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Daidai Wang,1,&ast; Hua Zhang,2,&ast; Lanfang Du,1,&ast; Qiangrong Zhai,1 Guangliang Hu,1 Wei Gao,1 Anyi Zhang,1 Sa Wang,1 Yajuan Hao,1 Kaijian Shang,1,3 Xueqing Liu,1 Yanxia Gao,4 Nijiati Muyesai,5 Qingbian Ma1 1Department of Emergency Medicine, Peking University Third Hospital, Beijing, People’s Republic of China; 2Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, People’s Republic of China; 3Department of Emergency Medicine, Second hospital of Shanxi Medical University, Shanxi, People’s Republic of China; 4Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 5Department of Emergency Medicine, Xinjiang Ulger Municipal People’s Hospital, Urumqi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qingbian Ma; Nijiati Muyesai, Tel +86 15611908229, Email maqingbian@126.com; muyassar11@aliyun.comPurpose: Acute aortic syndrome is a constellation of life-threatening medical conditions for which rapid assessment and targeted intervention are important for the prognosis of patients who are at high risk of in-hospital death. The current study aims to develop and externally validate an early prediction mortality model that can be used to identify high-risk patients with acute aortic syndrome in the emergency department.Patients and Methods: This retrospective multi-center observational study enrolled 1088 patients with acute aortic syndrome admitted to the emergency departments of two hospitals in China between January 2017 and March 2021 for model development. A total of 210 patients with acute aortic syndrome admitted to the emergency departments of Peking University Third Hospital between January 2007 and December 2021 was enrolled for model validation. Demographics and clinical factors were collected at the time of emergency department admission. The predictive variables were determined by referring to the results of previous studies and the baseline analysis of this study. The study’s endpoint was in-hospital death. To assess internal validity, we used a fivefold cross-validation method. Model performance was validated internally and externally by evaluating model discrimination using the area under the receiver-operating characteristic curve (AUC). A nomogram was developed based on the binary regression results.Results: In the development cohort, 1088 patients with acute aortic syndromes were included, and 88 (8.1%) patients died during hospitalization. In the validation cohort, 210 patients were included, and 20 (9.5%) patients died during hospitalization. The final model included the following variables: digestive system symptoms (OR=2.25; P=0.024), any pulse deficit (OR=7.78; P< 0.001), creatinine (μmol/L)(OR=1.00; P=0.018), lesion extension to iliac vessels (OR=4.49; P< 0.001), pericardial effusion (OR=2.67; P=0.008), and Stanford type A (OR=10.46; P< 0.001). The model’s AUC was 0.838 (95% CI 0.784– 0.892) in the development cohort and 0.821 (95% CI 0.750– 0.891) in the validation cohort, and the Hosmer–Lemeshow test showed p=0.597. The fivefold cross-validation demonstrated a mean accuracy of 0.94, a mean precision of 0.67, and a mean recall of 0.13.Conclusion: This risk prediction tool uses simple variables to provide robust prediction of the risk of in-hospital death from acute aortic syndrome and validated well in an independent cohort. The tool can help emergency clinicians quickly identify high-risk acute aortic syndrome patients, although further studies are needed for verifying the prospective data and the results of our study.Keywords: acute aortic syndrome, risk prediction model, emergency department

Chen Shao, Kun Shang, Huaibao Xu, Yu Zhang, Zhichao Pei, Yuxin Pei Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling, Shaanxi 712100, China Background: Photodynamic therapy is a safe, noninvasive modality for cancer therapy, in which the photosensitizer (PS) is a crucial component. Hypericin (Hy) is a promising PS; however, its clinical application is significantly limited by its poor hydrophilicity. Materials and methods: To overcome the clinical application limitation of Hy, a novel strategy is developed here by entrapping Hy into polydopamine (PDA) film formed on the surface of magnetic iron oxide nanoparticles (MNPs) through the self-polymerization of dopamine under alkaline condition. The amount of Hy in the Hy-entrapped PDA–MNP composite nanoparticles (denoted as PHMs) was measured by spectrophotometry. Furthermore, lactose, as the targeting ligand to asialoglycoprotein receptors, was conjugated to the surface of the PHMs by taking advantage of the spontaneous reaction of PDA with amino groups. Results: Spectrophotometry analysis revealed that the amount of Hy in the PHMs was 72 µmol g-1 PHMs. The fabricated Hy-entrapped glyconanoparticle (Lac-PHM) exhibited excellent water dispersibility, stability, and selectivity for asialoglycoprotein receptors overexpressing HepG2 cells. Atomic absorption spectroscopy analysis showed that the amount of the Lac-PHMs taken in HepG2 cells was 2.1-fold higher than that of the triethylene glycol-modified PHMs. The results of intracellular reactive oxygen species generation detection, cytotoxicity study, and apoptosis detection indicated that the Lac-PHMs had a satisfying photodynamic effect to HepG2 cells. Conclusion: The strategy developed in this work offers great potential for delivery of a variety of hydrophobic PSs. Keywords: hypericin, photodynamic therapy, glyconanoparticles, targeted drug delivery

