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Macrophage polarization plays essential and diverse roles in most diseases, such as atherosclerosis, adipose tissue inflammation, and insulin resistance. Homeostasis dysfunction in M1/M2 macrophage polarization causes pathological conditions and inflammation. Neuroinflammation is characterized by microglial activation and the concomitant production of pro-inflammatory cytokines, leading to numerous neurodegenerative diseases and psychiatric disorders. Decreased neuroinflammation can be obtained by using natural compounds, including flavonoids, which are known to ameliorate inflammatory responses. Among flavonoids, quercetin possesses multiple pharmacological applications and regulates several biological activities. In the present study, we found that quercetin effectively inhibited the expression of lipocalin-2 in both macrophages and microglial cells stimulated by lipopolysaccharides (LPS). The production of nitric oxide (NO) and expression levels of the pro-inflammatory cytokines, inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX)-2, were also attenuated by quercetin treatment. Our results also showed that quercetin significantly reduced the expression levels of the M1 markers, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, and IL-1β, in the macrophages and microglia. The M1 polarization-associated chemokines, C–C motif chemokine ligand (CCL)-2 and C-X-C motif chemokine ligand (CXCL)-10, were also effectively reduced by the quercetin treatment. In addition, quercetin markedly reduced the production of various reactive oxygen species (ROS) in the microglia. The microglial phagocytic ability induced by the LPS was also effectively reduced by the quercetin treatment. Importantly, the quercetin increased the expression levels of the M2 marker, IL-10, and the endogenous antioxidants, heme oxygenase (HO)-1, glutamate-cysteine ligase catalytic subunit (GCLC), glutamate-cysteine ligase modifier subunit (GCLM), and NAD(P)H quinone oxidoreductase-1 (NQO1). The enhancement of the M2 markers and endogenous antioxidants by quercetin was activated by the AMP-activated protein kinase (AMPK) and Akt signaling pathways. Together, our study reported that the quercetin inhibited the effects of M1 polarization, including neuroinflammatory responses, ROS production, and phagocytosis. Moreover, the quercetin enhanced the M2 macrophage polarization and endogenous antioxidant expression in both macrophages and microglia. Our findings provide valuable information that quercetin may act as a potential drug for the treatment of diseases related to inflammatory disorders in the central nervous system.

Sensors are devices that can detect changes in the external environment and convert them into signals. They are widely used in fields like industrial automation, smart homes, medical devices, automotive electronics, and the Internet of Things (IoT), enabling real-time data collection to enhance system intelligence and efficiency. With advancements in technology, sensors are evolving toward miniaturization, high sensitivity, and multifunctional integration. This paper employs the Direct Sampling Method (DSM) and neural networks to reconstruct the shape of perfect electric conductors from the sensed electromagnetic field. Transverse electric (TE) electromagnetic waves are transmitted to illuminate the conductor. The scattered fields in the x- and y-directions are measured by sensors and used in the method of moments for forward scattering calculations, followed by the DSM for initial shape reconstruction. The preliminary shape data obtained from the DSM are then fed into a U-net for further training. Since the training parameters of deep learning significantly affect the reconstruction results, extensive tests are conducted to determine optimal parameters. Finally, the trained neural network model is used to reconstruct TE images based on the scattered fields in the x- and y-directions. Owing to the intrinsic strong nonlinearity in TE waves, different regularization factors are applied to improve imaging quality and reduce reconstruction errors after integrating the neural network. Numerical results show that compared to using the DSM alone, combining the DSM with a neural network enables the generation of high-resolution images with enhanced efficiency and superior generalization capability. In addition, the error rate has decreased to below 15%.

The conversion of textile water sludge (TWS) into solid recovered fuel (SRF) represents a promising approach to addressing environmental challenges, advancing waste-to-energy strategies, and promoting circular economy principles. This manuscript explores Taiwan’s innovative efforts in SRF production from textile industry waste, highlighting its integration into industrial processes, regulatory frameworks, and global relevance. The study examines the key technological processes involved, including sorting, drying, and torrefaction, which enhance fuel properties such as calorific value and combustion efficiency. Challenges related to raw material availability, quality control, economic viability, and public perception are analyzed alongside potential solutions such as advanced processing technologies, government incentives, and industry collaboration. Comparisons with international practices reveal Taiwan’s leadership in leveraging textile water sludge as a feedstock while identifying opportunities for further alignment with global standards and scalability. The environmental benefits of SRF, including waste reduction and greenhouse gas mitigation, are juxtaposed with risks like emissions control and high production costs. This comprehensive review underscores the potential of SRF production from textile water sludge as a sustainable solution for waste management and energy generation, contributing to Taiwan’s net-zero emissions goals and offering valuable insights for global adoption.

