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Abstract 3-dB couplers, which are commonly used in photonic integrated circuits for on-chip information processing, precision measurement, and quantum computing, face challenges in achieving robust performance due to their limited 3-dB bandwidths and sensitivity to fabrication errors. To address this, we introduce topological physics to nanophotonics, developing a framework for topological 3-dB couplers. These couplers exhibit broad working wavelength range and robustness against fabrication dimensional errors. By leveraging valley-Hall topology and mirror symmetry, the photonic-crystal-slab couplers achieve ideal 3-dB splitting characterized by a wavelength-insensitive scattering matrix. Tolerance analysis confirms the superiority on broad bandwidth of 48 nm and robust splitting against dimensional errors of 20 nm. We further propose a topological interferometer for on-chip distance measurement, which also exhibits robustness against dimensional errors. This extension of topological principles to the fields of interferometers, may open up new possibilities for constructing robust wavelength division multiplexing, temperature-drift-insensitive sensing, and optical coherence tomography applications.

Abstract Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Studies have indicated that immune dysfunction plays a central role in the pathogenesis of sepsis. Dendritic cells (DCs) play a crucial role in the emergence of immune dysfunction in sepsis. The major manifestations of DCs in the septic state are abnormal functions and depletion in numbers, which are linked to higher mortality and vulnerability to secondary infections in sepsis. Apoptosis is the most widely studied pathway of number reduction in DCs. In the past few years, there has been a surge in studies focusing on regulated cell death (RCD). This emerging field encompasses various forms of cell death, such as necroptosis, pyroptosis, ferroptosis, and autophagy-dependent cell death (ADCD). Regulation of DC’s RCD can serve as a possible therapeutic focus for the treatment of sepsis. Throughout time, numerous tactics have been devised and effectively implemented to improve abnormal immune response during sepsis progression, including modifying the functions of DCs and inhibiting DC cell death. In this review, we provide an overview of the functional impairment and RCD of DCs in septic states. Also, we highlight recent advances in targeting DCs to regulate host immune response following septic challenge. Graphical Abstract

Abstract While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

Abstract Mitochondria are the centers of energy and material metabolism, and they also serve as the storage and dispatch hubs of metal ions. Damage to mitochondrial structure and function can cause abnormal levels and distribution of metal ions, leading to cell dysfunction and even death. For a long time, mitochondrial quality control pathways such as mitochondrial dynamics and mitophagy have been considered to inhibit metal-induced cell death. However, with the discovery of new metal-dependent cell death including ferroptosis and cuproptosis, increasing evidence shows that there is a complex relationship between mitochondrial quality control and metal-dependent cell death. This article reviews the latest research results and mechanisms of crosstalk between mitochondrial quality control and metal-dependent cell death in recent years, as well as their involvement in neurodegenerative diseases, tumors and other diseases, in order to provide new ideas for the research and treatment of related diseases.

Abstract Background Liver ischemia/reperfusion (I/R) injury is usually caused by hepatic inflow occlusion during liver surgery, and is frequently observed during war wounds and trauma. Hepatocyte ferroptosis plays a critical role in liver I/R injury, however, it remains unclear whether this process is controlled or regulated by members of the DEAD/DExH-box helicase (DDX/DHX) family. Methods The expression of DDX/DHX family members during liver I/R injury was screened using transcriptome analysis. Hepatocyte-specific Dhx58 knockout mice were constructed, and a partial liver I/R operation was performed. Single-cell RNA sequencing (scRNA-seq) in the liver post I/R suggested enhanced ferroptosis by Dhx58 hep−/− . The mRNAs and proteins associated with DExH-box helicase 58 (DHX58) were screened using RNA immunoprecipitation-sequencing (RIP-seq) and IP-mass spectrometry (IP-MS). Results Excessive production of reactive oxygen species (ROS) decreased the expression of the IFN-stimulated gene Dhx58 in hepatocytes and promoted hepatic ferroptosis, while treatment using IFN-α increased DHX58 expression and prevented ferroptosis during liver I/R injury. Mechanistically, DHX58 with RNA-binding activity constitutively associates with the mRNA of glutathione peroxidase 4 (GPX4), a central ferroptosis suppressor, and recruits the m6A reader YT521-B homology domain containing 2 (YTHDC2) to promote the translation of Gpx4 mRNA in an m6A-dependent manner, thus enhancing GPX4 protein levels and preventing hepatic ferroptosis. Conclusions This study provides mechanistic evidence that IFN-α stimulates DHX58 to promote the translation of m6A-modified Gpx4 mRNA, suggesting the potential clinical application of IFN-α in the prevention of hepatic ferroptosis during liver I/R injury.

