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In response to the situation in the Huanghuai region, where corn fertilization and pesticide application primarily rely on manual methods such as hand broadcasting fertilizer and using manual backpack sprayers, resulting in low levels of mechanization, this study designed an electric integrated work vehicle in line with the trend of developing new energy. The vehicle is powered by six 12 V, 100 Ah lead-acid batteries and integrates the functions of fertilization and pesticide spraying. It can achieve precise hole-fertilization, applying fertilizer to a depth of 100 to 150 mm near the roots of corn, and can also perform multi-row pesticide spraying. The vehicle’s electronic control system is divided into two functional areas: 220 V and 24 V. The walking system uses a 220 V, 2 kW AC servo motor, which is driven by converting the voltage of the 72 V battery group into a 220 V sine wave AC through an inverter, and the motor speed can be adjusted. The working width is adjusted by two fixed electric cylinders at the top of the rear wheel frame. The user can preset the width through the control panel, and during operation, the electric cylinders can be automatically controlled to the optimal working width via a whisker-type limit switch. Analysis using ADAMS software shows that when the vehicle speed is 2, 3, and 5 km per hour, the opening angles of the duckbill controller are 66°, 58°, and 48°, respectively, indicating that the higher the speed, the smaller the opening angle. This shortens the fertilization interval time and makes the fertilization spacing more stable. The maximum opening angle of the adjacent duckbill is 25°, indicating that the fertilization amount remains stable. When the vehicle is moving in reverse, the duckbill always remains closed, and at different speeds, the opening angle change curve of the duckbill controller is smooth and regular. This vehicle significantly improves the efficiency and precision of corn planting. However, improvements are still needed in battery technology, control system optimization, and the high cost of electric agricultural machinery to promote the widespread application of agricultural mechanization.

This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as Rtotal, is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within Rtotal, and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception–reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. Rtotal has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity.

In complex marine environments, extracting target features from acoustic signal is very difficult, making the targets hard to be recognized. Therefore, it is necessary to perform denoising method on the acoustic signal to highlight the target features. However, training deep learning denoising models requires a large mount of acoustic data with labels and obtaining labels with real measured data is also extremely difficult. In this paper, an enhanced simulation algorithm, which considers integrating features of target line spectrum and ocean environmental noise, is proposed to construct a large-scale training sample set. Additionally, a deep convolutional denoising model is presented, which is first train on simulated data and directly applied to real measured data for denoising, enabling line spectrum to be significantly displayed in the time-frequency spectrogram. The results on simulation experiments and sea trials demonstrate that the proposed method can significantly reduce ocean noise while preserving the characteristics of target line spectrum. Furthermore, the experiments demonstrate that the proposed convolutional denoising model has transferability and generalization, making it suitable for denoising underwater acoustic signal in different marine areas.

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement.

To address the problems of missed and false detections caused by target occlusion and lighting variations, this paper proposes a recognition model based on YOLOv5 with bi-level routing attention to achieve precise real-time small object recognition, using the problem of tailhook recognition for carrier-based aircraft as a representative application. Firstly, a module called D_C3, which combines deformable convolution, was integrated into the backbone network to enhance the model’s learning ability and adaptability in specific scenes. Secondly, a bi-level routing attention mechanism was employed to dynamically focus on the regions of the feature map that are more likely to contain the target, leading to more accurate target localization and classification. Additionally, the loss function was optimized to accelerate the bounding box regression process. The experimental results on the self-constructed CATHR-DET and the public VOC dataset demonstrate that the proposed method outperforms the baselines in overall performance.

