Search

As a typical East Asian country transitioning from a production-based social insurance system to a modern free social insurance system, South Korea has set a precedent for other Asian countries. This study adopts data from 2006, 2010, and 2012 Korean Longitudinal Study of Aging (KLoSA) and uses a fixed effect analysis of the quasi-natural experiment to conclude that the increase in the Korean pension contribution rate has a limited negative impact on social security participation. The degree of filial piety of children, including the financial support of those children, rather than the number of children, affects the participation of the elderly in social security insurance. Thus, an alternative relationship between family pensions and social pensions presents itself. This paper discusses the mechanism that factors influence the willingness to participate in pension insurance with reduced replacement rates in an extended family of a multigenerational population. After systematically delineating the background of South Korea's welfare system, this paper compares the similarities between China and South Korea in this regard and proposes that China and other East and Southeast Asian countries can learn from South Korea's example.

Abstract The development of theragnostic nanosystems integrating FRET (fluorescence resonance energy transfer) imaging and chemodynamic therapy (CDT) for accurate diagnosis and effective treatment of lung tumors is still a big challenge. Herein, a peptide‐assembled 3D DNAzyme motor nanodevice is engineered for a self‐powered FRET amplifier profiling human neutrophil elastase (HNE) and self‐supplied H2O2 enhancing CDT. The nanodevice is prepared by depositing AuNPs on ZIF‐8, in which ZIF‐8 co‐loaded the lysosomal targeting peptide‐modified copper peroxides (PCPs) and hairpins (H1, H2, and H3), AuNPs are co‐labeled by DNAzyme‐peptide (DP) conjugate and H3. In the tumor micro‐environment, HNE driven 3D DNAzyme walker followed by an exponential amplification constructed by a synergistic cross‐activation between hybridization chain reaction and DNAzyme, generating a self‐powered FRET amplifier. The FRET amplifier specifically measures HNE with a sensitivity of 0.026 pM, and successfully images exogenous HNE in living cells and monitors HNE in mouse models. Moreover, the PCPs can target lysosomes, reducing lysosome escape. The self‐supplying H2O2 undertaken by PCPs improves the Cu (II)‐catalyzed Fenton‐like reaction, effectively causing cell apoptosis to inhibit tumor growth. Significantly, the nanodevice successfully screens inhibitors and discriminates the HNE level in normal and lung cancer tissues, suggesting that the nanodevice provides an effective tool for the diagnosis and treatment of lung tumors.

Background Tumor-infiltrating regulatory T cells (TI-Tregs) are well-adapted to thrive in the challenging tumor microenvironment (TME) by undergoing metabolic reprogramming, notably shifting from glycolysis to mitochondrial oxidative phosphorylation (OXPHOS) for energy production. The extracellular matrix is an important component of the TME, contributing to the regulation of both tumor and immune cell metabolism patterns by activating mechanosensors such as YAP. Whether YAP plays a part in regulating TI-Treg mitochondrial function and the underlying mechanisms are yet to be elucidated.Methods To gain insights into the effect of matrix stiffness on YAP activation in Tregs, alterations in stiffness were performed both in vitro and in vivo. YAP conditional knockout mice were used to determine the role of YAP in TI-Tregs. RNA-seq, quantitative PCR, flow cytometry, lentivirus infection and mitochondrial function assay were employed to uncover the mechanism of YAP modulating mitochondrial function in TI-Tregs. A YAP inhibitor and a low leucine diet were applied to tumor-bearing mice to seek the potential antitumor strategy.Results In this study, we found that YAP, as a mechanotransducer, was activated by matrix stiffness in TI-Tregs. A deficiency in YAP significantly hindered the immunosuppressive capability of TI-Tregs by disrupting mitochondrial function. Mechanically, YAP enhanced mitochondrial OXPHOS by upregulating the transcription of Lars2 (Leucyl-tRNA synthetase 2, mitochondrial), which was essential for mitochondrial protein translation in TI-Tregs. Since Lars2 relied much on its substrate amino acid, leucine, the combination of a low leucine diet and YAP inhibitor synergistically induced mitochondrial dysfunction in TI-Tregs, ultimately restraining tumor growth.Conclusions This finding uncovered a new understanding of how YAP shapes mitochondrial function in TI-Tregs in response to mechanical signals within the TME, making the combined strategy of traditional medicine and diet adjustment a promising approach for tumor therapy.

