bioinformatics analysis and then discusses recent studies on the various factors, such as diet, breed, gender, animal performance, and heat stress, that influence the GI microbiota and thereby ruminant nutrition.

This paper develops deep learning (DL) based beamforming approaches for multi-antenna interference channels where several base stations (BSs) individually optimize their own beamforming vectors in a decentralized manner. By exploiting the optimal beam structure, we propose an efficient method for beam decisions and coordination among BSs based solely on local information. Moreover, we show that the proposed approach allows a scalable design with respect to the number of users. We also present novel training strategies for the proposed deep neural networks, validating its potential as an innovative decentralized beamforming methodology. Consequently, the proposed DL based decentralized beamforming framework can achieve various optimal beamforming strategies. Numerical results demonstrate the advantages of the proposed framework over conventional methods.

In this paper, a newly developed 300 GHz channel sounder is presented followed by a detailed description of test measurements and the subsequent results obtained to validate its working. An elucidation of the high-resolution double-directional channel measurement in a typical conference room scenario precedes the comparison of the results obtained for the current campaign to that obtained from an earlier campaign at 60 GHz for a similar setup. It is observed that a similar number of clusters for both the bands under investigation for all the transmitter (Tx) and receiver (Rx) positions are obtained from the generated power spectra. Any deviation is theorized to be caused either due to the usage of lower measurement bandwidth in the 60 GHz campaign or due to limited elevation expanse in the 300 GHz measurement. To identify the interacting objects (IOs) causing the clusters, environment-embedded angular power spectra (APS) and ray tracing simulations are used. The large-scale parameters (LSPs) are also evaluated for both campaigns. It is observed that signal propagation at 300 GHz is dominated more by the line-of-sight (LoS) path compared to 60 GHz. The results are also compared with other similar results from the literature.

As the world warms up to the idea of millimeter wave (mmWave) communication and fifth generation (5G) mobile networks, realization slowly dawns that the data rate, latency, throughput, and other performance metrics that are used to assess a new wireless communication technology will not be enough to support the demands of envisioned futuristic applications. Thus there is an eagerness to further climb up the frequency ladder to use the large swathes of available spectrum in the 0.1 – 10 THz band which is expected to act as the key technology enabler to fulfill the requirements of the sixth generation (6G) wireless communication and possibly even beyond. Channel measurement and modeling are crucial to the design and deployment of future wireless communication systems and researchers across the globe are putting their best foot forward to accelerate the process. The current article presents comprehensive assimilation of research efforts in the context of THz channel sounding. A detailed overview of the current channel sounding techniques is first introduced followed by their relevance to THz band channel measurement. An in-house novel channel sounder developed for THz band measurement is also briefly introduced in this context. The paper next provides elaborate dissemination of various measurement campaigns in the band of interest followed by the modeling techniques that are available in the literature and are being adopted for the THz band. Post the description of different challenges and future research directions in the context of sounding, measurement, and modeling the article is concluded.

Obtaining experimental evidence of a liquid-liquid phase transition in supercooled water is challenging due to the rapid crystallization. Here the authors drive low-density amorphous ice to the conditions of liquid-liquid coexistence using ultrafast laser heating and observe the liquid-liquid phase transition with femtosecond x-ray laser pulses.

Studies on the factors of the COVID-19 pandemic that influence architecture and spaces have presented various, often contradictory, findings, and the same is true for studies making predictions. Considering this, this study aims to use the Delphi technique, an analytical method for synthesizing the opinions of experts across diverse fields to determine major issues in the COVID-19 and post-COVID-19 eras as wells as the architectural and urban spaces in which future changes are expected. This study derived keywords representing major trends and issues that would lead to changes in architectural and urban spaces in the COVID-19 and post-COVID-19 eras, predicted the change patterns for each keyword, and determined the architectural and urban spaces expected to undergo major changes. The experts predicted these keywords to show a variety of changes, including the pattern of increasing influence during the COVID-19 pandemic and then decreasing in influence after the pandemic, the pattern of small influence during the pandemic and the increase in influence after the pandemic, and the pattern of greater influence during and after the pandemic. Furthermore, they predicted that most of the post-COVID-19 changes would occur in the housing sector. Developing architectural guidelines that could incorporate these changes is thus necessary.

