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This study examined the observed PM2.5 concentration across 247 underground stations consisting of Line-1 to Line-8 of the Seoul Metro from April 2021 to March 2023 in order to understand general characteristics of underground PM2.5 air quality. Approximately, in one-thirds of underground stations (85 stations), annual averaged PM2.5 concentration are over 35 µg m−3. Moreover, in 30 underground stations (approximately 12%), it exceeds 50 µg m−3, the recommended 24-hour maintenance standard for PM2.5 concentration in underground stations. We found that PM2.5 concentration is considerably influenced by both internal and external factors. Among the internal factors (i.e., depth, the number of passengers and operation frequency), the frequency of subway operation significantly affects changes in PM2.5 concentration however, various internal factors may act in combination. In terms of external factor, there are positive correlation coefficients (r = 0.15–0.95) between daily averaged PM2.5 concentration in underground station and that of the outdoor observatory closest to each underground station. In particular, in underground stations with high PM2.5 concentration, the correlation with outdoor PM2.5 air quality was low, suggesting that for better air quality in underground stations, we need to focus more on reducing the inherent emission from underground stations in highly polluted stations, but for less polluted stations, we need to improve outdoor air quality as well. We believe that this study may provide insights for effective future PM2.5 air quality management in underground stations. [ABSTRACT FROM AUTHOR]

Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we focus on the local patterns in simplicial complexes called simplets, a generalization of graphlets. We study the problem of counting simplets of a given size in a given simplicial complex. For this problem, we extend a sampling algorithm based on color coding, from graphs to simplicial complexes, with essential technical novelty. We theoretically analyze our proposed algorithm named SC3, showing its correctness, unbiasedness, convergence, and time/space complexity. Through extensive experiments on sixteen real-world datasets, we show the superiority of SC3 in terms of accuracy, speed, and scalability, compared to the baseline methods. We use the counts given by SC3 for simplicial complex analysis, especially for characterization, which is further used for simplicial complex clustering, where SC3 shows a strong ability of characterization with domain-based similarity. Additionally, we explore a variant of simplet counting (specifically, estimating the relative counts of simplets) under realistic scenarios where the entire simplicial complex is not provided at once but can only be partially accessed, for instance, through a limited number of API calls. For such scenarios, we propose a random-walk-based sampling algorithm, SCRW, and analyze its theoretical properties. In our experiments, SCRW requires, on average, 16.5 × less memory than SC3, while the speed-accuracy trade-offs provided by the two methods are comparable. [ABSTRACT FROM AUTHOR]

Sarcopenia is a rapidly rising health concern in the fast-aging countries, but its demanding diagnostic process is a hurdle for making timely responses and devising active strategies. To address this, our study developed and evaluated a novel sarcopenia diagnosis system using Stimulated Muscle Contraction Signals (SMCS), aiming to facilitate rapid and accessible diagnosis in community settings. We recruited 199 adults from Wonju Severance Christian Hospital between July 2022 and October 2023. SMCS data were collected using surface electromyography sensors with the wearable device exoPill. Their skeletal muscle mass index, handgrip strength, and gait speed were also measured as the reference. Binary classification models were trained to classify each criterion for diagnosing sarcopenia based on the AWGS cutoffs. The binary classification models achieved high discriminative abilities with an AUC score near 0.9 in each criterion. When combining these criteria evaluations, the proposed sarcopenia diagnosis system performance achieved an accuracy of 89.4% in males and 92.4% in females, sensitivities of 81.3% and 87.5%, and specificities of 91.0% and 93.8%, respectively. This system significantly enhances sarcopenia diagnostics by providing a quick, reliable, and non-invasive method, suitable for broad community use. The promising result indicates that SMCS contains extensive information about the neuromuscular system, which could be crucial for understanding and managing muscle health more effectively. The potential of SMCS in remote patient care and personal health management is significant, opening new avenues for non-invasive health monitoring and proactive management of sarcopenia and potentially other neuromuscular disorders. [ABSTRACT FROM AUTHOR]

Ammonia borane (AB), with a 19.6-wt% H2 content, is a promising hydrogen storage material for polymer electrolyte membrane fuel cells (PEMFC). However, traditional thermal decomposition of boric acid generates ammonia, which is detrimental to fuel cells. This study explores the use of silicic acid (SA) as a catalyst for AB, yielding 12.0 wt% of H2 at approximately 100 °C, making it suitable for fuel-cell operation. Notably, when the AB mass ratio is increased to 90 wt%, the reaction temperature increases slightly, yet it produces up to 12.3 wt% of H2. Fourier transform infrared (FT-IR) and X-ray photoelectron spectroscopy (XPS) analyses confirm that SA contains more silanol groups than SiO₂. Pyridine infrared analysis reveals that SA has a higher distribution of Lewis acid sites, which play a vital role in the dehydrogenation of AB. In addition, when in-situ mass spectrometry analysis is performed, ammonia is not detected, indicating that no filtration is required for fuel-cell applications. In conclusion, this study demonstrates that SA enhances the dehydrogenation of AB at low temperatures, achieving a high H2 yield without ammonia production. This makes SA a promising catalyst for efficient and safe H2 storage in fuel cells, with potential applications in mobile and aerial vehicles. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard. A lesion was classified as 'detected' when the minimum lumen diameter (MLD) identified by manual QCA fell within the boundaries of the lesion delineated by AI-QCA or visual estimation. The detected lesions were evaluated in terms of diameter stenosis (DS), MLD, and lesion length (LL). AI-QCA accurately detected lesions with a sensitivity of 93% (1705/1828) and showed strong correlations with manual QCA for DS, MLD, and LL (R² = 0.65, 0.83 and 0.71, respectively). In views targeting the major vessels, the proportion of undetected lesions by AI-QCA was less than 4% (56/1492). For lesions in the side branches, AI-QCA also demonstrated high sensitivity (> 92%) in detecting them. Compared to visual estimation, AI-QCA showed significantly better lesion detection capability (93% vs. 69%, p < 0.001), and had a higher probability of detecting all lesions in images with multiple lesions (86% vs. 33%, p < 0.001). The updated AI-QCA demonstrated robust performance in lesion detection and quantification without operator intervention, enabling reproducible vessel analysis. The automated process of AI-QCA has the potential to optimize angiography-guided interventions by providing quantitative metrics. [ABSTRACT FROM AUTHOR]

