Search

Purpose: This study aimed to investigate the student's experiences of social and emotional learning in a community-based youth soccer program grounded in the hybridization of the teaching personal and social responsibility model and the cooperative learning model. Methods: This research was guided by a case study design. Qualitative data were collected over 28 weeks. Twenty-three children (n = 23) participated in the study. Eleven individual interviews, six focus group interviews, two rounds of student drawing activities, and 22 researcher's self-reflection journals were taken during the program. Inductive analysis and constant comparison were used for data analysis. Results: Four themes were drawn from the collected data: trying your best, respecting each other, learning and working as a team, and making your responsibilities at home and school. Conclusion: The study provided evidence that the hybridization of teaching personal and social responsibility and cooperative learning could be an effective pedagogical approach for students' social and emotional learning development in sports-based youth development programs. [ABSTRACT FROM AUTHOR]

High-frequency coherent acoustic phonons hold immense value in characterizing the coupling between magnetic, lattice, and electronic properties, offering nanometer-scale spatial resolution within the ultrafast timescale. However, efficiently propagating intense sub-THz coherent acoustic phonons across diverse materials remains a formidable challenge. Here, we demonstrate that using vanadium dioxide (VO2) as a transducer can induce enhanced coherent acoustic pulses that propagate efficiently (∼90%) into TiO2 due to excellent acoustic impedance matching and minor lattice interface mismatch compared with traditional metals such as Pt, Au, and Cu. Employing time-resolved pump–probe reflectivity spectroscopy, we observe pronounced coherent phonon oscillations reaching up to 0.164 THz from the longitudinal acoustic mode along the c axis in VO2/TiO2. Furthermore, the temperature and pump fluence dependence of the coherent phonon oscillation signals suggest that the metallic state of VO2 responds to these large coherent acoustic phonons. [ABSTRACT FROM AUTHOR]

Oxygen vacancies (OV) constitute significant defects in TiO2 and various other metal oxides, profoundly influencing heterogeneous catalytic reactions. In this investigation, anatase TiO2 samples with different OV concentrations were synthesized by adjusting both the atmosphere and temperature. Characterization results reveal a gradual increase in relative OV content with higher H2 reduction temperatures, contrasting with observations made under an air atmosphere. Through acetylene semi-hydrogenation reactions, it was validated that elevated OV content promotes the reaction activity of acetylene hydrogenation. Kinetic studies have shown that the acetylene reaction rate is positively correlated with the OV content. Density functional theory (DFT) calculations demonstrated that hydrogen undergoes homolytic cleavage on TiO2 surfaces devoid of OV, whereas surfaces containing OV experience heterolytic cleavage, resulting in a significant decrease in hydrogen activation energy and thereby enhancing reaction activity. Furthermore, OV demonstrate higher adsorption affinity for acetylene, thereby facilitating its hydrogenation. On OV-rich surfaces, the rate-determining step for acetylene semi-hydrogenation is the initial hydrogenation which requires less energy (0.14 eV). Conversely, on OV-lacking surfaces, the rate-determining step shifts to the second hydrogenation which needs to overcome an energy of 5.03 eV. Consequently, acetylene can be efficiently hydrogenated to ethylene on TiO2 surfaces with OV. Based on our experimental findings, TiO2 samples obtained through hydrogen reduction at 200 °C demonstrate optimal performance for acetylene semi-hydrogenation, achieving a 100% conversion rate and 95% ethylene selectivity under the employed reaction conditions. [ABSTRACT FROM AUTHOR]

Embodied referring expression (REVERIE) is a challenging task that requires an embodied agent to autonomously navigate in unseen environment and locate the target object specified in the given instruction. One main challenge in this task is the scarcity of data, leading to low generalization ability and poor performance of the agent in unseen environments. To address these issues, we propose VISIONARY (VISION-Aware enhancement with Reminding scenes generated by captions), which leverages advanced pre-trained models as a source of common sense and generates additional valuable information from both linguistic and visual aspects based on the embodied agent's visual input to guide the navigation decision-making process. Specifically, the reminding scene generation mechanism is proposed to describe the observed scene in detail and generate corresponding reminding scenes, which can effectively enrich the input and serve as a supplement to the training data. Additionally, the caption-aware module and the adaptive fusion module are proposed to, respectively, inject the generated scene description and reminding scene into the model, thereby enhancing the navigation efficiency and generalization ability of the agent. Extensive experiments conducted on the REVERIE benchmark demonstrate the effectiveness of our proposed methods, achieving improvements of 2.34 and 2.32% on the key metrics SPL and RGSPL, respectively, in unseen environments compared to the previous state-of-the-art method. The code is available at https://github.com/tpxbps/visionary. [ABSTRACT FROM AUTHOR]

Intersection of education and deep learning method of artificial intelligence (AI) is gradually becoming a hot research field. Education will be profoundly transformed by AI. The purpose of this review is to help education practitioners understand the research frontiers and directions of AI applications in education. This paper reviews the applications of deep learning in education and provides a visualized bibliometric analysis. The data of this paper come from Education Resources Information Center (ERIC) and Web-of-Science (WOS). These two data bases are searched to identify research of deep learning-based education applications from 2015 to 2023. CiteSpace is used to analyze the number of publications, authors, institutions, and keywords of articles that are related to deep learning and education. This paper reviews and systematically analyzes the educational applications of deep learning in the following six aspects: learning effect prediction, educational game, learning recommendation, automatic scoring, assisted teaching and medical education. Based on the visualized bibliometric analysis, the path and inflection point of research evolution can be inferred, the potential motivation of research can be analyzed, and the frontier of research can be explored. At present, AI can enable teachers to focus more on teaching and personalized interactions with students, enhancing rather than replacing human instructions. [ABSTRACT FROM AUTHOR]

With the widespread adoption of electric vehicles (EVs), battery failures have become a significant concern. To safeguard the lives and property of drivers, accurate and prompt diagnosis of battery failures is crucial. This paper proposes a novel battery fault diagnosis method based on the Relative-Range-Feature (RRF) and an improved Theil index, utilizing actual operating data from EVs. Firstly, the trend component of the voltage is extracted utilizing Singular Spectrum Analysis (SSA) to eliminate noise from the original voltage data. Secondly, to address the challenge of inconspicuous early fault features, a new RRF feature extraction method is introduced. This method significantly amplifies the feature differences between normal and faulty batteries. An improved Theil index is proposed to achieve prompt fault detection with high robustness. Finally, a faulty battery localization algorithm that combines Multidimensional scaling (MDS) and Mahalanobis distance (MD) is proposed. This algorithm excels in distinguishing faulty batteries from normal ones. Validation using actual vehicle data demonstrates that the proposed algorithm offers significant advantages in reliability, effectiveness, and robustness. Compared to the information entropy and correlation coefficient methods, this method demonstrates superior applicability. [ABSTRACT FROM AUTHOR]