Due to its exceptional quality as a biomedical metal, stainless steel is often utilized to produce a broad range of medical tools. The resistance of stainless steel to corrosion is a key indicator of how long and how effective it will serve its intended purpose, and it is an important factor in determining the biocompatibility of the material. However, due to the complex physiological environment within the human body, the corrosion management of medical-grade stainless steel is facing several challenges. In this article, an overview of the factors that influence the corrosion performance of medical-grade stainless steel is provided, and new technologies and methods that have been developed in recent years to improve the corrosion resistance are discussed. These cutting-edge methods are expected to improve the corrosion resistance and longevity of medical-grade stainless steel, providing strong support for the increased applicability of the material in the medical industry.

Abstract Background N6-methyladenosine (m6A) is dysregulated in various cancers, including colorectal cancer (CRC). Herein, we assess the diagnostic potential of peripheral blood (PB) m6A levels in CRC. Methods We collected PB from healthy controls (HCs) and patients with CRC, analyzed PB RNA m6A levels and the expression of m6A-related demethylase genes FTO and ALKBH5, cocultured CRC cells with PB mononuclear cells (PBMCs), and constructed an MC38 cancer model. Results PB RNA m6A levels were higher in the CRC than that in HCs. The area under the curve (AUC) of m6A levels (0.886) in the CRC was significantly larger compared with carbohydrate antigen 199 (CA199; 0.666) and carcinoembryonic antigen (CEA; 0.834). The combination of CEA and CA199 with PB RNA m6A led to an increase in the AUC (0.935). Compared with HCs, the expression of FTO and ALKBH5 was decreased in the CRC. After coculturing with CRC cells, the PBMCs RNA m6A were significantly increased, whereas the expression of FTO and ALKBH5 decreased. Furthermore, m6A RNA levels in the PB of MC38 cancer models were upregulated, whereas the expression of FTO and ALKBH5 decreased. Conclusions PB RNA m6A levels are a potential diagnostic biomarker for patients with CRC.

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO2, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control.

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards.

Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan–Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group ( P

BackgroundThe diagnosis and treatment of cancer triggers not only a negative psychological response for the patient, but also a positive psychological outcome. Positive dyadic coping, as a form of coping for mental health outcomes, can maintain or reestablish internal stability between the patient and his or her spouse, resulting in positive physical and psychological changes. However, there is a paucity of research on body image, dyadic coping, and post-traumatic growth in breast cancer patients. The purpose of this study was to explore the relationship and pathways between body image, dyadic coping, and post-traumatic growth in breast cancer patients.MethodsA cross-sectional study was conducted from November 2022 to November 2023 at a tertiary care hospital in Wuxi, Jiangsu, China. This study was conducted among 154 breast cancer patients treated at the Affiliated Hospital of Jiangnan University, all of whom completed demographic and clinical information questionnaires, Body image self-rating questionnaire for breast cancer (BISQ-BC), Dyadic Coping Inventory (DCI) and Post Traumatic Growth Inventory (PTGI). A Pearson correlation analysis was used to explore the relationship between body image, dyadic coping, and post-traumatic growth. Structural equation modeling was used to analyze the path relationships among the three and to explore the mediating role of dyadic coping.ResultsThe level of body image was negatively correlated with post-traumatic growth (r = −0.462, p

Abstract Objective To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. Materials and methods We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. Results The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. Conclusion CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

This survey provides an overview of recent advancements in wireless soil sensing technologies, including the sensors and communication systems used to manage and enhance soil health and crop production for sustainable agricultural practices. The surveyed technologies are categorized by the target sensing parameters, such as moisture, temperature, salinity, pH, nutrient concentration, pressure, and heavy metal content. Special focus is placed on the passive and semi-passive sensing principles for soil data collection, communication systems for transmitting the sensed data, and machine learning approaches for data processing and analysis to assist in informed decision-making in the realization of sustainable agriculture. By comparing existing technologies, we highlight the advantages and disadvantages of these sensing mechanisms, summarize existing challenges of the surveyed soil sensing systems, and identify future research directions that need attention.

