Search

With the continued exploration of the universe, there is an increasingly urgent need to address the health challenges arising from spaceflight. In space, astronauts are exposed to radiation, confinement and isolation, circadian rhythm dysregulation, and microgravity conditions that are different from those on Earth. These risk factors jeopardize astronauts’ health, thus affecting the quality of space missions. Among these factors, gravitational changes influence the balance between oxidation and antioxidants, stimulating the production of reactive oxygen species (ROS), finally leading to oxidative stress (OS). OS leads to oxidative damage of biomolecules such as lipids, proteins, and DNA, which causes the development of various diseases. The occurrence of OS is increased in microgravity and affects multiple systems, including the musculoskeletal, cardiovascular, nervous, and immune systems. In this review, we discuss the mechanisms of OS, the physiological effects on different systems caused by OS in microgravity environment, and potential treatments for OS. Finally, treatment strategies for oxidative stress in microgravity are summarized, providing some promising approaches for protecting the health of astronauts in future space exploration.

The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing downstream tasks in intelligent shearer operations and maintenance. Currently, named entity recognition in the field of shearer maintenance primarily relies on fine-tuning-based methods; however, a gap exists between pretraining and downstream tasks. In this paper, we introduce prompt learning and large language models (LLMs), proposing a named entity recognition method for shearer maintenance based on multi-level clue-guided prompt learning (MCP). This method consists of three key components: (1) the prompt learning layer, which encapsulates the information to be identified and forms multi-level sub-clues into structured prompts based on a predefined format; (2) the LLM layer, which employs a decoder-only architecture-based large language model to deeply process the connection between the structured prompts and the information to be identified through multiple stacked decoder layers; and (3) the answer layer, which maps the output of the LLM layer to a structured label space via a parser to obtain the recognition results of structured named entities in the shearer maintenance domain. By designing multi-level sub-clues, MCP enables the model to extract and learn trigger words related to entity recognition from the prompts, acquiring context-aware prompt tokens. This allows the model to make accurate predictions, bridging the gap between fine-tuning and pretraining while eliminating the reliance on labeled data for fine-tuning. Validation was conducted on a self-constructed knowledge corpus in the shearer maintenance domain. Experimental results demonstrate that the proposed method outperforms mainstream baseline models in the field of shearer maintenance.

Background/Objectives: Irritable bowel syndrome (IBS) is a complex disorder affecting 10% of the global population, but the underlying mechanisms remain poorly understood. By integrating multifluid metabolomics, we aimed to identify metabolite markers of IBS in a large population-based cohort. Methods: We included individuals from TwinsUK with and without IBS, ascertained using the Rome III criteria, and analysed serum (232 cases, 1707 controls), urine (185 cases, 1341 controls), and stool (186 cases, 1284 controls) metabolites (Metabolon Inc.). Results: After adjusting for covariates, and multiple testing, 44 unique metabolites (25 novel) were associated with IBS, including lipids, amino acids, and xenobiotics. Androsterone sulphate, a sulfated steroid hormone precursor, was associated with lower odds of IBS in both urine (0.69 [95% confidence interval = 0.56–0.85], p = 2.34 × 10−4) and serum (0.75 [0.63–0.90], p = 1.54 × 10−3. Moreover, suberate (C8-DC) was associated with higher odds of IBS in serum (1.36 [1.15–1.61]; p = 1.84 × 10−4) and lower odds of IBS in stool (0.76 [0.63–0.91]; p = 2.30 × 10−3). On the contrary, 32 metabolites appeared to be fluid-specific, including indole, 13-HODE + 9-HODE, pterin, bilirubin (E,Z or Z,Z), and urolithin. The remaining 10 metabolites were associated with IBS in one fluid with suggestive evidence (p < 0.05) in another fluid. Finally, we identified androgenic signalling, dicarboxylates, haemoglobin, and porphyrin metabolism to be significantly over-represented in individuals with IBS compared to controls. Conclusions: Our results highlight the utility of a multi-fluid approach in IBS research, revealing distinct metabolic signatures across biofluids.

Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from 90.00% to 98.33%, with the NN model demonstrating the best performance. Specifically, it achieved 96.67% accuracy for single-maturity classification, 98.33% for single-variety classification, and 96.67% for dual maturity and variety classification, along with 98.09% precision, 97.92% recall, and 97.91% F1 score. Feature importance analysis indicated that the NN model exhibited the most balanced reliance on various VOCs, contributing to its optimal performance with the broad-spectrum VOC detection method, GC-IMS. Overall, these findings underscore the potential of NN modeling for accurate and efficient fruit classification based on integrated VOC profiles.