Background: Adverse childhood experiences (ACEs) and positive childhood experiences (PCEs) impact adult health. However, differences in ACEs, PCEs, and mental health have not been extensively studied among Asian Americans. Objective: To examine the association between childhood experiences and adult mental health in first and second generation Asian Americans. Design: This study used data from the 2021-2022 California Health Interview Survey (CHIS), an address-based sampling of noninstitutionalized Californians conducted online or by phone. Participants: Asian American respondents aged 18-65 years. Exposure: Fifteen different ACEs and seven different PCEs. Main Measures: Adjusted prevalence ratios (aPR) of severe psychological distress for each generation. Survey weights were applied to all analyses for population-based representation. Key Results: 5,744 Asian Americans (48.0% male, 16.4% aged 18-25) were included in the current study. We found that second generation Asian Americans experienced a greater prevalence of ACEs (65.4% reported ≥1 ACE vs 47.5% in first generation Asian Americans) and lower prevalence of PCEs (32.1% reported ≤2 PCEs vs 22.6% in first generation Asian Americans). Second generation Asian Americans were more likely to report ≥4 ACEs (aPR, 1.46; 95% CI, 1.13 to 1.88) and ≤2 PCEs (aPR, 1.51; 95% CI, 1.29 to 1.78) relative to first generation Asian Americans. Second generation Asian Americans with ≥4 ACEs or ≤2 PCEs were more likely to report severe psychological distress (aPR, 2.54; 95% CI, 1.55 to 4.17 and aPR, 1.48; 95% CI, 1.03 to 2.13, respectively) relative to first generation Asian Americans. When examining ACEs and PCEs individually, domestic, physical, and verbal abuse; divorce; racism; and lacking support systems were significantly associated with severe psychological distress in second generation Asian Americans. Conclusions: Second generation Asian Americans are more likely to experience more ACEs, fewer PCEs, and poorer mental health as a result. Our study indicates that physicians should screen for childhood experiences and leverage trauma-informed care among Asian American subpopulations. [ABSTRACT FROM AUTHOR]

The interest in highly sensitive sensors is rapidly increasing for detecting very tiny signals for Internet of Things devices. Here, we achieve ultra-sensitive correlated breathable sensors based on freestanding VO2 membranes. We fabricate the membranes by growing VO2 films onto sacrificial Sr3Al2O6 layer grown on SrTiO3, selectively dissolving the Sr3Al2O6 in water, and then rendering freestanding VO2 membrane on nanomesh. The nanomeshes are extremely flexible, sweat permeable, and readily skin-adhesive. The resistance of the VO2 membranes is reversibly tuned by human's tiny mechanical stimuli and breath stimuli. The stimuli modulate the Peierls dimerization of one-dimensional V−V chains in the VO2 lattice which concomitantly controls the electron correlation and hence resistivity. Since our breathable sensors operate based on quantum-mechanical correlation effects, their sensitivity is 1−2 orders of magnitude higher than conventional tactile and respiratory sensors based on other materials. Thus, the freestanding membranes of correlated oxides on epidermal nanomeshes are multifunctional platforms for developing ultra-sensitive correlated breathable sensors. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of a recently developed Lagrangian stochastic model (LSM), which solves the transport equations for turbulence probability density functions (PDFs), for simulating the unstable atmospheric surface layer (ASL). The simulated statistics are compared with the Monin-Obukhov similarity theory (MOST) predictions for mean gradients, standard deviations, turbulent Prandtl number, turbulence kinetic energy budgets, and turbulence PDFs. The LSM successfully captures many aspects of ASL structure and turbulence characteristics, particularly the mean gradients and standard deviations of potential temperature, specific humidity, and vertical velocity, which align closely with MOST predictions. However, the model shows limitations in reproducing the stability dependency of mean gradients of horizontal wind speed. Additionally, the model underestimates the probability of high vertical velocity fluctuations, leading to underprediction of turbulent transport and thermal convection initiation. The simulated turbulent Prandtl number shows dependency on stability but is generally weaker than MOST predictions. The study identifies that the parameterizations of dissipation, pressure redistribution, and pressure transport need to be enhanced to ensure that they have correct stability dependency, in order to improve the model's accuracy in simulating the ASL. [ABSTRACT FROM AUTHOR]

Pyrochlore oxide (Y2Ru2O7) has been identified as a promising catalyst for the oxygen evolution reaction (OER) in advanced green energy strategies. However, its electrochemical inertness necessitates the exploration of an effective strategy to facilitate electronic modulation. This study proposes a surface modification approach involving the integration of defective NiFe (D-NiFe) nanoparticles onto a Y2Ru2O7 (YRO) support (YRO@D-NiFeP/Ru) using a Prussian blue analog (PBA). Numerous cyanide (CN) vacancies are generated through the oxidation treatment of the NiFe PBA grown on the YRO support, yielding a defective PBA precursor (YRO@D-PBA). Subsequent annealing facilitates the transformation to the D-NiFe nanoparticles on the YRO support (YRO@D-NiFeP/Ru), which augments the exposure of Ni3+ active sites beneficial for the OER. Moreover, the reduction of Ru cations from YRO results in the exsolution of Ru nanoparticles, which promotes synergistic charge transfer from the nanoparticles to the interior of Y2Ru2O7. Consequently, YRO@D-NiFeP/Ru exhibits a remarkable voltage of 1.49 V at 10 mA·cm−2 and the lowest Tafel slope of 42.4 mV·dec−1. In addition, a Zn–air battery constructed with YRO@D-NiFeP/Ru exhibits an outstanding power density of 136.2 mW·cm−2 and high charge–discharge stability, confirming the applicability of YRO@D-NiFeP/Ru in metal-air batteries. [ABSTRACT FROM AUTHOR]

Many real-world datasets are represented as tensors, i.e., multi-dimensional arrays of numerical values. Storing them without compression often requires substantial space, which grows exponentially with the order. While many tensor compression algorithms are available, many of them rely on strong data assumptions regarding its order, sparsity, rank, and smoothness. In this work, we propose TensorCodec, a lossy compression algorithm for general tensors that do not necessarily adhere to strong input data assumptions.TensorCodec incorporates three key ideas. The first idea is neural tensor-train decomposition (NTTD) where we integrate a recurrent neural network into Tensor-Train Decomposition to enhance its expressive power and alleviate the limitations imposed by the low-rank assumption. Another idea is to fold the input tensor into a higher-order tensor to reduce the space required by NTTD. Finally, the mode indices of the input tensor are reordered to reveal patterns that can be exploited by NTTD for improved approximation. In addition, we extend TensorCodec to enable the lossy compression of tensors with missing entries, often found in real-world datasets. Our analysis and experiments on 8 real-world datasets demonstrate that TensorCodec is (a) Concise: it gives up to 7.38 × more compact compression than the best competitor with similar reconstruction error, (b) Accurate: given the same budget for compressed size, it yields up to 3.33 × more accurate reconstruction than the best competitor, (c) Scalable: Its empirical compression time is linear in the number of tensor entries, and it reconstructs each entry in logarithmic time. Our code and datasets are available at https://github.com/kbrother/TensorCodec. [ABSTRACT FROM AUTHOR]