Abstract Background Hypervirulent Klebsiella pneumoniae (hvKp) infection-induced sepsis-associated acute lung injury (ALI) has emerged as a significant clinical challenge. Increasing evidence suggests that activated inflammatory macrophages contribute to tissue damage in sepsis. However, the underlying causes of widespread macrophage activation remain unclear. Methods BALB/c mice were intravenously injected with inactivated hvKp (iHvKp) to observe lung tissue damage, inflammation, and M1 macrophage polarization. In vitro, activated RAW264.7 macrophage-derived exosomes (iHvKp-exo) were isolated and their role in ALI formation was investigated. RT-PCR was conducted to identify changes in exosomal miRNA. Bioinformatics analysis and dual-luciferase reporter assays were performed to validate MSK1 as a direct target of miR-155-5p. Further in vivo and in vitro experiments were conducted to explore the specific mechanisms involved. Results iHvKp successfully induced ALI in vivo and upregulated the expression of miR-155-5p. In vivo, injection of iHvKp-exo induced inflammatory tissue damage and macrophage M1 polarization. In vitro, iHvKp-exo was found to promote macrophage inflammatory response and M1 polarization through the activation of the p38-MAPK pathway. RT-PCR revealed exposure time-dependent increased levels of miR-155-5p in iHvKp-exo. Dual-luciferase reporter assays confirmed the functional role of miR-155-5p in mediating iHvKp-exo effects by targeting MSK1. Additionally, inhibition of miR-155-5p reduced M1 polarization of lung macrophages in vivo, resulting in decreased lung injury and inflammation induced by iHvKp-exo or iHvKp. Conclusions The aforementioned results indicate that exosomal miR-155-5p drives widespread macrophage inflammation and M1 polarization in hvKp-induced ALI through the MSK1/p38-MAPK Axis. Graphical Abstract

Summary: Interfacial investigation for fine-tuning microenvironment has recently emerged as a promising method to optimize the electrochemical CO2 reduction system. The electrical double layer located at the electrode-electrolyte interface presents a particularly significant impact on electrochemical reactions. However, its effect on the activity and selectivity of CO2 electrocatalysis remains poorly understood. Here, we utilized two-dimensional mica flakes, a material with a high dielectric constant, to modify the electrical double layer of Ag nanoparticles. This modification resulted in a significant enhancement of current densities for CO2 reduction and an impressive Faradaic efficiency of 98% for CO production. Our mechanistic investigations suggest that the enhancement of the electrical double layer capacitance through mica modification enriched local CO2 concentration near the reaction interface, thus facilitating CO2 electroreduction.

Background Temporary stoma formation is common in Crohn’s disease (CD), while stoma reversal is associated with postoperative morbidity. This study aimed to evaluate the postoperative outcomes of split stoma reversal, SSR (i.e., exteriorization of proximal and distal ends of the stoma through a small common opening) and end stoma closure, ESC (i.e., the proximal stump externalized, and distal end localized abdominally.Methods Patients with CD who underwent stoma reversal surgeries between January 2017 and December 2021 were included. Demographic, clinical, and postoperative data were collected and analyzed to evaluate outcomes of reversal surgery.Results A total of 255 patients who underwent stoma reversal surgeries met the inclusion criteria. SSR was superior to ESC in terms of operative time (80.0 vs. 120.0, p = 0.0004), intraoperative blood loss volume (20.0 vs. 100.0, p = 0.0002), incision length (3.0 vs. 15.0, p