In urban areas of China, several plant species are commonly used for ornamental purposes. Many of these plants have demonstrated a good capacity to resist these varied urban stresses, and it would be relevant to verify whether they can be grown on polluted sites and demonstrate some phytoremediation potential. Twelve ornamental shrub species were chosen to be tested for tolerance to inorganic contaminants and capacity to absorb and concentrate heavy metals in their aerial parts. A large split-plot trial comprising 20 plots was set up, and soil was spiked with different metals (Cu, Pb and Zn). In general, all twelve shrub species performed well regardless of the treatment. Two Hibiscus species, H. mutabilis and H. syriacus “Hamabo” were particularly productive. In terms of capacity to uptake metals, two of the best performing species were Spiraea japonica, for copper, and Nandina domestica, for lead. Bioconcentration and transfer factors were low. This could be related to weak development of the root systems in these recently established plants. Species with high yield, such as the two Hibiscus species, presented more interesting values in terms of quantity of metal extracted, and could eventually be recommended for decontamination of soils polluted by inorganics.

Ying Zhang,1,* Xiaowen Wu,1,* Chenxi Hou,1 Kun Shang,1 Kui Yang,1 Zhimin Tian,2 Zhichao Pei,1 Yongquan Qu,2 Yuxin Pei1 1Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry and Pharmacy, Northwest A&F University, Yangling, Shaanxi, People’s Republic of China; 2Center for Applied Chemical Research, Frontier Institute of Science and Technology, Xi’an Jiaotong University, Xi’an, People’s Republic of China *These authors contributed equally to this work Objective: The aim was to produce the first report of assembling degradable stimuli-responsive dithio-polydopamine coating with a cancer target unit for synergistic and targeted drug delivery. Methods: A multifunctional drug delivery system was constructed by coating a dual-responsive dithio-polydopamine (PDS) on porous CeO2 nanorods and subsequent conjugation of lactose derivative, where the PDS was formed by self-polymerization of dithio-dopamine (DOPASS). Results: The multifunctional drug delivery system displayed excellent cancer targeted ability resulting from the conjugation of lactose derivative, which could specifically recognize the overexpressed asialoglycoprotein receptors on the surface of HepG2 cells. It also showed a dual-responsive property of glutathione and pH, achieving controllable drug release from the cleavage of disulfide bond and subsequent degradation of PDS in cancer cells. Moreover, the degradation of PDS led to the exposure of CeO2 nanorods, which has a synergistic anticancer effect due to its cytotoxicity to cancer cells. Conclusion: This work presents a good example of a rational design towards synergistic and targeted DDS for cancer chemotherapies. Keywords: degradable polydopamine, cerium oxide nanoparticles, dual-responsiveness, targeted drug delivery, synergistic anticancer

Many cancers exhibit resistance to chemotherapy, resulting in a poor prognosis. The transcription factor NRF2, activated in response to cellular antioxidants, plays a crucial role in cell survival, proliferation, and resistance to chemotherapy. This factor may serve as a promising target for therapeutic interventions in esophageal carcinoma. Recent research suggests that NRF2 activity is modulated by ubiquitination mediated by the KEAP1-CUL3 E3 ligase complex, highlighting the importance of deubiquitination. However, the specific deubiquitinase responsible for regulating NRF2 in esophageal cancer remains unknown. In this study, a novel regulator of the NRF2 protein, Ubiquitin-Specific Protease 35 (USP35), has been identified. Mechanistically, USP35 modulates NRF2 stability through enzymatic deubiquitination. USP35 interacts with NRF2 and facilitates its deubiquitination. Knockdown of USP35 leads to a notable increase in NRF2 levels and enhances the sensitivity of cells to chemotherapy. These findings suggest that the USP35-NRF2 axis is a key player in the regulation of therapeutic strategies for esophageal cancer.

