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Abstract Previous studies on the association between age-related macular degeneration (AMD) and rheumatoid arthritis (RA) have shown conflicting results. We sought to assess the association between AMD with/without visual disability (VD) and the risk of RA using National Health Insurance data in South Korea. In total, 3,537,293 individuals who underwent health checkups in 2009 were included and followed until 2019. Participants with VD were defined as those with loss of vision or a visual field defect as certified by the Ministry of Health and Welfare of Korea. Using multivariable adjusted Cox regression analysis, RA hazard ratios were estimated for control and AMD with/without VD groups. In total, 43,772 participants (1.24%) were diagnosed with RA. Individuals with AMD were at higher risk of RA compared to controls, regardless of the presence of VD (aHR 1.11; 95% CI 1.02–1.21). Among individuals with AMD, different risk levels of RA were observed between those without VD (aHR 1.13; 95% CI 1.03–1.21) and those with VD (aHR 0.90; 95% CI 0.64–1.27). AMD was associated with a higher risk of RA, which remained significant as a trend even after adjusting for lifestyle factors and comorbidities.

Abstract The demand for high-resolution and large-area imaging systems for non-destructive wafer inspection has grown owing to the increasing complexity and extremely fine nature of semiconductor processes. Several studies have focused on developing high-resolution imaging systems; however, they were limited by the tradeoff between image resolution and field of view. Hence, computational imaging has arisen as an alternative method to conventional optical imaging, aimed at enhancing the aforementioned parameters. This study proposes a method for improving the resolution and field of view of an image in a lens-less reflection-type system. Our method was verified by computationally restoring the final image from diffraction images measured at various illumination positions using a visible light source. We introduced speckle illumination to expand the numerical aperture of the entire system, simultaneously improving image resolution and field of view. The image reconstruction process was accelerated by employing a convolutional neural network. Using the reconstructed phase images, we implemented high-resolution topography and demonstrated its applicability in wafer surface inspection. Furthermore, we demonstrated an ideal diffraction-limited spatial resolution of 1.7 μm over a field of view of 1.8 × 1.8 mm2 for the topographic imaging of targets with various surface roughness. The proposed approach is suitable for applications that simultaneously require high throughput and resolution, such as wafer-wide integrated metrology, owing to its compact design, cost-effectiveness, and mechanical robustness.

Abstract Background We aimed to investigate whether there are sex differences in disease activity measures among patients with axial spondyloarthritis (axSpA) and to determine any potential impact on the assessment of treatment responses to tumor necrosis factor alpha inhibitors (TNFi). Methods Using the Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry data, we compared sex differences in changes in the Bath Ankylosing Spondylitis Disease Activity Score (BASDAI) and Ankylosing Spondylitis Disease Activity Score (ASDAS) levels at baseline and one year after TNFi initiation in patients with axSpA. Results This study included 1,753 patients with axSpA who started or changed TNFi, of whom 1,343 (76.6%) were male. At baseline, the mean BASDAI and ASDAS scores of all patients were 5.98 and 3.6, respectively. The BASDAI changes between baseline and the one-year follow-up were independently associated with sex (𝛽 = 0.343, p = 0.011), whereas ASDAS was not (𝛽 = 0.079, p = 0.235). When judging the effect of TNFi at one-year of treatment, male patients were more likely to be assessed as effective by the BASDAI-based criterion (ΔBASDAI ≥ 50% or ≥ 2; OR 1.700, 95% CI 1.200–2.406), while the ASDAS-based criterion (ΔASDAS ≥ 1.1) showed no significant difference between sexes (OR 0.993, 95% CI 0.678–1.455), after adjusting for other baseline characteristics. Conclusions The changes in disease activity before and after TNFi use were significantly different between sexes when measured by BASDAI, but not ASDAS. TNFi treatment effects may be interpreted differently between sexes depending on the disease activity measure used.

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self‐compliance attribute in 4F2 memristor arrays. Evaluations of a 24 × 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13‐fold decline in vector‐matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16‐level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated.

Abstract Background High-risk stage III colon cancer has a considerably poorer prognosis than stage II and low-risk stage III colon cancers. Nevertheless, most guidelines recommend similar adjuvant treatment approaches for all these stages despite the dearth of research focusing on high-risk stage III colon cancer and the potential for improved prognosis with intensive adjuvant treatment. Given the the proven efficacy of triplet chemotherapy in metastatic colorectal cancer treatment, the goal of this study is to evaluate the oncologic efficacy and safety of mFOLFIRINOX in comparison to those of the current standard of care, mFOLFOX 6, as an adjuvant treatment for patients diagnosed with high-risk stage III colon cancer after radical resection. Methods This multicenter, randomized (1:1), open-label, phase II trial will assess and compare the effectiveness and toxicity of mFOLFIRINOX and mFOLFOX 6 in patients with high-risk stage III colon cancer after radical resection. The goal of the trial is to enroll 312 eligible patients, from 11 institutes, aged between 20 and 70 years, with an Eastern Cooperative Oncology Group (ECOG) performance status of 0–2, or between 70 and 75 with an ECOG performance status of 0. Patients will be randomized into two arms - Arm A, the experimental arm, and Arm B, the reference arm - and will receive 12 cycles of mFOLFIRINOX and mFOLFOX 6 every 2 weeks, respectively. The primary endpoint of this study is the 3-year disease-free survival, and secondary endpoints include the 3-year overall survival and treatment toxicity. Discussion The Frost trial would help determine the oncologic efficacy and safety of adjuvant triplet chemotherapy for high-risk stage III colon cancers and ultimately improve prognoses. Trial registration ClinicalTrials.gov NCT05179889, registered on 17 December 2021.