Abstract Studies suggest that mangiferin (MAF) has good therapeutic effects on chronic bronchitis and hepatitis. Also, it is one of the antiviral ingredients in Anemarrhena asphodeloides Bunge. However, its effect on the LPS‐induced inflammation and intestinal flora during sepsis remains unclear yet. In the present study, LPS‐stimulated inflammation RAW264.7 cells and LPS‐induced sepsis mice were used to evaluate the efficacy of MAF in vitro and in vivo. 16S rDNA sequencing was performed to analyze the characteristics of intestinal flora of the sepsis mice. It has been demonstrated that MAF (12.5 and 25 μg/mL) significantly inhibited protein expressions of TLR4, MyD88, NF‐κB, and TNF‐α in the LPS‐treated cells and reduced the supernatant TNF‐α and IL‐6 levels. In vivo, MAF (20 mg/kg) markedly protected the sepsis mice and reduced the serum TNF‐α and IL‐6 levels. Also, MAF significantly downregulated the protein expressions of TLR4, NF‐κB, and MyD88 in the livers. Importantly, MAF significantly attenuated the pathological injuries of the livers and small intestines. Further, MAF significantly increased proportion of Bacteroidota and decreased the proportions of Firmicutes, Desulfobacterota, Actinobacteriota, and Proteobacteria at phylum level, and it markedly reduced the proportions of Escherichia‐Shigella, Pseudoalteromonas, Staphylococcus at genus level. Moreover, MAF affects some metabolism‐related pathways such as citrate cycle (TCA cycle), lipoic acid metabolism, oxidative phosphorylation, bacterial chemotaxis, fatty acid biosynthesis, and peptidoglycan biosynthesis of the intestinal flora. Thus, it can be concluded that MAF as a treatment reduces the inflammatory responses in vitro and in vivo by inhibiting the TLR4/ MyD88/NF‐κB pathway, and corrects intestinal flora imbalance during sepsis to some degree.

BackgroundChina exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China’s pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks.MethodsRetrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny.ResultsVarious diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation.ConclusionThis investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19’s disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

BackgroundInsulin-like Growth Factor-1 (IGF-1) plays a crucial role in the growth and metabolic functions of various tissues and cells in the body. Recently, there has been increased attention to the association between IGF-1 and osteoarthritis (OA). However, there is controversy in current research regarding the correlation between IGF-1 levels and OA. Furthermore, the specific manner in which Body Mass Index (BMI), a key risk factor for OA, mediates the impact of IGF-1 levels on OA remains unclear.ObjectThis study aimed to investigate the bidirectional causal link between IGF-1 levels and OA in four body regions, and to explore how BMI influences the impact of IGF-1 on these types of OA.MethodTwo-sample Mendelian Randomization (MR) and its combined forms were utilized to investigate the bidirectional relationship between IGF-1 levels and four types of OA, as well as the mediating role of BMI in the impact of IGF-1 levels on OA. Data from various Genome-Wide Association Studies (GWAS) and multiple analytical methods, including inverse variance weighted, MR-Egger regression, and weighted median were utilized. Sensitivity analyses, such as MR-Egger intercept, Cochran Q test, leave-one-out, and MR-PRESSO, were conducted to ensure the robustness of the results.ResultsHigher IGF-1 levels are correlated with an increased risk for knee (OR, 1.07; 95% CI, 1.01–1.03; p = 1.49e-01; q = 9.86e-03), hip (OR, 1.13; 95% CI, 1.06–1.20; p = 7.61e-05; q = 7.44e-05), and hand OA (OR, 1.09; 95% CI, 1.01–1.17; p = 1.88e-02; q = 1.15e-02), but not spine OA but not spine OA (OR, 1.05; 95% CI, 0.99–1.10; p = 9.20e-02; q = 5.52e-02). Different types of OA do not affect IGF-1 levels. BMI mediates the increase in OA risk associated with higher IGF-1, including indirect spine OA risk through BMI.ConclusionThe study elucidates the bidirectional causality between IGF-1 levels and OA in various body parts, highlighting BMI’s mediating role in the impact of IGF-1 levels on OA. This provides valuable insights for OA prevention, diagnosis, and treatment strategies. Future research will expand our study to include a broader spectrum of ethnicities and explore the underlying mechanisms involved.