ABSTRACTThe interaction between adherent-invasive Escherichia coli (AIEC) and intestinal macrophages is implicated in the pathogenesis of Crohn’s disease (CD). However, its role in intestinal fibrogenesis and the underlying molecular mechanisms are poorly understood. In addition, miRNAs such as let-7b may participate in AIEC-macrophage interactions. In this study, we identified that the colonization of AIEC in the ileum was associated with enhanced intestinal fibrosis and reduced let-7b expression by enrolling a prospective cohort of CD patients undergoing ileocolectomy. Besides, AIEC-infected IL-10−/− mice presented more severe intestinal fibrosis and could be improved by exogenous let-7b. Mechanistically, intestinal macrophages were found to be the main target of let-7b. Transferring let-7b-overexpressing macrophages to AIEC-infected IL-10−/− mice significantly alleviated intestinal fibrosis. In vitro, AIEC suppressed exosomal let-7b derived from intestinal epithelial cells (IECs), instead of the direct inhibition of let-7b in macrophages, to promote macrophages to a fibrotic phenotype. Finally, TGFβR1 was identified as one target of let-7b that regulates macrophage polarization. Overall, the results of our work indicate that AIEC is associated with enhanced intestinal fibrosis in CD. AIEC could inhibit exosomal let-7b from IECs to promote intestinal macrophages to a fibrotic phenotype and then contributed to fibrogenesis. Thus, anti-AIEC or let-7b therapy may serve as novel therapeutic approaches to ameliorate intestinal fibrosis.

ABSTRACTPolyunsaturated fatty acids (PUFAs) have been shown to exacerbate Crohn’s disease (CD) by promoting lipid peroxidation (LPO) of intestinal epithelial cells (IECs). Dysbiosis of the gut microbiota may play a crucial role in this process. CD patients often exhibit an increased abundance of Escherichia coli (E. coli) in the gut, and the colonization of adherent-invasive E. coli (AIEC) is implicated in the initiation of intestinal inflammation in CD. However, the impact of AIEC on LPO remains unclear. In this study, we observed that AIEC colonization in the terminal ileum of CD patients was associated with decreased levels of glutathione peroxidase 4 (GPX4) and ferritin heavy chain (FTH) in the intestinal epithelium, along with elevated levels of 4-Hydroxynonenal (4-HNE). In vitro experiments demonstrated that AIEC infection reduced the levels of GPX4 and FTH, increased LPO, and induced ferroptosis in IECs. Furthermore, arachidonic acid (AA) and docosahexaenoic acid (DHA) supplementation in AIEC-infected IECs significantly aggravated LPO and ferroptosis. However, overexpression of GPX4 rescued AIEC-induced LPO and ferroptosis in IECs. Our results further confirmed that AIEC with AA supplementation, associated with excessive LPO and cell death in IECs, worsened colitis in the DSS mouse model and induced enteritis in the antibiotic cocktail pre-treatment mouse model in vivo. Moreover, treatment with ferrostatin-1, a ferroptosis inhibitor, alleviated AIEC with AA supplementation-induced enteritis in mice, accompanied by reduced LPO and cell death in IECs. Our findings suggest that AIEC, in combination with PUFA supplementation, can induce and exacerbate intestinal inflammation, primarily through increased LPO and ferroptosis in IECs.

Recently, a cellular automata learning and prediction (CALP) model was proposed that considered images as living cells and used cellular automata to evolve and synthetically augment handwritten data. In this paper, another data augmentation method is proposed inspired by the flocking pattern of birds. It is proposed that any image sample in a handwritten data set can be represented as an assembly of birds. Each bird is specifically located at a point or pixel to collectively form an image. Using a well-defined flocking mechanism, the new positions for these birds can be calculated. Each snapshot of the new position can be considered as a new version of the original image, thus generating more data. The best flocking pattern is determined using biologically-inspired genetic algorithms. The quality of the synthetic data is demonstrated by using it in a handwritten pattern recognition system. For this purpose, the CALP framework is used with few modifications. It is shown that using the new data along with the original data to train classifiers improves the accuracy of classifiers than when they are trained using only the original data. Following the same test bed as CALP, 25 different data sets and classifier pairs were used for experimentation. The experimental results show that by also using the data set generated through flocking, the performance of the base classifiers is significantly improved even when smaller training data is used. We also compare the performance of the proposed technique with other state-of-the-art oversampling methods.