Mechanoluminescence platforms, combining phosphors with elastic polymer matrix, have emerged in smart wearable technology due to their superior elasticity and mechanically driven luminescent properties. However, their luminescence performance often deteriorates under extreme elastic conditions owing to a misinterpretation of polymer matrix behavior. Here, we unveil the role of the polymer matrices in mechanoluminescence through an interface-triboelectric effect driven by elasticity, achieving both high elasticity and brightness. By investigating interactions between elastic polymers and copper doped zinc sulfide microparticles, we reveal that elasticity significantly governed triboelectric effects for mechanoluminescence. In particular, high negative triboelectricity emerged as the key to overcoming poor triboelectric effect in extreme elastic conditions. This led to the discovery of polybutylene adipate-co-terephthalate silane and polycarbonate silane, achieving remarkable elasticity over 100% and a brightness of 139 cd/m2. These findings offer fundamental insights to select the optimal polymer matrix based on systematic parameters for various smart wearable applications. Mechanoluminescent platforms have emerged in smart wearable technology, however, their luminescence performance often deteriorates under extreme elastic conditions owing to a misinterpretation of polymer matrix behavior. Here, the authors study the role of polymer matrices in mechanoluminescence via elasticity-driven interface-triboelectric effects providing insights into selecting optimal polymers for smart wearable applications. [ABSTRACT FROM AUTHOR]

The growing demand for lithium, driven by its critical role in lithium-ion batteries (LIBs) and other applications, has intensified the need for efficient extraction methods from aqua-based resources such as seawater. Among various approaches, 2D channel membranes have emerged as promising candidates due to their tunable ion selectivity and scalability. While significant progress has been made in achieving high Li+/Mg2+ selectivity, enhancing Li+ ion selectivity over Na+ ion, the dominant monovalent cation in seawater, remains a challenge due to their similar properties. This review provides a comprehensive analysis of the fundamental mechanisms underlying Li+ selectivity in 2D channel membranes, focusing on the dehydration and diffusion processes that dictate ion transport. Inspired by the principles of biological ion channels, we identify key factors—channel size, surface charge, and binding sites—that influence energy barriers and shape the interplay between dehydration and diffusion. We highlight recent progress in leveraging these factors to enhance Li+/Na+ selectivity and address the challenges posed by counteracting effects in ion transport. While substantial advancements have been made, the lack of comprehensive principles guiding the interplay of these variables across permeation steps represents a key obstacle to optimizing Li+/Na+ selectivity. Nonetheless, with their inherent chemical stability and fabrication scalability, 2D channel membranes offer significant potential for lithium extraction if these challenges can be addressed. This review provides insights into the current state of 2D channel membrane technologies and outlines future directions for achieving enhanced Li+ ion selectivity, particularly in seawater applications. [ABSTRACT FROM AUTHOR]

The lymphatic system plays a crucial role in maintaining physiological homeostasis and regulating immune responses. Traditional imaging modalities such as magnetic resonance imaging, computerized tomography, and positron emission tomography have been widely used to diagnose disorders in the lymphatic system, including lymphedema, lymphangioma, lymphatic metastasis, and Castleman disease. Nano-fluorescence technology has distinct advantages—including naked-eye visibility, operational simplicity, portability of the laser, and real-time visibility—and serves as an innovative alternative to traditional imaging techniques. This review explores recent advancements in nano-fluorescence imaging aimed at enhancing the resolution of lymphatic structure, function, and immunity. After delineating the fundamental characteristics of lymphatic systems, it elaborates on the development of various nano-fluorescence systems (including nanoparticles incorporating fluorescent dyes and those with intrinsic fluorescence) while addressing key challenges such as photobleaching, limited tissue penetration, biocompatibility, and signal interference from biomolecules. Furthermore, this review highlights the clinical applications of nano-fluorescence and its potential integration into standard diagnostic protocols. Ongoing advancements in nanoparticle technology underscore the potential of nano-fluorescence to revolutionize the diagnosis and treatment of lymphatic disease. [ABSTRACT FROM AUTHOR]

DNA methylation is an epigenetic mark that plays a critical role in regulating gene expression. DNA methyltransferase (DNMT) inhibitors, inhibit global DNA methylation and have been a key tool in studies of DNA methylation. A major bottleneck is the lack of tools to induce global DNA methylation. Here, we engineered a CRISPR based approach, that we initially designed, to enable site-specific DNA methylation. Using the synergistic activation mediator (SAM) system, we unexpectedly find that regardless of the targeted sequence any sgRNA induces global genome-wide DNA methylation. We term this method SAM-DNMT3A and show that induction of global DNA methylation is a unique vulnerability in ER-positive breast cancer suggesting a therapeutic approach. Our findings highlight the need of caution when using CRISPR based approaches for inducing DNA methylation and demonstrate a method for global induction of DNA methylation. DNA methylation is an epigenetic mark that plays a critical role in many biological processes. Here, we describe the development of SAM-DNMT3A a tool for induction of genome wide DNA methylation. Using SAM-DNMT3A we show that DNA methylation is a unique vulnerability in ER+ breast cancer. [ABSTRACT FROM AUTHOR]

As control algorithms become increasingly sophisticated, delivering improved performance at the expense of greater complexity, practical experiments often become unfeasible. To address this challenge, this study introduces a novel rapid control prototyping (NRCP) approach based on model-based design (MBD) using MATLAB/Simulink, STM32CubeMX software, and field-oriented control strategies for permanent magnet synchronous motors. Compared with existing rapid control prototyping methods, our NRCP design offers several advantages: it simplifies model construction by utilizing only basic Simulink modules, minimizes dependency on MATLAB/Simulink toolboxes by only requiring Embedded Code conversion to C language, and ensures strong compatibility as the experimental code involves only C language. To demonstrate the feasibility and efficiency of this approach, sensor-based and sensorless control models were developed using the MBD method. The practicality of the NRCP was successfully validated through sensor-based and sensorless experiments using an ARM Cortex-M4-based STM32 microcontroller. [ABSTRACT FROM AUTHOR]

Background & purpose: Tremor is a common movement disorder diagnosed employing electrophysiological methods. Today, machine learning (ML) algorithms can efficiently analyze image-based data. Thus, we subjected the dynamics of essential tremor (ET) to video-based analysis. Methods: We enrolled 59 ET patients and 48 age-matched normal controls. The Clinical Rating Scale for Tremor was used to score tremors. All subjects used a smartphone to record an image designed especially for this study while both stationary and in motion. The trajectories were divided into lower bandpass-filtered and bandpass-filtered (BPF) groups based on the frequency. We extracted seven trajectory features, including the angle, velocity, homogeneity, pitch, power, entropy, and cosine. We used Student's t-test to compare the features of the ET patients and normal controls. A Random Forest model was employed to rank feature importance. Five ML models (random forest, k-nearest neighbors, support vector machine, decision tree, and multi-layer perceptron) were applied to estimate diagnostic accuracy. Results: Significant differences in most of the features of the BPF signals were evident between the two groups. The velocity and homogeneity of the BPF trajectory were highest in the stationary and motion phases, respectively. The highest accuracy levels in the stationary, motion, and combined phases for predicting ET were 0.901, 0.757, and 0.892, respectively. Conclusions: Features of ET tremor were evident in image-based data, enabling analysis of the tremor dynamics. ML analyses distinguished ET subjects from normal controls; however, more research is needed. [ABSTRACT FROM AUTHOR]

