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Fluorescence imaging is a promising approach for in vivo real-time two-dimensional (2D) visualization of biological structures. However, it is still a challenging problem to acquire depth information conveniently from 2D images. Herein, we developed a visible/second near-infrared (NIR-II) ratiometric fluorescence strategy to acquire tissue depth from 2D fluorescence imaging. A ratiometric fluorescent probe TAC NSs that emitted red and NIR-II fluorescence was prepared through electrostatic interactions between poly-(ethylene-imine)-modified Ti3C2 MXene nanosheets and CdTe and Ag2S quantum dots (QDs). The visible/NIR-II fluorescence ratio decreased exponentially with increasing tissue depth, and its natural logarithm (ln) values shows good linear relationship with tissue depth in the range of 0–1.0 mm and 1–2.5 mm, respectively. Notably, the ratiometric fluorescence at a fixed tissue depth is not affected by the concentration of TAC NSs, indicating that the tissue depth analysis is not interfered by the nonuniform tissue distribution of TAC NSs. Moreover, the TAC NSs exhibited efficient photothermal conversion ability for tumor ablation. This work can provide a strategy for acquiring three-dimensional information from in vivo fluorescence imaging. [ABSTRACT FROM AUTHOR]

The issue of phrasal verb learning has caused much discussion and attracted vigorous investigation. Inspired by the theory of conceptual metaphor, a pedagogical experiment was conducted to investigate whether an approach focussing on sense extension of particle "out" in terms of conceptual metaphors can enhance the learning of phrasal verbs containing "out." One control group was instructed with general reading materials embedded with various phrasal verbs, and two experimental groups with self-constructed lessons, in which six sense types of "out" were identified and corresponding phrasal verbs were selected, and in addition, an L1 lexical item was employed to illustrate the mechanism of metaphorical extension for one experimental group. The result showed that learners receiving a cognitive inspired approach to instruction achieved significantly better learning outcomes than those receiving the traditional approach, not only on taught items but also on untaught items. However, the use of L1 lexical items to illustrate sense extension was not as effective as expected in boosting the learning of "out-phrasal verbs." It was concluded by discussing the benefit of the proposed approach to phrasal verb instruction and, more importantly, the need of teaching materials providing more profound understanding of phrasal verbs so as to facilitate phrasal verb learning.

Taking the endophytic fungus UJ3-2, isolated from Urtica fissa, as the experimental material, this study aimed to explore the composition of its metabolites and the underlying mechanisms by which it inhibits Staphylococcus aureus. Initially, the MIC, MBC, inhibitory curves, biofilm growth, and extracellular nucleic acids and proteins of S. aureus in response to the metabolites were measured. Secondly, PI staining and SEM were used to evaluate the impact of the metabolites on the integrity of the cell wall and overall morphology of S. aureus. Additionally, UPLC-MS was employed to analyze the composition of the secondary metabolites. The UJ3-2 strain was identified as Xylaria grammica based on ITS sequencing and designated as Xylaria grammica UJ3-2. Our results revealed that the metabolites of UJ3-2 exhibited excellent in vitro antibacterial activity against S. aureus, with both MIC and MBC values of 3.125 mg/mL. The inhibitory curve confirmed that 1 MIC of UJ3-2 metabolites could completely inhibit the growth of S. aureus within 24 h. With increasing concentrations of UJ3-2 metabolites, the growth of S. aureus biofilms was significantly suppressed, and obvious leakage of nucleic acids and proteins was observed. PI fluorescence staining indicated that various concentrations of UJ3-2 metabolites disrupted the integrity of the S. aureus cell membrane. SEM observation revealed that the treated S. aureus surfaces became rough, and the bacteria shrank and adhered to each other, showing a dose-dependent effect. UPLC-MS analysis suggested that the main components of the fermented metabolites were 6-oxocineole (17.92%), (S)-2-acetolactate (9.91%), 3-methyl-cis,cis-muconate (4.36%), and 8-oxogeranial (3.17%). This study demonstrates that the endophytic fungus UJ3-2 exhibits remarkable in vitro antibacterial effects against S. aureus, primarily by enhancing the permeability of the S. aureus cell membrane, causing the leakage of its intracellular contents, and altering the bacterial surface morphology to inhibit the pathogen. The endophytic fungus UJ3-2 has a good antibacterial effect on S. aureus, which gives it certain application prospects in the screening and industrial production of new and efficient natural antibacterial active substances. [ABSTRACT FROM AUTHOR]