Abstract In Drosophila larvae, nociceptive mdIV sensory neurons detect diverse noxious stimuli and prompt a nociceptive rolling response. Intriguingly, the same neurons also regulate stereotyped larval movement. The channels responsible for transducing these stimuli into electric signals are not yet fully identified. Here we undertook genetic and electrophysiological analysis of Ppk19, a member of the Deg/ENaC family of cationic channels. ppk19 mutants exhibited an impaired nociceptive rolling response upon mechanical force and acid, but no impairment in response to noxious temperature and gentle touch. Mutants also exhibited defective larval movement. RNAi against ppk19 in mdIV neurons likewise produced larvae with defects in mechanical and acid nociception and larval movement, but no impairment in detection of heat and gentle touch. Cultured cells transfected with ppk19 produced currents in acid and hypotonic solution, suggesting that ppk19 encodes an ion channel that responds to acid and cell swelling. Taken together, these findings suggest that Ppk19 acts in mdIV neurons as a proton- and mechano-gated ion channel to mediate acid- and mechano-responsive nociception and larval movement.

Although COVID-19 has severely affected the global economy, information technology (IT) employees managed to perform most of their work from home. Telecommuting and remote work have promoted a demand for IT services in various market sectors, including retail, entertainment, education, and healthcare. Consequently, computer and information experts are also in demand. However, producing IT, experts is difficult during a pandemic owing to limitations, such as the reduced enrollment of international students. Therefore, researching increasing software productivity is essential; this study proposes a code similarity determination model that utilizes augmented data filtering and ensemble strategies. This algorithm is the first automated development system for increasing software productivity that addresses the current situation—a worldwide shortage of software dramatically improves performance in various downstream natural language processing tasks (NLP). Unlike general-purpose pre-trained language models (PLMs), CodeBERT and GraphCodeBERT are PLMs that have learned both natural and programming languages. Hence, they are suitable as code similarity determination models. The data filtering process consists of three steps: (1) deduplication of data, (2) deletion of intersection, and (3) an exhaustive search. The best mating (BM) 25 and length normalization of BM25 (BM25L) algorithms were used to construct positive and negative pairs. The performance of the model was evaluated using the 5-fold cross-validation ensemble technique. Experiments demonstrate the effectiveness of the proposed method quantitatively. Moreover, we expect this method to be optimal for increasing software productivity in various NLP tasks.

Bovine fecal microbiota is important for host health and its composition can be affected by various factors, such as diet, age, species, breed, regions, and environments. The objective of this study was to evaluate the impact of diet and gender on fecal microbiota in Korean native Hanwoo cattle. The 16S rRNA gene amplicon sequencing of fecal microbiota was conducted from 44 Hanwoo cattle divided into four groups: (1) 11 heifers fed an oat hay plus total mixed ration (TMR) diet for breeding (HOTB), (2) 11 heifers fed an early fattening TMR diet (HEFT), (3) 11 steers fed the early fattening TMR diet (SEFT), and (4) 11 steers fed the late fattening TMR diet (SLFT). Firmicutes and Bacteroidota were the first and second most dominant phyla in all the samples, respectively. The Firmicutes/Bacteroidota (F/B) ratio associated with feed efficiency was significantly greater in the SLFT group than in the other groups. At the genus level, Romboutsia, Paeniclostridium, and Turicibacterwere the most abundant in the SLFT while Akkermansia, Bacteroides, and Monoglobus were the most abundant in the HOTB group. Although the same early fattening TMR diet was fed to Hanwoo heifers and steers, Marvinbryantia and Coprococcus were the most abundant in the HEFT group while Alistipes and Ruminococcus were the most abundant in the SEFT group. Shannon and Simpson diversity indices were significantly lower in the SLFT group than in the other groups. Distribution of fecal microbiota and functional genetic profiles were significantly different among the four treatment groups. The present study demonstrates that different diets and genders can affect fecal microbiota and the F/B ratio may be associated with feed efficiency in Hanwoo cattle. Our results may help develop strategies to improve gut health and productivity through manipulation of fecal microbiota using the appropriate diet considering Hanwoo cattle gender.