Abstract Actively controlling the polarization states of terahertz (THz) waves is essential for polarization-sensitive spectroscopy, which has various applications in anisotropy imaging, noncontact Hall measurement, and vibrational circular dichroism. In the THz regime, the lack of a polarization modulator hinders the development of this spectroscopy. We theoretically and experimentally demonstrate that conjugated bilayer chiral metamaterials (CMMs) integrated with Ge2Sb2Te5 (GST225) active components can achieve nonvolatile and continuously tunable optical activity in the THz region. A THz time-domain spectroscopic system was used to characterize the device, showing a tunable ellipticity (from ‒36° to 0°) and rotation of the plane polarization (from 32° to 0°) at approximately 0.73 THz by varying the GST225 state from amorphous (AM) to crystalline (CR). Moreover, a continuously tunable chiroptical response was experimentally observed by partially crystallizing the GST225, which can create intermediate states, having regions of both AM and CR states. Note that the GST225 has an advantage of nonvolatility over the other active elements and does not require any energy to retain its structural state. Our work allows the development of THz metadevices capable of actively manipulating the polarization of THz waves and may find applications for dynamically tunable THz circular polarizers and polarization modulators for THz emissions.

Abstract Miniaturized nonvolatile reconfigurable optical components with a subwavelength thickness, extremely compact size, high-speed response, and low power consumption will be the core of next-generation all-optical integrated devices and photonic computing to replace traditional bulky optical devices and integrated circuits, which are reaching physical limitations of Moore’s law. Metasurfaces, as ultrathin planar surfaces, have played a major role in controlling the amplitude, phase, and polarization of electromagnetic waves and can be combined with various active modulation methods to realize a variety of functional devices. However, most existing reconfigurable devices are bounded in volatile nature with constant power to maintain and single functionality, which restricts their further extensive applications. Chalcogenide phase change materials (PCM) have attracted considerable attention due to their unique optical properties in the visible and infrared domains, whereas in the terahertz (THz) regime, research on the reversible phase transition in large-scale areas and applications of Ge2Sb2Te5 (GST) are still under exploration. Here, we achieved reversible, repeated, and large-area switching of GST with the help of optical and thermal stimuli. Large-area amorphization with a 1 cm diameter of GST is realized by using a single laser pulse. Then, we incorporate GST into metasurface designs to realize nonvolatile, reconfigurable, multilevel, and broadband terahertz modulators, including the anomalous deflector, metalens, and focusing optical vortex (FOV) generator. Experimental results verify the feasibility of multilevel modulation of THz waves in a broadband frequency range. Moreover, the modulators are reusable and nonvolatile. The proposed approach presents novel avenues of nonvolatile and reconfigurable metasurface designs and can enable wide potential applications in imaging, sensing, and high-speed communications.

Deep learning methods have achieved state-of-the-art performance on synthetic aperture radar (SAR) target recognition tasks in recent years. However, obtaining sufficient SAR images for training these deep learning methods is costly in time and labor. This paper focuses on recognizing targets with a few training samples, that is, few-shot target recognition. We combine deep neural networks’ powerful feature representation capabilities with the nonparametric flexibility of Gaussian processes (GPs) and propose a few-shot recognition model based on deep kernel learning. Deep neural networks map input samples into a low-dimensional embedding space. GPs employ a family of kernel functions to measure the similarity between embedded samples and classify them. During training, the model builds diverse related tasks to learn kernel functions with parameters shared across few-shot tasks. These learned kernel functions define common prior knowledge that can be transferred to unseen tasks. During testing, the model can recognize novel tasks with a few samples based on learned kernel functions. We conducted extensive experiments on a widely-used real SAR dataset to evaluate the model’s effectiveness. The test results demonstrate that our model is superior to several recently proposed few-shot recognition methods.