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector–matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Abstract Background Chest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usually fails when clinically applied to various machines. To overcome this problem, we propose an image manipulation pipeline. Methods A total of 15,010 chest x-rays from systems with different generators/detectors were retrospectively collected from five institutions from May 2020 to February 2021. We developed an AI model to classify pulmonary abnormalities using x-rays from a single system. Then, we externally tested its performance on chest x-rays from various machine specifications. We compared the area under the receiver operating characteristics curve (AUC) of AI models developed using conventional image processing pipelines (histogram equalization [HE], contrast-limited histogram equalization [CLAHE], and unsharp masking [UM] with common data augmentations) with that of the proposed manipulation pipeline (XM-pipeline). Results The XM-pipeline model showed the highest performance for all the datasets of different machine specifications, such as chest x-rays acquired from a computed radiography system (n = 356, AUC 0.944 for XM-pipeline versus 0.917 for HE, 0.705 for CLAHE, 0.544 for UM, p $$\le$$ ≤ 0.001, for all) and from a mobile x-ray generator (n = 204, AUC 0.949 for XM-pipeline versus 0.933 for HE, p = 0.042, 0.932 for CLAHE (p = 0.009), 0.925 for UM (p = 0.001). Conclusions Applying the XM-pipeline to AI training increased the diagnostic performance of the AI model on the chest x-rays of different machine configurations. Relevance statement The proposed training pipeline would successfully promote a wide application of the AI model for abnormality screening when chest x-rays are acquired using various x-ray machines. Key points • AI models developed using x-rays of a specific machine suffer from generalization. • We proposed a new image processing pipeline to address the generalization problem. • AI models were tested using multicenter external x-ray datasets of various machines. • AI with our pipeline achieved the highest diagnostic performance than conventional methods. Graphical Abstract

Objectives Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities. Methods An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census. Results The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were “very positive.” Conclusions This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.

Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). Materials and methods We retrospectively collected 100 patients (median age, 61 years [IQR, 53–70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). Results The median effective dose was 0.16 (IQR, 0.14–0.18) mSv for QLD CT and 0.65 (IQR, 0.57–0.71) mSv for LDCT. The radiologists’ evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P

Summary: Poly(ADP-ribosyl)ation (PARylation), catalyzed mainly by poly(ADP-ribose) polymerase (PARP)1, is a key posttranslational modification involved in DNA replication and repair. Here, we report that TIMELESS (TIM), an essential scaffold of the replisome, is PARylated, which is linked to its proteolysis. TIM PARylation requires recognition of auto-modified PARP1 via two poly(ADP-ribose)-binding motifs, which primes TIM for proteasome-dependent degradation. Cells expressing the PARylation-refractory TIM mutant or under PARP inhibition accumulate TIM at DNA replication forks, causing replication stress and hyper-resection of stalled forks. Mechanistically, aberrant engagement of TIM with the replicative helicase impedes RAD51 loading and protection of reversed forks. Accordingly, defective TIM degradation hypersensitizes BRCA2-deficient cells to replication damage. Our study defines TIM as a substrate of PARP1 and elucidates how the control of replisome remodeling by PARylation is linked to stalled fork protection. Therefore, we propose a mechanism of PARP inhibition that impinges on the DNA replication fork instability caused by defective TIM turnover.

To mimic the homeostatic functionality of biological neurons, a split-gate field-effect transistor (S-G FET) with a charge trap layer is proposed within a neuron circuit. By adjusting the number of charges trapped in the Si3N4 layer, the threshold voltage (Vth) of the S-G FET changes. To prevent degradation of the gate dielectric due to program/erase pulses, the gates for read operation and Vth control were separated through the fin structure. A circuit that modulates the width and amplitude of the pulse was constructed to generate a Program/Erase pulse for the S-G FET as the output pulse of the neuron circuit. By adjusting the Vth of the neuron circuit, the firing rate can be lowered by increasing the Vth of the neuron circuit with a high firing rate. To verify the performance of the neural network based on S-G FET, a simulation of online unsupervised learning and classification in a 2-layer SNN is performed. The results show that the recognition rate was improved by 8% by increasing the threshold of the neuron circuit fired.

