A structural model of EFL teachers' physical activity, emotion regulation, and competence for online teaching.

BACKGROUND: The COVID-19 pandemic has prompted a rapid shift to online teaching, placing unprecedented demands on educators' physical and mental well-being. However, the relationship between English as a Foreign Language (EFL) teachers' physical activity, emotion regulation, and competence for online teaching remains underexplored. OBJECTIVES: This study aimed to investigate the interplay between EFL teachers' physical activity, emotion regulation strategies, and competence for online teaching. RESULTS: Structural equation modeling revealed significant direct and indirect effects, indicating that physical activity positively influences emotion regulation, which, in turn, enhances teachers' competence for online instruction. Furthermore, emotion regulation was found to mediate the relationship between physical activity and online teaching competence. CONCLUSIONS: These findings underscore the importance of promoting physical activity among EFL teachers as a means to enhance their emotion regulation skills and competence for online teaching, particularly in the context of the COVID-19 pandemic. IMPLICATIONS: The study highlights the need for targeted interventions aimed at supporting EFL teachers' well-being and professional development, with implications for educational policies, teacher training programs, and institutional support structures in the digital learning landscape.

In silico and in vivo discovery of antioxidant sea cucumber peptides with antineurodegenerative properties.

Since oxidative stress is often associated with neurodegenerative diseases, antioxidants are likely to confer protection against neurodegeneration. Despite an increasing number of food-derived peptides being identified as antioxidants, their antineurodegenerative potentials remain largely unexplored. Here, a sea cucumber peptide preparation - the peptide-rich fraction of <3 kDa (UF<3K) obtained by ultrafiltration from Apostichopus japonicus protein hydrolyzate - was found to protect PC12 cells and Caenorhabditis elegans from neurodegeneration by reducing oxidative stress and apoptosis, demonstrating its in vitro and in vivo neuroprotective effects. As many food-originated peptides are cryptides (cryptic peptides - short amino acid sequences encrypted in parent proteins) released in quantities by protein hydrolysis, UF<3K was subjected to sequencing analysis. As expected, a large repertoire of peptides were identified in UF<3K, establishing a sea cucumber cryptome (1238 peptides in total). Then 134 peptides were randomly selected from the cryptome (>10%) and analyzed for their antioxidant activities using a number of in silico bioinformatic programs as well as in vivo experimental assays in C. elegans. From these results, a novel antioxidant peptide - HoloPep#362 (FETLMPLWGNK) - was shown to not only inhibit aggregation of neurodegeneration-associated polygluatmine proteins but also ameliorate behavioral deficits in proteotoxicity nematodes. Proteomic analysis revealed an increased expression of several lysosomal proteases by HoloPep#362, suggesting proteostasis maintenance as a mechanism for its antineurodegenerative action. These findings provide an insight into the health-promoting potential of sea cucumber peptides as neuroprotective nutraceuticals and also into the importance of training in silico peptide bioactivity prediction programs with in vivo experimental data.

Exploring the differential effects of psychological resilience and social support in mitigating post-traumatic psychiatric symptoms: real-world network analysis of front-line rescuers.

BACKGROUND: Although both psychological resilience and social support are widely believed to be effective in alleviating post-traumatic psychiatric symptoms in individuals with traumatic events, there has been a lack of comparative analysis of their intervention effects on different post-traumatic psychiatric symptoms. Furthermore, previous studies have mostly failed to control for potential confounding effects caused by different traumatic events. AIMS: We used the novel network analysis approach to examine the differential moderating effects of psychological resilience and social support on post-traumatic psychiatric symptoms, controlling for the confounding effects of traumatic events. METHOD: We recruited 264 front-line rescuers who experienced the same traumatic event. Quantified edge weights and bridge expected influence (BEI) were applied to compare the alleviating effects of psychological resilience and social support. RESULTS: Our study revealed distinct correlations in a sample of front-line rescuers: social support negatively correlates more with psychosomatic symptoms, notably fatigue in depressive networks and sleep disturbance in post-traumatic stress disorder (PTSD) networks, whereas psychological resilience shows fewer such correlations. Quantitative analysis using BEI indicated that psychological resilience more effectively suppresses depressive and anxiety symptom networks, whereas social support more significantly inhibits PTSD symptom networks. CONCLUSIONS: The current study represents the first attempt to examine the differential effects of psychological resilience and social support on post-traumatic outcomes in real-world emergency rescuers, controlling for the confounding effect of traumatic events. Our results can act as the theoretical reference for future precise and efficient post-trauma psychological interventions.