Metamaterials composed of metallic antennae arrays are used as they possess extraordinary optical transmission (EOT) in the terahertz (THz) region, whereby a giant forward light propagation can be created using constructive interference of tunneling surface plasmonic waves. However, numerous applications of THz meta-devices demand an active manipulation of the THz beam in free space. Although some studies have been carried out to control the EOT for the THz region, few of these are based upon electrical modulation of the EOT phenomenon, and novel strategies are required for actively and dynamically reconfigurable EOT meta-devices. In this work, we experimentally present that the EOT resonance can be coupled to optically reconfigurable chalcogenide metamaterials which offers a reversible all-optical control of the THz light. A modulation efficiency of 88% in transmission at 0.85 THz is experimentally observed using the EOT metamaterials, which is composed of a gold (Au) circular aperture array sitting on a non-volatile chalcogenide phase change material (Ge2Sb2Te5) film. This comes up with a robust and ultrafast reconfigurable EOT over 20 times of switching, excited by a nanosecond pulsed laser. The measured data have a good agreement with finite-element-method numerical simulation. This work promises THz modulators with significant on/off ratios and fast speeds.

It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control.

Zinc manganese oxide (ZMO) system represents a notable family of mixed transition metal oxides (MTMOs) because of their superiority of the high theoretical capacity, adequacy of natural content, and low cost. However, the methods to match both the reliable synthesis and the designable construction of large-sized two-dimensional (2D) ZMO nanosheets are still considered as grand challenges. Herein, we have successfully realized the preparation of 2D ZMO nanosheets with large lateral sizes up to ∼20 μm by simple pyrolysis of 2D metal–organic framework (MOF) nanosheets precursor. The growth mechanism of 2D MOF is proposed to be based on the lamellar micelles formed by polyvinyl pyrrolidone (PVP). The obtained 2D and porous ZMO nanosheets exhibit high specific capacity as well as good rate capability. More importantly, the as-prepared ZMO electrode shows a remarkable capacity increment upon cycling (from 832 mAh g−1 at the 2nd cycle to 1418 mAh g−1 at the 700th cycle, at 1 A g−1). Through simple adjustment of the calcination temperature, the valence state of Mn species in the yielding ZMO samples can be fine-tuned. Through systematic investigation towards these ZMOs containing different Mn species, the extra specific capacity is revealed to be chiefly on account of the arising of the valence state of Mn upon the cycling process. Moreover, it is disclosed that the higher-valent Mn the pristine ZMO contains, the more additional capacity it gains upon cycling. We believe that this work will inspire more detailed analysis on the relationship between the valence state of Mn and extra capacity.

Terahertz spectroscopy is a powerful tool for investigating the properties and states of biological matter. Here, a systematic investigation of the interaction of THz wave with “bright mode” resonators and “dark mode” resonators has been conducted, and a simple general principle of obtaining multiple resonant bands has been developed. By manipulating the number and positions of bright mode and dark mode resonant elements in metamaterials, we realized multi-resonant bands terahertz metamaterial structures with three electromagnetic-induced transparency in four-frequency bands. Different carbohydrates in the state of dried films were selected for detection, and the results showed that the multi-resonant bands metamaterial have high response sensitivity at the resonance frequency similar to the characteristic frequency of the biomolecule. Furthermore, by increasing the biomolecule mass in a specific frequency band, the frequency shift in glucose was found to be larger than that of maltose. The frequency shift in glucose in the fourth frequency band is larger than that of the second band, whereas maltose exhibits an opposing trend, thus enabling recognition of maltose and glucose. Our findings provide new insights into the design of functional multi-resonant bands metamaterials, as well as new strategies for developing multi-band metamaterial biosensing devices.