Previous research has demonstrated that episodic future thinking (EFT) can enhance prospective memory (PM); however, its effects on older adults have been less explored. This study examines the impact of EFT training on PM in both older and younger adults under varying delay intervals. Experiment 1 employed a 2 (EFT training: present vs. absent) × 2 (age: younger adults vs. older adults) × 2 (delay interval: 5 min vs. 20 min) between-subjects design. The results revealed a significant main effect of EFT training (p < 0.001), indicating that such training improves PM performance. Among younger adults, a significant difference in PM performance was found between the trained and untrained groups (p = 0.03), while among older adults, this difference was only marginally significant. This suggests that the facilitative effect of EFT training is more pronounced in younger adults. Additionally, there was a significant main effect of delay interval (p = 0.01), with shorter intervals yielding better PM performance than longer intervals. Experiment 2 focused on the impact of specificity in EFT training on PM in both age groups. A 2 (training: specific vs. non-specific) × 2 (age: younger vs. older adults) × 2 (delay interval: 5 min vs. 20 min) between-subjects design was used. Results indicated that older adults in the specific training group outperformed those in the non-specific training group (p = 0.03), whereas no difference was observed among younger adults. This finding suggests that specific training is more effective for enhancing prospective memory in older adults. Moreover, older adults exhibited differences based on the delay interval, with a 20 min interval impairing performance (p = 0.04), while younger adults showed no difference between the two intervals. These findings will be discussed in relation to the Multiprocess Model and the Preparatory Attention and Memory Processes Theory.

As early career researchers, postdocs play an irreplaceable and crucial role in scientific research, especially in highly competitive fields. Given the importance of the postdoc community, it is essential to explore their engagement in the academic labor market. Employment in academic departments should be the ideal career choice for postdocs, but this is not always the case. In recent years, an increasing number of postdocs have chosen to leave the ivory tower, which is often the result of a dynamic integration of the work environment and individual cognition. This study is based on the public data from the “Nature 2023 Postdoc Survey” and empirically analyzes the predictive relationship between different dimensions of environmental perception and postdoc academic career choices, as well as whether there are differences in predictive relationships across various fields. Difference analysis suggested that males and those working in their native country tend to have greater satisfaction in their environment perception. Correlation analysis revealed that postdocs’ environmental perception is significantly and positively correlated with academic career choices. Regression results indicated that institutional environment, organizational environment, living environment, and support environment can all predict postdoc academic career choices, with significant disciplinary differences in these predictive effects. It is recommended that mental health, salary and benefits, job security, and professional training be focused on to improve the working environment for early career researchers. Additionally, it is necessary to increase inclusive support for vulnerable postdoc groups and enhance their expectations for academic careers.

Recycling is a crucial waste management option because of the increasing amount of waste generated and the limited space in landfills. However, traditional recycling processes, which require individuals to deliver large quantities of waste to recycling centers, can discourage participation. To address this issue, this study expanded upon the technology acceptance model (TAM) by incorporating perceived risk and social influence to examine residents’ intentions to adopt recycling vending machines. This study used partial least squares structural equation modeling based on the data collected from 525 individuals in Jiangsu Province, China. This study’s findings indicate that TAM components, such as attitudes, perceived usefulness, and perceived ease of use, positively influence residents’ intentions and behaviors to adopt recycling vending machines. Additionally, perceived usefulness and ease of use significantly affected attitudes toward recycling vending machines. This study also found that social influence had a significant positive impact on perceived usefulness and ease of use, while perceived risk negatively influenced these factors. Furthermore, attitude played a crucial mediating role, with additional factors impacting intentions and behaviors through attitude. Overall, this research can help stakeholders such as waste management companies to understand residents’ concerns and improve the implementation of recycling vending machines.