Evidence for multiferroicity in single-layer CuCrSe2.

Multiferroic materials, which simultaneously exhibit ferroelectricity and magnetism, have attracted substantial attention due to their fascinating physical properties and potential technological applications. With the trends towards device miniaturization, there is an increasing demand for the persistence of multiferroicity in single-layer materials at elevated temperatures. Here, we report high-temperature multiferroicity in single-layer CuCrSe2, which hosts room-temperature ferroelectricity and 120 K ferromagnetism. Notably, the ferromagnetic coupling in single-layer CuCrSe2 is enhanced by the ferroelectricity-induced orbital shift of Cr atoms, which is distinct from both types I and II multiferroicity. These findings are supported by a combination of second-harmonic generation, piezo-response force microscopy, scanning transmission electron microscopy, magnetic, and Hall measurements. Our research provides not only an exemplary platform for delving into intrinsic magnetoelectric interactions at the single-layer limit but also sheds light on potential development of electronic and spintronic devices utilizing two-dimensional multiferroics.

Adaptive under-sampling strategy for fast imaging in compressive sensing-based atomic force microscopy.

Compressive sensing (CS) can reconstruct the rest information almost without distortion by advanced computational algorithm, which significantly simplifies the process of atomic force microscope (AFM) scanning with high imaging quality. In common CS-AFM, the partial measurements randomly come from the whole region to be measured, which easily leads to detail loss and poor image quality in regions of interest (ROIs). Consequently, important microscopic phenomena are missed probably. In this paper, we developed an adaptive under-sampling strategy for CS-AFM to optimize the process of sampling. Under a certain under-sampling ratio, the weight coefficient of ROIs and regions of base (ROBs) were set to control the distribution of under-sampling points and corresponding measurement matrix. A series of simulations were completed to demonstrate the relationship between the weight coefficient of ROIs and image quality. After that, we verified the effectiveness of the method on our homemade AFM. Through a lot of simulations and experiments, we demonstrated how the proposed method optimized the sampling process of CS-AFM, which speeded up the process of AFM imaging with high quality.

Organizing Photosensitive and Photothermal Single-Sites Uniformly in a Trimetallic Metal-Organic Framework for Efficient Photocatalytic Hydrogen Evolution.

Effective combination of the photosensitivity and photothermal property in photocatalyst is vital to achieve the maximum light utilization for superior photocatalytic efficiency. Herein, this work successfully organizes photosensitive Cd-NS single-sites and photothermal Ni-NS single-sites uniformly at a molecular level within a tailored trimetallic metal-organic framework. The optimized Ho6-Cd0.76Ni0.24-NS exhibits a superior photocatalytic hydrogen evolution rate of 40.06 mmol g-1 h-1 under visible-light irradiation and an apparent quantum efficiency of 29.37% at 420 nm without using cocatalysts or photosensitizers. A systematical mechanism study reveals that the uniformly organized photosensitive and photothermal single-sites have synergistic effect, which form ultrashort pathways for efficient transport of photoinduced electrons, suppress the recombination of photogenerated charge carriers, hence promote the hydrogen evolution activity. This work provides a promising approach for organizing dual-functional single-sites uniformly in photocatalyst for high-performance photocatalytic activity.

[Technical Status and Development Trend of Medical Electron Linear Accelerators].