Abstract Background The objective of this study was to identify potential biomarkers for predicting response to MSC therapy by pre-MSC treatment plasma proteomic profile in severe COVID-19 in order to optimize treatment choice. Methods A total of 58 patients selected from our previous RCT cohort were enrolled in this study. MSC responders (n = 35) were defined as whose resolution of lung consolidation ≥ 51.99% (the median value for resolution of lung consolidation) from pre-MSC to 28 days post-MSC treatment, while non-responders (n = 23) were defined as whose resolution of lung consolidation

Determining the navigation line is critical for the automatic navigation of agricultural robots in the farmland. In this research, considering a wheat field as the typical scenario, a novel navigation line extraction algorithm based on semantic segmentation is proposed. The data containing horizontal parallax, height, and grayscale information (HHG) is constructed by combining re-encoded depth data and red–green–blue (RGB) data. The HHG, RGB, and depth data are used to achieve scene recognition and navigation line extraction for a wheat field. The method includes two main steps. First, the semantic segmentation of the wheat, ground, and background are performed using a fully convolutional network (FCN). Second, the navigation line is fitted in the camera coordinate system on the basis of the semantic segmentation result and the principle of camera pinhole imaging. Our segmentation model is trained using 508 randomly selected images from a data set, and the model is tested on 199 images. When labelled data are used as the reference benchmark, the mean intersection over union (mIoU) of the HHG data is greater than 95%, which is the highest among the three types of data. The semantic segmentation methods based on the RGB and HHG data show higher navigation line extraction accuracy rates (with the absolute value of the angle deviation less than 5°) than the compared methods. The mean and standard deviation of the angle deviation of the two methods are within 0.1° and 2.0°, while the mean and standard deviation of the distance deviation are less than 30 mm and 60 mm, respectively. These values meet the basic requirements of agricultural machinery field navigation. The novelty of this work is the proposal of a navigation line extraction algorithm based on semantic segmentation in wheat fields. This method is high in accuracy and robustness to interference from crop occlusion.

Frequency response capability is necessary for wind farms (WFs) in the power system with a high proportion of renewable energy. In frequency control, WFs need to meet the grid’s power demand and avoid obvious additional damage to wind turbines (WTs) due to frequency control. In terms of meeting the grid power demand, the actual power characteristics of WTs need to be accurately described. An improved Gaussian Mixture Model (GMM) is used to model the actual measured output of WTs. The wind farm control center can accordingly issue achievable effective power commands to the WTs in the frequency response. In terms of avoiding the structural fatigue increase of WTs, a two-degree-of-freedom control strategy is designed, which simultaneously controls the generator torque and pitch angle and achieves fatigue suppression through multi-objective model predictive control (MOMPC). The simulation results show that the proposed control system can improve the frequency response capability of the WF and reduce the fatigue load of WTs, which has obvious application prospects.

Abstract Objective To assess electrocardiogram (ECG) for risk stratification in inferior ST‐elevation myocardial infarction (STEMI) patients within 24 h. Methods Three hundred thirty‐four patients were divided into four ECG‐based groups: Group A: R V1

ObjectivesThis study aimed to analyze active compounds and signaling pathways of CH applying network pharmacology methods, and to additionally verify the molecular mechanism of CH in treating AP.Materials and methodsNetwork pharmacology and molecular docking were firstly used to identify the active components of CH and its potential targets in the treatment of AP. The pancreaticobiliary duct was retrogradely injected with sodium taurocholate (3.5%) to create an acute pancreatitis (AP) model in rats. Histological examination, enzyme-linked immunosorbent assay, Western blot and TUNEL staining were used to determine the pathway and mechanism of action of CH in AP.ResultsNetwork pharmacological analysis identified 168 active compounds and 276 target proteins. In addition, there were 2060 targets associated with AP, and CH had 177 targets in common with AP. These shared targets, including STAT3, IL6, MYC, CDKN1A, AKT1, MAPK1, MAPK3, MAPK14, HSP90AA1, HIF1A, ESR1, TP53, FOS, and RELA, were recognized as core targets. Furthermore, we filtered out 5252 entries from the Gene Ontology(GO) and 186 signaling pathways from the Kyoto Encyclopedia of Genes and Genomes(KEGG). Enrichment and network analyses of protein-protein interactions predicted that CH significantly affected the PI3K/AKT signaling pathway, which played a critical role in programmed cell death. The core components and key targets showed strong binding activity based on molecular docking results. Subsequently, experimental validation demonstrated that CH inhibited the phosphorylation of PI3K and AKT in pancreatic tissues, promoted the apoptosis of pancreatic acinar cells, and further alleviated inflammation and histopathological damage to the pancreas in AP rats.ConclusionApoptosis of pancreatic acinar cells can be enhanced and the inflammatory response can be reduced through the modulation of the PI3K/AKT signaling pathway, resulting in the amelioration of pancreatic disease.