Mango (Mangifera indica L.) is one of the most significant tropical and subtropical fruit species, with high ecological and economic value. However, research on the in vitro culture of mangoes is relatively weak, so establishing an efficient and stable mango plant regeneration system is of great significance. In this study, a preliminary mango regeneration system was established with Mangifera indica L. cv. Keitt from young branches as the starting explants. The results showed that the optimal plant growth regulator (PGR) formula for direct adventitious shoot induction on the branches was 1 mg/L 6-benzylaminopurine (6-BA) + 0.1 mg/L a-naphthaleneacetic acid (NAA), with an adventitious shoot induction rate of 73.63% and an average of 6.76 adventitious shoots. The optimal basal medium for adventitious shoot induction was wood plant medium (WPM), with an adventitious shoot induction rate of 63.87% and an average of 5.21 adventitious shoots. The optimal culture medium for adventitious shoot elongation was WPM + 1 mg/L 6-BA + 0.5 mg/L NAA, with an adventitious shoot elongation rate of 89.33% and an average length of 5.17 cm. The optimal formula for the induction of mango rooting was Douglas fir cotyledon revised medium (DCR) + 3 mg/L indole-3-butyric acid (IBA), with a maximum rooting rate of 66.13% and an average rooting quantity of 6.43. The genetic fidelity of the in vitro-regenerated plants was evaluated using inter-simple sequence repeat (ISSR) molecular markers. There was no difference between the in vitro-regenerated plants and the parent plant. This study provides an efficient and stable propagation system for Mangifera indica L., laying the foundation for its rapid propagation and genetic improvement.

Chinese sour bamboo shoot is a traditionally, spontaneous fermented food that is particularly popular due to its complex and distinctive flavor. The volatile compounds of sour bamboo shoot originate mainly from the raw materials and the microbial fermentation. To reveal the correlation between microorganisms and flavor, third-generation sequencing and Gas Chromatography-Ion Mobility Spectrometry were applied to analyze the dynamics of microbial communities at the species level and volatile compounds during sour bamboo shoot fermentation. The abundance of Lactobacillus acetotolerans and Lactobacillus fermentum increased during the fermentation, while Lactobacillus amyloliquefaciens decreased at first but then began to rise. At the end of fermentation, Lactobacillus amyloliquefaciens and Lactobacillus acetotolerans became the predominant species. A total of sixty-seven volatile compounds, which included twenty-three esters, nineteen alcohols, eight ketones, six aldehydes, six aromatic hydrocarbons, four acids and one ether, were identified. These compounds constituted the primary flavor of sour bamboo, which created a complex flavor of sour bamboo shoot. Among them, the contents of acetic acid, propionic acid, and isoamyl alcohol gradually increased during the fermentation process, and they became the main volatile compounds. Furthermore, the correlation between microorganisms and volatile compounds was investigated through two-way Orthogonal Partial Least Squares (O2PLS), which revealed a positive correlation between Lactobacillus amylolyticus and ethyl propanoate. Additionally, the abundance of Lactobacillus acetotolerans and Lactobacillus fermentum was found to be positively correlated with 2-heptenal. These findings provide a theoretical basis for understanding the formation mechanism of sour bamboo shoot flavor and the standardized production of high-quality sour bamboo shoots.

Palmitoylethanolamide (PEA) exhibits multiple skincare functions such as anti-nociceptive and anti-inflammatory effects. However, its topical application is limited due to its difficulty in bypassing the stratum corneum barrier, relatively low bioavailability, and low stability. Herein, elastic nano-liposomes (ENLs) with excellent deformability and elasticity were utilized as a novel drug delivery system to encapsulate PEA to overcome the abovementioned issues and enhance the biological effects on the skin. ENL was prepared with phosphatidylcholine, cholesterol, and cetyl-PG hydroxyethyl palmitamide with a molar ratio mimicking skin epidermal lipids, and PEA was loaded. The PEA-loaded ENL (PEA-ENL) demonstrated efficient transdermal delivery and enhanced skin retention, with negligible cytotoxicity toward HaCaT cells and no allergic reaction in the human skin patch test. Notably, PEA-ENL treatment increased cell migration and induced significant regulation in the expression of genes associated with anti-nociceptive, anti-inflammatory, and skin barrier repair. The mechanism of the anti-nociceptive and anti-inflammatory effects of PEA was further investigated and explained by molecular docking site analysis. This novel PEA-ENL, with efficient transdermal delivery efficiency and multiple skincare functionalities, is promising for topical application.