Purpose: To address the issue that laboratory methods cannot meet the need foron-site rapid testingat the grassroot level, a quick method for detecting the authenticity of meat products was established based on multienzyme isothermal rapid amplification (MIRA) coupled with lateral flow dipstick (LFD). Methods: Pig, cattle, sheep, chicken, and duck-specific mitochondrial gene sequences were selected through GenBank, and intra-species conserved and inter-species specific target fragments were compared using the DNAStar Megalign software. According to the design principles of MIRA primers and probes, target gene-specific primer and probe sets were designed and screened systematically through positive, negative and blank controls to determine the species-specific primers and probes. The reaction system, temperature, and time were optimized. Analytical figures of merit of the method including DNA extraction, sensitivity, detection limit, false positive rate and false negative rate were evaluated to verify its validity. Results: Compared with real-time fluorescent polymerase chain reaction (PCR), the average false positive rate of this method was 3.49%, and the false negative rate was 3.54%, which meets the requirements of on-site rapid testing. Conclusion: In this study, the MIRA-LFD technique was established for 5 common meats, namely, cattle, sheep, pig, chicken and duck, and its validity and feasibility were confirmed, realizing on-site rapid testing of the authenticity of meat products.

Objective: This study aimed to investigate the effects of different processing temperature on texture profile and flavor of dried edible bird's nest. Methods: With dried edible bird's nest as the main raw material, the texture profile analysis of edible bird's nest at different processing temperature was carried out by physical property analyzer. The volatile components of edible bird's nest at different processing temperature were determined by electronic nose and solid phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). Results: It was found that the adhesion of cooked edible bird's nest decreased significantly (P＜0.01), but its resilience increased significantly (P＜0.05) with the increase of temperature. 12 kinds of effective flavor substances were detected in the cooked edible bird's nest by SPME-GC-MS, including: 3 alcohols, 2 aldehydes, 2 esters, 3 ketones, 1 acid, and 1 ether, accounting for 6.23%, 49.34%, and 54.85% of the total detected substances in the cooked edible bird's nest at 95 ℃, 105 ℃ and 115 ℃, respectively. Conclusion: Different processing temperature affects the taste of edible bird's nest and the overall odor profile after processing. Moreover, higher cooking temperature tends to make the egg white-like flavor stronger. The volatile substances in the bird's nest stew at 95 ℃ are mainly acids, the bird's nest cooked at 105 ℃ mainly produces alcohols, and the edible bird's nest cooked at 115 ℃ mainly produces ketones and alcohols.

Nowadays, a wide variety of labels of items are widely available, and human consumption is increasingly tailored to meet their individual needs. So, many businesses are starting to focus on improving the functionality of modern packaging. Sensorial paradigms and emotional reactions could change during the user-product interaction lifecycle. The designer's emotional imagination and past experiences are the backbone of conventional product package design, which has limitations due to unmanageable content and an absence of professional advice—the majority of previous research on emotional image analysis aimed to forecast the most common viewer emotions. Since the feelings a picture evokes are quite individual and vary from viewer to viewer, this overarching feeling isn't always enough for practical uses. The research presented an approach to packaging design evaluation based on image emotion perception computing (PDE-IEPC), which combines emotion perception technology with a deep LSTM (Long short-term model), resulting in an immersive and dynamic experience for the human senses. Emotion Perception Computing's Dynamic Multi-task Hypergraph Learning (DMHL) approach considers graphical data, social context, spatial evolution, and location, among other criteria, to evaluate packaging designs efficiently based on their emotional impact. Image-Emotion-Social-Net is a large dataset used to evaluate multidimensional and categorical attitude representation. The dataset is sourced from Flickr and contains over 1 million images presented by over 9000 users. Personalized emotion categorization is an area where research on this dataset shows that the suggested strategy outperforms many modern techniques. The experimental results show that the proposed method achieves a high packaging design quality rate of 94.1 %, a performance success rate of 97.5 %, and a mean square error rate of 2 % compared to other existing methods.

Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.

In this paper, we first use CNNS to collect rich samples of folk dance music, establish a model framework and a functional system framework, and conduct a comprehensive process analysis of the music. Then, the instrumental features, frequency features, and timbre features are extracted to obtain the spectral information. In the stage of chord analysis and encoding, a multivariate chord encoding model is established based on the acquired spectral information, including two parts: chord representation preprocessing and chord encoding. By utilizing this model, the chord structure of music was successfully and accurately encoded, allowing for analysis with up to 98% accuracy. Furthermore, significant recall results were achieved, reaching over 0.9, which suggests that the extracted chord features are highly reliable and accurate in recognizing musical chord information.