Objectives: The study examined the effects of illness perception on mental health among patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the moderating role of coping style in this effect. Methods: The current study has collected the data of MAFLD patients at baseline and 8 months after. In both baseline and follow-up surveys, structural questionnaires were adopted to assess participants' illness perception (BIPQ), coping strategies (Brief COPE) and psychological distress (GHQ-12). A cross-lagged panel model was constructed to examine the associations between illness perception and psychological distress over 8 months. The moderating role of coping in the relationship between illness perception and psychological distress was addressed. Results: A total of 155 participants diagnosed with MAFLD were enrolled in the baseline survey, of which 71 patients engaged in the 8-month follow-up. The results showed that severe illness perceptions (perceiving MAFLD with more negative consequence, more negative emotions, lower coherence, and less treatment control) were associated with increased psychological distress 8 months later (β = 0.171, p < 0.05). Though approach coping showed an overall positive effect (β = 0.169, p < 0.05), people using it as a dominant strategy tend to be more subjected to the impacts of negative illness perceptions. Conclusion: Threatening perceptions about MAFLD were associated with more psychological distress over time, and coping style has moderated the effects of illness perceptions. Healthcare providers should be aware of the impacts of negative illness perceptions, particularly when developing coping skill training programs to promote well-being and recovery. [ABSTRACT FROM AUTHOR]

Crude oil spill accidents cause extreme environmental damage, with huge economic costs that include the loss of oil resources. Despite the advances in conventional oil spill cleanup technology, such as dispersants, absorbents, and skimmers, there is still a need for more efficient solutions. In this study, we introduce a Joule-heated stearic acid/carbon nanotube-wrapped polyurethane sponge (SCPU) for rapid and all-weather recovery of leaked crude oil. SCPU is both hydrophobic and oleophilic and is electrically conductive. When 6 V were applied across the SCPU, the surface temperature increased to 146 °C within 70 s, which reduced the viscosity of crude oil and shortened the recovery time by over 99%. In addition, the Joule-heated SCPU can continuously recover crude oil floating on the water surface when connected to a peristaltic pump. This Joule-heated SCPU has a high crude oil adsorption capacity, a scalable preparation method, and is mechanically stable, and thus is expected to provide an efficient and practical solution for responding to crude oil spill accidents. [ABSTRACT FROM AUTHOR]

Supervised learning, a popular tool in modern science and technology, thrives on huge amounts of labeled data. Physics-enhanced deep neural networks offer an effective solution to alleviate the data burden by incorporating an analytical model that interprets the underlying physical processes. However, it completely fails in tackling systems without analytical solution, where wave scattering systems with multiple input multiple output are typical examples. Herein, we propose a concept of deep empirical neural network (DENN) that is a hybridization of a deep neural network and an empirical model, which enables seeing through an opaque scattering medium in an untrained manner. The DENN does not rely on labeled data, all while delivering as high as 58% improvement in fidelity compared with the supervised learning using 30000 data pairs for achieving the same goal of optical phase retrieval. The DENN might shed new light on the applications of deep learning in physics, information science, biology, chemistry and beyond. Tu et al. demonstrate that the unique integration of a convolutional neural network with an empirical model measured for an optical scattering system can unscramble the scattering and see through the opaque medium in an untrained fashion, featured as a deep empirical neural network. [ABSTRACT FROM AUTHOR]

Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data. [ABSTRACT FROM AUTHOR]

Carbon Capture, Utilization and Storage (CCUS) technology is recognized as a pivotal strategy to mitigate global climate change. The CO2 storage and enhanced oil recovery (CCUS-EOR) technology not only enhances oil recovery rates but also contributes to significant reductions in CO2 emissions, with significant social and economic benefits. This paper examines the application of CO2-EOR technology in both enhancing oil recovery and facilitating geological CO2 storage, and analyzes its implementation status and differences in the United States and China. Through experimental investigations conducted in a specific oilfield, we analyze the effects of dissolved CO2 on the viscosity–temperature characteristics, yield value under pressure, stability, and rheological properties of crude oil and produced fluids. Additionally, we assess the demulsification effectiveness of various demulsifiers. Our findings indicate that both dissolved CO2 in crude oil and emulsions exhibit non-Newtonian fluid behavior characterized by shear thinning, and the viscosity decreases with the increase in temperature and pressure. Furthermore, the presence of dissolved CO2 exacerbates the oil–water separation phenomenon in produced fluids, thereby diminishing emulsion stability. The increase in emulsion concentration and the increase in emulsification temperature are both conducive to improving the emulsification rate. These research results provide critical insights for pipeline design and pump selection in oilfield production processes. [ABSTRACT FROM AUTHOR]

The chestnut-winged cuckoo (Clamator coromandus) is a bird species known for its brood parasitism, laying eggs in the nests of other bird species. However, there is a paucity of genetic information available for this species and their genus Clamator. In this study, we present the first complete mitochondrial genome sequence of C. coromandus and compare it with other species within the Cuculidae family. The mitogenome is a closed circular molecule consisting of 17,082 bp with an organization typical of the mitochondrial genomes of Cuculidae. Alignment of the control regions across Cuculidae species revealed substantial genetic variation and a significant abundance of AT content. A significant difference was detected in AT-skews between brood-parasitic and parental-care species. A distinctive long poly-C sequence was located at the 5′ end of domain I. Phylogenetically, C. coromandus is more closely related to Piaya cayana than Ceuthmochares aereus. The phylogenetic analysis indicated a general divergence between species with brood parasitism and those with parental care, with transitions between these behaviors within brood parasitism branches, suggesting multiple evolutionary occurrences of these traits. The complete mitogenome of C. coromandus serves as a valuable resource for further investigation into the taxonomic status and phylogenetic history of Clamator species. [ABSTRACT FROM AUTHOR]

