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Abstract We investigated the clinical and radiologic predictors of unruptured symptomatic intracranial artery dissection (IAD) outcomes. Unruptured symptomatic IAD patients who underwent vessel wall magnetic resonance imaging (VW-MRI) and time-of-flight magnetic resonance angiography (TOF-MRA) within 1 month after symptom onset, followed for over 12 months were included. Baseline features predicting the clinical outcome of recurrent symptoms and radiologic outcomes of aneurysmal dilatation and occlusion were analyzed using logistic regression analysis. The Kaplan–Meier method calculated the median time to morphological stability. Patients with aneurysmal dilatation were categorized into progressive and non-progressive enlargement subgroups. Seventy-three IADs from 65 patients were included. All patients showed benign clinical course (mRS 0–1). No baseline features were predictive of recurrent symptoms. Aneurysmal dilatation was associated with increased outer diameter in baseline VW-MRI (OR, 23.15; 95% CI, 3.78–141.75, P

Products coated with flexible film can be stored in wound rolls after the winding process. However, if the roll product experiences excessive internal stress, it can lead to various defects in the coated layer. To alleviate the stress within the wound roll, a taper tension profile is utilized to progressively reduce tension as the winding length increases. As a substantial amount of web is wound onto the core, the moment of inertia of the wound roll increases. It has been observed that, as the winding length increases, the tension control performance of the taper tension tends to deteriorate. We propose an advanced taper tension profile to enhance tension control performance and validates the effectiveness of this profile through winding experiments. It was observed that, compared to the parabolic taper tension, maximum tension error could be reduced by approximately 16.1 % throughout the winding process. This reduction in tension error enables the preservation of the radial stress reduction effect of the taper tension, resulting in a 13.3 % decrease in the maximum radial stress compared to the parabolic taper tension. The proposed taper tension profile contributes to a more uniform quality of the coated layer, especially as the maximum winding length is extended.

Abstract This study investigates the effects of an air layer on drag reduction and turbulence dynamics in channel flow over a superhydrophobic surface (SHS). Employing the OpenFOAM platform, direct numerical simulation was conducted to investigate turbulent channel flow with an air layer over an SHS. The simulations, which take into account the interaction between water and air, analyze various parameters such as velocity distribution, drag reduction (DR), Reynolds stress, turbulent kinetic energy (TKE), and coherent structures near the water–air interface. The presence of an air layer significantly alters the velocity distribution, leading to higher velocities at the interface compared to simulations without the air layer. Notably, the thickness of the air layer emerges as an important factor, with larger thicknesses resulting in increased velocities and drag reduction. This study underscores the substantial impact of the air layer on TKE near the superhydrophobic surface, emphasizing its role in understanding and optimizing drag reduction. Furthermore, the nonlinear relationship between slip velocity, Q contours, and coherent structures near the SHS are investigated.

Abstract The unique characteristics of an anti‐ambipolar switch (AAS) device exhibit Λ‐shaped transfer responses (namely delta conductance) and present unique opportunities to overcome the limit of silicon‐based, complementary metal‐oxide‐semiconductor (CMOS) logic circuits. It is crucial because a device that only turns on under a certain bias range can be utilized to simplify the logic circuit and reduce the device count and circuit area required to perform logic functions. In this study, a physically scalable AAS device is investigated using ZnO and dinaphtho[2,3‐b:2′,3′‐f]thieno[3,2‐b]thiophene as heterojunction structures to reduce the operating voltage and enhance the peak current and peak‐to‐valley ratio of the AAS device. Moreover, novel logic circuits for AND, OR, XOR, DEMUX, and half‐adder functions are demonstrated using AAS devices. AAS device‐based logic circuits exhibit power‐efficiency characteristics (≈49 times lower than that of the 90‐nm silicon‐based CMOS inverter) and reduce the transistor count and the circuit area by ≈67% and ≈70%, respectively. These results indicate that the use of AAS device‐based logic circuits can be a promising approach to overcome the physical scaling limit of current CMOS technology.

Abstract Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Abstract Background Photosynthetic microalgae are known for their sustainable and eco-friendly potential to convert carbon dioxide into valuable products. Nevertheless, the challenge of self-shading due to high cell density has been identified as a drawback, hampering productivity in sustainable photoautotrophic mass cultivation. To address this issue, mutants with altered pigment composition have been proposed to allow a more efficient light diffusion but further study on the role of the different pigments is still needed to correctly engineer this process. Results We here investigated the Chlamydomonas reinhardtii Δzl mutant with zeaxanthin as the sole xanthophyll. The Δzl mutant displayed altered pigment composition, characterized by lower chlorophyll content, higher chlorophyll a/b ratio, and lower chlorophyll/carotenoid ratio compared to the wild type (Wt). The Δzl mutant also exhibited a significant decrease in the light-harvesting complex II/Photosystem II ratio (LHCII/PSII) and the absence of trimeric LHCIIs. This significantly affects the organization and stability of PSII supercomplexes. Consequently, the estimated functional antenna size of PSII in the Δzl mutant was approximately 60% smaller compared to that of Wt, and reduced PSII activity was evident in this mutant. Notably, the Δzl mutant showed impaired non-photochemical quenching. However, the Δzl mutant compensated by exhibiting enhanced cyclic electron flow compared to Wt, seemingly offsetting the impaired PSII functionality. Consequently, the Δzl mutant achieved significantly higher cell densities than Wt under high-light conditions. Conclusions Our findings highlight significant changes in pigment content and pigment–protein complexes in the Δzl mutant compared to Wt, resulting in an advantage for high-density photoautotrophic cultivation. This advantage is attributed to the decreased chlorophyll content of the Δzl mutant, allowing better light penetration. In addition, the accumulated zeaxanthin in the mutant could serve as an antioxidant, offering protection against reactive oxygen species generated by chlorophylls.