Background: Traditionally, Melicope pteleifolia has been used for heat clearing and detoxifying, traumatic injuries, bruise, and snake or insect bite. Up to now, its effect on sepsis remains unknown. Methods: Minimum inhibitory concentration (MIC) and dynamic antibacterial curve, and bacteria-induced sepsis mouse models were used to assess the anti-bacterial effect of Melicope pteleifolia ethanol extract (MPE) in vitro and in vivo. Lipopolysaccharide (LPS)-stimulated RAW264.7 cells and cecum ligation and puncture (CLP)-induced sepsis mouse model were used to assess the anti-inflammatory effect of MPE in vitro and in vivo. Results: MPE treatment significantly inhibited the growth of some pathogenic bacteria and increased survival rates of the sepsis mice challenged with the bacteria. In addition, MPE markedly reduced increased supernatant TNF-α and IL-6 levels. Further, it remarkably downregulated protein expressions of TLR4, MyD88, and NF-κB in the LPS-treated cells. Similarly, MPE significantly increased the survival rate, reduced the serum TNF-α and IL-6 levels of the CLP-induced sepsis mice. Importantly, it attenuated the CLP-induced pathological injuries of small intestines. Moreover, the anti-inflammatory effects of MPE in vitro and in vivo were not significantly abolished in presence of TAK-242 or JSH-23. In addition, MPE changed composition of intestinal flora at phylum and genus levels. Further, MPE affect some metabolism pathways such as carbohydrate, lipid metabolism, other amino acids metabolism, drug resistance, and others. Kaempferol (KAF), quercetin (QC), and N-p-coumaroyl-tyramine (NPC) were characteristic components possessing moderate binding to pathogenic associated molecular patterns (PAMPs) kdo2-Lipid A ammonium (KLA) and ODN 1826. Conclusion: In summary, MPE exerts anti-bacterial and anti-inflammatory effects, and its anti-inflammatory activity is independent of the TLR4/MyD88/NF-κB pathway.

ABSTRACT: Intestinal microbiota regulates the host metabolism, including fat metabolism and muscle development in mammals; however, studies on the interactions between the gut microbiome and in chickens with respect to fat metabolism and muscle development are still rare. We established a germ-free (GF) chicken model to determine the transcriptomes and metabolomes of GF and specific-pathogen-free (SPF) chickens. Transcriptome analysis showed 1,282 differentially expressed genes (DEGs) in GF and SPF chickens. The expression levels of some genes related to muscle formation were very high in SPF chickens but low in GF chickens, suggesting that GF chickens had poorer muscle development ability. In contrast, the expression levels of some fat synthesis-related genes were very low in SPF chickens but high in GF chickens, suggesting that GF chickens had a more potent fat-synthesizing ability. Metabolome analysis revealed 62 differentially expressed metabolites (DEMs) in GF and SPF chickens, of which 35 were upregulated and 27 were downregulated. Furthermore, the Pearson correlation coefficient (PCC) was calculated, and an interaction network was constructed to visualize the crosstalk between the genes, metabolites, and gut microbiota in GF and SPF chickens. The top 10 gut microbiota were positively correlated with lipid metabolism including13(S)-HpODE and 9(S)-HpOTrE, and genes related to muscle development, while were negatively correlated with genes related to fat synthesis. In conclusion, this study indicated that chicken intestinal microbiota regulate host metabolism, inhibit fat synthesis, and may promote muscle development.

Abstract A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions.

Abstract Coal spontaneous combustion is determined by a variety of factors. Testing can describe the changes incoal spontaneous combustion with various factors, however, the dependence of spontaneous combustion on various factors is unclear. Therefore, reliability theory was used to deduce the functional relationship of the dependence of coal spontaneous combustion on various factors, and construct a model algorithm for predicting the probability of occurrence of coal spontaneous combustion, which is adopted to evaluate and rank the degree of influence of various factors on coal spontaneous combustion. Effective prevention methods are proposed by strengthening the role of the most important factors. The results show that, by taking the duration of coal heating to 150°C as the measurement standard of coal spontaneous combustion, the duration of the initial stage of coal heating increases linearly with the increase of specific heat capacity, thermal conductivity, and moisture content of coal. With the increase of oxygen concentration, oxidation rate, and initial temperature of the coal, the duration of the initial stage of coal heating decreases exponentially. With the increase of gas flow seepage velocity in the coal body, the duration of the initial heating stage changes in a parabolic manner. At the same time, the probability of spontaneous combustion decreases exponentially with the increase of specific heat capacity and moisture content of the coal. The probability of coal spontaneous combustion increases linearly with the increase of coal thermal conductivity, oxygen concentration, gas seepage velocity, and rate of oxidation. The dependence of coal spontaneous combustion probability on different factors can be expressed as follows: coal temperature > gas seepage velocity > specific heat capacity > oxidation rate > oxygen concentration > moisture content > thermal conductivity.