Abstract The colloidal dispersion in a nonpolar medium is an essential material for electrophoretic displays (EPD) with low‐power consumption. A uniform‐sized superparamagnetic iron oxide nanoparticle (SPION) is a promising candidate for EPD, which exhibits tunable structural color by Bragg diffraction. In this study, the surface of SPION is charged in a nonpolar medium by inverse micelles of Solsperse‐17k, an oil‐soluble polymeric surfactant. A photonic ink of SPION dispersion exhibits simultaneous magnetochromism and electrochromism. The photonic ink is encapsulated via a complex coacervation process, in which double layers of gelatin/gum Arabic form a stable shell for µ‐capsule. The µ‐capsules show tunable structural colors, which depends upon the size of SPION in photonic ink. The increased surfactant content in photonic ink brings about a decrease in µ‐capsule size due to a reduced surface tension. A lowered gelatin concentration during coacervation results in a smaller µ‐capsule, which exhibits an electrical color tunability. Optical characterization using a confocal microscopy enables 3D visualization of the inner structure of µ‐capsules and the formation of particle chain structure of SPION in H‐field. The encapsulated photonic ink exhibits magnetochromism for 1 year, illuminating the long‐term stability of µ‐capsules developed in this study.

Landslides are prevalent natural disasters in mountainous regions worldwide, and the debris flows that accompany them are considered a significant cause of topographical changes. Landslide-debris flows cause property damage and casualties if they occur in densely populated areas, such as cities and rural areas. Sediments entering a dam or reservoir lake can compromise the integrity and functionality of the facility. To minimize such damage, this phenomenon should be elucidated through numerical models and quantitative analyses performed. Despite South Korea having approximately 18,000 dams and reservoirs, with approximately 70% of the country being mountainous, research on landslides and debris flows occurring in the dam and reservoir basins remains insufficient. However, such studies are essential for the continuous operation and management of dams/reservoirs. This study focused on analyzing the damage caused by landslide-debris flow events in a dam or reservoir basin. We established different scenarios based on the distribution of vegetation in the basin to determine the impact of vegetation on slope stability and debris flows.

We propose an outlier-resilient Gaussian process regression (GPR) model supported by support vector machine regression (SVMR) for kinetic profile inference. GPR, being a non-parametric regression using Bayesian statistics, has advantages in that it imposes no constraints on profile shapes and can be readily used to integrate different kinds of diagnostics, while it is vulnerable to the presence of even a single outlier among a measured dataset. As an outlier classifier, an optimized SVMR is developed based only on the measurements. Hyper-parameters of the developed GPR model with informative prior distributions are treated in two different ways, i.e. maximum a posteriori (MAP) estimator and marginalization using a Markov Chain Monte Carlo sampler. Our SVMR-supported GPR model is applied to infer ion temperature T _i profiles using measured data from the KSTAR charge exchange spectroscopy system. The GPR-inferred T _i profiles with and without an outlier are compared and show prominent improvement when the outlier is removed by the SVMR. T _i profiles inferred with the MAP estimator and the marginalization scheme are compared. They are noticeably different when observation uncertainties are not small enough, and the marginalization scheme generally provides a smoother profile.

Identifying potential sources of suspended sediment (SS) in headwater catchments is crucial for water quality management. To differentiate these potential SS sources, we investigated the distribution of two fallout radionuclides (FRNs), 137Cs and 210Pbex, using gamma spectrometry along with soil organic matter (SOM) analysis in a headwater catchment with five potential SS sources: cultivated land, non-harvested forest floor, eroded hillslope, harvested forest floor, and stream bank. The 137Cs and 210Pbex concentrations and the SOM content were considerably higher in the harvested forest floor materials than in the other four potential SS source materials. FRN concentrations revealed distinct properties according to the type of potential SS sources. Specifically, the combination of FRNs (with the effect of SOM content removed) associated with the mineral fraction and SOM showed distinguishable differences among the potential SS sources, except for no difference between cultivated land and eroded hillslope. Therefore, SOM and FRNs, or their combination, can be effective indices to differentiate or trace potential SS sources on various land use/land cover types within a catchment. Further field tests will allow the tracing techniques that bind FRNs with SOM to contribute to understanding SS transport from non-point sources within a catchment.