Introduction: The CH1 domain of IgG antibodies controls assembly and secretion, mediated by the molecular chaperone BiP via the endoplasmic reticulum protein quality control (ERQC) mechanism. However, it is not clear whether the variable domains are necessary for this process.Methods: Here, we generated IgG1 antibodies in which the V domain (VH and/or VL) was either removed or replaced, and then assessed expression, assembly, and secretion in HEK293 cells.Results: All Ig variants formed a covalent linkage between the Cγ1 and Cκ, were successfully secreted in an assembled form. Replacement of the cognate Vκ with a non-secretory pseudo Vκ (ψVκ) hindered secretion of individual or assembled secretion of neither heavy chains (HCs) nor light chains (LCs). The ψLC (ψVκ-Cκ) exhibited a less folded structure compared to the wild type (wt) LC, as evidenced by enhanced stable binding to the molecular chaperone BiP and susceptibility to proteolytic degradation. Molecular dynamics simulation demonstrated dramatic alterations in overall structure of ψFab (Fd-ψLC) from wt Fab.Discussion: These findings suggest that V domains do not initiate HC:LC assembly and secretion; instead, the critical factor governing IgG assembly and secretion is the CH-CL pairing. Additionally, the structural integrity of the VL domain is crucial for IgG secretion. These data offer valuable insight into the design of bioactive molecules based on an IgG backbone.

Currently, GPUs face significant challenges due to limited off-chip bandwidth (BW) and memory capacity during DNN training. To address these bottlenecks, we propose a memory access-triggered near-data processing matNDP architecture that offloads memory- and communication-bound operations. With matNDP, normal memory accesses also serve as implicit NDP requests to enable NDP in a non-invasive manner without modifying core-side ISA/microarchitecture/SW, for practicality. Additionally, matNDP enables on-the-fly NDP where the data already supplied in normal memory requests for compute-bound operations are also used for NDP; thus, matNDP can overlap even dependent kernels while also reducing memory traffic. Moreover, with the overlap, memory bandwidth (BW) underutilized by GPU cores can be used by NDP units to improve performance under the same total memory BW. The matNDP units can be deployed to heterogeneous memory devices in a system. First, we deploy them near GPU’s memory controllers. Secondly, our NDP units are deployed in memory expanders connected to multiple GPUs to create an NDP-enabled memory eXpander Network (NDPXNet). It can entirely offload gradient reduction and optimizer in data-parallel training, achieving additional speedups while eliminating redundancy in memory usage and optimizer execution. Thus, we 1) enable NDP without core HW/SW changes, 2) overlap the execution of dependent layers, and 3) offload both memory- and communication-bound operations from GPUs in DNN training. Through our deep learning compiler support, NDP kernels can be generated automatically without any model code modification. Consequently, matNDP can improve training throughput by up to $2.73\times $ and reduce energy by up to 41.4%.

Abstract Doped Mott insulators are the starting point for interesting physics such as high temperature superconductivity and quantum spin liquids. For multi-band Mott insulators, orbital selective ground states have been envisioned. However, orbital selective metals and Mott insulators have been difficult to realize experimentally. Here we demonstrate by photoemission spectroscopy how Ca2RuO4, upon alkali-metal surface doping, develops a single-band metal skin. Our dynamical mean field theory calculations reveal that homogeneous electron doping of Ca2RuO4 results in a multi-band metal. All together, our results provide evidence for an orbital-selective Mott insulator breakdown, which is unachievable via simple electron doping. Supported by a cluster model and cluster perturbation theory calculations, we demonstrate a type of skin metal-insulator transition induced by surface dopants that orbital-selectively hybridize with the bulk Mott state and in turn produce coherent in-gap states.

Purpose To propose standardized and feasible imaging protocols for constructing artificial intelligence (AI) database in acute stroke by assessing the current practice at tertiary hospitals in South Korea and reviewing evolving AI models. Materials and Methods A nationwide survey on acute stroke imaging protocols was conducted using an electronic questionnaire sent to 43 registered tertiary hospitals between April and May 2021. Imaging protocols for endovascular thrombectomy (EVT) in the early and late time windows and during follow-up were assessed. Clinical applications of AI techniques in stroke imaging and required sequences for developing AI models were reviewed. Standardized and feasible imaging protocols for data curation in acute stroke were proposed. Results There was considerable heterogeneity in the imaging protocols for EVT candidates in the early and late time windows and posterior circulation stroke. Computed tomography (CT)-based protocols were adopted by 70% (30/43), and acquisition of noncontrast CT, CT angiography and CT perfusion in a single session was most commonly performed (47%, 14/30) with the preference of multiphase (70%, 21/30) over single phase CT angiography. More hospitals performed magnetic resonance imaging (MRI)-based protocols or additional MRI sequences in a late time window and posterior circulation stroke. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) were most commonly performed MRI sequences with considerable variation in performing other MRI sequences. AI models for diagnostic purposes required noncontrast CT, CT angiography and DWI while FLAIR, dynamic susceptibility contrast perfusion, and T1-weighted imaging (T1WI) were additionally required for prognostic AI models. Conclusion Given considerable heterogeneity in acute stroke imaging protocols at tertiary hospitals in South Korea, standardized and feasible imaging protocols are required for constructing AI database in acute stroke. The essential sequences may be noncontrast CT, DWI, CT/MR angiography and CT/MR perfusion while FLAIR and T1WI may be additionally required.