Purpose: Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments. Patients and Methods: An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results: The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk. Conclusion: An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL. [ABSTRACT FROM AUTHOR]

The large-scale coke oven is a developing trend in coking technology, and the influence of the industrial coke oven size on the quality of metallurgical coke needs to be revealed. Under the same raw coals and blending ratios, metallurgical cokes were obtained from top-loading coke ovens with carbonization chamber heights of 6 and 7 m, respectively. The macroscopic quality and microstructure of the refined metallurgical cokes were comprehensively tested. The results showed that the conventional cold and thermal strength indexes of Coke-7m were better than those of Coke-6m. The comprehensive thermal strength at multiple temperatures further showed that the thermal strength advantage of Coke-7m was mainly manifested at low temperatures (1050 and 1100 °C) or a high temperature (1300 °C), where the solution-loss reaction mode of coke tended to be the "uniform reaction model" or "unreacted core model". At medium temperatures, the solution-loss reaction mode tended to be the "gradient reaction model", which had a greater destructive effect on the thermal strength of coke. At this time, the thermal strengths of the two cokes were close. Microstructure characterization revealed that industrial coke oven size mainly affected the carbon structure of coke but had little effect on pore structure. The advantage of Coke-7m regarding carbon structure is an important reason for its superior macroscopic quality compared to Coke-6m. [ABSTRACT FROM AUTHOR]

Recent studies confirmed that pyroptosis is involved in the progression of pulmonary hypertension (PH), which could promote pulmonary artery remodeling. Urolithin A (UA), an intestinal flora metabolite of ellagitannins (ETs) and ellagic acid (EA), has been proven to possess inhibitory effects on pyroptosis under various pathological conditions. However, its role on PH remained undetermined. To investigate the potential of UA in mitigating PH, mice were exposed to hypoxia (10% oxygen, 4 weeks) to induce PH, with or without UA treatment. Moreover, in vitro experiments were carried out to further uncover the underlying mechanisms. The in vivo treatment of UA suppressed the progression of PH via alleviating pulmonary remodeling. Pyroptosis-related genes were markedly upregulated in mice models of PH and reversed after the administration of UA. In accordance with that, UA treatment significantly inhibited hypoxia-induced pulmonary arterial smooth muscle cell (PASMC) pyroptosis via the AMPK/NF-κB/NLRP3 pathway. Our results revealed that UA treatment effectively mitigated PH progression through inhibiting PASMC pyroptosis, which represents an innovative therapeutic approach for PH. [ABSTRACT FROM AUTHOR]

This study proposes a reinforcement learning‐based finite‐time cross‐media tracking control approach for a slender body cross‐media vehicle encountering unknown hydrodynamics, wind, and wave disturbances. Initially, a reinforcement learning framework consisting of the actor neural network and critic neural network is constructed. The critic neural network monitors the actions of the actor neural network and approximates the cost function, while the actor neural network estimates the unknown hydrodynamics and disturbances, minimising the cost function to optimise performance. Subsequently, the command filter featuring finite‐time convergence is formulated, effectively managing the corresponding filter error through a proposed error compensating signal. By integrating these techniques, a reinforcement learning‐based finite‐time control strategy is developed, circumventing the singularity issue inherent in traditional finite‐time backstepping strategies. Comparative analysis with existing methods demonstrates the strong robustness of the proposed scheme against unknown hydrodynamics and disturbances, ensuring finite‐time convergence of the system's states and optimising controller performance. Finally, simulations confirm the effectiveness and superiority of the presented approach. [ABSTRACT FROM AUTHOR]

Background The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. Methods ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. Results The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P <0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P <0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P <0.001). Conclusions ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals. [ABSTRACT FROM AUTHOR]