Abstract Background The use of face masks remains contentious, with international variation in practice. Their prevalence in the UK, is likely to increase due to new legislation. Clear information regarding the appropriate use of masks is needed, to ensure compliance with policies to reduce transmission of COVID-19. We aimed to assess the impact of visual representations of guidance, or infographics, upon the knowledge of appropriate face mask usage in a representative UK cohort. Methods Adult patients were recruited to this randomised internet-based questionnaire study during the 12–14 May 2020 from across the UK. Respondents viewed one of five public health stimuli regarding the use of face masks, or no stimulus. The groups accessed aids by the European Centre for Disease Control (EUCDC), World Health Organisation (WHO), Singaporean Ministry of Health (SMOH), text from the UK government (UK Gov), or an infographic designed by the Behavioural Insights Team (BIT). The primary outcome was to evaluate the effect of each infographic upon participants’ recall of face mask technique, sentiments and willingness to wear a face covering. Secondary outcomes included the effect of symptomology and socio-demographic factors. Results 4099 respondents were randomised (1009 control, 628 EUCDC, 526 WHO, 639 SMOH, 661 UKGOV and 606 BIT). Stimuli from the WHO, SMOH and BIT demonstrated significantly higher average recall scores compared to the controls (7.40 v. 7.38 v. 7.34 v. 6.97, P

Aerospace control software is the most important part of aerospace software. Since its potential defects endanger life and safety, there are strict requirements on product quality. Therefore, efficient and reliable software testing is essential. The traditional testing method has been challenging to meet its development requirements, and software automation testing has gradually become the main tool for testing aerospace control software. For the automation testing of aerospace control software, the core problem is to locate the GUI widgets on the software screenshots and identify their intent, which directly affects the accuracy of the test. Because of this, we use the widget recognition technology based on image matching and use the image understanding and analysis technology to extract the widget image in the screenshots. After obtaining the widget image, we use a convolutional neural network to extract image features and use the encoder module to encode the extracted information features as a tensor. The decoder module generates a word sequence conditional on tensor and previous output based on the encoded information. We also conduct an empirical study to evaluate the accuracy of widget recognition and intention generation. For widget recognition, our average IoU reached 0.81. For widget intent generation, our model BLEU-1 is 0.567, BLEU-2 is 0.356, BLEU-3 is 0.261, BLEU-4 is 0.131. The results show that our method is very effective.

Abstract The rapid development of wearable, highly integrated, and flexible electronics has stimulated great demand for on-chip and miniaturized energy storage devices. By virtue of their high power density and long cycle life, micro-supercapacitors (MSCs), especially those with interdigital structures, have attracted considerable attention. In recent years, tremendous theoretical and experimental explorations have been carried out on the structures and electrode materials of MSCs, aiming to obtain better mechanical and electrochemical properties. The high-performance MSCs can be used in many fields, such as energy storage and medical assistant examination. Here, this review focuses on the recent progress of advanced MSCs in fabrication strategies, structural design, electrode materials design and function, and integrated applications, where typical examples are highlighted and analyzed. Furthermore, the current challenges and future development directions of advanced MSCs are also discussed.

Abstract The unmanned aerial vehicle (UAV) can extend the network coverage and improve the system throughput for 5th generation (5G) communication systems; hence, it receives a lot of attention recently. This paper considers the problem of channel predictive precoding for UAV-enabled cache-assisted B5G multi-input multi-output (MIMO) systems. A novel channel precoder predictor is proposed, in which the prediction is conducted on a non-linear vector space—Grassmannian manifold. The predictor at the receiver utilizes the current and previous channel matrices to solve the precoder at the next time and then feeds it back to the transmitter for precoding. More specifically, two sub-matrices are extracted from the channel right singular matrices and modeled as two points on the Grassmannian manifold. Then, the geodesic between the two points is conducted. Unlike the conventional method in which the tangent vector at the previous point is parallel transported along the geodesic, we predict the next point by use of the geodesic equation directly. We analyze the computational complexity of the proposed method and demonstrate the superiority of the proposed method by comparing with the conventional one. Besides, we adopt a general Ricean channel model in the UAV MIMO system, where both the Kronecker model and Jake’s model are incorporated. The effects of various channel model parameters on the system performance in terms of the chordal error of channel predictor and the optimum step are thoroughly investigated.

Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students' acquisition of skills and knowledge and positively mediated dental technology students' CAD performance.

The oncogenic fusion protein AML1-ETO has the ability of AML1 to interact with DNA but blocks AML1-dependent transcription. Here the authors report that histone lysine methyltransferase EZH1 interacts with AML1-ETO and methylates AML1-ETO at lysine 43, promoting AML1-ETO transcriptional repression in leukemia.

In this paper, we investigate the application of using software-defined radio (SDR) and surface acoustic wave (SAW) device for wireless measurement of the response of in situ sensors. SDR uses software to realize different communication functions. After collecting the magnitude and phase of the response at discrete frequencies, we apply inverse Fourier transform to analyze the time domain responses which, in turn, allows for monitoring the changes of the response of the in situ sensor. We employ microwave signal flow graph concepts to improve the quality of the received signals. Comparing the normalized results obtained by SDR with those obtained from a commercial vector network analyzer (VNA), we demonstrate that the results are sufficiently close, and the SDR-based experiments can provide satisfactory measurement of the in-situ sensors. The objective is to eventually employ this wireless measurement system for soil nutrient sensing.

Background: Isolated colonic Crohn’s disease (cCD) responds less well to induction therapy with exclusive enteral nutrition (EEN) compared with ileal or ileocolonic disease in adult patients; therefore, we aimed to identify the factors that influence the response to EEN and develop a predictive nomogram model to optimize the use of EEN in cCD patients. Materials and methods: Eighty-five cCD patients treated with EEN as first-line therapy at our center between 1 June 2012 and 30 June 2018 were retrospectively analyzed as the primary cohort. The primary endpoint was clinical remission after EEN therapy. Potential predictive factors for the efficacy of EEN were assessed by univariate and multivariate analyses, and a nomogram to predict the response to EEN therapy in cCD patients was designed. Another 19 cCD patients were retrospectively included in the validation cohort to verify the accuracy of the nomogram model. Results: The clinical remission rates for the primary cohort and validation cohort were 52.9% and 47.4%, respectively. Pancolitis was the greatest contributor to the risk of failure to respond to EEN [odds ratio (OR) = 4.896; 95% confidence interval (CI) = 1.223–19.607; p = 0.025], lean body mass index (LBMI), colonic lesion features, simple endoscopic scores for Crohn’s disease, C-reactive protein before treatment and ∆prealbumin were also related to the efficacy of EEN in cCD. The nomogram model showed robust discrimination, with an area under the receiving operating characteristic curve of 0.906. Conclusion: Several predictive factors for response to EEN therapy in cCD adult patients were identified, and a promising nomogram that can predict the effect of EEN in cCD was developed.

Educator wellbeing has broad implications for students and schools. Current approaches to address this problem are generally resource-intensive. This trial used novel nudges to increase wellbeing and decrease burnout among educators and other school-based faculty. We designed a light touch intervention where T1 received evidence-based wellbeing weekly text messages and T2 received weekly messages plus leadership endorsement emails. We evaluated this intervention in a large-scale three-arm RCT with participants (n=1,155) from K-12 schools in Manitoba, Alberta, and British Columbia. When compared to the control group, we saw no significant difference between the control group and T1 and T2 groups on burnout or wellbeing. The failure of these evidence-based text messages in increasing educators' wellbeing and reducing their burnout highlights both the difficulty of addressing this problem and the importance of learning lessons from trials with null results to contribute to our knowledge base of improving educators' wellbeing.

We report the development of a new ultrasensitive approach for label-free DNA detection using [...]