Purpose: Hypoderma pantholopsum is a parasite that parasitizes Tibetan antelopes (Pantholops hodgsonii). This study aims was to reveal the genetic diversity within H. pantholopsum and contribute to the protection of Tibetan antelope. Methods: H. pantholopsum was subjected to mitochondrial genome sequencing, annotation, and phylogenetic analysis. PhyloSuite and BEAST were used to construct phylogenetic tree and divergence time for the parasite. Results: The complete H. pantholopsum genome was 16,265 bp in length. The complete mitochondrial genomes contained 37 typical genes, which included 13 protein-coding genes (PCGs), 22 tRNAs, and 2 rRNAs. Phylogenetic trees constructed based on the 18S rRNA, 28S rRNA, and mitochondrial genome sequences showed that H. pantholopsum clustered on the same branch as the Hypoderma species in the GenBank database. According to the divergence time for the COI gene, H. pantholopsum emerged and differentiated approximately 11.59 million years ago (Mya), which indicates that H. pantholopsum appeared much earlier than H. bovis and H. sinense in the genus Hypoderma. Conclusion: The present study explored that the complete mitochondrial genome of H. pantholopsum, along with the phylogenetic evolution, and divergence time estimation, provide valuable data for future investigations into the phylogeny and differentiation of Hypoderma species on the Qinghai-Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Phase equilibria in the ZrO2–YO1.5–NbO2.5 system were experimentally studied at 1300°C, and a comparison with the existing ZrO2–YO1.5–TaO2.5 (ZYTO) was conducted. Ternary fluorite phases on the Y:Nb > 1 side extend from binary ZrO2‐YO1.5 to YO1.5–NbO2.5 system, generating a continuous solid solution region. Furthermore, it was observed that three phases of M‐YNbO4, fluorite, and m‐ZrO2 were in equilibrium. On the Y:Nb < 1 side, two three‐phase equilibrium regions were measured, namely M‐YNbO4+m‐ZrO2+O‐Nb2Zr6O17 and h‐Nb2O5+O‐Nb2Zr6O17+M‐YNbO4. The tetragonal YNbO4 and ZrO2 phases both extend nearly along with Y:Nb = 1. The t‐ZrO2 phases with slightly lower stabilizer content were found to transform into their monoclinic polymorph during the cooling process. However, in the ZrO2–YNbO4 subsystem, the tetragonal ZrO2 was nontransformable due to larger YO1.5 and NbO2.5 co‐dopants, and the maximum solubility is about 18.1 mol%. Upon cooling, the tetragonal YNbO4 phase, even with the maximum ZrO2 content, is transformable to monoclinic form. The maximum solubility of ZrO2 in YNbO4 is determined to be about 20.7 mol%. Finally, the high‐temperature liquidus in the ZrO2–YNbO4 system was first determined based on the cooling traces, and a vertical section diagram was proposed for further speculation. A eutectic reaction of Liquid→YNbO4+ZrO2 was discovered, and the eutectic composition and temperature were measured as 39.2ZrO2–29.2YO1.5–31.6NbO2.5 and 1813°C. This study provides the basis for thermodynamic assessment of ZrO2–YO1.5–NbO2.5 and is valuable for the design and development of the ZrO2–YO1.5–NbO2.5‐based ceramic materials. [ABSTRACT FROM AUTHOR]

Early detection of abnormal battery characteristics is crucial for ensuring personnel safety and minimizing property damage. Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning method based on improved kurtosis index is proposed, which is capable of capturing the weak abnormal features of the battery and issuing warnings. Furthermore, a faulty battery cell localization method based on two-dimension feature extraction and Isolation Forest algorithm is proposed to capture minor fault features and accurately locate faulty cells. Finally, experiments conducted on four operational electric vehicles demonstrated that the early fault warning method could promptly detect faults and issue alarms, while the faulty battery cell positioning method accurately identified the faulty cell, achieving 100 $\% $ % Accuracy and 0 $\% $ % False Positive Rate. This confirms the effectiveness of the proposed method. Additionally, Bootstrap was used to evaluate the performance metrics, further verifying the robustness of the method. [ABSTRACT FROM AUTHOR]

Purpose: A 3D finite element (FE) model based on the double ellipsoidal heat source was developed to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process. This paper aims to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process by developing a 3D finite element (FE) model based on the double ellipsoidal heat source. Design/methodology/approach: Experimental thermal cycle curves and residual stresses were obtained by thermocouples and X-ray diffraction, respectively. The validity of the model was verified by the corresponding experimental results. Findings: The deposition process of the upper pass led to the partial remelting of the lower deposited pass. The thermal process of the current-deposited pass alleviated the stress concentration in the previous-formed passes. A more uniform temperature distribution could be obtained by using the reciprocating deposition path. Compared to the reciprocating deposition path, the peak values of the transverse and longitudinal tensile residual stresses of the deposited sample under the unidirectional deposition path were reduced by 15 MPa and increased by 13 MPa, respectively. The heat conduction in the deposited passes could be improved by extending the inter-pass cooling time appropriately. With an increase in the inter-pass cooling time, the longitudinal residual stress in the middle region of sample along longitudinal and transverse directions showed increase and decrease–increase trends, respectively, while the transverse residual stress exhibited decrease trend. Originality/value: This study enhances the understanding of temperature and stress fields evolution during the multilayer and multi-pass cold metal transfer-WAAM processes of magnesium alloy and provides the reference for parameter optimization. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) causes leading death worldwide. Mitophagy and lactate metabolism accumulation are distinctive features of LUAD. We aimed to identify lactate-related genes (LRGs) signatures based on mitophagy for predicting prognosis and immune response in LUAD. Methods: The gene expression and clinical data were downloaded from TCGA and GEO database. First, the subtype analysis was analyzed based on 29 mitophagy genes. Survival, immune, and function differences between the different subtypes were analyzed. Then, based on mitophagy genes and 14 LRGs, the best LRGs were screened to construct a risk score model and combined with clinical factors to establish a nomogram for predicting patient survival. Finally, the expression level and molecular function of the key candidate gene OGDH were verified by in vitro experiments. Results: All the LUAD samples were divided into 2 subtypes: sub1 and sub2. The sub2 possessed worse survival. Immune score, immune checkpoint genes, and human leucocyte antigen genes in sub1 were higher than in sub2. Six optimal mitophagy-related LRGs were used to construct a risk score model. A high-risk score indicates poorer survival, higher tumor mutation burden, and higher drug sensitivity. The nomogram was robust in predicting LUAD survival. The experiments in vitro showed that knockdown of OGDH inhibited the proliferation, migration and invasion in LUAD cells. Conclusions: A nomogram based on the construction of the mitophagy-related lactate genes predicts prognosis and immune response in LUAD. These results could help with risk stratification and targeted therapy for LUAD. [ABSTRACT FROM AUTHOR]

PTFE's exceptional low-temperature resistance makes it an ideal material for cryogenic aerospace fuel seals. However, the current understanding of its mechanical behaviors at low temperatures remains limited, posing potential design vulnerabilities. This study investigates the quasi-static mechanical properties of PTFE over a temperature range from 77 K to room temperature, with a particular focus on the effects of temperature and strain rate, which are key variables in sealing applications. The experimental conditions are categorized into ambient temperature (293 K), conventional low temperatures (233 K–293 K), and cryogenic temperatures (77 K–223 K). Testing was conducted at crosshead speeds of 1, 10, and 100 mm/min to evaluate their influence. The results reveal significant phase transition characteristics in PTFE's low-temperature mechanical properties, with the effects of phase transitions far outweighing those of strain rate, leading to dramatic changes in the stress response, from elasticity through plasticity to fracture. Further analysis indicates that these effects are concentrated around the transition state temperature zone, with slower changes observed at other temperatures. This study highlights the critical importance of considering phase transitions in the low-temperature design of PTFE structures. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate prediction models for estimating the activity index (AI) of kidney histopathology in lupus nephritis (LN) using clinical and laboratory data. Methods: This study used single-center data from patients with kidney biopsy-confirmed LN between January 2012 and March 2022. The development and validation datasets were temporally cut. We discriminated AI > 10 and ≤ 10 as high and mild/moderate activity status, respectively. We constructed four models for AI: Model 1 included demographic information; Model 2 additionally incorporated data of systemic conditions; Model 3 further included kidney-specific conditions; and Model 4 included all the aforementioned predictors. Logistic regression was employed in Models 1 to 3, while Model 4 utilized least absolute shrinkage and selection operator for predictor selection and model building. Internal validation was performed using 1000 bootstrap resampling, while external validation was performed in the temporal validation dataset. Both calibration and discrimination metrics were evaluated. Results: There were 160 patients in the development dataset and 70 patients in the validation dataset. In the temporal validation, all the models achieved acceptable calibration and excellent discrimination. Model 2 which contained relatively fewer predictors achieved the highest area under the receiver operator characteristic curve of 0.86 (95% confidence interval 0.76 to 0.94). Conclusion: Our Model 2 incorporating demographic and systemic indicators exhibited good performance in estimating the AI of LN. We thus provide a simple yet effective algorithm to predict AI in patients with LN, potentially aiding clinicians in non-invasively assessing disease activity and guiding treatment decisions. Key Points • We developed a prediction model (Model 2) incorporating demographic and systemic indicators to predict AI in patients with LN. • The prediction model can aid clinicians in noninvasively assessing disease activity and guiding treatment decisions. [ABSTRACT FROM AUTHOR]