More than 70% of tumor patients require radiotherapy. Medical electron linear accelerators are important high-end radiotherapy equipment for tumor radiotherapy. With the application of artificial intelligence technology in medical electron linear accelerator, radiotherapy has evolved from ordinary radiotherapy to today's intelligent radiotherapy. This study introduces the development history, working principles and system composition of medical electron linear accelerators. It outlines the key technologies for improving the performance of medical linear electron accelerators, including beam control, multi-leaf collimator, guiding technology and dose evaluation. It also looks forward to the development trend of major radiotherapy technologies, such as biological guided radiotherapy, FLASH radiotherapy and intelligent radiotherapy, which provides references for the development of medical electron linear accelerators.

Bottom-Up Construction of Rhombic Lamellar CoNi-MOFs for the Electrochemical Sensing of H2S.

Lamellar metal-organic frameworks (MOFs) have attracted significant attention in the field of electrochemical sensing due to their abundant open active sites and specific electron conductivity. Herein, by employing a bottom-up synthesis strategy, rhombic lamellar heterometallic CoNi-MOFs with varying thicknesses are constructed. This is achieved by using 4-methylpyridine as a capping agent based on the (4,6)-linked Co2(azpy)2(bptc) (azpy = 4,4'-azopyridine, bptc = 3,3',5,5'-biphenyltetracarboxylic acid) structure with a fsc topology and by introducing Ni species simultaneously. To mitigate sulfur deposition on electrodes, the triple pulse amperometry (TPA) method is employed. Among the synthesized lamellar CoNi-MOFs, lamellar CoNi-MOF-3 with the minimum thickness exhibits an optimal electrochemical sensing performance toward hydrogen sulfide, with a sensitivity of 119.3 µA·mM-1·cm-2 in the linear range of 2-2000 µM. This study pioneers a new approach to the controlled construction and electrochemical activity modification of lamellar MOF materials.

Cell membrane coated nanoparticles: cutting-edge drug delivery systems for osteoporosis therapy.

Osteoporosis, characterized by a reduction in bone mineral density, represents a prevalent skeletal disorder with substantial global health implications. Conventional therapeutic strategies, exemplified by bisphosphonates and hormone replacement regimens, though effective, encounter inherent limitations and challenges. Recent years have witnessed the surge of cell-membrane-coated nanoparticles (CMNPs) as a promising intervention for osteoporosis, leveraging their distinct attributes including refined biocompatibility, heightened pharmaceutical payload capacity, as well as targeted drug release kinetics. However, a comprehensive review consolidating the application of CMNPs-based therapy for osteoporosis remains absent within the existing literature. In this review, we provide a concise overview of the distinctive pathogenesis associated with osteoporosis, alongside an in-depth exploration of the physicochemical attributes intrinsic to CMNPs derived from varied cellular sources. Subsequently, we explore the potential utility of CMNPs, elucidating emerging trends in their deployment for osteoporosis treatment through multifaceted therapeutic approaches. By linking the notable attributes of CMNPs with their roles in mitigating osteoporosis, this review serves as a catalyst for further advances in the design of advanced CMNPs tailored for osteoporosis management. Ultimately, such progress is promising for enhancing outcomes in anti-bone loss interventions, paving the way for clinical translation in the near future.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection.

Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

Starter molds and multi-enzyme catalysis in koji fermentation of soy sauce brewing: A review.

Soy sauce is a traditional fermented food produced from soybean and wheat under the action of microorganisms. The soy sauce brewing process mainly involves two steps, namely koji fermentation and moromi fermentation. In the koji fermentation process, enzymes from starter molds, such as protease, aminopeptidase, carboxypeptidase, l-glutaminase, amylase, and cellulase, hydrolyze the protein and starch in the raw ingredients to produce short-chain substances. However, the enzymatic reactions may be diminished after being subjected to moromi fermentation due to its high NaCl concentration. These enzymatically hydrolyzed products are further metabolized by lactic acid bacteria and yeasts during the moromi fermentation process into organic acids and aromatic compounds, giving soy sauce a unique flavor. Thus, the starter molds, such as Aspergillus oryzae, Aspergillus sojae, and Aspergillus niger, and their secreted enzymes play crucial roles in soy sauce brewing. This review comprehensively covers the characteristics of the starter molds mainly used in soy sauce brewing, the enzymes produced by starter molds, and the roles of enzymes in the degradation of raw material. We also enumerate current problems in the production of soy sauce, aiming to offer some directions for the improvement of soy sauce taste.