Lithium-ion batteries (LIBs) are well known for their energy efficiency and environmental benefits. However, increasing their energy density compromises their safety. This study introduces a novel ceramic-coated separator to enhance the performance and safety of LIBs. Electrospinning was used to apply a coating consisting of an alumina (Al2O3) ceramic and polyacrylic acid (PAA) binder to a polypropylene (PP) separator to significantly improve the mechanical properties of the PP separator and, ultimately, the electrochemical properties of the battery cell. Tests with 2032-coin cells showed that the efficiency of cells containing separators coated with 0.5 g PAA/Al2O3 was approximately 10.2% higher at high current rates (C-rates) compared to cells with the bare PP separator. Open circuit voltage (OCV) tests revealed superior thermal safety, with bare PP separators maintaining stability for 453 s, whereas the cells equipped with PP separators coated with 4 g PAA/Al2O3 remained stable for 937 s. The elongation increased from 88.3% (bare PP separator) to 129.1% (PP separator coated with 4 g PAA/Al2O3), and thermal shrinkage decreased from 58.2% to 34.9%. These findings suggest that ceramic/PAA-coated separators significantly contribute to enhancing the thermal safety and capacity retention of high-energy-density LIBs.

Silicon-based electrodes offer a high theoretical capacity and a low cost, making them a promising option for next-generation lithium-ion batteries. However, their practical use is limited due to significant volume changes during charge/discharge cycles, which negatively impact electrochemical performance. This study proposes a practical method to increase silicon content in lithium-ion batteries with minimal changes to the manufacturing process by using dual-layer electrodes (DLEs). These DLEs are fabricated with two slurries containing silicon and graphite as active materials. Notably, the electrode with the silicon as the outermost layer on top of the graphite layer (Si-on-top) demonstrated a superior initial capacity of 935 mAh/g and retained 70% of its capacity (537 mAh/g) after 100 cycles at 0.5 C. In contrast, a single-layered electrode (SLE) with a silicon–graphite mixture retained only 50.3% of its capacity (370 mAh/g) under the same conditions. These findings suggest that DLEs, particularly with the silicon layer located on top, effectively increase silicon content in the negative electrode while remaining compatible with existing manufacturing processes. This approach offers a realistic strategy for enhancing the performance of lithium-ion batteries without significant process modifications.

Abstract Background Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets. Methods The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients. Results The trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load. Conclusions This study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

Artificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients’ genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research.

Abstract Finding d-electron heavy fermion states has been an important topic as the diversity in d-electron materials can lead to many exotic Kondo effect-related phenomena or new states of matter such as correlation-driven topological Kondo insulator. Yet, obtaining direct spectroscopic evidence for a d-electron heavy fermion system has been elusive to date. Here, we report the observation of Kondo lattice behavior in an antiferromagnetic metal, FeTe, via angle-resolved photoemission spectroscopy, scanning tunneling spectroscopy and transport property measurements. The Kondo lattice behavior is represented by the emergence of a sharp quasiparticle and Fano-type tunneling spectra at low temperatures. The transport property measurements confirm the low-temperature Fermi liquid behavior and reveal successive coherent-incoherent crossover upon increasing temperature. We interpret the Kondo lattice behavior as a result of hybridization between localized Fe 3dxy and itinerant Te 5pz orbitals. Our observations strongly suggest unusual cooperation between Kondo lattice behavior and long-range magnetic order.

The unprecedented medium-resolution ( R _λ ∼ 1500–3500) near- and mid-infrared (1–18 μ m) spectrum provided by JWST for the young (140 ± 20 Myr) low-mass (12–20 M _Jup ) L–T transition (L7) companion VHS 1256 b gives access to a catalog of molecular absorptions. In this study, we present a comprehensive analysis of this data set utilizing a forward-modeling approach applying our Bayesian framework, ForMoSA . We explore five distinct atmospheric models to assess their performance in estimating key atmospheric parameters: T _eff , log( g ), [M/H], C/O, γ , f _sed , and R . Our findings reveal that each parameter’s estimate is significantly influenced by factors such as the wavelength range considered and the model chosen for the fit. This is attributed to systematic errors in the models and their challenges in accurately replicating the complex atmospheric structure of VHS 1256 b, notably the complexity of its clouds and dust distribution. To propagate the impact of these systematic uncertainties on our atmospheric property estimates, we introduce innovative fitting methodologies based on independent fits performed on different spectral windows. We finally derived a T _eff consistent with the spectral type of the target, considering its young age, which is confirmed by our estimate of log( g ). Despite the exceptional data quality, attaining robust estimates for chemical abundances [M/H] and C/O, often employed as indicators of formation history, remains challenging. Nevertheless, the pioneering case of JWST’s data for VHS 1256 b has paved the way for future acquisitions of substellar spectra that will be systematically analyzed to directly compare the properties of these objects and correct the systematics in the models.

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego.