Extra energy consumption, inefficient nitrogen removal, and excessive sludge production are major challenges faced by wastewater treatment plants (WWTPs) that rely on the traditional activated sludge process. Fermentation of wasted activated sludge (WAS) and novel nitrogen removal technologies based on partial nitrification (PN) have emerged as promising solutions to these issues. Recent studies have revealed an innovative strategy that integrates these two processes by supplementing fermentation liquid into activated sludge to induce PN. This review summarizes the research progress on PN establishment induced by the fermentation process. The microbiology and establishment methods of PN are briefly introduced, followed by a detailed discussion on the process, influencing factors, and product characteristics of WAS fermentation. The core section focuses on the side-stream and main-stream approaches of fermentation-induced PN, comparing their performance and application prospects. The potential mechanisms are explored, with an emphasis on the roles of free ammonia for the side-stream approach and the high tolerance of ammonium oxidizers to in-site fermentation stress for the main-stream approach. Finally, the limitations of the current research and future perspectives are discussed, highlighting the need for further investigation into microbial ecology, process optimization, and long-term stability. This review aims to provide insights into the synergistic integration of WAS fermentation and PN for sustainable and energy-efficient wastewater treatment.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that leads to deformities and disabilities in patients. Conventional treatment focuses on delaying progression; therefore, new treatments are necessary. The present study reported a novel ionic liquid transdermal platform for efficient RA treatment, and the underlying mechanism was elucidated using FTIR, 1H-NMR, Raman, XPS, and molecular simulations. The results showed that the reversibility of the semi-ionic hydrogen bonding facilitated high drug loading and enhanced drug permeability. Actarit’s drug loading had an approximately 11.34-times increase. The in vitro permeability of actarit and ketoprofen was improved by 5.46 and 2.39 times, respectively. And they had the same significant effect in vivo. Furthermore, through the integration of network pharmacology, Western blotting (WB), and radiology analyses, the significant osteoprotective effects of SIHDD-PSA (semi-ionic H-bond double-drug pressure-sensitive adhesive transdermal patch) were revealed through the modulation of the JAK-STAT pathway. The SIHDD-PSA significantly reduced paw swelling and inflammation in the rat model, and stimulatory properties evaluation confirmed the safety of SIHDD-PSA. In conclusion, these findings provide a novel approach for the effective treatment of RA, and the semi-ionic hydrogen bonding strategy contributes a new theoretical basis for developing TDDS.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

Time-based prospective memory (TBPM) refers to the ability of an individual to successfully execute an expected plan in the future at a certain time point or after a definite period of time. This study investigated the potential developmental mechanism of school-age children’s TBPM ability from the perspective of time monitoring. Experiment 1 used a between-subjects design of three ages (7, 9, 11) × two TBPM types (time point, time period) to investigate the trends and characteristics of two types of TBPM in children aged 7–11 years old. Experiment 2 used a between-subjects design of three ages (7, 9, 11) × two monitoring conditions (free monitoring, fixed monitoring) to investigate differences in two types of TBPM and monitoring behavior among school-age children under different monitoring conditions. These results showed that the age effect of TBPM was affected by the type of prospective memory (PM) and that time-point PM performance was significantly better than time-period PM performance among school-age children. These findings indicate that clear and definite external cues are helpful for school-age children in performing TBPM tasks. Moreover, there were significant differences found in the TBPM performance of school-age children under different time monitoring conditions. The performance of older children was significantly better than that of younger children. This indicates that older children can better allocate their attentional resources and use time monitoring strategies to improve their performance in PM tasks. Accordingly, this study showed that the TBPM ability of 7- to 11-year-old children is in continuous development and that the time monitoring behavior in the TBPM task is affected by task type and monitoring conditions.

National parks (NPs) are the flagship protected areas in China’s conservation network and play a key role in the ecological protection of core objects and important natural landscapes. However, the shortage of spatial optimization methods based on quantitative indicators has limited the spatially explicit identification of national parks. Therefore, in this study, we selected the main area of the Tarim River (MTR) in Xinjiang as an example to optimize the spatial boundary of a national park. We constructed an evaluation system based on representativeness, ecological importance, and the foundation of the existing protected area. Subsequently, we comprehensively employed a species distribution model to simulate the habitat of the primary conservation targets. Additionally, we spatially optimized the region using an integer linear model, considering multiple scenarios. The results of this study show that fewer than 30% of the existing protected areas in the MTR protect conservation objects. Using different protection objects and quantitative goals, the spatial optimization results for the eight protection scenarios show that the most effective national park establishment scenario covers a total area of 15,009.3 km2, which is 8157.5 km2 more than the protected area already in place and would include the Tarim Populus National Nature Reserve and Luntai Tarim Populus Forest Park. The opportunity cost to be paid for protection according to this boundary is approximately USD 115.14 million. The protection ratios of each protection object for the expanded park are more than 50%, which is 27.7% higher than the protection effect produced by the existing protected area, so we recommend that Tarim National Park be set up on the basis of this boundary.