Detection of Biomolecules Using Solid-State Nanopores Fabricated by Controlled Dielectric Breakdown.

Nanopore sensor technology is widely used in biomolecular detection due to its advantages of low cost and easy operation. In a variety of nanopore manufacturing methods, controlled dielectric breakdown has the advantages of a simple manufacturing process and low cost under the premise of ensuring detection performance. In this paper, we have made enhancements to the applied pulses in controlled dielectric breakdown and utilized the improved dielectric breakdown technique to fabricate silicon nitride nanopores with diameters of 5 to 15 nm. Our improved fabrication method offers the advantage of precise control over the nanopore diameter (±0.4 nm) and enhances the symmetry of the nanopore. After fabrication, we performed electrical characterization on the nanopores, and the IV characteristics exhibited high linearity. Subsequently, we conducted detection experiments for DNA and protein using the prepared nanopores to assess the detection performance of the nanopores fabricated using our method. In addition, we also give a physical model of molecule translocation through the nanopores to give a reasonable explanation of the data processing results.

Reconstructing atmospheric 129I deposition over 170 years with the varved sediment in the Sihailongwan Maar Lake, northeast China.

Long-term deposition of atmospheric radioactive iodine-129 (129I) is important for assessing the impact of human nuclear activities (HNAs), but still not well understood in East Asia. In this study, we quantitatively reconstructed the deposition history of airborne 129I using varved sediment from Sihailongwan Maar Lake (SHLW) in northeast China. Our results revealed significant increases in 129I concentrations and 129I/127I atomic ratios since the 1950s, indicating the influence of HNAs on the environment and marking the onset of the Anthropocene. The variation of 129I in the investigated site can be primarily attributed to the global fallout of ANWT as well as nuclear fuel reprocessing in Europe, Russia and the USA. Notably, neither the Chernobyl nor the Fukushima nuclear accidents have had any discernable impact on the SHLW Lake. Over the past 170 years (1846-2021), the reconstructed fluxes indicate a rapid increase in 129I deposition from the early 1950s until the 1970s followed by dramatic changes thereafter. The measured 129I fluxes range between (1.26-349) × 109 atoms m-2 yr-1 in the SHLW Lake, which are consistent with similar latitude zones across East Asia, but differ significantly from those observed in high-elevation glaciers within the Northern Hemisphere due to prevailing atmospheric circulation patterns. The total 129I inventory was calculated to be 11.9 × 1012 atoms m-2, with natural and anthropogenic 129I accounting for 2.86 % and 97.1 %, respectively, suggesting an overwhelming artificial contribution. The reconstructed fluxes and inventory of atmospheric 129I deposition quantitatively distinguish the natural and artificial contributions, and provide a novel insight into the historical environmental impact of HNAs in East Asia and the characteristics of the Anthropocene.

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study.

BACKGROUND: Previous research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated. METHODS: A total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety. RESULTS: Four distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances. CONCLUSION: To the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

Highly Specific Sulfadiazine Detection Using a Two-Dimensional Europium-Organic Coordination Polymer.

Sulfadiazine (SFZ) is an inexpensive large-consumption antibiotic used for treat bacterial infections but an excess of residues in food can be harmful. Fast and specific luminescence detection of SFZ is highly challenging because of the interference of structurally similar antibiotics. In this work, we develop a two-dimensional europium-organic coordination polymer with excellent luminescence and water stability for highly specific detection of SFZ in the range of 0-0.2 mM. Structural analysis shows that the high stability of coordination polymer is due to the high coordination number of europium ion and the special chelating coordination structure of ligand. The experiment results revealed that the high selectivity and effectively luminescence quenched behaviour of coordination polymer toward SFZ is caused by highly efficient inner filter effect.

Phase retrieval from single-shot square wave fringe based on image denoising using deep learning.