Background and objectives There has been no validated tool for objectively quantifying the overall condition and characteristics of the scalp. This study aimed to establish and validate a new classification and scoring system for evaluating scalp conditions. Methods The Scalp Photographic Index (SPI) using a trichoscope grades five features of scalp conditions (dryness, oiliness, erythema, folliculitis, and dandruff) on a score of 0–3. To evaluate the validity of SPI, SPI grading was performed by three experts on the scalps of 100 subjects along with a dermatologist’s assessment of the scalps and a scalp-related symptom survey. For reliability assessment, 20 healthcare providers performed SPI grading for the 95 selected photographs of the scalp. Results SPI grading and the dermatologist’s scalp assessment showed good correlations for all five scalp features. Warmth showed a significant correlation with all features of SPI and the subjects’ perception of a scalp pimple had a significant positive correlation with the folliculitis feature. SPI grading demonstrated good reliability with excellent internal consistency (Cronbach’s α = 0.90) and strong inter- and intra-rater reliability (Kendall’s W = 0.84, ICC(3,1)=0.94). Conclusions SPI is an objective, reproducible, and validated numeric system for classifying and scoring scalp conditions.

See the article, “Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuantⓇ vs. DeepBrainⓇ in the Korean Population: Correlation with Cranial Shape”, in volume 84 on page 1080-1090 (https://doi.org/10.3348/jksr.2023.0006).

An investigation of comprehensive service requirements and their importance across various stakeholders is needed to develop systematic swallowing healthcare services. The present study examined the service requirements and their importance in developing comprehensive swallowing healthcare services from diverse stakeholders, including healthy seniors, individuals at risk, patients, guardians, and medical professionals, within the context of dysphagia. A series of survey-based interviews with 35 participants from the 5 stakeholder groups identified a comprehensive array of requirements pertinent to swallowing healthcare. Subsequently, a larger survey of 125 participants was administered using a 7-point Likert scale to evaluate the perceived importance of the identified requirements. A total of 46 distinct requirements were identified across the prevention/screening, evaluation/diagnosis, and treatment/training stages of swallowing healthcare. Accessibility, customization, usability, practicality, and information availability were features commonly identified across the three stages. Evaluation of the importance of these requirements yielded ratings ranging from 5.0 to 6.6. Significantly higher ratings were found for requirements associated with the evaluation/diagnosis and treatment/training stages, underscoring the stakeholder groups’ pronounced preference for services directed at these specific stages of the healthcare continuum. The study offers insights into the multifaceted requirements of stakeholders in swallowing healthcare, emphasizing the necessity for personalized, easily accessible, and comprehensive services. These findings serve as a foundational framework on targeted interventions and tailored services designed to address the diverse requirements of stakeholders for swallowing healthcare.

In fabricating functional layers, including thin-film transistors and conductive electrodes, using roll-to-roll (R2R) processing on polymer-based PET film, the instability of the slot-die coating meniscus under a high-speed web impedes functional layer formation with the desired thickness and width. The thickness profiles of the functional layers significantly impact the performance of the final products. In this study, we introduce an electrohydrodynamic (EHD)-based voltage application module to a slot-die coater to ensure the uniformity of the cross-machine direction (CMD) thickness profile within the functional layer and enable a stable, high-speed R2R process. The module can effectively control the spreadability of the meniscus by utilizing variations in the surface tension of the ink. The effectiveness of the EHD module was experimentally verified by applying a high voltage to a slot-die coater while keeping other process variables constant. As the applied voltage increases, the CMD thickness deviation reduces by 64.5%, and the production rate significantly increases (up to 300%), owing to the formation of a stable coated layer. The introduction of the EHD-based application module to the slot-die coater effectively controlled the spreadability of the meniscus, producing large-area functional layers.

The roll-to-roll manufacturing system is utilized for processing flexible webs into functional films, offering high productivity through continuous multi-stage processing. In the converting span, which encompasses coating, printing, and drying processes, precise tension control and appropriate tension settings are crucial to ensure the quality of the final product. Particularly during the drying process, where solvent removal occurs, thermal deformation of the web induces additional elongation, amplifying the impact of tension on coating layer quality and printing patterns. Consequently, the drying process necessitates more accurate tension control compared to other span, necessitating the application of feedforward control that considers web thermal deformation. However, the current tension control logic employs an imprecise temperature distribution within the dry span as an input, resulting in inadequate tension control performance and defects such as uneven coating layers and registration errors. This research presents the development of a feedforward tension controller that employs Finite Element Method (FEM)-based web temperature distribution inputs to enhance tension control performance during the drying span. The tension control performance was compared and analyzed based on the drying span web temperature prediction method, affirming that higher accuracy in predicting the web temperature distribution leads to improved tension control performance. Experimental results from an industrial-scale roll-to-roll system demonstrate that the proposed model enhances tension control accuracy during the drying process by 27.76% compared to the existing control logic. Furthermore, the effectiveness of the tension control logic in enhancing the surface quality of functional layers was confirmed through surface quality analysis using solid oxide fuel cell (SOFC) electrolyte layers.