Background: Patients achieving pathological complete response (pCR) post-neoadjuvant chemoradiotherapy (nCRT) and surgery for locally advanced esophageal squamous cell carcinoma (ESCC) have a favorable prognosis. However, recurrence occurs in approximately 20–30% of all patients, with few studies evaluating their prognostic factors. We identified these prognostic factors, including inflammation-based markers, in patients with ESCC showing pCR after nCRT and surgery. Patients and Methods: Patients with ESCC undergoing esophagectomy post-nCRT (January 2007–August 2017) were studied. Survival analysis evaluated 5-year overall (OS) and recurrence-free survival (RFS). Risk factors, including inflammation factors, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio (PLR), were analyzed using Cox-proportional hazards model. Results: Overall, 123patients participated herein. After a median follow-up duration of 67 months (44–86 months), 17 patients (12.3%) had recurrent disease. The 5-year OS and RFS rates were 71.6% and 68.0%, respectively. In the multivariable analysis, older age (≥ 60 years) [hazard ratio (HR) 3.228, 95% confidence interval (CI) 1.478–7.048, p = 0.003], higher pretreatment T stage (≥ T3; HR 2.563, 95% CI 1.335–4.922, p = 0.005), nonapplication of induction chemotherapy (HR 2.389, 95% CI 1.184–4.824, p = 0.015), and higher post-nCRT PLR (≥ 184.2; HR 2.896, 95% CI 1.547–5.420, p = 0.001) were poor independent prognostic factors for 5-year RFS. The patient group with three to four identified factors with poor outcomes exhibited a 5-year RFS rate of 46.2%. Conclusions: Significant prognostic factors include higher post-nCRT PLR, older age, higher clinical T stage, and nonapplication of induction chemotherapy. Identifying higher recurrence risk patients is crucial for tailored follow-up and treatment. [ABSTRACT FROM AUTHOR]

A critical bottleneck toward all-solid-state batteries lies in how the solid(electrode)-solid(electrolyte) interface is fabricated and maintained over repeated cycles. Conventional composite cathodes, with crystallographically distinct electrode/electrolyte interfaces of random particles, create complexities with varying (electro)chemical compatibilities. To address this, we employ an epitaxial model system where the crystal orientations of cathode and solid electrolyte are precisely controlled, and probe the interfaces in real-time during co-sintering by in situ electron microscopy. The interfacial reaction is highly dependent on crystal orientation/alignment, especially the availability of open ion channels. Interfaces bearing open ion paths of NCM are more susceptible to interdiffusion, but stabilize with the early formed passivation layer. Conversely, interfaces with closed ion pathway exhibit stability at intermediate temperatures, but deteriorate rapidly at high temperature due to oxygen evolution, increasing interfacial resistance. The elucidation of these distinct interfacial behaviors emphasizes the need for decoupling collective interfacial properties to enable rational design in solid-state batteries. A bottleneck in solid-state batteries is the solid electrode-electrolyte interface being maintained over repeated cycles. Here, the authors use an epitaxial model system to probe and control the interface in real time. [ABSTRACT FROM AUTHOR]

A debris flow barrier is a structure used to prevent erosion caused by debris flow, and is typically installed in countries with mountainous areas, such as Korea. However, owing to earth pressure, several debris flow barriers become damaged. Geofoam is a reinforcement material widely used in the design of debris flow barriers, not only in dams/retaining walls but also in bridges and landscaping. The purpose of this study was to examine the lateral earth pressure reduction effect of geofoam on debris flow barriers, and to find an optimized geofoam shape that satisfies specific conditions. In this study, the debris flow barrier, backfill, geofoam, and foundation were modeled using ABAQUS, a two-dimensional finite element method program. Models A-C were simulated and compared at backfill lengths of 10, 20, and 40 m. The debris flow barrier model was used at a mountain slope of 25°. To determine the geofoam shape and analyze the stability coefficients for sliding and overturning, ten diverse scenarios were considered. According to the results, Scenario 10, with a reversed triangle shape of geofoam, was the most effective in all cases, which reduces the earth pressure significantly. In the case of Model C, a reversed triangle geofoam reinforcement reduces the lateral earth pressure on the backside of the structure by an average of 34.43%. [ABSTRACT FROM AUTHOR]

This study assessed the relationship between daily life changes and mental health in Korean adolescents during the COVID-19 pandemic. Data from middle and high school students who responded to the relevant survey items the 2021 Korea Youth Risk Behavior Survey were analyzed (n = 53,868). The survey assessed changes in economic status, physical activity, skipping breakfast, drinking, smoking, and mental health, including stress, depression, generalized anxiety disorder, and suicidal ideation, due to the COVID-19 pandemic. The overall prevalence of perceived stress, depression, generalized anxiety disorder, and suicidal ideation was 38.6%, 26.4%, 12.2%, and 12.4%. Adolescents whose economic status worsened due to COVID-19 had a higher risk of perceived stress, depression, generalized anxiety disorder, and suicidal ideation. Changes in physical activity and breakfast consumption due to the COVID-19 pandemic were significantly associated with increases in perceived stress, depression, generalized anxiety disorder, and suicidal ideation. Adolescents who increased their alcohol consumption had the highest risk of perceived stress, depression, generalized anxiety disorder, and suicidal ideation. Changes in economic status and health behaviors caused by the COVID-19 pandemic had a negative effect on the mental health of Korean adolescents. These results can be used to identify adolescents at high risk of developing mental health problems. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease is defined by inflammation and immune dysregulation. This study investigated the effects of Gα13 liver-specific knockout (LKO) on proximal and distal colons of dextran sodium sulfate (DSS)-induced mice in conjunction with a high-fat diet (HFD). HFD improved body weight gain and disease activity index scores. Gα13LKO exerted no improvement. In the proximal colon, HFD augmented the DSS effect on Il6, which was not observed in Gα13LKO mice. In the distal colon, HFD plus DSS oppositely fortified an increase in Tnfa and Cxcl10 mRNA in Gα13LKO but not WT. Il6 levels remained unchanged. Bioinformatic approaches using Gα13LKO livers displayed bile acid and cholesterol metabolism-related gene sets. Cholic acid and chenodeoxycholic acid levels were increased in the liver of mice treated with DSS, which was reversed by Gα13LKO. Notably, mice treated with DSS showed a reduction in hepatic ABCB11, CYP7B1, CYP7A1, and CYP8B1, which was reversed by Gα13LKO. Overall, feeding HFD augments the effect of DSS on Il6 in the proximal colon of WT, but not Gα13LKO mice, and enhances DSS effect on Tnfa and Cxcl10 in the distal colon of Gα13LKO mice, suggesting site-specific changes in the inflammatory cytokines, potentially resulting from changes in BA synthesis and excretion. [ABSTRACT FROM AUTHOR]