Retinal vessels have been good predictive and prognostic imaging biomarkers for systemic or eye diseases. Numerous studies have shown that the two retinal vein occlusion entities may correlate with cardiovascular and cerebrovascular events or primary open-angle glaucoma. This study aims to investigate if there is a disparity in the correlations between branch RVO (BRVO) and central RVO (CRVO) with systemic disorders or POAG, thus explaining the pathogenic difference between BRVO and CRVO. This retrospective case-control study enrolled 59 RVO subjects (118 eyes), including 25 CRVO and 34 BRVO subjects, who received routine eye and brain MRI examinations. The geometric characteristics of the caliber of the retinal and cerebral blood vessels and the optic nerve subarachnoid space width (ONSASW) were measured. Multivariable logistic regression analysis showed that ONSASW at 3 mm behind the globe (p = 0.044) and the relative retinal venular calibers (p = 0.031) were independent risk factors for the CRVO-affected eyes group in comparison with the BRVO-affected eyes group after adjusting for age, duration of hypertension, BMI, and IOP. In the CRVO-affected eyes, narrower relative retinal arteriolar calibers (p = 0.041) and wider relative venular calibers (p = 0.011) were independent risk factors compared with the CRVO-contralateral normal eyes when adjusting for IOP. We concluded that BRVO may be more associated with cerebrovascular diseases, and CRVO may be correlated with primary angle glaucoma. The geometric characteristics difference between the retinal and cerebrovascular may explain the pathological difference between CRVO and BRVO.

In the past, the shift in career patterns and the unprecedented disruptions caused by events such as COVID-19 have posed notable challenges for job seekers. This holds particularly true for college students who are preparing to enter the workforce. In this context, enhancing career adaptability plays a vital role in shaping their career development. The primary objective of this research was to investigate the relationship between career education skills and career adaptability among 273 undergraduate students in China. Additionally, the study aimed to explore the mediating effect of career decision-making self-efficacy in shaping this relationship. The findings of the correlation analysis indicate a significant positive correlation between career education skills and career adaptability. Moreover, the results of the mediation model revealed that career education skills significantly contribute to improving career adaptability along with the mediating effect of college students’ self-efficacy in making career decisions. This study suggests that universities should prioritize the development and expansion of career education initiatives. They should not only help establish clear career goals for college students but also cultivate a positive and flexible career outlook to assist them in better adapting to various changes that may arise throughout their career journeys.

The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment’s state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines.

Recently, electrical stimulation, as a non-pharmacological physical stimulus, has been widely exploited in biomedical and clinical applications due to its ability to significantly enhance cell proliferation and differentiation. As a kind of dielectric material with permanent polarization characteristics, electrets have demonstrated tremendous potential in this field owing to their merits of low cost, stable performance, and excellent biocompatibility. This review provides a comprehensive summary of the recent advances in electrets and their biomedical applications. We first provide a brief introduction to the development of electrets, as well as typical materials and fabrication methods. Subsequently, we systematically describe the recent advances of electrets in biomedical applications, including bone regeneration, wound healing, nerve regeneration, drug delivery, and wearable electronics. Finally, the present challenges and opportunities have also been discussed in this emerging field. This review is anticipated to provide state-of-the-art insights on the electrical stimulation-related applications of electrets.

Atmospheric profiles are important input parameters for atmospheric radiative transfer models and atmospheric parameter inversions. The construction of regionally representative reference atmospheric profiles can provide basic data for global atmospheric and environmental research. Most reference atmospheric profile databases commonly used lag behind in updating frequency. These databases usually have limited spatial and temporal resolution and differ greatly from the real atmospheric state. To present the real atmospheric state, this article constructs the Global Reference Atmospheric Profile Database (GRAP) based on ACE-FTS satellite products of 2021 and 2022, AIRS satellite products and ERA5 reanalysis data of 2022 u6sing a random forest regression model and a hierarchical mean algorithm. The radiance spectrum of FY-3E HIRAS-II using different profile databases was simulated and compared with the measured spectrum. The results show that GRAP spectral simulations fit better with the measured HIRAS-II spectrum. Comparing the CO2, CH4, O3 and N2O profiles of GRAP, AFGL, MIPAS, RTTOV and NDACC ground station profiles in equatorial, mid-latitude summer and polar winter, the results show that GRAP has high spatial and temporal resolution and better fits the current real atmospheric state. Comparing the temperature profiles of eight regions in China, the results illustrate that GRAP is a better representation of the state of the atmosphere in the Chinese region. GRAP can provide fundamental atmospheric data for radiative transfer studies and atmospheric parameter inversions.