To address the current issues of frequent tower crane accidents, imperfect supervision systems and the low adaptability of existing algorithms, a novel collision risk warning model for tower cranes is proposed. The model comprises a state-awareness module and a collision risk assessment module. The skeleton key points of the tower crane are proposed and constructed in the state-awareness module. On this basis, the You Only Look Once (YOLO) v8 algorithm detects the tower crane and its skeleton key points. The ByteTrack algorithm is used to track skeleton key points in real time. In the collision risk assessment module, the speed analysis method for the skeleton key points of the tower crane and the minimum safety distance assessment method between different tower cranes are proposed and compiled. Finally, the effectiveness and robustness of this collision risk warning model are verified by taking three practical projects as examples. The research results demonstrate that the precision of the box, the precision of key points, the mAP@0.5 of the box, and the mAP@0.5 of key points are 96.18%, 96.10%, 92.85%, and 92.53%, respectively. This paper's method implements an intelligent assessment of tower crane collision risk. The model exhibits accurate visualization information in practical engineering applications and has broad application prospects. [ABSTRACT FROM AUTHOR]

Soil salinity poses a significant environmental challenge for the growth and development of blueberries. However, the specific mechanisms by which blueberries respond to salt stress are still not fully understood. Here, we employed a comprehensive approach integrating physiological, metabolomic, and transcriptomic analyses to identify key metabolic pathways in blueberries under salt stress. Our findings indicate that blueberries primarily adapt to salt stress by modulating pathways associated with carbohydrate metabolism, organic acid metabolism, amino acid metabolism, and various organic compounds. Key metabolites involved in this response include sucrose, propionic acid, and palmitic acid. A total of 241 transcription factors were differentially expressed, with significant involvement from families such as AP2, Dof, GATA, WRKY, and TCP. Notably, the galactose metabolism pathway was associated with 5 DAMs and 24 DEGs, while the starch and sucrose metabolism pathway contained 5 DAMs and 23 DEGs, highlighting their crucial roles in mitigating salt stress. Overexpression of VcGolS3 in transgenic Arabidopsis conferred tolerance to salt and drought stresses, primarily evidenced by a significant increase in GolS enzyme activity and reduced ROS accumulation. This study provides valuable insights into the molecular mechanisms underlying the blueberry response to salt stress and lays the groundwork for breeding salt- and drought-tolerant blueberry varieties.Key message: The key gene VcGolS3 involved in blueberry stress resistance was screened through a combined analysis of transcriptomics and metabolomics, and it was confirmed that the gene plays an important role in plant resistance to salt and drought stresses. [ABSTRACT FROM AUTHOR]

Background: The microbes residing in ruminant gastrointestinal tracts play a crucial role in converting plant biomass to volatile fatty acids, which serve as the primary energy source for ruminants. This gastrointestinal tract comprises a foregut (rumen) and hindgut (cecum and colon), which differ in structures and functions, particularly with respect to feed digestion and fermentation. While the rumen microbiome has been extensively studied, the cecal microbiome remains much less investigated and understood, especially concerning the assembling microbial communities and overriding pathways of hydrogen metabolism. Results: To address this gap, we comparatively investigated the composition, capabilities, and activities of the rumen and the cecum microbiome using goats as an experimental ruminant model. In situ measurements showed significantly higher levels of dissolved hydrogen and acetate in the cecum than in the rumen. Increased dissolved hydrogen indicated distinct processes and reduced coupling between fermentative H2 production and utilization, whereas higher levels of acetate could be caused by slower VFA absorption through cecal papillae than through the rumen papillae. Microbial profiling indicated that the cecum harbors a greater abundance of mucin-degrading microbes and fermentative hydrogen producers, whereas the rumen contains a higher abundance of fibrolytic fermentative bacteria, hydrogenotrophic respiratory bacteria, and methanogenic archaea. Most strikingly, reductive acetogenic bacteria were 12-fold more abundant in the cecum. Genome-resolved metagenomic analysis unveiled that the cecum acetogens are both phylogenetically and functionally distinct from those found in the rumen. Further supporting these findings, two in vitro experiments demonstrated a marked difference in hydrogen metabolism pathways between the cecum and the rumen, with increased acetate production and reduced methanogenesis in the cecum. Moreover, comparative analysis across multiple ruminant species confirmed a strong enrichment of reductive acetogens in the hindguts, suggesting a conserved functional role. Conclusions: These findings highlight an enrichment of acetogenesis in a key region of the gastrointestinal tract and reshape our understanding of ruminant hydrogen metabolism and how the H2 can be managed in accord to livestock methane mitigation efforts. 4u9SCdfN3oG9CLNNa6zYDa Video Abstract [ABSTRACT FROM AUTHOR]

Background: Lymphatic leakage is a common complication after radical cystectomy and pelvic lymph node dissection (PLND) for muscle-invasive bladder cancer (MIBC).This study aimed to investigate the risk factors contributing to postoperative lymphatic leakage in patients with MIBC. Materials and methods: A total of 534 patients undergoing radical cystectomy and PLND were enrolled in the retrospective study at Peking University Third Hospital from January 2010 to July 2023. Patients were categorized into lymphatic leakage(n = 254)and non-leakage groups (n = 280) and compared demographic, perioperativ and pathologic factors. Multivariate logistic regression was applied to identify risk factors for lymphatic leakage. Spearman correlation was used to analyze the relationship between lymph leakage ratio and risk factors. Results: Patients with lymphatic leakage had significantly higher rates of receiving extended PLND (19.7% vs. 11.4%, p = 0.008), higher total number of dissected lymph nodes (median 11 vs. 8, p < 0.001), longer hospital stays (median 13 vs. 11 days, p < 0.001), higher postoperative hypoalbuminemia rate (56.7% vs. 36.4%, p < 0.001) and higher fever rate (14.2% vs. 8.6%, p = 0.04) compared to the non-leakage group. On multivariate analysis, higher number of dissected lymph nodes (OR 3.278, 95% CI 1.135–9.471, p = 0.028) was found to be a independent risk factor for lymphatic leakage. Additionally, a positive correlation was observed between the numbers of dissected lymph nodes and lymphatic leakage rate (R = 0.456, p = 0.013). Conclusions: The increased number of dissected lymph nodes is associated with a heightened risk of lymphatic leakage following radical cystectomy for MIBC. [ABSTRACT FROM AUTHOR]