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies.

Triangular mesh data structures are principal in computer graphics, serving as the foundation for many 3D models. To effectively utilize these 3D models across diverse industries, it is important to understand the model’s overall shape and geometric features thoroughly. In this work, we introduce a novel method for generating camera paths that emphasize the model’s local geometric characteristics. This method uses a toroidal patch-based spatial data structure, approximating the mesh’s faces within a predetermined tolerance ϵ, encapsulating their geometric intricacies. This facilitates the determination of the camera position and gaze path, ensuring the mesh’s key characteristics are captured. During the path construction, we create a bounding cylinder for the mesh, project the mesh’s faces and associated toroidal patches onto the cylinder’s lateral surface, and sequentially select grids of the cylinder containing the highest number of toroidal patches as we traverse the lateral surface. The centers of the selected grids are used as control points for a periodic B-spline curve, which serves as our foundational path. After initial curve generation, we generated camera position and gaze path from the curve by multiplying factors to ensure a uniform camera amplitude. We applied our method to ten triangular mesh models, demonstrating its effectiveness and adaptability across various mesh configurations.

Direct and real-time observation of the excess holes in an element-specific and energylevel-specific manner is a challenging task. Here the authors present a complete hole dynamics of photo-excited anatase TiO2 with 100 femto-second resolution.

Motivated by regulating/eliminating the population of herbivorous pests, we investigate a discrete-time plant–herbivore model with two different constant control strategies (removal versus reduction), and formulate the corresponding optimal control problems when its dynamics exhibits varied types of bi-stability and fluctuating environments. We provide basic analysis and identify the critical factors to characterize the optimal controls and the corresponding plant–herbivore dynamics such as the control upper bound (the effectiveness level of the implementation of control measures) and the initial conditions of the plant and herbivore. Our results show that optimal control could be easier when the model has simple dynamics such as stable equilibrium dynamics under constant environment or the model exhibits chaotic dynamics under fluctuating environments. Due to bistability, initial conditions are important for optimal controls. Regardless of with or without fluctuating environments, initial conditions taken from the near the boundary makes optimal control easier. In general, the pest is hard to be eliminated when the control upper bound is not large enough. However, as the control upper bound is increased or the initial conditions are chosen from near the boundary of the basin of attractions, the pest can be manageable regardless of the fluctuating environments.

Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.

The crimping of copper terminals via hand-operated and hydraulic compressors is used to generate a compressive force between a terminal and a wire, generally on a worksite. However, this equipment often causes compression defects because non-uniform pressure is applied to the terminal surface in the radial direction during crimping. When the crimped terminal is connected to electrical parts such as the power transmission system, a low-quality crimped terminal can separate from the wire strands, increasing resistance to current flow through the terminal, energy loss, and the risk of fire due to overheating. For this reason, Magnetic Pulse Crimping (MPC), which can yield highly durable crimped terminals with uniform quality, has recently been developed. This process uses only the magnetic force generated by high electromagnetic interaction between the crimping coil part and the surface of the terminal, without physical contact. The objective of this research was to confirm the superiority of the MPC process over the conventional crimping process and then analyze the effects of the main process parameters, including the crimping length and the charge energy on the crimping part, so that this new process can be applied at worksites. To realize these goals, copper terminals and 35 mm2 copper wire strands were employed, and various types of crimping parts were manufactured under different crimping conditions. In particular, the distribution of electromagnetic force on the crimped parts were analyzed via numerical analysis. The crimping part performance was improved when the MPC process was applied to terminal crimping. In particular, decreasing the crimping length led to increased crimping quality, while increasing the charge energy caused increases in the compression ratio and pullout strength. However, excessively high charge energy caused the edge to break the wire strands; therefore, it is important to select the proper charge energy.