The interest rate risk of commercial banks is closely related to the effectiveness of the LPR mechanism. In this paper, the interest rate market is divided into three stages according to the process of interest rate marketization, and a six-sector New Keynesian MS-DSGE model is constructed by adding monetary regulation variables. The parameters in the model that are influenced by interest rate marketization and the new LPR mechanism are transformed by Markov zone transformation. The interest rate risk of commercial banks is measured by the impulse response functions of uncertain interest rate shocks to the interest rate sensitivity gap and the duration gap, and the volatility characteristics of interest rate risk in different stages of interest rate marketization are compared and analyzed. The results show that in the third stage (2019-present), the LPR mechanism is transformed from old to new. Under the δR = 0.5 shock, the commercial lending interest rate tends to stabilize around 0.00038, and the borrowing interest rate tends to 0 in all 3 differentiated target parameters in the 10th period. For this reason, the differentiated interest rate pricing behaviors of commercial banks should be considered comprehensively, and the LPR target should be set reasonably so as to get the optimal policy effect.

BackgroundExposure to high-intensity X-ray irradiation induces numerous defects in the thin films of optical components applied to X-ray free-electron laser (XFEL) facilities. These defects result in structural damage to the material and degradation of macroscopic properties, thereby affecting its service life and significantly compromising the reliability and stability of XFEL facilities.PurposeThis paper aims to design and implement an automated irradiation simulation software based on Python called automatic irradiation simulation based on LAMMPS (AISL) to support the simulation research of XFEL radiation damage to materials using molecular dynamics methods.MethodsAISL was designed to facilitate high-throughput automated studies on XFEL radiation damage by exploring the process of radiation-induced defects and promoting the accumulation of data for radiation-resistant materials. This software package established an automated workflow pipeline for simulation tasks, encompassing aspects such as batch submission management, scheduling of simulation tasks, reliable storage of computational data, and post-processing of thermodynamic information. Additionally, the metadata was automatically recorded in a radiation damage simulation database based on MongoDB, so were the workflow information, and calculation result files of the simulation. Finally, the utilization of AISL in simulating radiation damage in metal thin films of XFEL optical components was demonstrated to verify the validation of AISL.Results & ConclusionsThe demonstration results indicate that AISL is an effective, user-friendly software for conducting high-throughput automated irradiation simulation studies. It significantly enhances the efficiency of material irradiation damage simulation calculations using LAMMPS.

In order to study the nonlinear remote monitoring system of the manipulator based on the network communication technology, a research method of the nonlinear remote monitoring system of the manipulator based on the network communication technology is proposed. In this article, in the Visual C++ environment, the transmission control protocol/internet protocol technology is used to build a remote monitoring system for the robotic arm, and it mainly realizes the function of the robotic arm running according to the trajectory in the remote. When storing video, you need to specify the ID of the video source, i.e., the synchronization source in the real-time transport protocol data packet header identifies the robot, which can monitor more clearly. The remote monitoring technology is widely used in the modern automation industry, which not only further extends the human space activity ability, but also removes the human from the dangerous and complex working environment. Therefore, the remote monitoring technology has broad application prospects.

Mt. Tianmu is one of the most important refugia of Tertiary relict plant taxa in Eastern China. We analyzed the habitat characteristics, community structure, demographic structure, and production methods of Tertiary relict deciduous forests at 1,100 m on Mt Tianmu (1,506 m), Zhejiang province, China. Emmenopterys henryi mostly occurred in unstable habitats with gap-formation or landslides, and these populations showed a sporadic regeneration pattern. E. henryi colonized and established at unstable sites by abundant wind-dispersed seeds. After colonization, E. henryi persisted for a long time and dominated in the canopy layer and even reached emergent layer due to its long lifespan and vegetative reproduction capability. It could thus be regarded as an undifferentiated climax pioneering species with an 'r-selected' life history that produced abundant minute seeds and experienced intermittent recruitment and often became pioneer species of secondary succession after moderate disturbance. Here, we discussed conservation strategies for Tertiary relict deciduous trees as climax pioneering species that accounted for the peculiarity of their habitat and population structure in broad-leaved forests. [ABSTRACT FROM AUTHOR]

Information dissemination and the associated change of individual behavior can significantly slow the spread of an epidemic. However, major social events which attract public attention will disturb information spread and affect epidemic transmission in ways that have not been readily quantified. We investigate the interplay between disease spreading and disease-related information dissemination in a two-layer network. We employ the SIR-UAU model with a time dependent coefficient to denote information dissemination. We found that major social events are equivalent to perturbations of information dissemination in certain time intervals and will consequently weaken the effect of information dissemination, and increase prevalence of infection. Our simulation results agree well with the trends observed from real-world data sets. We found that two specific major events explain the trend of the coronavirus epidemic in the US: the online propaganda and international agenda setting of Donald Trump early in 2020 and the 2020 US Presidential Election.