This study proposes a practical fault detection and prediction method by addressing a margin-maximized hyperspace. The proposed method is effective for a highly imbalanced dataset without any supervision, which is a frequently occurring and challenging problem in real-world applications. The proposed method has three characteristics. First, knowledge-based feature manipulation is executed to provide sufficient information for a neural network. Second, a regulated variational autoencoder transforms distinct input features into a latent space, which ensures high accuracy and robustness. Third, the obtained latent space is confirmed to statistically allocate two extremes of major (normal) and minor (faulty) clusters at an origin and unity, maximizing the sensitivity to classify faults. The effectiveness of the proposed method is demonstrated through field measurements of elevator door-strokes and showed high sensitivity to separate each cluster along with locational constancy compared to other autoencoders. Therefore, the proposed method is effective for real-world applications with scarce fault measurements.

Existing methods for generating virtual character videos focus on improving either appearance or motion. However, achieving both photo- and motion-realistic characters is critical in real services. To address both aspects, we propose Fake to Real Portrait Control (F2RPC), a unified framework for image destylization and face reenactment. F2RPC employs a blind face restoration model to circumvent GAN inversion limitations, such as identity loss and alignment sensitivity, while preserving GAN’s generation quality. This framework includes two novel sub-modules: AdaGPEN for destylization and PCGPEN for reenactment, both leveraging the same restoration model as a backbone. AdaGPEN exploits GAN prior of the restoration model via blending features from the original and its blurred image using the AdaMix block. PCGPEN reenacts the input image to follow the input motion condition via flow-based feature editing. These components function in an end-to-end manner, enhancing efficiency and lowering computational overhead. We evaluate F2RPC using synthetic character dataset and high-resolution talking face datasets for destylization and reenactment, respectively. The results show that F2RPC outperforms the combined use of state-of-the-art methods for destylization (i.e., DualStyleGAN) and reenactment (i.e., StyleHEAT). F2RPC improves the FID by 26.4% and preserves identity similarity by 95% more at a resolution of $512\times 512$ video. This evidences F2RPC’s efficacy and superiority in the photo- and motion-realistic virtual character video generation task.

According to previous studies on internal combustion engine applications using deep learning, deep learning model should be individually optimized and trained to predict different phenomena. This study introduces task transfer learning to predict transient nitrogen oxides (NO $_{\mathrm {x}}$ ), soot, and total hydrocarbon (THC) emissions, which are the major emissions from diesel engines. Using the concept of task transfer learning, when there is a pretrained model relevant to the target task, the model can be transferred to predict another phenomenon by training only the last two layers with hyperparameters of the pretrained model. This concept omits the need for optimizing and training separate models that can save computational time and cost. The results of task transfer learning were evaluated using Worldwide Harmonized Light Vehicles Test Procedure (WLTP) cycle data, which are representative transient cycles of the internal combustion engine, and all possible transfer cases with NOx, soot, and THC emissions were investigated. The R2 values of pretrained NOx, soot, and THC models were 0.9780, 0.9215, and 0.9390, respectively. The R2 gaps between the pretrained and transferred models were within 0.012, with a value of 0.0015 for the NOx emission, 0.011 for the soot emission, and 0.0115 for the THC emission. The relative mean absolute errors (MAEs) to the maximum emission values were approximately 0.57-0.82% for NOx emissions, 0.69-2.02% for soot emissions, and 1.52-2.42% for THC emissions. These accuracy results were comparable to the accuracy of the emission measurement device, which was better than that of the sensors for practical use in vehicles. The results indicated that task transfer learning was valid for predicting emissions of an internal combustion engine, and it achieved efficient organization of prediction models using a pretrained model.

Facial landmark detection is an essential task in face-processing techniques. Traditional methods, however, require expensive pixel-level labels. Semi-supervised facial landmark detection has been explored as an alternative, but previous approaches only focus on training-oriented issues (e.g., noisy pseudo-labels in semi-supervised learning), neglecting task-oriented issues (i.e., the quantization error in landmark detection). We argue that semi-supervised landmark detectors should resolve the two technical issues simultaneously. Through a simple experiment, we found that task- and training-oriented solutions may negatively influence each other, thus eliminating their negative interactions is important. To this end, we devise a new heatmap regression framework via hybrid representation, namely HybridMatch. We utilize both 1-D and 2-D heatmap representations. Here, the 1-D and 2-D heatmaps help alleviate the task-oriented and training-oriented issues, respectively. To exploit the advantages of our hybrid representation, we introduce curriculum learning; relying more on the 2-D heatmap at the early training stage and gradually increasing the effects of the 1-D heatmap. By resolving the two issues simultaneously, we can capture more precise landmark points than existing methods with only a few annotated data. Extensive experiments show that HybridMatch achieves state-of-the-art performance on three benchmark datasets, especially showing 26.3% NME improvement over the existing method in the 300-W full set at 5% data ratio. Surprisingly, our method records a comparable performance, 5.04 (challenging set in the 300-W) to the fully-supervised facial landmark detector 5.03. The remarkable performance of HybridMatch shows its potential as a practical alternative to the fully-supervised model.