The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA. This study aimed to evaluate the performance of a CV-based video analysis system, based on OpenCV algorithm, to detect surgical interruptions and analyze surgical fluency in EEA. The accuracy of the CV-based video analysis was investigated, and the time required for operative video review using CV-based analysis was compared to that of manual review. The dominant color of each frame in the EEA video was determined using OpenCV. We developed an algorithm to identify events of surgical interruption if the alterations in the dominant color pixels reached certain thresholds. The thresholds were determined by training the current algorithm using EEA videos. The accuracy of the CV analysis was determined by manual review, and the time spent was reported. A total of 46 EEA operative videos were analyzed, with 93.6%, 95.1%, and 93.3% accuracies in the training, test 1, and test 2 data sets, respectively. Compared with manual review, CV-based analysis reduced the time required for operative video review by 86% (manual review: 166.8 and CV analysis: 22.6 minutes; P<.001). The application of a human-computer collaborative strategy increased the overall accuracy to 98.5%, with a 74% reduction in the review time (manual review: 166.8 and human-CV collaboration: 43.4 minutes; P<.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency (nasal phase: 14.9, sphenoidal phase: 15.9, and sellar phase: 4.9 interruptions/10 minutes; P<.001) and duration (nasal phase: 67.4, sphenoidal phase: 77.9, and sellar phase: 31.1 seconds/10 minutes; P<.001) of surgical interruptions. A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (early: 4.9 and late: 2.9 interruptions/10 minutes; P=.03) and duration (early: 41.1 and late: 19.8 seconds/10 minutes; P=.02) of surgical interruptions during the sellar phase. CV-based analysis had a 93% to 98% accuracy in detecting the number, frequency, and duration of surgical interruptions occurring during EEA. Moreover, CV-based analysis reduced the time required to analyze the surgical fluency in EEA videos compared to manual review. The application of CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery. ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020.

Feline infectious peritonitis virus (FIPV) is an alphacoronavirus that causes a nearly 100% mortality rate without effective treatment. Here we report a 3.3-Å cryoelectron microscopy (cryo-EM) structure of the serotype I FIPV spike (S) protein, which is responsible for host recognition and viral entry. Mass spectrometry provided site-specific compositions of densely distributed high-mannose and complex-type N-glycans that account for 1/4 of the total molecular mass; most of the N-glycans could be visualized by cryo-EM. Specifically, the N-glycans that wedge between 2 galectin-like domains within the S1 subunit of FIPV S protein result in a unique propeller-like conformation, underscoring the importance of glycosylation in maintaining protein structures. The cleavage site within the S2 subunit responsible for activation also showed distinct structural features and glycosylation. These structural insights provide a blueprint for a better molecular understanding of the pathogenesis of FIP.

Porcine epidemic diarrhea (PED) is a highly contagious swine disease caused by porcine epidemic diarrhea virus (PEDV). PED causes enteric disorders with an exceptionally high fatality in neonates, bringing substantial economic losses in the pork industry. The trimeric spike (S) glycoprotein of PEDV is responsible for virus-host recognition, membrane fusion, and is the main target for vaccine development and antigenic analysis. The atomic structures of the recombinant PEDV S proteins of two different strains have been reported, but they reveal distinct N-terminal domain 0 (D0) architectures that may correspond to different functional states. The existence of the D0 is a unique feature of alphacoronavirus. Here we combined cryo-electron tomography (cryo-ET) and cryo-electron microscopy (cryo-EM) to demonstrate in situ the asynchronous S protein D0 motions on intact viral particles of a highly virulent PEDV Pintung 52 strain. We further determined the cryo-EM structure of the recombinant S protein derived from a porcine cell line, which revealed additional domain motions likely associated with receptor binding. By integrating mass spectrometry and cryo-EM, we delineated the complex compositions and spatial distribution of the PEDV S protein N-glycans, and demonstrated the functional role of a key N-glycan in modulating the D0 conformation. Hsu and co-workers integrate cryo-electron tomography, cryo-electron microscopy and mass spectrometry to reveal the structural polymorphism of a pig coronavirus spike protein within intact viral particles, and how glycosylation modulates the conformational changes pertinent to host recognition.

In this paper, we propose an inductive line tunneling FET using Epitaxial Tunnel Layer with Ge-Source and Charge Enhancement Insulation (CEI ETL GS-iTFET). The CEI ETL GS-iTFET allows full overlap between the gate and source regions, thereby enhancing the line tunneling. In addition, a germanium layer is introduced on the source side to form a heterojunction, effectively improving the device's conduction current. An ETL is incorporated to combat point tunneling leakage, resulting in a steeper subthreshold swing. Furthermore, a CEI consisting of Si3N4 is introduced between the germanium source and the Schottky metal, which effectively reduces carrier losses in the inversion layer and improves the overall device performance. This study presents a calibration-based approach to simulations, taking into account practical process considerations. Simulation results show that at VD = 0.2 V, the CEI ETL GS-iTFET achieves an average subthreshold swing (SSavg) of 30.5 mV/dec, an Ion of 3.12 × 10–5 A/μm and an Ion/Ioff ratio of 1.81 × 1010. These results demonstrate a significantly low subthreshold swing and a high current ratio of about 1010. In addition, the proposed device eliminates the need for multiple implantation processes, resulting in significant manufacturing cost reductions. As a result, the CEI ETL GS-iTFET shows remarkable potential in future low-power device competition.