Complex coacervation plays an important role in various fields. Here, the influences of the backbone chemistry and ionic functional groups of five pairs of oppositely charged polyelectrolytes on complex coacervation were investigated. These pairs include synthetic polymers with aliphatic hydrocarbon backbones, peptides with amide bonds, and carbohydrates with glycosidic linkages. Despite sharing identical charged groups, specific pairs displayed distinct liquid/liquid and liquid/solid phase separations depending on the polyelectrolyte mixing ratio, buffer, and ionic strength. The coacervate phase boundary broadened in the orders: glycosidic linkages > amide backbone > aliphatic hydrocarbon backbone, and Tris-phosphate > Tris-acetate > Tris-chloride buffers. Coacervates prepared from polyelectrolytes with lower solubilities in water resisted disassembly at high salt concentrations, and their merge rate was slow. These observations suggest that the hydrophobic segments in polyelectrolytes interfere with the formation of complex coacervates; however, following coacervate formation, the hydrophobic segments render the coacervates stable and elastic. Complex coacervation is propelled by the electrostatic association between oppositely charged polyelectrolytes, but the factors that drive complex coacervation have yet to be fully understood. Here, the authors investigate the influence of the backbone chemistry and ionic functional groups of five pairs of oppositely charged polyelectrolytes on complex coacervation. [ABSTRACT FROM AUTHOR]

Affective disorders are frequently associated with disrupted circadian rhythms. The existence of rhythmic secretion of central serotonin (5-hydroxytryptamine, 5-HT) pattern has been reported; however, the functional mechanism underlying the circadian control of 5-HTergic mood regulation remains largely unknown. Here, we investigate the role of the circadian nuclear receptor REV-ERBα in regulating tryptophan hydroxylase 2 (Tph2), the rate-limiting enzyme of 5-HT synthesis. We demonstrate that the REV-ERBα expressed in dorsal raphe (DR) 5-HTergic neurons functionally competes with PET-1—a nuclear activator crucial for 5-HTergic neuron development. In mice, genetic ablation of DR 5-HTergic REV-ERBα increases Tph2 expression, leading to elevated DR 5-HT levels and reduced depression-like behaviors at dusk. Further, pharmacological manipulation of the mice DR REV-ERBα activity increases DR 5-HT levels and affects despair-related behaviors. Our findings provide valuable insights into the molecular and cellular link between the circadian rhythm and the mood-controlling DR 5-HTergic systems. Circadian nuclear receptor, REV-ERBa regulates the rhythmic serotonin synthesis in the central nervous system, highlighting the molecular connections between circadian clock and mood regulation [ABSTRACT FROM AUTHOR]

Photocatalysis provides a versatile approach to redox activation of various organic substrates for synthetic applications. To broaden the scope of photoredox catalysis, developing catalysts with strong oxidizing or reducing power in the excited state is imperative. Catalysts that feature highly cathodic oxidation potentials and long lifetimes in their excited states are particularly in demand. In this research, we demonstrate the catalytic utility of two-coordinate Au(I) complex photocatalysts that exhibit an exclusive ligand-to-ligand charge-transfer (LLCT) transition in C–C cross-coupling reactions between N-heterocycles and (hetero)aryl halides, including redox-resistant (hetero)aryl chlorides. Our photocatalysis system can steer reactions under visible-light irradiation at a catalyst loading as low as 0.1 mol% and exhibits a broad substrate scope with high chemo- and regioselectivity. Our mechanistic investigations provide direct spectroscopic evidence for each step in the catalysis cycle and demonstrate that the LLCT-active Au(I) complex catalysts offer several benefits, including strong visible-light absorption, a 210 ns-long excited-state lifetime without short-lived components, and a 91% yield in the production of free-radical intermediates. Given the wide structural versatility of the proposed catalysts, we envision that our research will provide useful insights into the future utilization of the LLCT-active Au(I) complex for organic transformations. Developing photoredox catalysts with long-lived excited states that feature highly cathodic oxidation potentials is imperative. Here, the authors demonstrate the C–C cross-coupling catalytic utility of two-coordinate LLCT-active Au(I) complex photocatalysts with visible-light absorption and a 210 ns-long excited-state lifetime. [ABSTRACT FROM AUTHOR]

Virtual metrology (VM) in semiconductor manufacturing is to predict product physical quality measurements using the processing information at a wafer fabrication process. The process state parameters from various sensors on production equipment are used for the accurate and reliable prediction of process outcomes, and several deep learning methods have been considered, including convolutional neural network (CNN). Recent studies in the literature have demonstrated the successful VM modeling with CNN for univariate-response prediction. Multivariate process outputs, however, are overlooked in deep learning-based VM modeling although the joint information among the process outputs can be used to improve the prediction performance. In this work, we propose a CNN-based multivariate VM model using multi-sensors process sensor data. We evaluate the proposed model on a real-life case for VM modeling at an etching process in wafer fabrication. [ABSTRACT FROM AUTHOR]

Most triblock copolymer-based physical hydrogels form three-dimensional networks through micellar packing, and formation of polymer loops represents a topological defect that diminishes hydrogel elasticity. This effect can be mitigated by maximizing the fraction of elastically effective bridges in the hydrogel network. Herein, we report hydrogels constructed by complexing oppositely charged multiblock copolymers designed with a sequence pattern that maximizes the entropic and enthalpic penalty of micellization. These copolymers self-assemble into branched and bridge-rich network units (netmers), instead of forming sparsely interlinked micelles. We find that the storage modulus of the netmer-based hydrogel is 11.5 times higher than that of the micelle-based hydrogel. Complementary coarse grained molecular dynamics simulations reveal that in the netmer-based hydrogels, the numbers of charge-complexed nodes and mechanically reinforcing bridges increase substantially relative to micelle-based hydrogels. In the formation of physical hydrogels from triblock copolymers, formation of polymer loops can result in topological defects. Here, the authors report the development of hydrogels from oppositely charged copolymers which assembled to form branched and bridge-rich networks. [ABSTRACT FROM AUTHOR]

Differentially methylated CpG sites (dmCpGs) that distinguish prostate tumour from adjacent benign tissue could aid in the diagnosis and prognosis of prostate cancer. Previously, the identification of such dmCpGs has only been undertaken in radical prostatectomy (RP) samples and not primary diagnostic tumour samples (needle biopsy or transurethral resection of the prostate). We interrogated an Australian dataset comprising 125 tumour and 43 adjacent histologically benign diagnostic tissue samples, including 41 paired samples, using the Infinium Human Methylation450 BeadChip. Regression analyses of paired tumour and adjacent benign samples identified 2,386 significant dmCpGs (Bonferroni p < 0.01; delta-β ≥ 40%), with LASSO regression selecting 16 dmCpGs that distinguished tumour samples in the full Australian diagnostic dataset (AUC = 0.99). Results were validated in independent North American (npaired = 19; AUC = 0.87) and The Cancer Genome Atlas (TCGA; npaired = 50; AUC = 0.94) RP datasets. Two of the 16 dmCpGs were in genes that were significantly down-regulated in Australian tumour samples (Bonferroni p < 0.01; GSTM2 and PRKCB). Ten additional dmCpGs distinguished low (n = 34) and high Gleason (n = 88) score tumours in the diagnostic Australian dataset (AUC = 0.95), but these performed poorly when applied to the RP datasets (North American: AUC = 0.66; TCGA: AUC = 0.62). The DNA methylation marks identified here could augment and improve current diagnostic tests and/or form the basis of future prognostic tests. [ABSTRACT FROM AUTHOR]