Abstract The infant gut microbiome has lifelong implications on health and immunity but there is still limited understanding of the microbiome differences and similarities between children in low- and middle-income countries (LMICs) vs. high-income countries (HICs). Here, we describe and compare the microbiome profile of children aged under 48 months in two urban areas: Maputo, Mozambique and Atlanta, USA using shotgun metagenomics. The gut microbiome of American children showed distinct development, characterized by higher alpha diversity after infancy, compared to the same age group of African children, and the microbiomes clustered separately based on geographic location or age. The abundances of antibiotic resistance genes (ARGs) and virulence factors (VFs) were significantly higher in Maputo children, driven primarily by several primary and opportunistic pathogens. Most notably, about 50% of Maputo children under the age of two were positive for enterotoxigenic (ETEC) and typical enteropathogenic (EPEC) Escherichia coli diagnostic genes while none of the Atlanta age-matched children showed such a positive signal. In contrast, commensal species such as Phocaeicola vulgatus and Bacteroides caccae were more abundant in Atlanta, potentially reflecting diets rich in animal protein and susceptibility to inflammatory diseases. Overall, our results suggest that the different environments characterizing the two cities have significant, distinctive signatures on the microbiota of children and its development over time. Lack of safe water, sanitation, and hygiene (WASH) conditions and/or unsafe food sources may explain the higher enteric pathogen load among children in Maputo.

Stem cells represent an ideal source for regenerative medicine; however, longitudinal assessment of stem cell phenotype and function is challenging. Contrastingly, a convolutional neural network (CNN) algorithm can automatically extract the image features and produce highly accurate image recognition. Thus, this study implements CNN to establish stable and reproducible cell culture experiments by predicting a unique morphology of pluripotent stem cell (PSC) lines. Interestingly, the algorithm distinguishes the PSC lines cultured in the different cell culture conditions, such as the presence or absence of small molecules and/or the long‐ or short‐term culture in our induced PSC (iPSC) models, which include iPSC lines with abnormal gene expression patterns and genomic abnormalities. Our deep learning technology accurately classifies the various cell lines with or without genetic defects using only the cell images, without any labeling process. This suggests that the CNN system may simplify the various tasks involving stable cell cultures and their differentiation.

Background : The outcomes of liver transplantation (LT) have improved, but actual 20-year survival data have rarely been presented. Methods : Longitudinal follow-up data of 20-year LT survivors were retrospectively analyzed. The LT database of our institution was searched to identify patients who underwent primary LT from January 2000 to December 2001. The study cohort of 251 patients was divided into three groups: 207 adults who underwent living donor LT (LDLT), 22 adults who underwent deceased donor LT (DDLT), and 22 pediatric patients who underwent LT. Results : Hepatitis B virus–associated liver cirrhosis and biliary atresia were the most common indications for adult and pediatric LT, respectively. Seven patients required retransplantation, including six who underwent DDLT and one who underwent LDLT. Twenty-two patients died within 3 months after LT and 69 died at later intervals. The overall survival rates at 1, 3, 5, 10, and 20 years were 86.4%, 79.6%, 77.7%, 72.8%, and 62.6%, respectively, in the adult LDLT group; 86.4%, 72.7%, 72.7%, 72.7%, and 68.2%, respectively, in the adult DDLT group; and 86.4%, 86.4%, 81.8%, 81.8%, and 77.3%, respectively, in the pediatric LT group (P=0.545). Common immunosuppressive regimens at 20 years included tacrolimus monotherapy, tacrolimus–mycophenolate dual therapy, cyclosporine monotherapy, and mycophenolate monotherapy. Conclusions: The present study is the first report of actual 20-year survival data from a Korean high-volume LT center. The graft and patient survival outcomes reflected the early experiences of LT in our institution, with long-term outcomes being similar regardless of graft type and patient age.

Most sexual organisms ensure that organelles are inherited from a single parent. Here, the authors describe OTU2p, a Chlamydomonas deubiquitinase that drives uniparental organelle inheritance without gametic dimorphism by preventing proteasome-mediated degradation exclusively in gametes of the plus mating type.

The ESA/Rosetta mission accompanied the Jupiter Family Comet 67P/Churyumov-Gerasimenko and provided a huge amount of data which are providing important results about cometary activity mechanisms. We summarize the results obtained within the ISSI International Team Characterization of 67P cometary activity, which studied dust and gas ejection in different stages of the comet’s orbit, by means of a data fusion between instruments onboard the Rosetta orbiter, i.e., the OSIRIS camera, the VIRTIS imaging spectrometer, the GIADA dust detector, the MIDAS atomic force microscope, the COSIMA dust mass spectrometer, and the ROSINA gas mass spectrometer, supported by numerical models and experimental work. The team reconstructed the motion of the dust particles ejected from the comet surface, finding a correlation between dust ejection and solar illumination as well as larger occurrence of fluffy (pristine) particles in less processed and more pebble-rich terrains. Dust activity is larger in ice-rich terrains, indicating that water sublimation is the dominant activity process during the perihelion phase. The comparison of dust fluxes of different particle size suggests a link between dust morphology and ejection speed, generation of micrometric dust from fragmentation of millimetric dust, and homogeneity of physical properties of compact dust particles across the 67P surface. The comparison of fluxes of refractory and ice particles suggests the occurrence of a small amount of ice in fluffy particles, which is released when they are fragmented. A new model of cometary activity has been finally developed, according to which the comet nucleus includes Water-Ice-Enriched Blocks (WEBs), that, when exposed by CO2 activity, are the main sources of water sublimation and dust ejection.