Lung cancer has long posed a severe threat to human life and health, and early detection as well as effective treatment can significantly improve the survival rates of patients. In recent years, deep learning has greatly propelled the advancement in image processing, yet its application in pulmonary nodule detection through deep learning is still at an early stage. Therefore, researching automatic pulmonary nodule detection technology based on deep learning, enhancing the performance of lung nodule detection, holds significant importance for improving the survival rates among lung cancer patients. This paper aims to address the above issues and propose a semi-supervised pulmonary nodule detection algorithm based on adversarial learning. A multi-task classification network is designed to distinguish between annotated and unannotated candidate pulmonary nodules while removing false-positive pulmonary nodules. The adversarial loss and maximum-minimum strategy are introduced into the training process, and the unannotated images and adversarial loss are used to update the 3D sliding window semantic segmentation network. To enable the adversarial loss to backpropagate, the algorithm makes some improvements to RoI pooling, connecting the 3D sliding window semantic segmentation network and multi-task classification network for end-to-end learning. In experiments, compared to the supervised lung nodule algorithm using only 50% labeled images, the semi-supervised lung nodule detection algorithm using 50% labeled images and 50% unlabeled images increased the average FROC from 0.677 to 0.717. [ABSTRACT FROM AUTHOR]

Objective: Clonostachys, a genus with rich morphological and ecological diversity in Bionectriaceae, has a wide distribution among diverse habitats. Several studies have reported Clonostachys fungi as effective biological agents against multiple fungal plant pathogens. To clarify the diversity and biocontrol mechanisms of the Clonostachys fungi, this study was undertaken to sequence and assemble the genomes of two C. chloroleuca and one C. rhizophaga. Data description: Here, we performed genomic sequencing of three strains of genus Clonostachys collected from the China General Microbiological Culture Collection Center (CGMCC) using Illumina HiSeq 2500 sequencing technology. Whole genome analysis indicated that their genomes consist of 58,484,224 bp with a GC content of 48.58%, 58,114,960 bp with a GC content of 47.74% and 58,450,453 bp with a GC content of 48.58%, respectively. BUSCO analysis of the genome assembly indicated that the completeness of these genomes was at least 98%. In summary, these datasets provide a valuable resource for ongoing studies that include further exploration of biological function, marker development, enhanced biological control ability of Clonostachys fungi, and population diversity. [ABSTRACT FROM AUTHOR]

Given a natural language query, code search aims to find matching code snippets from a codebase. Recent works are mainly designed for mainstream programming languages with large amounts of training data. However, code search is also needed for domain-specific programming languages, which have fewer training data, and it is a heavy burden to label a large amount of training data for each domain-specific language. To this end, we propose DAFCS, a data augmentation framework with curriculum learning for few-shot code search tasks. Specifically, we first collect unlabeled codes in the same programming language as the original codes, which can provide additional semantic signals to the original codes. Second, we employ an occlusion-based method to identify key statements in code fragments. Third, we design a set of new key-based augmentation operations for the original codes. Finally, we use curriculum learning to reasonably schedule augmented samples for training well-performing models. We conduct retrieval experiments on a public dataset and find that DAFCS surpasses state-of-the-art methods by 5.42% and 5.05% in the Solidity and SQL domain-specific languages, respectively. Our study shows that DAFCS, which adopts data augmentation and curriculum learning strategies, can achieve promising performance in few-shot code search tasks. [ABSTRACT FROM AUTHOR]

Background: Childhood pneumonia (CP) is a common respiratory infectious disease in children with high morbidity and mortality. However, differential changes in miRNAs may interact with the regulation of CP. The aim of this paper was to discuss the miR-200b expression in CP and its diagnostic and prognostic value. Methods: RT-qPCR was used to measure the miR-200b expression in venous blood. ROC curve was adopted to assess the diagnostic ability of miR-200b in children with CP. Logistic analysis was adopted to study the risk factors are related to CP. Kaplan-Meier was employed to analyze the prognostic ability of miR-200b in CP. Transfection of MRC-5 cells in vitro was used to verify the impact of miR-200b on cell proliferation and apoptosis. Results: The expression of miR-200b in serum of CP children was upregulated. ROC analysis prompted that upregulation of miR-200b could be effective in distinguishing between CP and healthy children. Compared with the good prognosis group, miR-200b expression was elevated in the poor prognosis group. Kaplan-Meier reminded that high expression of miR-200b led to a poor prognosis in CP. Cells experiments demonstrated that transfection with miR-200b inhibitor encouraged cell proliferation and inhibited apoptosis, whereas transfection with miR-200b mimic had the contrary result. Conclusions: miR-200b had high diagnostic and prognostic value for children with CP and may become a biomarker for clinical diagnosis and prognosis. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecasting is crucial for effective smart grid management, given the intermittent nature of PV generation. To address these challenges, this paper proposes the Temporal Bottleneck-enhanced Bidirectional Temporal Convolutional Network with Multi-Head Attention and Autoregressive (TB-BTCGA) model. It introduces a temporal bottleneck structure and Deep Residual Shrinkage Network (DRSN) into the Temporal Convolutional Network (TCN), improving feature extraction and reducing redundancy. Additionally, the model transforms the traditional TCN into a bidirectional TCN (BiTCN), allowing it to capture both past and future dependencies while expanding the receptive field with fewer layers. The integration of an autoregressive (AR) model optimizes the linear extraction of features, while the inclusion of multi-head attention and the Bidirectional Gated Recurrent Unit (BiGRU) further strengthens the model's ability to capture both short-term and long-term dependencies in the data. Experiments on complex datasets, including weather forecast data, station meteorological data, and power data, demonstrate that the proposed TB-BTCGA model outperforms several state-of-the-art deep learning models in prediction accuracy. Specifically, in single-step forecasting using data from three PV stations in Hebei, China, the model reduces Mean Absolute Error (MAE) by 38.53% and Root Mean Square Error (RMSE) by 33.12% and increases the coefficient of determination ( R 2 ) by 7.01% compared to the baseline TCN model. Additionally, in multi-step forecasting, the model achieves a reduction of 54.26% in the best MAE and 52.64% in the best RMSE across various time horizons. These results underscore the TB-BTCGA model's effectiveness and its strong potential for real-time photovoltaic power forecasting in smart grids. [ABSTRACT FROM AUTHOR]