Transmembrane protein 9 (TMEM9) is a transmembrane protein that regulates lysosomal acidification by interacting with the v-type ATPase complex. However, the role of TMEM9 in the lysosome-dependent autophagy machinery has yet to be identified. In this study, we demonstrate that the lysosomal protein TMEM9, which is involved in vesicle acidification, regulates Rab9-dependent alternative autophagy through its interaction with Beclin1. The cytosolic domain of TMEM9 interacts with Beclin1 via its Bcl-2-binding domain. This interaction between TMEM9 and Beclin1 dissociates Bcl-2, an autophagy-inhibiting partner, from Beclin1, thereby activating LC3-independent and Rab9-dependent alternative autophagy. Late endosomal and lysosomal TMEM9 apparently colocalizes with Rab9 but not with LC3. Furthermore, we show that multiple glycosylation of TMEM9, essential for lysosomal localization, is essential for its interaction with Beclin1 and the activation of Rab9-dependent alternative autophagy. These findings reveal that TMEM9 recruits and activates the Beclin1 complex at the site of Rab9-dependent autophagosome to induce alternative autophagy. [ABSTRACT FROM AUTHOR]

Based on density functional theory (DFT) calculations, we first examine a detailed study of CeO2 in both crystalline and amorphous forms, assessing their structural, electronic, mechanical, and surface characteristics for chemical mechanical planarization (CMP). We explored the atomic structures of CeO2, uncovering distinct atomic configurations and consistent bonding in both phases. The crystalline form of CeO2 is characterized by a distinct bandgap of 1.84 eV and a higher degree of stiffness, evidenced by a bulk modulus of 178.10 GPa. Conversely, the amorphous phase of CeO2 features a smaller bandgap, measured at 1.39 eV, and a reduced stiffness with a bulk modulus of 155.13 GPa, beneficial for reducing CMP scratches. Notably, the amorphous CeO2 forms oxygen vacancies more readily, leading to a higher concentration of Ce3+ ions, lower surface energy (0.92 J/m2), and enhanced adhesion with SiO2 (adhesion work of 0.73 J/m2) compared to crystalline CeO2. Our DFT calculations reveal the unique physicochemical properties of amorphous CeO2, such as its softer nature and stronger SiO2 adhesion, providing valuable insights for optimizing CMP processes. [ABSTRACT FROM AUTHOR]

Image-based lineage tracing enables tissue turnover kinetics and lineage potentials of different adult cell populations to be investigated. Previously, we reported a genetic mouse model system, Red2Onco, which ectopically expressed mutated oncogenes together with red fluorescent proteins (RFP). This system enabled the expansion kinetics and neighboring effects of oncogenic clones to be dissected. We now report Red2Flpe-SCON: a mosaic knockout system that uses multicolor reporters to label both mutant and wild-type cells. We develop the Red2Flpe mouse line for red clone-specific Flpe expression, as well as the FRT-based SCON (Short Conditional IntrON) method to facilitate tunable conditional mosaic knockouts in mice. We use the Red2Flpe-SCON method to study Sox2 mutant clonal analysis in the esophageal epithelium of adult mice which reveal that the stem cell gene, Sox2, is less essential for adult stem cell maintenance itself, but rather for stem cell proliferation and differentiation. Inducible genetic mosaics can provide information about cellular lineages that are otherwise difficult to obtain. Here the authors report a mosaic knockout system called Red2Flpe-SCON, which allows lineage tracing of wild-type and mutant cells using a multicolour fluorescent reporter in mice. [ABSTRACT FROM AUTHOR]

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering. [ABSTRACT FROM AUTHOR]

Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defects) detection. However, since abnormal states (with defects) occur much less frequently than normal ones (without defects) in a manufacturing process, the number of sensor data samples collected from a normal state is usually much more than that from an abnormal state. This issue causes imbalanced training data for classification analysis, thus deteriorating the performance of detecting abnormal states in the process. It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set. To achieve this goal, this paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The novelty of our approach is that a standard GAN and classifier are jointly optimized with techniques to stabilize the learning process of standard GAN. The diverse and high-quality generated samples provide balanced training data to the classifier. The iterative optimization between GAN and classifier provides the high-performance classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes. [ABSTRACT FROM AUTHOR]

The reliable diagnosis of electrical submersible pump (ESP) failure is a vital process for establishing of the optimal production strategies and achieving minimum development costs. Although traditional ammeter charts and nodal analysis are commonly used for ESP failure diagnosis, the techniques have limitations, as it requires manpower and is difficult to diagnose the failure in real-time. Therefore, in this study, ESP failure diagnosis was performed using the principal component analysis (PCA). First, 11 types of 9,955 pieces of data were acquired from a newly constructed ESP experimental system for 300 days. During the experimental period, ESP failure occurred twice with a significant drop in performance: first on day 112 and second on day 271. The PCA model was constructed with the 8,928 pieces of normal status data and tested with the 1,027 pieces of normal and failure status data. Three principal components were extracted from the measured data to identify the patterns of the normal and failure status. Based on the logistic regression method to analyze the efficiency of the PCA model, it was found out that the developed PCA model showed an accuracy of 93.3%. Therefore, the PCA model was found to be reliable and effective for the ESP failure diagnosis and performance analysis. [ABSTRACT FROM AUTHOR]

Stereoselective reactions have played a vital role in the emergence of life, evolution, human biology, and medicine. However, for a long time, most industrial and academic efforts followed a trial-and-error approach for asymmetric synthesis in stereoselective reactions. In addition, most previous studies have been qualitatively focused on the influence of steric and electronic effects on stereoselective reactions. Therefore, quantitatively understanding the stereoselectivity of a given chemical reaction is extremely difficult. As proof of principle, this paper develops a novel composite machine learning method for quantitatively predicting the enantioselectivity representing the degree to which one enantiomer is preferentially produced from the reactions. Specifically, machine learning methods that are widely used in data analytics, including Random Forest, Support Vector Regression, and LASSO, are utilized. In addition, the Bayesian optimization and permutation importance tests are provided for an in-depth understanding of reactions and accurate prediction. Finally, the proposed composite method approximates the key features of the available reactions by using Gaussian mixture models, which provide suitable machine learning methods for new reactions. The case studies using the real stereoselective reactions show that the proposed method is effective and provides a solid foundation for further application to other chemical reactions. [ABSTRACT FROM AUTHOR]