Age-related diseases, such as Parkinson’s disease, Alzheimer’s disease, cardiovascular diseases, cancers, and age-related macular disease, have become increasingly prominent as the population ages. Oxygen is essential for living organisms, but it may also cause disease when it is transformed into reactive oxygen species via biological processes in cells. Most of the production of ROS occurs in mitochondrial complexes I and III. The accumulation of ROS in cells causes oxidative stress, which plays a crucial role in human ageing and many diseases. Nuclear factor-erythroid 2-related factor 2 (Nrf2) is a key antioxidant transcription factor that plays a central role in many diseases and ageing in general. It regulates many downstream antioxidative enzymes when cells are exposed to oxidative stress. A basic-region leucine zipper (bZIP) transcription factor, MAF, specifically the small MAF subfamily (sMAFs), forms heterodimers with Nrf2, which bind with Maf-recognition elements (MAREs) in response to oxidative stress. The role of this complex in the human retina remains unclear. This review summarises the current knowledge about Nrf2 and its downstream signalling, especially its cofactor—MAF, in ageing and diseases, with a focus on the retina. Since Nrf2 is the master regulator of redox homeostasis in cells, we hypothesise that targeting Nrf2 is a promising therapeutic approach for many age-related diseases.

Sanghuangporus sanghuang is a large wood-decaying mushroom highly valued in traditional Chinese medicine due to its medicinal properties, including hypoglycemic, antioxidant, antitumor, and antibacterial properties effects. Its key bioactive compounds include flavonoids and triterpenoids. Specific fungal genes can be selectively induced by fungal elicitors. To investigate the effect of fungal polysaccharides derived from Perenniporia tenuis mycelia on the metabolites of S. sanghuang, we conducted metabolic and transcriptional profiling with and without elicitor treatment (ET and WET, respectively). Correlation analysis showed significant differences in triterpenoid biosynthesis between the ET and WET groups. In addition, the structural genes associated with triterpenoids and their metabolites in both groups were verified using quantitative real-time polymerase chain reaction (qRT-PCR) and high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS). Through metabolite screening, three triterpenoids were identified: betulinol, betulinic acid, and 2-hydroxyoleanolic acid. Excitation treatment increased the level of betulinic acid by 2.62-fold and 2-hydroxyoleanolic acid by 114.67-fold compared to WET. The qRT-PCR results of the four genes expressed in secondary metabolic pathways, defense gene activation, and signal transduction showed significant variation between the ET and WET groups. Overall, our study suggests that the fungal elicitor induced the aggregation of pentacyclic triterpenoid secondary metabolites in S. sanghuang.

MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.

Terahertz (THz) technology is an emerging field that is promising for future applications. Nonlinear optical (NLO) materials can effectively convert incident light into the THz frequency range using optics methods. Ionic-type organic π-conjugated NLO crystals containing electron donor-π-acceptor motifs have long attracted attention for their possibility to achieve large nonlinear optical coefficients. In this paper, an overview of the recent progress in ionic-type organic NLO crystals for highly efficient THz wave generation is presented. The substitution design strategies of cations and anions, for increasing optical nonlinearities and reducing absorptions in different structure series, are summarized. In addition, the progress in crystal growth and their THz output performance are also discussed.

Three different pathways for the atomic iodine plus water trimer reaction I + (H2O)3 → HI + (H2O)2OH were preliminarily examined by the DFT-MPW1K method. Related to previous predictions for the F/Cl/Br + (H2O)3 reactions, three pathways for the I + (H2O)3 reaction are linked in terms of geometry and energetics. To legitimize the results, the “gold standard” CCSD(T) method was employed to investigate the lowest-lying pathway with the correlation-consistent polarized valence basis set up to cc-pVQZ(-PP). According to the CCSD(T)/cc-pVQZ(-PP)//CCSD(T)/cc-pVTZ(-PP) results, the I + (H2O)3 → HI + (H2O)2OH reaction is predicted to be endothermic by 47.0 kcal mol−1. The submerged transition state is predicted to lie 43.7 kcal mol−1 above the separated reactants. The I···(H2O)3 entrance complex lies below the separated reactants by 4.1 kcal mol−1, and spin-orbit coupling has a significant impact on this dissociation energy. The HI···(H2O)2OH exit complex is bound by 4.3 kcal mol−1 in relation to the separated products. Compared with simpler I + (H2O)2 and I + H2O reactions, the I + (H2O)3 reaction is energetically between them in general. It is speculated that the reaction between the iodine atom and the larger water clusters may be energetically analogous to the I + (H2O)3 reaction. The iodine reaction I + (H2O)3 is connected with the analogous valence isoelectronic bromine/chlorine reactions Br/Cl + (H2O)3 but much different from the F + (H2O)3 reaction. Significant difference with other halogen systems, especially for barrier heights, are seen for the iodine systems.