Abstract The progress of artificial intelligence and the development of large‐scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low‐power neural network implementation approaches and neuromorphic computing systems are being highlighted as potential candidates. Specifically, the development of high‐density and reliable synaptic devices, which are the key elements of neuromorphic systems, is of particular interest. In this study, an 8 × 16 memcapacitor crossbar array that combines the technological maturity of flash cells with the advantages of NAND flash array structure is presented. The analog properties of the array with high reliability are experimentally demonstrated, and vector‐matrix multiplication with extremely low error is successfully performed. Additionally, with the capability of weight fine‐tuning characteristics, a spiking neural network for CIFAR‐10 classification via off‐chip learning at the wafer level is implemented. These experimental results demonstrate a high level of accuracy of 92.11%, with less than a 1.13% difference compared to software‐based neural networks (93.24%).

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

Purpose Bowel dysfunction commonly occurs in patients with locally advanced rectal cancer treated with a multimodal approach of chemoradiation therapy (CRT) combined with sphincter-preserving rectal resection. This study investigated the decline in anorectal function using sequential anorectal manometric measurements obtained before and after the multimodal treatment as well as at a 1-year follow-up. Methods This was a retrospective cohort study conducted in a single center. The study population consisted of patients with locally advanced mid- to low rectal cancer who received the preoperative CRT followed by sphincter-preserving surgery from 2012 to 2016. The anorectal manometric value measured after each treatment modality was compared to demonstrate the degree of decline in anorectal function. A generalized linear model of repeated measures was performed using the manometric values measured pre- and post-CRT, and at 12 months postoperatively. Results Overall, 100 patients with 3 consecutive manometric data were included in the final analysis. In the overall cohort study, the mean resting and maximal squeezing pressures showed insignificant decrement post-neoadjuvant CRT. At a 1-year postoperative follow-up, the maximal squeezing pressure significantly decreased. The maximal rectal sensory threshold demonstrated significant reduction consecutively after each following treatment (P

Abstract This study aimed to evaluate the relative risk of malignancy in patients with Takayasu’s arteritis compared to that in the general population. This retrospective nationwide cohort study used data from the Korean Health Insurance Review and Assessment Service database. All newly diagnosed patients with Takayasu’s arteritis were identified between January 2009 and December 2019. They were observed until the diagnosis of malignancy, death, or end of the observational period, December 2020. The standardized incidence ratios (SIRs) of the overall and site-specific malignancies were estimated and compared with the incidence of cancer in the general population retrieved from the National Cancer Registry. We identified 1449 newly diagnosed patients with Takayasu’s arteritis during the observational period (9196 person-years). A total of 74, 66, and 8 patients had overall, solid, and hematologic malignancies, respectively. The risks of overall [SIR, 1.62; 95% confidence interval (CI) 1.27–2.03], solid (SIR, 1.51; 95% CI 1.17–1.92), and hematologic (SIR, 4.05; 95% CI 1.75–7.98) malignancies were increased compared to those in the general population. In solid malignancies, breast (SIR, 2.07; 95% CI 1.16–3.42) and ovarian (SIR, 4.45; 95% CI 1.21–11.39) cancers had an increased risk. In hematologic malignancies, the risk of myelodysplasia increased (SIR, 18.02; 95% CI 3.72–52.66). Immunosuppressive agent use was not associated with malignancy. There was no specific period when cancer more frequently occurred. An increased risk of malignancy was observed in patients with Takayasu’s arteritis compared to that in the general population in this large-scale nationwide population study of Korean health insurance data.

Abstract Undernutrition is a risk factor of tuberculosis (TB), but the association between duration of undernutrition and occurrence of TB is inconclusive. The objective of this study was to determine whether there is a difference in occurrence of TB according to the duration of undernutrition expressed as accumulated number of underweight in Republic of Korea, an intermediate TB burden country. The National Health Insurance database was used. Eligible subjects were individuals who received a national health examination between 1 and 2009 and 31 December 2010, and who also had received health examinations for four consecutive years prior to 2009. Finally included individuals in the analysis were followed until 31 December 2017. Accumulated number of underweight was defined as the number of times recorded as underweight over four consecutive years. The outcome of the study was newly diagnosed TB according to accumulated number of underweight. Among a study population of 2,396,434, TB was identified in 9,322 (3.89%) cases. The highest accumulated number of underweight was significantly associated with occurrence of TB (adjusted hazard ratio [aHR] 2.563, 95% CI 2.319–2.833). This association remained consistent after adjusting for demographic factors and underlying diseases (aHR 3.326, 95% CI 3.004–3.84). In stratified analysis based on age, sex, diabetes (DM), hypertension (HTN), and waist circumference (WC) in metabolic syndrome (MS), age and sex were identified as effect modifiers. Occurrence of TB was significantly higher in the group with the highest accumulated number of underweight under 65 years of age.