Background: Co-existent pulmonary tuberculosis and lung cancer (PTB-LC) represent a unique disease entity often characterized by missed or delayed diagnosis. This study aimed to investigate the clinical and radiological features of patients diagnosed with PTB-LC. Methods: Patients diagnosed with active PTB-LC (APTB-LC), inactive PTB-LC (IAPTB), and LC alone without PTB between 2010 and 2022 at our institute were retrospectively collected and 1:1:1 matched based on gender, age, and time of admission. Symptoms and clinicopathological features were compared among the three groups of patients. Logistic regression was employed for risk factor analysis. Results: Compared to LC or IAPTB-LC, patients with APTB-LC exhibited higher proportions of weight loss (p < 0.001) and fever (p < 0.001) at the time of diagnosis. Additionally, radiological features such as nodules (p = 0.007), tree-in-bud (p < 0.001), cavitation (p < 0.001), and calcification (p < 0.001) were significantly more prevalent in APTB-LC patients compared to the other groups. Patients with APTB-LC were more susceptible to lymph node involvement (p < 0.001) and distant metastasis (p = 0.006) compared to those with IAPTB-LC or LC alone. Additionally, in comparison to LC alone, patients with IAPTB-LC exhibited more complex symptoms, imaging features, and lymph node metastases. Logistic regression results indicated that factors such as BMI, fever, patchy shadow, cavitation, neck or supraclavicular lymph node metastasis, and liver injury favor the diagnosis of APTB-LC over LC alone. The pre-diagnostic model exhibited robust performance, achieving area under the curve (AUC) values of 0.864 in the training set and 0.830 in the test set. Conclusion: Our results indicate that PTB-LC is a distinct disease characterized by complex clinicopathological features and a more aggressive nature. Based on our findings, we recommend conducting TB-related tests for LC patients who exhibit relevant risk factors or are identified as high-risk cases according to the pre-diagnostic model. [ABSTRACT FROM AUTHOR]

Climate change poses a significant threat to economic development and necessitates immediate action to achieve net-zero greenhouse gas emissions. This study investigates the feasibility of hydrogen systems as a comprehensive solution for decarbonizing the distillery industry, a sector often deemed "hard-to-treat" from service perspective. Through a qualitative case study of a whiskey distillery in Scotland, insights from senior management at both the distillery and the hydrogen system provider reveal the potential of hydrogen to not only serve as an energy source but also to address broader sustainability challenges within the food and agriculture sector. While advantages are evident, the study also identifies challenges related to infrastructure, cost, and the need for shifts in government policy, user attitudes, and investor perspectives. This research serves as a precursor to broader sectoral studies aimed at developing pathways toward net-zero emissions, emphasizing the importance of collaboration among stakeholders to unlock the full potential of hydrogen as a transformative solution for achieving a sustainable future. [ABSTRACT FROM AUTHOR]

Preventing secondary crises resulting from emergency incidents in engineering projects is a crucial and complex task for project operation management. Public opinion and its underlying sentiment can act as reliable indicators, reflecting the progression of emergency incidents, and warrant serious consideration. With the advent of Web 2.0, the management of online public opinion (OPO) through social platforms has advanced significantly. However, previous research has overlooked the diverse categories of participants contributing to OPO evolution. This article proposes an optimised bounded confidence model (BCM) for sentiment OPO evolution under emergency situations at railway stations, incorporating multiple participant categories. A conceptual model based on eleven assumptions is developed, involving four key participants (netizens, media, opinion leaders, and government) structured into four sub-processes. To illustrate this model, the case of the Wuhan railway stations' blockade during the COVID-19 outbreak is examined. This case study demonstrates the initial data acquisition and simulation process. The standard simulation results are recorded, followed by a multiple-sensitivity analysis to investigate the impact of various critical factor combinations on OPO evolution. Finally, policy recommendations are provided to government departments to enhance their response to emergency situations, particularly those involving railway stations, thereby ensuring public safety. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to investigate the association between sarcopenia and liver fibrosis in patients aged 18–59 years with metabolic dysfunction-associated steatotic liver disease (MASLD) and to assess the potential of sarcopenia as a risk factor for the progression of liver fibrosis. Methods: The study included 821 patients with MASLD in the US cohort and 3,405 patients with MASLD in the Chinese cohort. Liver controlled attenuation parameters (CAP) and liver stiffness measurements (LSM) were assessed by vibration-controlled transient elastography (VCTE) to evaluate the extent of hepatic steatosis and fibrosis. Sarcopenia was assessed by measuring appendicular skeletal muscle mass (ASM) and calculating ASMI. To analyze the relationship between sarcopenia, ASMI, and liver fibrosis, logistic regression models, multivariate-adjusted models, and restricted cubic spline (RCS) models were employed, with stratification and interaction analyses. Results: The results demonstrated that patients with sarcopenia exhibited a markedly elevated risk of significant liver fibrosis, advanced liver fibrosis, and cirrhosis compared to those without sarcopenia in both cohorts. After adjusting for confounding variables, sarcopenia was identified as an independent risk factor for the progression of liver fibrosis in patients with MASLD. A significant negative correlation was observed between ASMI and the severity of liver fibrosis, with a progressive reduction in the risk of liver fibrosis associated with increasing ASMI. Additionally, a non-linear feature was evident in some liver fibrosis indicators. Subgroup analysis further corroborated the finding that the harmful effect of sarcopenia on liver fibrosis was consistent across all identified subgroups. Conclusion: Sarcopenia may be associated with the progression of liver fibrosis in patients with MASLD. Monitoring ASMI may assist in identifying individuals at an elevated risk of liver fibrosis in MASLD patients. [ABSTRACT FROM AUTHOR]

We designed a NIR-II fluorescence probe, Cys(StBu)-Gly-Phe-Leu-Gly-Lys(AF3)-CBT (AF3-CBT), which forms a dually quenched nanoparticle AF3-NP. Under CTSB cleavage, NIR-II fluorescence turns "On" for sensitive imaging of CTSB. AF3-NP showed 6.1-fold, 3.2-fold, and 2.1-fold higher NIR-II fluorescence intensities compared to those of the control group in vitro, in cells, and in vivo, respectively. [ABSTRACT FROM AUTHOR]