Measurement of the degree of polarization of backscattering light from rough surfaces plays an important role in targets-detection applications. The polarization bidirectional reflectance function is the key to establish the relation between the polarization states of incidence and backscattering light. For the purpose of obtaining a polarized bidirectional reflectance distribution function (pBRDF) of a realistic, complicated target, it is decomposed as typical geometric surfaces and analytically calculated as the degree of polarization of the backscattering light, using a microfacet model, under conditions in which the scale of the target is far less than the target distance. In an experiment testing several typical geometric models, the results coincided with the theoretical calculation. The degree of polarization varied substantially as the rotation angle of the target changed, but showed little dependence on the size of target. The results have potential in applications discriminating between targets at different spatial orientations.

The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).

Florfenicol is an excellent antibiotic and is widely used in animal bacterial diseases. However, its poor water solubility leads to various problems, such as poor absorption and bioavailability. The development of nanocrystals is one of the most useful methods for solubilizing florfenicol, which often requires solubility data of florfenicol in different mixed solvents. In this work, the solubility of florfenicol was determined by the gravimetric method in methanol + water, ethanol + water, 1-propanol + water, and isopropanol + water binary solvents at temperatures from 278.15 to 318.15 K. In these four mixed solvents, the solubility of florfenicol increased with the increase in temperature. The solubility of florfenicol in methanol + water mixed solvent increases with the decrease in water ratio, while the solubility of florfenicol in ethanol + water, 1-propanol + water, or isopropanol + water mixed solvents increased first and then decreased with the decrease in water ratio, indicating a cosolvency phenomenon. The modified Apelblat model, CNIBS/R-K model, Jouyban–Acree model, and NRTL model were used to correlate the solubility data of florfenicol in four binary solvents. RMSD values indicated that the calculated values are in good agreement with the experimental solubility data for all four models, among which the CNIBS/R-K model provides the best correlation.

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.

The human retina, which is part of the central nervous system, has exceptionally high energy demands that requires an efficient metabolism of glucose, lipids, and amino acids. Dysregulation of retinal metabolism disrupts local energy supply and redox balance, contributing to the pathogenesis of diverse retinal diseases, including age-related macular degeneration, diabetic retinopathy, inherited retinal degenerations, and Macular Telangiectasia. A better understanding of the contribution of dysregulated metabolism to retinal diseases may provide better therapeutic targets than we currently have.

In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. Consistent with these changes, dramatic human modifications within the coastal zone stand out, and the coastline is altered from an undisturbed natural area to an artificial coastline. However, very few studies have attempted to quantitatively analyze the relationship between subsidence depression and human activities. Here, the subsidence characteristics of the different land-use types in the Yellow River Delta are examined, and their spatiotemporal trends are quantified using a long-term satellite-observed time series of 30 years (1984–2017) regarding the land use map in combination with the InSAR-derived vertical ground deformations during three typical periods (P1: 1992–2000, P2: 2007–2010, and P3: 2016–2017). Noticeably, the highest subsidence rates were observed in areas where substantial human activities were observed, such as the subsidence in the salt fields ranging from 13 mm/year to 32 mm/year to 453 mm/year, respectively. Moreover, through the land-use prediction of Land Change Modeler (LCM), it is found that the salt field area will be further expanded in the future. The ecological vulnerability of the Yellow River Delta coastal zone should receive more attention in the future in terms of planning environmental protection strategies.

A large number of modular multilevel converters (MMC) are connected to HVDC transmission systems nowadays. This paper aims at the short-circuit fault in the DC line of the HVDC transmission system and the problem of capacitor voltage imbalance in MMC, proposing a new type of MMC sub-module, which has both the DC fault self-clearing ability and the capacitor voltage self-balancing ability. This sub-module combines the topology of half bridge and full bridge. It uses the reverse capacitor voltage to forcibly turn off the conducting diode to block the fault current loop. At the same time, the two capacitances charge and discharge states are consistent by utilizing the operating mode of the sub-module. It is possible to directly achieve a self-balancing capacitor voltage without complex balancing voltage control. The MATLAB/Simulink simulation verifies the effectiveness of the DC fault blocking capability and capacitor voltage balance capability of the proposed sub-module.