Monitoring of cytokine storm markers is critical for determining the disease progression and treatment efficacy. In this study, we developed a novel fourplex electrochemical (EC) biosensor using nanohole array (NHA) structures containing several nanoholes on a single electrode for detecting cytokines with a low limit. A framework was developed for fabricating and optimizing multiplexed EC sandwich immunoassays using NHA electrodes because an NHA can enhance the EC detection of molecular binding by controlling the binding sites of captured biomolecules. For detecting multiple cytokines, the captured antibodies were immobilized on the surface-modified NHA and sandwich immunoassays with amperometric signal amplifications using poly-streptavidin horseradish peroxidase conjugates and 3,3′,5,5′-tetramethylbenzidine. Under the optimal conditions, the EC NHA immunosensors exhibited high sensitivity and specificity with a limit of detection of 1 pg/mL and a detection range of 1–300 pg/mL. The results revealed that the developed NHA sensor exhibits reliable EC characteristics and could be used to analyze multiple cytokines using small sample volume and subsequently developed for other applications.

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.

Memristor devices can be utilized for various computing applications, and stochastic computing is one of them. The intrinsic stochastic characteristics of the memristor cause unpredictable current fluctuations by the capture and emission of electrons in a trap site. Herein, a true random number generator (TRNG) using the random telegraph noise (RTN) of the memristor as an entropy source is proposed. TiOx/Al2O3 memristors are fabricated, and the capture time probability of the RTN characteristics is modulated to 50% with varying read‐voltage and device conductance state. In addition, the TRNG operations with the RTN current signals as entropy sources are experimentally demonstrated with a customized breadboard, and the randomness is verified with the National Institute of Standards and Technology (NIST) tests.

Memristor‐based ternary content‐addressable memory (TCAM) has emerged as an alternative to conventional static random‐access memory (SRAM)‐based TCAM because of its high‐density integration and zero‐static energy consumption. Herein, 0T2R TCAM operation on a 32 × 32 passive memristor crossbar circuit is experimentally verified. The effective margin, which is the difference between the match case and 1‐bit mismatch case, is improved through precise tuning operations. Moreover, the number of mismatch bits and match cases can be accurately detected thanks to the linear relationship between the number of mismatch bits and match‐line current. In addition, the nonideal effects in the passive crossbar array including dynamic voltage drop and sneak current are analyzed through SPICE studies. These results indicate that the proposed TCAM operating conditions can ensure stable TCAM operation in larger arrays.

BackgroundAn artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. ObjectiveWe aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. MethodsThis retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. ResultsThe AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). ConclusionsThe combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Background: A small number of studies have suggested an association between systemic lupus erythematosus (SLE) and Parkinson’s disease (PD). Objectives: This study aimed to determine the risk of incident PD among Korean patients with SLE. Design: A nationwide retrospective cohort study using the claims database of the National Health Insurance Service of Korea was conducted. Methods: Patients above 40 years of age diagnosed with SLE between 2010 and 2015 were included in the study. The primary outcome of the study was incident PD, defined by registration in the rare intractable diseases program for PD and an ICD-10 code of G20. Subjects were followed until PD diagnosis or the end of 2017. We estimated the cumulative incidence of PD among the SLE cohort and compared this to that in a 1:5 age- and sex-matched control group. Results: Totals of 11,615 SLE cases and 58,075 matched controls were identified. The cumulative incidence rate of PD was 0.7 per 1000 person-years in the SLE cohort. The crude hazard ratio (HR) of incident PD was 1.71 (95% CI: 1.25–2.36) among the SLE cohort compared to the control group. The HR was 1.59 (95% CI: 1.15–2.20) after adjustment for age, sex, income, and baseline comorbidities. Conclusion: This study demonstrated that patients with SLE had an increased risk of incident PD compared to non-SLE controls. Further research is required to determine the mechanism underlying this and to elucidate the precise role of systemic inflammation in the development of PD in patients with SLE.

Abstract Background Previous studies have shown that the incidence and risk factors of gout differs according to sex. However, little research has been done on the association between reproductive factors and gout. We conducted an analysis of a large nationwide population-based cohort of postmenopausal women to determine whether there is an association between reproductive factors and the incidence of gout. Methods A total of 1,076,378 postmenopausal women aged 40–69 years who participated in national health screenings in 2009 were included in the study. The outcome was the occurrence of incident gout, which was defined using the ICD-10 code of gout (M10) in the claim database. Cox proportional hazard models were used for the analyses and stratified analyses according to body mass index (BMI) and the presence/absence of chronic kidney disease (CKD) were performed. Results The mean follow-up duration was 8.1 years, and incident cases of gout were 64,052 (incidence rate 7.31 per 1000 person-years). Later menarche, earlier menopause, and a shorter reproductive span were associated with a high risk of gout. No association between parity and gout incidence was observed. Use of oral contraceptives (OC) and hormone replacement therapy (HRT) were associated with an increased risk of gout. The association between reproductive factors and gout was not statistical significant in the high BMI group. The effects of OC and HRT usage on gout were not significant in the CKD group. Conclusion Shorter exposure to endogenous estrogen was associated with a high risk of gout. Conversely, exposure to exogenous estrogen such as OC and HRT was associated with an increased risk of gout.