Background: To propose the bladder mucosal smoothness (BMS) grade and validate a predictive model including MRI parameters preoperatively that can evaluate the early recovery of urinary continence (UC) after laparoscopic radical prostatectomy (LRP). Methods: A retrospective analysis was conducted on 203 patients (83 patients experienced UI at the three-month follow-up) who underwent LRP in our medical center and were diagnosed with prostate cancer (PCa) from June 2016 to March 2020. Patients' clinicopathological data were collected. Prostate volume (PV), membranous urethra length (MUL), intravesical prostatic protrusion length (IPPL), and BMS grade were measured by MRI. The total sample was randomly divided into a training set (n = 142) and a validation set (n = 61). A model was developed to predict the risk of urinary incontinence (UI) at three months after LRP. Results: Age group, clinical T stage group, BMS grade group, PV group, IPPL group, and MUL group differed significantly between patients in the UI group and the UC group (all P values < 0.05). Multivariate analysis identified 3 MRI-related predictors selected for the prediction model: BMS grade (1 odds ratio [OR] 0.17, 95% CI 0.11–0.66; P value = 0.024) (2 + 3 OR 0.17, 95% CI 0.04–0.66; P value = 0.011), IPPL (> 5 mm OR 0.17, 95% CI 0.1–0.64; P = 0.004), and MUL (≥ 14 mm OR 6.41, 95% CI 2.72–15.09; P value < 0.001). The model achieved a highest area under the curve of 0.900 in the training set and the validation set. The sensitivity and specificity of the prediction model were 0.800 and 0.816. Conclusion: Our study confirmed that patients with lower BMS grade are associated with early recovery of urinary continence after LRP. A prediction model was developed and validated to evaluate the early recovery of urinary continence after LRP. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Uterine Corpus Endometrial Carcinoma (UCEC) represents a common malignant neoplasm in women, with its prognosis being intricately associated with available therapeutic interventions. In the past few decades, there has been a burgeoning interest in the role of mitochondria within the context of UCEC. Nevertheless, the development and application of prognostic models predicated on mitochondrial-related genes (MRGs) in UCEC remains in the exploratory stages. This study utilized RNA sequencing data and clinical information from the TCGA database to identify differentially expressed MRGs (DEMRGs) between UCEC and normal groups that are associated with overall survival (OS). Patients were randomly assigned to training and testing cohorts in a 1:1 ratio. In the training cohort, a risk model based on DEMRGs was developed using Lasso Cox regression analysis. Subsequently, patients in both cohorts were stratified into high-risk and low-risk groups based on their median risk scores. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, ROC curves, and nomograms. Additionally, further analyses including functional enrichment, immune landscape assessment, prediction of response to ICB therapy, mutation profiling, and drug sensitivity analysis elucidated biological distinctions between the identified risk groups. We established a risk model incorporating eight MRGs. Patients classified within he high-risk group exhibited significantly poorer prognoses relative to those in the low-risk group. Functional enrichment analysis identified substantial differences in biological processes and signaling pathways between the high-risk and low-risk cohorts. Immune landscape analysis showed that patients with elevated risk scores exhibited significant immunosuppressive and immune evasion mechanisms. Conversely, low-risk patients exhibited higher expression of human leukocyte antigen (HLA) family members and immune checkpoint genes (ICGs) compared to their high-risk counterparts.Consequently, low-risk patients showed greater responsiveness to immunotherapy and potential small molecule drugs, whereas high-risk patients were more susceptible to chemotherapy. The mitochondrial-related risk model formulated in this study demonstrates efficacy in predicting both prognosis and response to immunotherapy in patients with UCEC, thereby providing a scientific basis for personalized treatment strategies. Future research endeavors should focus on further validating the clinical utility of this model and investigate the specific mechanisms of the identified MRGs in UCEC. [ABSTRACT FROM AUTHOR]

Fluorescence imaging in the second near-infrared region (NIR-II, 1000–1700 nm) enables high-resolution visualization of deep-tissue biological architecture and physiopathological events, due to the reduced light absorption, scattering and tissue autofluorescence. Numerous versatile NIR-II fluorescent probes have been reported over the past decades. In this review, we first provide a detailed account of the advantages of fluorescence imaging in the NIR-II region. Following this, the classification, design and performance optimization strategies of NIR-II fluorescent probes are systematically discussed, along with a broad range of biomedical applications in vivo. Finally, the discussion extends to the next generation of fluorescent probes for in vivo imaging and the challenges and perspectives for the clinical translation of fluorescence imaging technology in the NIR-II region. [ABSTRACT FROM AUTHOR]

Electric polarization typically originates from non-centrosymmetric charge distributions in compounds. In elemental crystalline materials, chemical bonds between atoms of the same element favor symmetrically distributed electron charges and centrosymmetric structures, making elemental ferroelectrics rare. Compared to atoms, elemental clusters are intrinsically less symmetric and can have various preferred orientations when they are assembled to form crystals. Consequently, the assembly of clusters with different orientations tends to break the inversion symmetry. By exploiting this concept, we show that sliding ferroelectricity naturally emerges in trilayer quasi-hexagonal phase (qHP) C60, a cluster-assembled carbon allotrope recently synthesized. Compared to many metallic or semi-metallic elemental ferroelectrics, trilayer qHP C60's have sizable band gaps and several ferroelectric structures, which are distinguishable by measuring their second-harmonic generation (SHG) responses. Some of these phases show both switchable out-of-plane and in-plane polarizations on the order of 0.2 pC/m. The out-of-plane and in-plane polarizations can be switched independently and enable an easy-to-implement construction of Van der Waals homostructures with ferroelectrically switchable chirality. [ABSTRACT FROM AUTHOR]

Interferon regulatory factor 3 (IRF3) is a central hub transcription factor that controls host antiviral innate immunity. The expression and function of IRF3 are tightly regulated by the post-translational modifications. However, it is unknown whether unanchored ubiquitination and deubiquitination of IRF3 involve modulating antiviral innate immunity against RNA viruses. Here, we find that USP5, a deubiquitinase (DUB) regulating unanchored polyubiquitin, is downregulated during host anti-RNA viral innate immunity in a type I interferon (IFN-I) receptor (IFNAR)-dependent manner. USP5 is further identified to inhibit IRF3-triggered antiviral immune responses through its DUB enzyme activity. K48-linked unanchored ubiquitin promotes IRF3-driven transcription of IFN-β and induction of IFN-stimulated genes (ISGs) in a dose-dependent manner. USP5 simultaneously removes both K48-linked unanchored and K63-linked anchored polyubiquitin chains on IRF3. Our study not only provides evidence that unanchored ubiquitin regulates anti-RNA viral innate immunity but also proposes a novel mechanism for DUB-controlled IRF3 activation, suggesting that USP5 is a potential target for the treatment of RNA viruses infectious diseases. Author summary: Innate immunity is the first line of defense against highly pathogenic RNA viruses. IRF3 plays a crucial role in initiating IFN-I-dependent antiviral innate immunity. Post-translational modifications, particularly ubiquitination, control the stability and transcription activity of IRF3. However, it is unknown whether unanchored ubiquitination and deubiquitination of IRF3 involve modulating antiviral innate immunity against RNA viruses. Here, we show that IFN-I-mediated USP5 suppression enhances K48-linked unanchored and K63-linked anchored ubiquitination of IRF3, thereby promoting IRF3 activation and further IFN-β transcription. We demonstrate a protective role of the IFN-I-mediated downregulation of USP5 during host infected with RNA viruses. Our study also shows the dual cleavage activity of USP5 in deconjugating K48-linked unanchored and K63-linked anchored polyubiquitin on IRF3, which provides insights into the molecular mechanisms governing host defense against RNA virus infection. [ABSTRACT FROM AUTHOR]