Although mice are social, multiple animals’ neural activities are rarely explored. To characterise the neural activities during multi-brain interaction, we simultaneously recorded local field potentials (LFP) in the prefrontal cortex of four mice. The social context and locomotive states predominately modulated the entire LFP structure. The power of lower frequency bands—delta to alpha—were correlated with each other and anti-correlated with gamma power. The high-to-low-power ratio (HLR) provided a useful measure to understand LFP changes along the change of behavioural and locomotive states. The HLR during huddled conditions was lower than that during non-huddled conditions, dividing the social context into two. Multi-brain analyses of HLR indicated that the mice in the group displayed high cross-correlation. The mice in the group often showed unilateral precedence of HLR by Granger causality analysis, possibly comprising a hierarchical social structure. Overall, this study shows the importance of the social environment in brain dynamics and emphasises the simultaneous multi-brain recordings in social neuroscience. [ABSTRACT FROM AUTHOR]

We establish a partial regularity result for solutions of parabolic systems with general φ -growth, where φ is an Orlicz function. In this setting we can develop a unified approach that is independent of the degeneracy of system and relies on two caloric approximation results: the φ -caloric approximation, which was introduced in Diening et al. (Calc Var Partial Differ Equ 56(4):27, 2017), and an improved version of the A -caloric approximation, which we prove without using the classical compactness method. [ABSTRACT FROM AUTHOR]

In this study, the impact of triisopropanolamine (TIPA) on both the crystallographic and surface properties of cubic and orthorhombic tricalcium aluminate (C3A) were investigated together with their hydration behavior and strength development. When TIPA was added during the grinding process, the pre-hydration and carbonation of C3A were effectively prevented, and crystal structural changes of C3A were confirmed. It leads to altering the hydration mechanism of C3A phases: in the case of cubic C3A, it promotes the formation of Al-hydrogarnet phases instead of OH-AFm phases even on the first day of curing. Similar hydration behavior was observed with orthorhombic C3A, but the phase transition of OH-AFm phases to Al-hydrogarnet occurs during a specific time period in 1–3 days. The latter was revealed as a very interesting endothermic reaction which can be the only heat absorptive behavior in complex cement hydration as reported so far. [ABSTRACT FROM AUTHOR]

Hypergraphs naturally represent group interactions, which are omnipresent in many domains: collaborations of researchers, co-purchases of items, and joint interactions of proteins, to name a few. In this work, we propose tools for answering the following questions in a systematic manner: (Q1) what are the structural design principles of real-world hypergraphs? (Q2) how can we compare local structures of hypergraphs of different sizes? (Q3) how can we identify domains from which hypergraphs are? We first define hypergraph motifs (h-motifs), which describe the overlapping patterns of three connected hyperedges. Then, we define the significance of each h-motif in a hypergraph as its occurrences relative to those in properly randomized hypergraphs. Lastly, we define the characteristic profile (CP) as the vector of the normalized significance of every h-motif. Regarding Q1, we find that h-motifs ' occurrences in 11 real-world hypergraphs from 5 domains are clearly distinguished from those of randomized hypergraphs. In addition, we demonstrate that CPs capture local structural patterns unique to each domain, thus comparing CPs of hypergraphs addresses Q2 and Q3. The concept of CP is naturally extended to represent the connectivity pattern of each node or hyperedge as a vector, which proves useful in node classification and hyperedge prediction. Our algorithmic contribution is to propose MoCHy, a family of parallel algorithms for counting h-motifs ' occurrences in a hypergraph. We theoretically analyze their speed and accuracy and show empirically that the advanced approximate version MoCHy-A + is up to 25 × more accurate and 32 × faster than the basic approximate and exact versions, respectively. Furthermore, we explore ternary hypergraph motifs that extends h-motifs by taking into account not only the presence but also the cardinality of intersections among hyperedges. This extension proves beneficial for all previously mentioned applications. [ABSTRACT FROM AUTHOR]

Establishing dependable, cost-effective electrical connections is vital for enhancing device performance and shrinking electronic circuits. MXenes, combining excellent electrical conductivity, high breakdown voltage, solution processability, and two-dimensional morphology, are promising candidates for contacts in microelectronics. However, their hydrophilic surfaces, which enable spontaneous environmental degradation and poor dispersion stability in organic solvents, have restricted certain electronic applications. Herein, electrohydrodynamic printing technique is used to fabricate fully solutionprocessed thin-film transistors with alkylated 3,4-dihydroxy-L-phenylalanine functionalized Ti3C2Tx (AD-MXene) as source, drain, and gate electrodes. The AD-MXene has excellent dispersion stability in ethanol, which is required for electrohydrodynamic printing, and maintains high electrical conductivity. It outperformed conventional vacuum-deposited Au and Al electrodes, providing thin-film transistors with good environmental stability due to its hydrophobicity. Further, thin-film transistors are integrated into logic gates and onetransistor-one-memory cells. This work, unveiling the ligand-functionalized MXenes’ potential in printed electrical contacts, promotes environmentally robust MXene-based electronics (MXetronics). [ABSTRACT FROM AUTHOR]

Although clinker has been used for many years, complicated mineralogical properties of clinker pose challenges for the precise quantification. In this study, the mineralogical and crystallographic properties of nine different clinkers according to grinding procedures were investigated. With the dry-grinding for 2 h, particle size reduction to 3 μm of median particle size with a substantial phase transition to an amorphous phase observed, to which alite (C3S) mainly contributed to the transition. Meanwhile, the crystallographic properties of the clinker phases were barely changed during the wet-grinding. In the wet-grinding program, the amount of ferrite solid solution (C4AF) with a high linear absorption coefficient was not underestimated. Furthermore, well-corrected preferred orientation effect on C3S was positively contributed to the analysis result of clinkers with the wet-grinding. Hence, it was suggested that the crystallographic effects observed in the wet-grinding program could produce more reliable results in phase analysis for the clinkers. [ABSTRACT FROM AUTHOR]

A single-column turbulence model for stratified atmospheric boundary layer (ABL), which solves the transport equations of turbulence probability density function (PDF) using a Lagrangian stochastic modeling (LSM) approach, is proposed in this study. This study adopts previously developed stochastic differential equations (SDEs) for particle velocity and temperature and extends the LSM to simulate inhomogeneous turbulence. The proposed LSM is tested for its ability to fully simulate statistics of inhomogeneous stratified turbulence. In the model, particles evolve by SDEs, and turbulence statistics are calculated by averaging the properties of particles. The model provides a full representation of turbulence PDF and simulates turbulent transport without any modeling assumption. The model performance is evaluated against large-eddy simulation (LES) results in the simulations of convective and stable ABL cases. For the convective ABL, LSM realistically simulates the entrainment process with the temperature and heat flux profiles that closely match with LES. The joint PDF simulated by LSM reproduces a curved and highly skewed shape, and some distinct features, like the asymmetric distribution of vertical velocity and the separation of the PDF in the entrainment zone, are simulated. LSM also reproduces the entrainment enhancement by wind shear in the simulation of sheared convective ABL. The LSM simulation of stable ABL predicts realistic turbulence intensity and mean field profiles, where Gaussian-like PDFs are simulated both in LSM and LES. [ABSTRACT FROM AUTHOR]