This paper developed a 60-GHz millimeter-wave cluster channel model for various office environments and investigated the impact of cluster characteristics on wireless data transmission. We conducted double-directional channel measurement campaigns in a university laboratory office environment to develop the channel model using a custom-developed channel sounder with an angular resolution of 6° and a delay resolution of $\mathrm {2.5~ns}$ at $\mathrm {58.32~GHz}$ for the measurements. The super-resolution multipath parameter estimation algorithm extracts multipath components from the measured data with a delay resolution of $\mathrm {0.1~ns}$ and an azimuth resolution of 0.1°, and the $K$ -PowerMeans algorithm identified multipath clusters. Based on the conventional 3rd Generation Partnership Project (3GPP) model, a scenario-categorized site-general model, we attempted to develop a highly accurate cluster model that can more accurately reproduce small-scale fading fluctuations in site-specific environments. The large- and small-scale parameters obtained from the measured data were extracted and compared with the 3GPP map-based hybrid channel model, a quasi-deterministic channel model. The results revealed strong site dependency in the inter-cluster and intra-cluster properties. Moreover, we assessed the impact of different channel parameters on system evaluation using single-user and multi-user multiple-input- multiple-output channel capacities.

Abstract A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial–temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial–temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.

Organic light-emitting diode (OLED) microdisplays have attracted much attention as displays for small form factor augmented reality (AR) devices. To realize glass-like thin and wide field of view (FoV) AR devices, we designed a display module with a high aspect ratio microdisplay and a thin optical component. For the high aspect ratio microdisplay, we developed the color OLED microdisplay with a 32:9 aspect ratio and a 0.8-inch diagonal ∼2,490-ppi CMOS backplane. To express color and reduce optical crosstalk, we fabricated the color filter (C/F) patterning directly on the white OLED. We also developed a pin mirror lens with 11 pin mirrors to improve the optical efficiency and quality with a thin lens. By combining the microdisplay with LetinAR’s pin mirror lens, we successfully demonstrated an AR device with a wide horizontal FoV of 46° but with a small form factor 4 mm lens.

ObjectivePreviously developed Intradialytic hypotension (IDH) prediction models utilize clinical variables with potential privacy protection issues. We developed an IDH prediction model using minimal variables, without the risk of privacy infringement.MethodsUnidentifiable data from 63,640 hemodialysis sessions (26,746 of 79 patients for internal validation, 36,894 of 255 patients for external validation) from two Korean hospital hemodialysis databases were finally analyzed, using three IDH definitions: (1) systolic blood pressure (SBP) nadir

Particulate matter (PM) produced in pig houses may contain microbes which can spread by airborne transmission, and PM and microbes in PM adversely affect human and animal health. To investigate the microbiome in PM from pig houses, nine PM samples were collected in summer 2020 inside and outside of pig houses located in Jangseong-gun, Jeollanam-do Province, Korea, comprising three PM samples from within a nursery pig house (I-NPH), three samples from within a finishing pig house (I-FPH), and three samples from outside of the pig houses (O-PH). Microbiomes were analyzed using 16S rRNA gene amplicon sequencing. Firmicutes was the most dominant phylum and accounted for 64.8%–97.5% of total sequences in all the samples, followed by Proteobacteria (1.4%–21.8%) and Bacteroidetes (0.3%–13.7%). In total, 31 genera were represented by > 0.3% of all sequences, and only Lactobacillus, Turicibacter, and Aerococcus differed significantly among the three PM sample types. All three genera were more abundant in the I-FPH samples than in the O-PH samples. Alpha diversity indices did not differ significantly among the three PM types, and a principal coordinate analysis suggested that overall microbial communities were similar across PM types. The concentration of PM did not significantly differ among the three PM types, and no significant correlation of PM concentration with the abundance of any potential pathogen was observed. The present study demonstrates that microbial composition in PM inside and outside of pig houses is similar, indicating that most microbe-containing PM inside pig houses leaks to the outside from where it, along with microbe-containing PM on the outside, may re-enter the pig houses. Our results may provide useful insights regarding strategies to mitigate potential risk associated with pig farming PM and pathogens in PM.