Since 2010, newly identified variants of porcine epidemic diarrhoea virus (PEDV) have caused high mortality in neonatal piglets which has devastated the swine industry. The spike (S) glycoprotein of PEDV contains multiple neutralizing epitopes and is a major target for PEDV neutralization and vaccine development. To understand the antigenicity of the new PEDV variant, we characterized the neutralizing epitopes of a new genotype 2b PEDV isolate from Taiwan, PEDV Pintung 52 (PEDV-PT), by the generation of neutralizing monoclonal antibodies (NmAbs). Two NmAbs, P4B-1, and E10E-1–10 that recognized the ectodomain of the full-length recombinant PEDV S protein and exhibited neutralizing ability against the PEDV-PT virus were selected. Recombinant truncated S proteins were used to identify the target sequences for the NmAbs and P4B-1 was shown to recognize the C-terminus of CO-26K equivalent epitope (COE) at amino acids (a.a.) 575–639 of the PEDV S. Interestingly, E10E-1–10 could recognize a novel neutralizing epitope at a.a. 435–485 within the S1 A domain of the PEDV S protein, whose importance and function are yet to be determined. Moreover, both NmAbs could not bind to linearized S proteins, indicating that only conformational epitopes are recognized. This data could improve our understanding of the antigenic structures of the PEDV S protein and facilitate future development of novel epitope-based vaccines.

This study explores the late coronavirus disease 2019 (COVID-19) pandemic, particularly, 2022Q1 to 2022Q3, to analyze the stock returns of industries expected to have a direct impact. The aim is to identify the factors influencing stock returns and to provide investment strategies, focusing on investment risk control (loss aversion). A total of 193 common stocks listed on the Taiwanese stock market were collected from four major industries: sports, restaurants, biotechnology, and epidemic prevention. Using the past information set (2021Q4): variables in three dimensions—firm characteristics, financial indicators, and corporate governance—are used to construct a stock investment strategy model. Empirical evidence shows the existence of differences in the stock investment advantages of some industries and firm characteristics and the lack of investment advantages of epidemic prevention stocks in 2020. The decision-tree model has a precision of 79.4% and an accuracy of 72.0%. The five most important factors affecting stock returns are market value, cash flow adequacy ratio, return on assets, Tobin’s Q, and price/book value. The scheme inducting three types of losses of return (loser) and one type of profit (winner) is summarized for reference. Second, it confirms the correlation between past information sets and stock returns, suggesting that Taiwan’s stock market was inefficient during the late stage of the COVID-19 pandemic. Finally, this study’s findings can be used as a reference for future fund managers and investors when handling similar events. JEL Classification: G11, G14, C88, I15 Plain Language Summary Stock Investment Losses Aversion during COVID-19 Pandemic This study explores the late COVID-19 pandemic, particularly, 2022Q1 to 2022Q3, to analyze the stock returns of industries expected to have a direct impact. The aim is to identify the factors influencing stock returns and to provide investment strategies, focusing on investment loss aversion. A total of 193 common stocks listed on the Taiwanese stock market were collected from four major industries: sports, restaurants, biotechnology, and epidemic prevention. Empirical evidence shows the existence of differences in the stock investment advantages of some industries and firm characteristics and the lack of investment advantages of epidemic prevention stocks in 2020. The decision-tree model has a precision of 79.4% and an accuracy of 72.0%. The five most important factors affecting stock returns are market value, cash flow adequacy ratio, return on assets, Tobin’s Q, and price/book value. The scheme inducting three types of losses of return (loser) and one type of profit (winner) is summarized for reference. Second, shows that Taiwan’s stock market was inefficient during the late stage of the COVID-19 pandemic. Finally, this study’s findings can be used as a reference for future fund managers and investors when handling similar events.

This study developed an efficient discharge measurement method that can be applied to estimate the streamflow of natural streams and artificial channels. The conventional methods that apply current meters to measure discharge are costly, time-consuming, and labor-intensive. Owing to a shortage of observers in streamflow measurement and for the safety of hydrologists and with advances in measurement techniques, many have strongly suggested the use of non-contact methods when determining streamflow. The non-contact methods that use floats or surface velocity radar to determine the streamflow are becoming more and more popular especially during periods of high water. However, it is not easy to estimate the surface velocity coefficient of each vertical directly for determining the mean velocity in each subsection. As the relationship between the mean surface velocity and mean velocity of a stream cross-section is constant, an efficient and accurate non-contact method of streamflow measurement could be further developed. Thus, streamflow can be estimated by the constant, the mean surface velocity, and cross-sectional area of a stream. The mean velocity of a cross-section, used for parameter calibration, is usually obtained from the discharge made based on the velocity-area principle and cross-sectional area. The surface velocity was measured at the vertical that is then used to estimate mean velocity of a subsection. Once the parameter is determined, streamflow can be obtained from the surface velocity. This approach was further applied to a natural stream and an artificial channel. Measurements were made to verify the reliability and accuracy of the proposed approach. The results show that the relationship between mean channel velocity and mean surface velocity is very stable in both a natural stream and an artificial channel because the streamflow differences, given by the proposed and the conventional method, are relatively insignificant. As a result, mean surface velocity can be used to determine the streamflow quickly and provides for a reliable and accurate measurement of streamflow.