Fringe-structured light measurement technology has garnered significant attention in recent years. To enhance measurement speed while maintaining a certain level of accuracy using binary fringe, this paper proposes a phase retrieval method with single-frame binary square wave fringe. The proposed method utilizes image denoising through deep learning to extract the phase, enabling the use of a trained image denoiser as a low-pass filter, which adaptively replaces the manual selection of the appropriate band-pass filter. The results demonstrate that this method achieves higher reconstruction accuracy than the traditional single-frame algorithm while preserving more object details.

EphA2-specific microvesicles derived from tumor cells facilitate the targeted delivery of chemotherapeutic drugs for osteosarcoma therapy.

Despite advances in surgery and chemotherapy, the survival of patients with osteosarcoma (OS) has not been fundamentally improved over the last two decades. Microvesicles (MVs) have a high cargo-loading capacity and are emerging as a promising drug delivery nanoplatform. The aim of this study was to develop MVs as specifically designed vehicles to enable OS-specific targeting and efficient treatment of OS. Herein, we designed and constructed a nanoplatform (YSA-SPION-MV/MTX) consisting of methotrexate (MTX)-loaded MVs coated with surface-carboxyl Fe3O4 superparamagnetic nanoparticles (SPIONs) conjugated with ephrin alpha 2 (EphA2)-targeted peptides (YSAYPDSVPMMS, YSA). YSA-SPION-MV/MTX showed an effective targeting effect on OS cells, which was depended on the binding of the YSA peptide to EphA2. In the orthotopic OS mouse model, YSA-SPION-MV/MTX effectively delivered drugs to tumor sites with specific targeting, resulting in superior anti-tumor activity compared to MTX or MV/MTX. And YSA-SPION-MV/MTX also reduced the side effects of high-dose MTX. Taken together, this strategy opens up a new avenue for OS therapy. And we expect this MV-based therapy to serve as a promising platform for the next generation of precision cancer nanomedicines.

The Influence of Light-Generated Radicals for Highly Efficient Solar-Thermal Conversion in an Ultra-Stable 2D Metal-Organic Assembly.

Solar-thermal water evaporation is a promising strategy for clean water production, which needs the development of solar-thermal conversion materials with both high efficiency and high stability. Herein, we reported an ultra-stable cobalt(II)-organic assembly NKU-123 with light-generated radicals, exhibiting superior photothermal conversion efficiency and high stability. Under the irradiation of 808 nm light, the temperature of NKU-123 rapidly increases from 25.5 to 215.1 °C in 6 seconds. The solar water evaporator based on NKU-123 achieves a high solar-thermal water evaporation rate of 1.442 and 1.299 kg m-2 h-1 under 1-sun irradiation with a water evaporation efficiency of 97.8 and 87.9 % for pure water and seawater, respectively. A detailed mechanism study revealed that the formation of light-generated radicals leads to an increase of spin density of NKU-123 for enhancing the photothermal effect, which provides insights into the design of highly efficient photothermal materials.

Exploring the Knowledge of An Outstanding Protein to Protein Interaction Transformer.

Protein-to-protein interaction (PPI) prediction aims to predict whether two given proteins interact or not. Compared with traditional experimental methods of high cost and low efficiency, the current deep learning based approach makes it possible to discover massive potential PPIs from large-scale databases. However, deep PPI prediction models perform poorly on unseen species, as their proteins are not in the training set. Targetting on this issue, the paper first proposes PPITrans, a Transformer based PPI prediction model that exploits a language model pre-trained on proteins to conduct binary PPI prediction. To validate the effectiveness on unseen species, PPITrans is trained with Human PPIs and tested on PPIs of other species. Experimental results show that PPITrans significantly outperforms the previous state-of-the-art on various metrics, especially on PPIs of unseen species. For example, the AUPR improves 0.339 absolutely on Fly PPIs. Aiming to explore the knowledge learned by PPITrans from PPI data, this paper also designs a series of probes belonging to three categories. Their results reveal several interesting findings, like that although PPITrans cannot capture the spatial structure of proteins, it can obtain knowledge of PPI type and binding affinity, learning more than binary PPI.