BackgroundYoung adults, particularly university students might be at greater risk of developing psychological distress, and exhibiting symptoms of anxiety and depression during the COVID-19 pandemic. The primary objective of this study was to explore and compare the determinants and predictors of mental health (anxiety and depression) during and after the COVID-19 lockdown among university students.MethodsThis was an observational, cross-sectional study with a sample size of 417 students. An online survey utilizing International Physical Activity Questionnaire-Short Form (IPAQ-SF), General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) was distributed to Universiti Tunku Abdul Rahman students via Google forms.ResultsDuring lockdown, family income [χ2 (1, n = 124) = 5.155, p = 0.023], and physical activity (PA) [χ2 (1, n = 134) = 6.366, p = 0.012] were associated with anxiety, while depression was associated with gender [χ2 (1, n = 75) = 4.655, p = 0.031]. After lockdown, family income was associated with both anxiety [χ2 (1, n = 111) = 8.089, p = 0.004], and depression [χ2 (1, n = 115) = 9.305, p = 0.002]. During lockdown, family income (OR = 1.60, p = 0.018), and PA (OR = 0.59, p = 0.011) were predictors for anxiety, while gender (OR = 0.65, p = 0.046) was a predictor for depression. After lockdown, family income was a predictor for both anxiety (OR = 1.67, p = 0.011), and depression (OR = 1.70, p = 0.009).ConclusionSignificant negative effects attributed to the COVID-19 lockdown, and certain factors predisposed to the worsening of mental health status in university students. Low family income, PA, and female gender were the major determinants and predictors linked to anxiety and depression.

High temperature can cause the thermal fracture of rock, which affects the permeability of rock. In order to investigate the effect of temperature on the microstructure and permeability of rock, the ultrasonic velocity, density and gas permeability of granite after thermal treatment at 50−800 ℃ were measured. With the aid of CT scanning technique, the microstructure of granite was extracted and reconstructed. The influence of variation of microstructure after thermal treatment on permeability was also discussed in detail. According to this, the applicability of the K-C model and its improved model at high temperatures was discussed and verified. Finally, combined with the pore fractal model, a temperature-permeability model of granite after thermal treatment was proposed. The results show that: 1) The change of the internal microstructure of granite can be observed obviously as temperature increases. Uneven thermal expansibility of minerals before 400 ℃ can contribute to generation of many small pores between rock particles, which constitutes the pore structure. As temperature increases, these pores expand rapidly and connect with each other, forming the pore-fracture network. 2) When the temperature of heat treatment is not higher than 600 ℃, the probability distribution curve of the shape factor of the internal granite microstructure is basically consistent. When the temperature is higher than 600 ℃, the peak value of the shape factor shifts to the left obviously, and the average shape factor decreases greatly. 3) In the stage of pores, the permeability of granite doesn’t change too much, which is around the value of 10−18 m2. In the stage of the pore-fracture network, the permeability of granite increases exponentially with the connection of fracture. The permeability of granite under 800 ℃ is 8×104 times of the normal atmospheric temperature condition. 4) According to the fitting results of porosity and permeability at 50−800 ℃ using four porosity-permeability models, Bayles model and Costa model are more reasonable to describe granite after thermal treatment, and the fitting results are higher than K-C model and S-R model. 5) When the temperature is lower than 600 ℃, there is basically no change in the shape of the internal microstructure of the granite, and the shape coefficient of K-C can be considered as a constant. This indicates that Bayles model and Costa model are applicable for granite when the temperature is lower than 600 ℃. 6) On the basis of Costa model, the temperature-permeability model of granite after thermal treatment is obtained by combining the pore fractal characteristic of high-temperature heated granite. The experimental data within the temperature range of 50−600 ℃ are used to verify the new model and the coefficient of determination of fitting result reaches 0.99.