The role of dairy products in cardiovascular disease (CVD) prevention remains controversial. This study investigates the association between dairy consumption and CVD incidence using data from the China Kadoorie Biobank and the UK Biobank, complemented by an updated meta-analysis. Among Chinese participants, regular dairy consumption (primarily whole milk) is associated with a 9% increased risk of coronary heart disease (CHD) and a 6% reduced risk of stroke compared to non-consumers. Among British participants, total dairy consumption is linked to lower risks of CVD, CHD, and ischemic stroke, with cheese and semi-skimmed/skimmed milk contributing to reduced CVD risk. Meta-analysis reveals that total dairy consumption is associated with a 3.7% reduced risk of CVD and a 6% reduced risk of stroke. Notably, inverse associations with CVD incidence are observed for cheese and low-fat dairy products. Current evidence suggests that dairy consumption, particularly cheese, may have protective effects against CVD and stroke. The role of dairy products in cardiovascular disease (CVD) prevention is debated. Here, the authors show that dairy consumption is associated with a lower risk of CVD and stroke, with cheese linked to reduced CVD risk, though the effects vary between Chinese and UK populations. [ABSTRACT FROM AUTHOR]

The high-amplitude and high-frequency wheel–rail impacts excited by wheel flat result in severe contact friction in axle-box bearing (ABB), and the friction can cause a temperature rise or even structural damage of ABB. To this end, a thermal analysis model considering the vehicle operation environment is proposed to analyze the temperature characteristics of ABB. Various coupling dynamics effects between the vehicle–track system and ABB are synergistically integrated. The heat conduction, convection, and radiation between the various components of ABB are also integrated into the thermal analysis modeling. By the field tests, the accuracy of the ABB thermal model is validated. The results obtained through the model simulation show that the ABB temperature increases with vehicle speed and wheel flat length, and the ABB temperature at the outside row is higher than that at the inside row. Moreover, the temperature of the roller, cage, inner ring, outer ring, and axle box increases following an ascending order. Specifically, the temperature at the small end of the roller is higher than that at the large end. The findings of this study can provide engineering guidelines for the condition monitoring of ABBs. [ABSTRACT FROM AUTHOR]

Magnesium slag is a by-product of the magnesium industry. As an auxiliary cementitious material incorporated into concrete, it can make full use of waste resources and has a certain potential for hydration and carbonation. To improve the mechanical properties of the concrete, the influence mechanism and strengthening mechanism of the carbon curing method on mechanical properties of magnesium slag concrete were investigated. The effects of different magnesium slag content and water-cement ratio on mechanical properties and ecological properties of carbon cured magnesium slag concrete were analyzed. Based on the phase composition and thermogravimetric composition of magnesium slag concrete, the carbonation mechanism of magnesium slag was revealed. The mechanical properties models of magnesium slag concrete with different carbon curing were constructed. The study shows that with the increase of the magnesium slag, the mechanical properties of carbon curing concrete first increase and then decrease. The optimum mechanical properties of concrete are 30% magnesium slag, and the compressive strength reaches 42.3 MPa. The content of magnesium slag increased from 0% to 60%, and the carbon fixation content was 14.60%, 11.87%, 11.69%, 16.90%, 19.80%, 14.78%, and 13.09%, respectively. With the increase of magnesium slag content, the content and grain size of magnesium oxide in concrete increase, which leads to more micro-bumps and depressions on the surface of the concrete structure. The hydration reaction and carbonation reaction of gelled materials are affected by magnesium ions, resulting in changes in the morphology and crystal structure of CaCO3 and MgCO3 reactants. [ABSTRACT FROM AUTHOR]

In recent years, steel-fiber-reinforced concrete (SFRC) has been increasingly applied in shield tunnel engineering. However, most research on SFRC segments focuses on the load-bearing capacity, while the tunnel deformation is an equally critical indicator that decides if the tunnel can operate safely during service conditions. Therefore, it is essential to also study the stiffness variations in SFRC segments, which is closely connected to the serviceability limit state (SLS). To investigate the influence of SFRC on segment stiffness, full-scale four-point bending tests and analytical calculations are carried out on both traditional reinforced concrete (RC) segments and SFRC segments with rebars. A C55 plain concrete is used in the RC segment, and for SFRC, 30 kg/m3 steel fibers are added. The segment stiffnesses are calculated and analyzed, and compared between test and analytical results. This study shows that the addition of steel fibers to traditional reinforced concrete segments can enhance the bending stiffness. This effect becomes apparent only after the segments crack. Initially, the effect is strong but then becomes weaker, with increasing load. The added 30 kg/m3 steel fibers generate a maximum of 33% in stiffness increment for a segment with 2.1% reinforcement. Further analysis indicates that the transfer of stresses in the cracked SFRC results in a stiffness improvement, but after cracking, the contribution of the reinforcement to the flexural resistance increases while the contribution of the SFRC gradually decreases. Thus, the effect is weak at high load levels. This paper contributes to a better understanding of the effect of SFRC on the stiffness of segments, as relevant for SLS requirements. [ABSTRACT FROM AUTHOR]

Incorporating specific defects into complex oxides facilitates the exploration of exotic phenomena and novel functionalities based on the intricate coupling between the defects and lattice/charge. However, methods for maximizing the density of specific defects while enhancing the desired properties have been rarely explored. In this study, the effect of N+ ion bombardment‐driven specific defects on the properties of bismuth ferrite (BFO) thin films was investigated. Furthermore, atomic structure characterization and computational processing revealed the displacement and orientation of the Fe atoms, which are linearly related to the degree of polarization. The ion bombardment introduced deep‐level trap states within the lattice, leading to a significant reduction in the leakage current and improved insulation performance of the films. By precisely engineering the defect content through N+ ion bombardment, the pure BFO thin films with remarkable and stable ferroelectric properties (remnant polarization, Pr = ∼116.8 µC·cm−2; leakage current, J = ∼1.5 × 10−8 A·cm−2) were fabricated. This innovative defect engineering‐based approach enables the customization and optimization of local ferroelectric order parameters, thereby establishing a solid foundation for designing functionalities across various functional material systems. [ABSTRACT FROM AUTHOR]

Evaluating the balance between a city's physical and socioeconomic environmental development is crucial for creating sustainable and livable urban spaces. Although they might appear contradictory, they jointly support the comprehensive sustainable urban development strategy. Traditional methods usually focus on assessing this balance from a specific perspective, such as how neighborhood greenery shapes real estate value. Yet, they fail to deliver a holistic balance assessment in developing the physical and socioeconomic dimensions. To fill this gap, this study introduces a research framework that measures this balance through house prices based on transferred bias. Using house price as an indicator shaped by both physical and socioeconomic environments, the framework first constructs a series of deep learning models to estimate house prices through street view images for each city. These models capture the relationship between neighborhood appearance and house price. Second, by leveraging transfer inference, we introduce neighborhood appearance from one city into the model trained from another city. This process identifies the transferred bias, which is the disparity between inaccurate inference resulting from a mismatched neighborhood appearance and the trained model. Through transferred bias, we can quantify the differences in physical and socioeconomic environments across cities and evaluate the urban balances of these two environments. The results show that the transferred bias effectively quantifies the disparities among cities in physical and socioeconomic environments, thereby facilitating further investigation into the urban balance between these two environments. [ABSTRACT FROM AUTHOR]