The development of analytical methods for endogenous therapeutic substances is a critical but challenging issue as obtaining a blank matrix without endogenous substance is impossible. To address this issue, we prepared a surrogate biological matrix by removing endogenous bile acids from rat plasma using a charcoal-stripped method and developed an analytical method for ursodeoxycholic acid (UDCA) and its conjugated metabolites, tauroursodeoxycholic (TUDCA) and glycoursodeoxycholic acid (GUDCA), including the use of surrogate matrices and protein precipitation method. In addition, we applied the bioanalytical method to investigate the bioavailability of UDCA-mixed micelle powder formulation (UDCA-MM). The oral bioavailability of UDCA in rats was calculated as 15.2% and increased 3.32-fold following the oral administration of UDCA-MM with the increased production of TUDCA without significant change in GUDCA. The UDCA-MM powder was prepared by thin-layer hydration and subsequent freeze-drying method in a ratio of UDCA/polysorbate 80/poloxamer 407 = 1:1:10 (w/w/w). The UDCA-MM was easily dispersed with a particle size of 16.5 ± 2.2 nm and solubility of 1120 ± 38 μg/mL, which represented a 175.3-fold increase in its solubility of UDCA. In conclusion, we developed and validated a simple and reliable bioanalytical method for UDCA, TUDCA, and GUDCA using the charcoal-stripped plasma as surrogate matrices. Our bioanalytical method successfully supported the assessment of the pharmacokinetics or bioavailability of UDCA, TUDCA, and GUDCA after the intravenous or oral dosing of UDCA and UDCA-MM. The UDCA-MM using poloxamer 407 and polysorbate 80 is a promising technique for increasing the solubility and oral absorption of UDCA. [ABSTRACT FROM AUTHOR]

Bioprinting, or bio-additive manufacturing, is a critical emerging field for transforming tissue engineering regenerative medicine to produce biological constructs and scaffolds in a layerwise fashion. Geometric accuracy and spatial distribution of scaffold porosity are critical factors associated with the quality of bio-printed tissue scaffolds. Determining optimal process parameters for tissue scaffold microextrusion 3D printing by traditional trial-and-error approaches is costly, labor-intensive, and time-consuming. In addition, effective in-process sensing techniques are needed to observe internal multilayer scaffold structures, such as porosity. Therefore, an in-process sensing platform based on integrated light scanning and microscopy was proposed to acquire in-process layer information during the fabrication of polymeric and hydrogel scaffolds. This work implements a customized sensing platform consisting of a 3D scanner and digital microscope for in-process quality monitoring of tissue scaffold biofabrication that provides in situ characterization of each printed layer's quality conditions (e.g., porosity). The proposed sensor-based in-process quality monitoring system can accurately capture layerwise porosity quality. Design of experiments (DoE) experimental analysis yielded a set of optimal process parameters that significantly improved the geometric accuracy and compressive modulus of thermoplastic- and hydrogel-based tissue scaffolds. The developed sensing system coupled with the DoE approach enables effective process parameter optimization to fabricate porous 3D-printed tissue scaffolds. It can significantly improve the quality and reproducibility of research associated with porous 3D-printed products, such as tissue scaffolds and membranes. [ABSTRACT FROM AUTHOR]

The intake plugging of an electrical submersible pump (ESP) has presented a formidable challenge to conventional ESP wells. Attention to cumulative solid deposition is essential since it intensifies the intake plugging severity and impedes ESP performance. We present a new approach to evaluate the ESP performance degradation during increased intake plugging severity. In particular, we employ the intake plugging factor, rate-derating factor, and affinity law to calculate the new ESP speed at different plugging conditions. We used Schlumberger PIPESIM software to perform nodal analysis of the newly calculated ESP speed. The result was validated using the actual field data and compared to the field cases that reported the intake plugging issue. The nodal analysis showed a steady maximum ESP head with zero rate derating at the shut-in point. The intake plugging factor caused a significant reduction in the ESP operating rate and increased pump intake pressure and annulus liquid level. Based on the existing intake plugging field data, we established the quantitative standard for the normal and abnormal intake plugging factor range. The observed results agreed with the field downhole data recorded during the intake plugging problem. We identified that regulating the ESP speed to the reduced operating rate could minimize unexpected pump stoppage. It is also possible to carefully monitor the intake plugging problem by combining the annulus liquid level, the signature of pump intake pressure, and a deadhead test. [ABSTRACT FROM AUTHOR]

Promising advances in membrane technology can lead to energy-saving and eco-friendly solutions in industrial sectors. This work demonstrates a highly selective membrane with ultrathin and highly interconnected organosiloxane polymer nanolayers by initiated chemical vapor deposition to effectively separate solutes within the molecular weight range of 150–300 g mol−1. We optimize the poly(1,3,5,7-tetravinyl-1,3,5,7-tetramethylcyclotetrasiloxane) membrane by adjusting both the thickness of the selective layer and the pore sizes of its support membranes. Notably, the 29 nm selective layer imparts a uniformly narrow molecular sieving property, providing a record-high solutesolute selectivity of 39.88 for different-sized solutes. Furthermore, a solutesolute selectivity of 11.04 was demonstrated using the real-world active pharmaceutical ingredient mixture of Acyclovir and Valacyclovir, key components for Herpes virus treatment, despite their molecular weight difference of less than 100 g mol−1. The highly interconnected membrane is expected to meet rigorous requirements for high-standard active pharmaceutical ingredient separation. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate clinicopathological and molecular tumour features associated with intratumoral pks+Escherichia coli (pks+E.coli+), pks+E.coli- (non-E.coli bacteria harbouring the pks island), Enterotoxigenic Bacteroides fragilis (ETBF) and Fusobacterium nucleatum (F. nucleatum). Methods: We screened 1697 tumour-derived DNA samples from the Australasian Colorectal Cancer Family Registry, Melbourne Collaborative Cohort Study and the ANGELS study using targeted PCR. Results: Pks+E.coli+ was associated with male sex (P < 0.01) and APC:c.835-8 A > G somatic mutation (P = 0.03). The association between pks+E.coli+ and APC:c.835-8 A > G was specific to early-onset CRCs (diagnosed<45years, P = 0.02). The APC:c.835-A > G was not associated with pks+E.coli- (P = 0.36). F. nucleatum was associated with DNA mismatch repair deficiency (MMRd), BRAF:c.1799T>A p.V600E mutation, CpG island methylator phenotype, proximal tumour location, and high levels of tumour infiltrating lymphocytes (Ps < 0.01). In the stratified analysis by MMRd subgroups, F. nucleatum was associated with Lynch syndrome, MLH1 methylated and double MMR somatic mutated MMRd subgroups (Ps < 0.01). Conclusion: Intratumoral pks+E.coli+ but not pks+E.coli- are associated with CRCs harbouring the APC:c.835-8 A > G somatic mutation, suggesting that this mutation is specifically related to DNA damage from colibactin-producing E.coli exposures. F. nucleatum was associated with both hereditary and sporadic MMRd subtypes, suggesting the MMRd tumour microenvironment is important for F. nucleatum colonisation irrespective of its cause. [ABSTRACT FROM AUTHOR]