Abstract Indirect reciprocity is a key mechanism that promotes cooperation in social dilemmas by means of reputation. Although it has been a common practice to represent reputations by binary values, either ‘good’ or ‘bad’, such a dichotomy is a crude approximation considering the complexity of reality. In this work, we studied norms with three different reputations, i.e., ‘good’, ‘neutral’, and ‘bad’. Through massive supercomputing for handling more than thirty billion possibilities, we fully identified which norms achieve cooperation and possess evolutionary stability against behavioural mutants. By systematically categorizing all these norms according to their behaviours, we found similarities and dissimilarities to their binary-reputation counterpart, the leading eight. We obtained four rules that should be satisfied by the successful norms, and the behaviour of the leading eight can be understood as a special case of these rules. A couple of norms that show counter-intuitive behaviours are also presented. We believe the findings are also useful for designing successful norms with more general reputation systems.

Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novel multi-layer heuristic algorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68% of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7%.

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.

Background: Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) has wide histologic diversity. This study investigated the prognostic impacts of cHCC-CCA histology according to the 2019 World Health Organization (WHO) classification. Methods: This retrospective observational study included 153 patients who underwent surgical resection for cHCC-CCA at Asan Medical Center between August 2012 and July 2019. Results: During the study period, 153 patients, 112 (73.2%) men and 41 (26.8%) women with a mean age of 56.4 ± 10.8 years, underwent R0 resection for cHCC-CCA. Mean tumor diameter was 4.2 ± 2.6 cm, and 147 (96.1%) patients had solitary tumors. According to 2019 WHO classification, 111 (72.5%) patients had cHCC-CCA alone, and 29 of them (26.1%) showed stem cell features. cHCC-CCA-intermediate cell carcinoma and cHCC-CCA-cholangiolocellular carcinoma were identified in 27 (17.6%) and 15 (9.8%), respectively. The 1-, 3-, and 5-year tumor recurrence and patient survival rates were 31.8% and 92.1%, 49.8% and 70.9%, and 59.0% and 61.7%, respectively. Univariate analyses revealed that significant prognostic factors were tumor size >5 cm, microscopic and macroscopic vascular invasion, lymph node metastasis, 8th American Joint Committee on Cancer (AJCC) tumor stage, and status of stem cell features. Multivariate analysis revealed 8th AJCC tumor stage and status of stem cell features as independent prognostic factors. 2019 WHO classification was not associated with post-resection prognosis. Conclusions: 2019 WHO classification was not associated with post-resection prognosis, thus was considered as simplified histologic classification requiring prognostic validation. We suggest that stem cell features should be included as an essential component of the pathology report for cHCC-CCA.

Propionic acid (HPr) is frequently accumulated in anaerobic digesters due to its thermodynamically unfavorable degradation reaction. Here, we identify key players in HPr oxidation and organic overloading recovery from metagenome-assembled genomes (MAGs) recovered from anaerobic digesters inoculated with HPr-enriched microbial consortia before initiating organic overloading. Two independent HPr-enrichment cultures commonly selected two uncultured microorganisms represented with high relative abundance: Methanoculleus sp002497965 and JABUEY01 sp013314815 (a member of the Syntrophobacteraceae family). The relative abundance of JABUEY01 sp013314815 was 60 times higher in bioaugmented bioreactors compared to their unaugmented counterparts after recovery from organic overloading. Genomic analysis of JABUEY01 sp013314815 revealed its metabolic potential for syntrophic propionate degradation when partnered with hydrogenotrophic methanogens (e.g., Methanoculleus sp002497965) via the methylmalonyl-CoA pathway. Our results identified at least two key species that are responsible for efficient propionate removal and demonstrate their potential applications as microbial cocktails for stable AD operation.

Abstract From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p

Microbial contamination is the inadvertent presence of microbes or their byproducts in materials or environments [...]

In this study, we introduce a novel design for a three-dimensional (3D) controller, which incorporates the omni-purpose stretchable strain sensor (OPSS sensor). This sensor exhibits both remarkable sensitivity, with a gauge factor of approximately 30, and an extensive working range, accommodating strain up to 150%, thereby enabling accurate 3D motion sensing. The 3D controller is structured such that its triaxial motion can be discerned independently along the X, Y, and Z axes by quantifying the deformation of the controller through multiple OPSS sensors affixed to its surface. To ensure precise and real-time 3D motion sensing, a machine learning-based data analysis technique was implemented for the effective interpretation of the multiple sensor signals. The outcomes reveal that the resistance-based sensors successfully and accurately track the 3D controller’s motion. We believe that this innovative design holds the potential to augment the performance of 3D motion sensing devices across a diverse range of applications, encompassing gaming, virtual reality, and robotics.

Salvage liver transplantation (LT) is a definite treatment for recurrent hepatocellular carcinoma (HCC) after hepatectomy. Combined hepatocellular carcinoma-cholangio-carcinoma (cHCC-CC) is not eligibly indicated for LT because of high post-transplant recurrence. We present a case of salvage living donor liver transplantation (LDLT) in a patient with tumor recurrence after surgical resection of cHCC-CC. A 61-year-old male patient diagnosed with chronic hepatitis B underwent right posterior sectionectomy for HCC. The pathological diagnosis revealed presence of a 3.2-cm-sized cHCC-CC with stem cell features and intermediate cell-subtype. Intrahepatic tumor recurrence occurred 9 months later and transarterial chemoembolization was performed. Due to the progress of recurrent tumors, ABO-incompatible LDLT was performed. The explant liver pathology revealed four small masses of cHCC-CC with stem cell features. Pulmonary metastasectomy and chest wall resection were performed for metastatic lesions at 10 months after LT. Multiple tumor recurrence was detected at 21 months after LT with progression despite sorafenib administration. Currently, the patient is alive for past 26 months after LT. In conclusion, the clinical sequences of this case suggest that the role of salvage LT for cHCC-CC is much more limited than that for HCC because the tumor biology of cHCC-CC is more aggressive compared with HCC.