Objective: To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field. Methods: The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table. Results: A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus. Conclusion: Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.

The maritime environment is complex and changeable, and an accurate maritime wireless channel model is particularly difficult to establish. This study investigates the sensitivity of a three-dimensional (3D) maritime ray tracing (RT) model for the use of the geometrical optics in radio channel characterizations of maritime environments. The channel measurement experiments are mainly carried out at three frequency points of 1.5, 1.8, and 2.5 GHz, and the simulation results of the model are compared and corrected with the measured received power. Analysis shows that the modified model simulation results at 1.5, 1.8, and 2.5 GHz frequency points are in good agreement with the actual measurement, and the RMS is within 7 dB. Moreover, the reverse RT algorithm has the advantages of fast calculation speed and high efficiency. The effects of the different sea conditions, frequencies, and distances on the propagation of electromagnetic waves are analyzed on the basis of this model.

AbstractBackground To investigate the cognition of cancer warning symptoms and cancer risk factors among Chinese college students, analyze the influencing factors, and explain the correlations between cancer cognition and cancer symptom discrimination, cancer fear and psychological distress.Methods Chinese college students were recruited in this cross-sectional study funded by a summer social practice activity in Yunnan Province, China. Cognition rates of cancer warning symptoms and cancer risk factors were evaluated using Cancer Warning symptoms Cognition Questionnaire (CWSCQ) and Cancer Risk Factors Cognition Questionnaire (CRFCQ), respectively. Factors associated with cognition of cancer warning symptoms, and factors associated with cognition of cancer risk factors were evaluated using multiple linear regression analysis. Interactions between cancer cognition, cancer symptom discrimination, psychological distress, and cancer fear were evaluated by structural equation modeling.Results There were 846 effective samples, with an effective rate of 80.9%. The cognition rates of cancer warning symptoms were from 47.9% to 84.4%, which were affected by cancer symptom discrimination, education, attitudes towards cancer screening, living expenses, drinking history, and ways to obtain cancer knowledge (p

Nitrogen (N) is an essential element required for sugar beet growth. Sugar beets with low N (LN) tolerance and high N use efficiency are excellent materials for breeding. Here, we comprehensively evaluated the morphological and physiological responses of nine sugar beet genotypes to LN supply. It was found that 0.5 mmol·L−1 N (LN) significantly influenced the performance of leaves and the topology of roots by reducing the bioproduction of chlorophyll a (Chl a) and soluble protein (SP) and the accumulation of N in leaves and roots (LNA and RNA), thus differentially restricting the growth (hypocotyl diameter, HD; root length, RL) and biomass (leaf and root fresh weight; LFW and RFW; leaf dry weight, LDW) of these sugar beets. Principal component and cluster analyses showed that 780016B/12 superior (F) exhibited excellent tolerance to LN; it had higher SOD activity (62.70%) and APX activity (188.92%) and a higher proline content (131.82%) than 92011 (G, LN sensitive). These attributes helped 780016B/12 superior (F) to better endure LN stress, and the morphology and N distribution changed to adapt to N deficiency, such that the root length increased by 112.48%, leaf area increased by 101.23%, and leaf nitrogen accumulation reached a peak of 14.13 g/plant. It seems that LN-tolerant genotypes increased their root length and surface area by reducing the difference in biomass, thereby expanding the contact between roots and soil, which was conducive to the absorption of nutrients (N) by sugar beets and helped distribute more assimilation products to the roots.

Human cartilage tissue can be categorized into three types: hyaline cartilage, elastic cartilage and fibrocartilage. Each type of cartilage tissue possesses unique properties and functions, which presents a significant challenge for the regeneration and repair of damaged tissue. Bionics is a discipline in which humans study and imitate nature. A bionic strategy based on comprehensive knowledge of the anatomy and histology of human cartilage is expected to contribute to fundamental study of core elements of tissue repair. Moreover, as a novel tissue-engineered technology, 3D bioprinting has the distinctive advantage of the rapid and precise construction of targeted models. Thus, by selecting suitable materials, cells and cytokines, and by leveraging advanced printing technology and bionic concepts, it becomes possible to simultaneously realize multiple beneficial properties and achieve improved tissue repair. This article provides an overview of key elements involved in the combination of 3D bioprinting and bionic strategies, with a particular focus on recent advances in mimicking different types of cartilage tissue.