Abstract Background This study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm. Methods The clinical characteristics and demographic data of patients with axial SpA included in the Korean College of Rheumatology Biologics and Targeted Therapy registry were investigated. The patterns of disease manifestations were examined using divisive hierarchical cluster analysis. After clustering, we compared the clinical characteristics of patients and the drug survival of TNFi between the classified groups. Results A total of 1042 patients were analysed. The cluster analysis classified patients into two groups: axial group predominantly showing isolated axial manifestations (n = 828) and extra-axial group more frequently showing extra-axial symptoms (n = 214). Almost all extra-axial symptoms (peripheral arthritis, enthesitis, uveitis, and psoriasis) were more frequently observed in the extra-axial group than in the axial group. Moreover, patients in the extra-axial group had shorter disease duration, later disease onset, and higher disease activity than those in the axial group. The disease activity was comparable between the two groups after 1 year of treatment with TNFi. Interestingly, the extra-axial group had a lower drug survival with TNFi than the axial group (p = 0.001). Conclusions Cluster analysis of patients with axial SpA using TNFi classified two distinct clinical phenotypes. These clusters had different TNFi drug survival, clinical characteristics, and disease activity.

Purpose Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%–96%) and specificity of 37% (95% CI: 26%–50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standarddose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2–6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710–56755) to 44840 (TPR, 38%; 95% CI: 35161–68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

Abstract Background Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. Methods Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. Results The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725–0.730] vs. 0.689 [0.606–0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576–0.658] vs. 0.565 [0.493–0.605], F1 score 0.841 [0.837–0.843] vs. 0.803 [0.732–0.828], AUC of the precision-recall curve 0.808 [0.763–0.829] vs. 0.754 [0.714–0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. Conclusions RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

BackgroundTo date, few studies have focused on risk factors for gout in young people, and large-scale studies on the relationship between metabolic syndrome (MetS) and gout are lacking. We aimed to investigate the association between gout and MetS in a large nationwide population-based cohort of young men who participated in national health examination.Materials and methodsCohort included men aged 20–39 years who participated in a health check-up in 2009–2012. A total of 3,569,104 subjects was included in the study, excluding those who had a previous diagnosis of gout or had renal impairment. The outcome was the occurrence of gout, which was defined using the diagnosis code of gout in the claims database. Cox proportional hazard model was used to evaluate the association between MetS and incident gout.ResultsMean follow-up duration was 7.35 ± 1.24 years and the incidence rate of gout was 3.36 per 1,000 person-years. The risk of gout in subjects with MetS was 2.4-fold higher than subjects without MetS. Among the components of MetS, hypertriglyceridemia and abdominal obesity showed the greatest association with gout. As the number of MetS components increased, the risk of gout increased. The association between gout and MetS was more pronounced in relatively young subjects and in low- or normal-weight subjects.ConclusionMetabolic syndrome is an important risk factor for the gout in young men. In particular, the association between MetS and gout was greater in young and non-obese men. Management of MetS in young men will be important for future gout prevention.

We investigated the impacts of lifestyle changes, namely, smoking, alcohol intake, and exercise, on the development of tuberculosis (TB) in patients with type 2 diabetes mellitus (T2DM). A retrospective population-based cohort study used data from the Korean National Health Insurance system database. We examined subjects diagnosed with T2DM and without previous history of TB between 2009 and 2012 who underwent two serial health examinations. The study participants were classified into each of the four groups based on changes in the patterns of smoking, alcohol intake, and exercise at the time of the second examination. The outcome of the study was newly diagnosed TB in patients with T2DM. Among 1,659,804 included subjects, TB was newly diagnosed with 10,288 subjects. Both consistent smokers (HR 1.406; 95% CI 1.333–1.483) and new smokers (HR 1.185; 95% CI 1.063–1.320) had a higher TB risk than smoking quitters (HR 1.107; 95% CI 1.009–1.216) and never smokers. Both consistent heavy drinkers (HR 1.281; 95% CI 1.172–1.399) and heavy drinking quitters (HR 1.247; 95% CI 1.147–1.356) had a higher TB risk than new heavy drinkers and never drinkers. With respect to exercise, persistent non-exercisers (HR 1.309; 95% CI 1.72–1.399) and exercise quitters (HR 1.164; 95% CI 1.066–1.271) had a higher TB risk than new exercisers. In the subgroup analysis, a significant interaction was observed between lifestyle changes and age. We found that lifestyle changes were associated with development of TB in patients with T2DM. These results suggest that lifestyle management could be a valuable strategy for control of TB in Korea.