Atmospheric Infrared Ultraspectral Sounder (AIUS) aboard the Chinese GaoFen-5 satellite was launched on 9 May 2018. It is the first hyperspectral occultation spectrometer in China. The spectral quality assessment of AIUS measurements at the full and representative spectral bands was presented by comparing the transmittance spectra of measurements with that of simulations. AIUS measurements agree well with simulations. Statistics show that more than 73% of the transmittance differences are within ±0.05 and more than 91% of the transmittance differences are within ±0.1. The spectral windows for O3, H2O, temperature, CO, CH4, and HCl were also analyzed. The comparison experiments indicate that AIUS data can provide reliable data for O3, H2O, temperature, CO, CH4, and HCl detection and dynamic monitoring. The H2O profiles were then retrieved from AIUS measurements, and the precision, resolution, and accuracy of the H2O profiles are discussed. The estimated precision is less than 1.3 ppmv (21%) below 57 km and about 0.9–2.4 ppmv (20–31%) at 60–90 km. The vertical resolution of H2O profiles is better than 5 km below 32 km and about 5–8 km at 35–85 km. Comparisons with MLS Level 2 products indicate that the mean H2O profiles of AIUS have a good agreement with those of MLS. The relative differences are mostly within ±10% at 16–75 km and about 10–15% at 16–20 km in 60∘–80∘ S. For 60∘–65 ∘ S in December, the relative differences are within ±5% between 22 km and 80 km. The H2O profiles retrieved from AIUS measurements are credible for scientific research.

The floating height of the strip in an air cushion furnace is a key parameter for the quality and efficiency of production. However, the high temperature and high pressure of the working environment prevents the floating height from being directly measured. Furthermore, the strip has multiple floating states in the whole operation process. It is thus difficult to employ a single model to accurately describe the floating height in different states. This paper presents a multi-model soft sensor to estimate the height based on state identification and the soft transition. First, floating states were divided using a partition method that combined adaptive k-nearest neighbors and principal component analysis theories. Based on the identified results, a hybrid model for the stable state, involving a double-random forest model for the vibration state and a soft-transition model, was created to predict the strip floating height. In the hybrid model for the stable state, a mechanistic model combined thick jet theory and the equilibrium equation of force to cope with the lower floating height. In addition, a novel soft-transition model based on data gravitation that further reflects the intrinsic process characteristic was developed for the transition state. The effectiveness of the proposed approach was validated using a self-developed air cushion furnace experimental platform. This study has important value for the process prediction and control of air cushion furnaces.

In the present study, the preliminary structure and in vitro antitumor activity of three exopolysaccharides (EPSs) from Streptococcus thermophilus CH9 were investigated. Then, three purified fractions of EPS-1a, EPS-2a, and EPS-3a were obtained by chromatography using DEAE-52 cellulose and Sephadex G-100, respectively. The average molecular weight of EPS-1a, EPS-2a, and EPS-3a, were 1.80 × 106, 1.06 × 106 and 1.05 × 106. The monosaccharide composition of EPS-3a was dramatically different from the others. The EPS-1a and EPS-2a were mainly composed of mannose, in a ratio of 69.82% and 57.09%, respectively, while EPS-3a was mainly composed of glucose (63.93%), without mannose. In addition, the surface morphology observed suggested that there were protein particles on the sugar chain of EPS-3a and EPS-3a was a protein-containing polysaccharide. Furthermore, EPS-3a exhibited higher antitumor activity against human liver cancer HepG2 cells in vitro. The antitumor activity of EPS-3a in HepG2 cells was associated with cell apoptosis. HE staining and Hoechst 33342 staining showed that with the treatment of EPS-3a, HepG2 cells had typical morphological changes. Flow cytometry analysis showed that the cell cycle was arrested at G0/G1 phase.

In this study, magnetic iron oxide nanoparticles coated with heparin (Hep-MION) were synthesized and the transcellular transport of the nanoparticles across epithelial cell monolayers on porous polyester membranes was investigated. An externally applied magnetic field facilitated the transport of the Hep-MION across cell monolayers. However, high Hep-MION concentrations led to an increased aggregation of nanoparticles on the cell monolayer after application of the magnetic field. Our results indicate that magnetic guidance of Hep-MION most effectively promotes transcellular transport under conditions that minimize formation of magnetically-induced nanoparticle aggregates. Across cell monolayers, the magnet’s attraction led to the greatest increase in mass transport rate in dilute dispersions and in high serum concentrations, suggesting that magnetic guidance may be useful for in vivo targeting of Hep-MION.