Background : Favorable outcomes achieved after deceased donor liver transplantation (DDLT) suggest that use of elderly donors may be an effective way to expand donor pool. Methods : This was a retrospective analysis of adult DDLT using elderly donors. It was a double-arm study that compared posttransplant outcomes to ascertain whether use of elderly donors (aged ≥76 years) has adverse effects on outcome of DDLT. Elderly donor study group included 14 donors aged ≥76 years and elderly donor control group comprised 39 donors aged 66–75 years. Results : Mean donor age of the study and control groups was 78.2±3.1 years and 68.9±2.7 years, respectively (P

Abstract Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).

There has been a long-standing expectation that the optical-resolution embodiment of photoacoustic tomography could have a substantial impact on gastrointestinal endoscopy by enabling microscopic visualization of the vasculature based on the endogenous contrast mechanism. Although multiple studies have demonstrated the in vivo imaging capability of a developed imaging device over the last decade, the implementation of such an endoscopic system that can be applied immediately when necessary via the instrument channel of a video endoscope has been a challenge. In this study, we developed a 3.38-mm diameter catheter-based, integrated optical-resolution photoacoustic and ultrasonic mini-probe system and successfully demonstrated its intra-instrument channel workability for the standard 3.7-mm diameter instrument channel of a clinical video endoscope based on a swine model. Through the instrument channel, we acquired the first in vivo dual-mode photoacoustic and ultrasonic endoscopic images from the esophagogastric junction of a swine. Further, in a rat colorectum in vivo imaging experiment, we visualized hierarchically developed mesh-like capillary networks with a hole size as small as ~50 µm, which suggests the potential level of image details that could be photoacoustically provided in clinical settings in the future.

Antifreeze proteins (AFPs) can inhibit the freezing of body fluid at subzero temperatures to promote the survival of various organisms living in polar regions. Type III AFPs are categorized into three subgroups, QAE1, QAE2, and SP isoforms, based on differences in their isoelectric points. We determined the thermal hysteresis (TH), ice recrystallization inhibition (IRI), and cryopreservation activity of three isoforms of the notched-fin eelpout AFP and their mutant constructs and characterized their structural and dynamic features using NMR. The QAE1 isoform is the most active among the three classes of III AFP isoforms, and the mutants of inactive QAE2 and SP isoforms, QAE2ACT and SPACT, displayed the full TH and IRI activities with resepect to QAE1 isoform. Cryopreservation studies using mouse ovarian tissue revealed that the QAE1 isoform and the active mutants, QAE2ACT and SPACT, more effectively preserved intact follicle morphology and prevented DNA double-strand break damage more efficiently than the inactive isoforms. It was also found that all active AFPs, QAE1, QAE2ACT, and SPACT, formed unique H-bonds with the first 310 helix, an interaction that plays an important role in the formation of anchored clathrate water networks for efficient binding to the primary prism and pyramidal planes of ice crystals, which was disrupted in the inactive isoforms. Our studies provide valuable insights into the molecular mechanism of the TH and IRI activity, as well as the cryopreservation efficiency, of type III AFPs.

Interest in utilizing organs from non–heart-beating donors (NHBDs) has increased because of the organ shortage. However, liver transplantation (LT) from NHBDs has been scarcely performed in Korea because only Maastricht category IV is legally permitted. We present one case of LT from an NHBD with extracorporeal membrane oxygenation (ECMO) support. The case was a 42-year-old male patient with alcoholic liver cirrhosis. The model for end-stage liver disease score was 28. The donor was a 47-year-old female who was diagnosed with brain death due to cerebral infarct, but cardiac arrest occurred before brain death was finally confirmed. Thus, venous-arterial type ECMO was initiated for circulatory support. In the operating room, asystole developed just after ECMO was stopped. After waiting for 5 minutes, cardiac death was declared. It took 6 minutes from skin incision to aorta perfusion. The recipient hepatectomy and graft implantation were performed according to the standard procedures of adult whole LT. The patient recovered from LT uneventfully and has been doing well for 9 years after LT. The use of NHBDs is a method to increase the potential pool of organ donors, thus changes toward enhanced public awareness and acceptance of donating organs, and legal support at the government level are necessary.

This study demonstrates a new resistive switching material, F-doped TiO2 (F:TiO2), fabricated by atomic layer deposition (ALD) with an in-house fluorine source for resistive random access memory (RRAM) devices. Controlling oxygen vacancies is required since RRAM uses resistive switching (RS) characteristics by redistributing oxygen ions in oxide, and poor oxygen defect control has been shown to significantly reduce RRAM reliability. Therefore, this study designed an F based RRAM device using fluorine anions rather than oxygen defect for the main agent of RS behavior. We developed the F:TiO2 RRAM material using a novel in situ doping method in ALD and investigated its RS behaviors. The Pt/F:TiO2/Pt device exhibited forming-less bipolar RS and self-rectifying behavior by fluorine anion migration, effectively reducing the sneak current in crossbar array architecture RRAM. The doped fluorine passivated and reduced oxygen related defects in TiO2, confirmed by x-ray photoelectron spectroscopy analysis. Adopting the F-based RS material by ALD provides a viable candidate for high reliability RRAM.