BackgroundThe impact of nirmatrelvir/ritonavir treatment on shedding of viable virus in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear.MethodsA prospective cohort study evaluating mildly ill COVID-19 patients was conducted. Virologic responses were compared between nirmatrelvir/ritonavir-treatment and supportive care groups. Risk factors and relevant clinical factors for shedding of viable virus were investigated.ResultsA total of 80 COVID-19 patients were enrolled and 222 sputum specimens were collected. Ten patients were dropped during follow-up, and 33 patients in the nirmatrelvir/ritonavir and 37 in the supportive care groups were compared. The median age was 67 years, and 67% were male. Clinical characteristics were similar between groups. Viral loads decreased significantly faster in the nirmatrelvir/ritonavir group compared with the supportive care group (P < 0.001), and the slope was significantly steeper (–2.99 ± 1.54 vs. –1.44 ± 1.52; P < 0.001). The duration of viable virus shedding was not statistically different between groups. In the multivariable analyses evaluating all collected specimens, male gender (OR 2.51, 95% CI 1.25–5.03, P = 0.010), symptom score (OR 1.41, 95% CI 1.07–1.87, P = 0.015), days from symptom onset (OR 0.72, 95% CI 0.59–0.88, P = 0.002), complete vaccination (OR 0.09, 95% CI 0.01–0.87, P = 0.038), and BA.2 subtype (OR 0.49, 95% CI 0.26–0.91, P = 0.025) were independently associated with viable viral shedding, while nirmatrelvir/ritonavir treatment was not.ConclusionNirmatrelvir/ritonavir treatment effectively reduced viral loads of SARS-CoV-2 Omicron variants but did not decrease the duration of viable virus shedding.

The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler–Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms.

To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels.

Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one‐selector–one‐memory (1S–1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage‐matching issues in constructing the 1S–1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence.

Background: In previous studies, cardiovascular (CV) risk was increased in patients with gout. The effects of uric acid–lowering therapy on CV risk in gout patients have been investigated in numerous studies; however, allopurinol and benzbromarone have rarely been compared. Objectives: To compare CV risk based on allopurinol and benzbromarone treatment in Korean gout patients. Design: A nationwide population-based retrospective cohort study. Methods: We used South Korea database of the Health Insurance Review and Assessment (HIRA) service to identify gout patients ⩾18 years of age who newly started allopurinol or benzbromarone between 2009 and 2015. The primary outcome of the study was the occurrence of a composite CV endpoint, which included coronary revascularization, hospitalization due to myocardial infarction, ischemic stroke, and transient ischemic attack. Cox proportional hazard regression analysis and Kaplan–Meier curves were used for analysis. Results: The study included 257,097 allopurinol initiators and 7868 benzbromarone initiators. Compared with allopurinol initiators, the adjusted hazard ratio (aHR) of the composite CV endpoint of benzbromarone initiators was 1.01 [95% confidence interval (CI): 0.83−1.21], which was not significantly different. The results did not change even when 1:3 propensity score matching was performed for baseline characteristics. In subgroup analysis of high-risk patients with CV disease, significant difference was not observed between allopurinol and benzbromarone initiators. Conclusion: In this study, significant difference was not found in CV risk between allopurinol and benzbromarone initiators. In the high-CV-risk group, the incidence of CV events did not differ between allopurinol and benzbromarone initiators.

In this paper, a physical unclonable function (PUF), a type of hardware security device, is proposed to overcome the limitations of existing security schemes. A $32\times 32$ crossbar array using TiOx/Al2O3- based memristors was fabricated, and electrical characteristics including its set voltage distribution were analyzed. The memristor switching characteristics model is described in a simplified space-charge-limited current (SCLC) regime. Based on this I-V model, selected bit-line current PUFs (SBC-PUFs) were designed with $32\times 32$ , $64\times 64$ , and $128\times 128$ crossbar arrays. The entropy source of these PUFs is the set voltage deviation in the fabricated memristors. Due to these characteristics, the SBC-PUF can exploit the broad resistance distribution near the switching region, including the internal resistance distributions of the high resistance state (HRS) and low resistance state (LRS). The SBC-PUF performance was evaluated for randomness/uniformity, correctness/reliability, and uniqueness by calculating the Hamming weight and intra/inter Hamming distance of challenge-response pairs (CRPs). The designed structure demonstrates high-security performance due to the high value of these indicators and the large number of CRPs. Furthermore, the devised PUF has a higher prediction error rate than arbiter PUF in machine learning attacks. This study verified that the SBC-PUF using the memristor of the crossbar array structure is safe enough to be used for hardware security.

Patients with systemic rheumatic diseases (SRD) are vulnerable for coronavirus disease (COVID-19). The Korean College of Rheumatology recognized the urgent need to develop recommendations for rheumatologists and other physicians to manage patients with SRD during the COVID-19 pandemic. The working group was organized and was responsible for selecting key health questions, searching and reviewing the available literature, and formulating statements. The appropriateness of the statements was evaluated by voting panels using the modified Delphi method. Four general principles and thirteen individual recommendations were finalized through expert consensus based on the available evidence. The recommendations included preventive measures against COVID-19, medicinal treatment for stable or active SRD patients without COVID-19, medicinal treatment for SRD patients with COVID-19, and patient evaluation and monitoring. Medicinal treatments were categorized according to the status with respect to both COVID-19 and SRD. These recommendations should serve as a reference for individualized treatment for patients with SRD. As new evidence is emerging, an immediate update will be required.

The fork protection complex (FPC), including the proteins TIMELESS and TIPIN, stabilizes the replisome to ensure unperturbed fork progression during DNA replication. Here the authors reveal that that SDE2, a PCNA-associated protein, plays an important role in maintaining active replication and protecting stalled forks by regulating the replication fork protection complex (FPC).

Abstract We discuss the sensitivity of the present and near-future axion dark matter experiments to a halo of axions or axion-like particles gravitationally bound to the Earth or the Sun. Such halos, assuming they are formed, can be searched for in a wide variety of experiments even when the axion couplings to matter are small, while satisfying all the present experimental bounds on the local properties of dark matter. The structure and coherence properties of these halos also imply novel signals, which can depend on the latitude or orientation of the detector. We demonstrate this by analyzing the sensitivity of several distinct types of axion dark matter experiments.

Abstract Cosmological relaxation of the electroweak scale is an attractive scenario addressing the gauge hierarchy problem. Its main actor, the relaxion, is a light spin-zero field which dynamically relaxes the Higgs mass with respect to its natural large value. We show that the relaxion is generically stabilized at a special position in the field space, which leads to suppression of its mass and potentially unnatural values for the model’s effective low-energy couplings. In particular, we find that the relaxion mixing with the Higgs can be several orders of magnitude above its naive naturalness bound. Low energy observers may thus find the relaxion theory being fine-tuned although the relaxion scenario itself is constructed in a technically natural way. More generally, we identify the lower and upper bounds on the mixing angle. We examine the experimental implications of the above observations at the luminosity and precision frontiers. A particular attention is given to the impressive ability of future nuclear clocks to search for rapidly oscillating scalar ultra-light dark matter, where the future projected sensitivity is presented.

In this study, we demonstrate both of digital and analog memory operations in InGaZnO (IGZO) memristor devices by controlling the electrode materials for neuromorphic application. The switching properties of the devices are determined by the initial energy barrier characteristics between the metal electrodes and the IGZO switching layer. Digital switching characteristics are obtained after the forming process when Schottky junction occurs at both of top and bottom electrodes. On the other hands, analog resistive switching is achieved when Schottky and Ohmic junctions exist at each side because the applied voltage modulates the Schottky barrier height through the Ohmic contact. In addition, the weight-update properties of the devices are verified depending on identical and incremental pulse schemes. The incremental pulse trains improve the linearity and variation of weight modulation, leading to the stable learning characteristics of neuromorphic system in terms of pattern recognition with MNIST hand-written digit images.

Biological disease-modifying antirheumatic drugs (bDMARDs) are highly effective agents for the treatment of inf lammatory arthritis; however, they also possess a potential risk for serious infection. Recently, with the rapid expansion of the bDMARDs market in Korea, reports of serious adverse events related to the agents have also increased, necessitating guidance for the use of bDMARDs. Current work entitled, “Expert consensus for the use of bDMARDs drugs for inflammatory arthritis in Korea,” is the first to describe the appropriate use of bDMARDs in the management of inflammatory arthritis in Korea, with an aim to provide guidance for the local medical community to improve the quality of clinical care. Twelve consensus statements regarding the use of bDMARDs for the management of rheumatoid arthritis and ankylosing spondylitis were generated. In this review, we provide detailed guidance on bDMARDs use based on expert consensus, including who should prescribe, the role of education, indications for use, and monitoring strategies for safety.

Here, a composite nanoparticle with an acid–base bifunctional structure has been reported for the transesterification of rapeseed oil to produce biodiesel. Triazole-PWA (PWA = 12-tungstophosphoric acid) composite materials with a hexahedral structure are produced using the precipitation method, showing the average particle diameters of 200–800 nm. XPS and FT-IR analyses indicate well-defined chemical bonding of triazole moieties to the PWA. The functionalization and immobilization of PWAs are investigated due to strong interactions with triazole, which significantly improves the thermal stability and even surface area of the heteropoly acid. Furthermore, various ratios of triazole and PWAs are examined using NH3-TPD and CO2-TPD to optimize the bi-functionality of acidity and basicity. The prepared nanomaterials are evaluated during the transesterification of rapeseed oil with methanol to analyze the effect of triazole addition to PWAs according to the different ratios. Overall, the bifunctional triazole-PWA composite nanoparticles exhibit higher fatty acid methyl ester (FAME) conversions than pure PWA nanoparticles. The optimized catalyst with a triazole:PWA ratio of 6:1 exhibits the best FAME-conversion performance due to its relatively large surface area, balance of acidity, and strong basicity from the well-designed chemical nano-structure.

In this work, two types of InGaZnO (IGZO) memristors were fabricated to confirm the conduction mechanism and degradation characteristics of memristors with different electrode materials. The IGZO memristor exhibits abrupt switching characteristics with the Pd electrode owing to the formation and destruction of conductive filaments but shows gradual switching characteristics with the p-type Si electrode according to the amount of generated oxygen vacancy. The electrical characteristics and conduction mechanisms of the device are analyzed using an energy band diagram and experimentally verified with random telegraph noise characteristics confirming the trap effects on the device conduction.

The optical response of inorganic two-dimensional semiconductors is dominated by Wannier-Mott excitons, but molecular systems can host localised Frenkel excitons. Here, the authors report strong optical response in a class of monolayer molecular J-aggregates due to the coherent Coulomb interaction between localised Frenkel excitons.

Background To investigate the prognostic impact of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement for the overall survival (OS) of patients with surgically treated lung adenocarcinomas. Methods A total of 689 patients with stage I–III lung adenocarcinomas (male:female = 334:355; median age, 64 years) underwent complete surgical resection between 2007 and 2013. The prognostic impact of EGFR mutation and ALK rearrangement on OS was analyzed using Cox regression analysis. Certain clinicopathological prognostic factors (i.e., age, sex, smoking status, nodule type, solid portion size, pathologic stage, adenocarcinoma subtype, and history of adjuvant chemotherapy) were included for adjustments of the hazard ratio (HR). Results EGFR mutation was observed in 438 patients (64%) and ALK rearrangement was seen in 28 patients (4%). Multivariable‐adjusted Cox regression demonstrated that the prognostic effect of EGFR mutation on OS differed by age (HR, exp.[−5.199 + 0.064*age]). The adjusted HR for EGFR mutation was 0.14 (95% CI: 0.05–0.36; P 0.05). Conclusions EGFR mutation was independently prognostic of the long‐term outcomes of patients with surgically treated lung adenocarcinomas. A more favorable prognostic effect was seen in younger than in older patients. ALK rearrangement was not associated with OS.

Background The growth rate of thymic epithelial tumors (TETs) and thymic cysts was investigated to determine whether they can be differentiated and clinico‐radiological predictors of interval growth was identified. Methods This retrospective study included 122 patients with pathologically proven thymic cysts (n = 56) or TETs (n = 66) who underwent two serial chest computed tomography scans at least eight weeks apart. Average diameters and attenuation were measured, volume‐doubling times (VDTs) were calculated, and clinical characteristics were recorded. VDTs were compared using the log‐rank test. Predictors of growth were analyzed using the log‐rank test and Cox regression analysis. Results The frequency of growth did not differ significantly between TETs and thymic cysts (P = 0.279). The VDT of thymic cysts (median 324 days) was not significantly different from that of the TETs (median 475 days; P = 0.808). Water attenuation (≤ 20 Hounsfield units) predicted growth in thymic cysts (P = 0.016; hazard ratio 13.2, 95% confidence interval 1.6–107.3), while lesion size (> 17.2 mm) predicted growth in TETs (P = 0.008 for size, P = 0.029 for size*time). For the growing lesions, the positive and negative predictive values of water attenuation for thymic cysts were 93% and 80%, respectively. Conclusion The frequencies of interval growth and VDTs were indistinguishable between TETs and thymic cysts. Water attenuation and lesion size predicted growth in thymic cysts and TETs, respectively. Among the growing lesions, water attenuation was a differential feature of thymic cysts.

In this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets. The source code is publicly released on the Github website.

To design metal nanoparticles (NPs) on a perovskite surface, the exsolution method has been extensively used for efficient catalytic reactions. However, there are still the challenges of finding a combination and optimization for the NPs’ control. Thus, we report in situ control of the exsolved Ni NPs from perovskite to apply as a catalyst for dry reforming of methane (DRM). The La0.8Ce0.1Ti0.6Ni0.4O3 (LCTN) is designed by Ce doping to incorporate high amounts of Ni in the perovskite lattice and also facilitate the exsolution phenomenon. By control of the eluted Ni NPs through exsolution, the morphological properties of exsolved Ni NPs are observed to have a size range of 10~49 nm, while the reduction temperatures are changed. At the same time, the chemical structure of the eluted Ni NPs is also changed by an increased reduction temperature to a highly metallic Ni phase with an increased oxygen vacancy at the perovskite oxide surface. The optimized composite nanomaterial displays outstanding catalytic performance of 85.5% CH4 conversion to produce H2 with a value of 15.5 × 1011 mol/s·gcat at 60.2% CO conversion, which shows the importance of the control of the exsolution mechanism for catalytic applications.

The fork protection complex (FPC), comprising the TIMELESS (TIM)-TIPIN heterodimer, acts as a scaffold of the replisome to support seamless DNA replication. We recently showed that SDE2, a PCNA-associated DNA replication stress regulator, maintains the integrity of the FPC, and together with TIM, protects stalled replication forks from nucleolytic degradation.