Antarctic krill oil has been proven to be able to promote the ovarian development of crustaceans, but its optimal application dose and potential regulatory mechanism in Macrobrachium rosenbergii are still unclear. In this study, five isonitrogenous and isolipidic diets with gradient additions of Antarctic krill oil (0%, 1.5%, 3%, 4.5%, and 6%) were served exposed to 8 weeks of feeding. The results show that 3–4.5% Antarctic krill oil supplementation significantly increases the weight gain rate and specific growth rate of M. rosenbergii (p < 0.05). In addition, 3–4.5% Antarctic krill oil supplementation significantly increased the content of hemolymph vitellogenin (VTG) and the levels of reproductive hormones, including methyl farnesoate (MF), estradiol (E2), and progesterone (P4) (p < 0.05). The differences in ovarian index, oocyte volume, yolk granule deposition in oocytes, and the transcription levels of VTG genes in hepatopancreas and ovarian tissues demonstrated that the addition of Antarctic krill oil significantly promoted ovarian development and vitellogenesis, especially at the 4.5% addition level. In terms of molecular signaling, this study confirms that the retinol metabolic signaling pathway, MF signaling pathway, steroid hormone signaling pathway, and ecdysone signaling pathway, along with their specific molecules, such as Farnesoic acid-O-methyltransferase (FAMeT), retinoid x receptor (RXR), ecdysone receptor (EcR), and estrogen-related receptor (ERR), are involved in the regulation of the ovarian development of M. rosenbergii by adding Antarctic krill oil at appropriate doses. The findings indicate that the supplementation of 4.5% Antarctic krill oil in the diet is optimal for stimulating the secretion of reproductive hormones in female M. rosenbergii, thereby promoting vitellogenesis and ovarian development.

The rake angles on both sides of the cutting edges of the hourglass worm gear hob significantly influence its cutting performance, which, in turn, plays a decisive role in the surface quality of the machined worm wheel. To balance the rake angles along the tooth height direction of each hob tooth and enhance the overall cutting performance of the hob, this paper proposes a method that utilizes a rotating paraboloid surface to generate the helical rake face of the hourglass worm gear hob. First, the conjugate condition equations for the rake face generated by the rotating paraboloid surface are derived. A mathematical model for the helical rake face of planar double-enveloping hourglass worm gear hob is established. This study explores the influence of two machining parameters on the rake angle, specifically the milling drive ratio coefficient k and the geometric parameter of a parabolic milling cutter p. Through a systematic analysis of the variations in rake angle at the dividing toroidal surface and along the tooth height direction, the optimal parameter values were identified as k = 0.9115 and p = 0.6834. The results show that, after optimization, the hob rake angle range is around ±4.7°, with a maximum rake angle difference of 6.3072° along the tooth height direction, and the rake angles on both sides of the teeth are more balanced. The structure of the rake face is more reasonable, reflecting the feasibility of rotating paraboloid tools for forming tools in the machining of complex surfaces.

Preclinical and clinical studies have suggested potential synergies of combining poly (ADP-ribose) polymerase (PARP) inhibitors and novel hormonal therapies (NHT) for patients with metastatic castration-resistant prostate cancer (mCRPC). We systematically searched PubMed, ClinicalTrials.gov and ASCO-GU annual meeting abstracts up to March 2023 to identify potential phase III trials reporting the use of combining PARP inhibitors with NHT in the first-line setting for mCRPC. A total of four phase III trials met the criteria for subsequent review. Emerging data suggested that the radiographic progression-free survival (rPFS) was significantly longer in the PARP inhibitor combined with NHT group versus the placebo plus NHT group for the first-line setting of biomarker-unselected mCRPC patients, especially for patients with homologous recombination repair (HRR) mutation (HRR m), and with the greatest benefit for BRCA1/2 mutation (BRCA1/2 m) populations. Final overall survival (OS) data of the PROpel trial indicated a significant improvement in median OS for mCRPC patients with HRR m and BRCA1/2 m receiving olaparib + abiraterone. Prior taxane-based chemotherapy might not influence the efficacy of the combination. Compared with the current standard-of-care therapies, combining NHT with PARP inhibitors could achieve a significant survival benefit in the first-line setting for mCRPC patients with HRR and BRCA1/2 mutations.

Quarantine policies during the coronavirus disease 2019 (COVID-19) pandemic prolonged time spent at home, leading to an unintended occurrence of sick building syndrome (SBS) symptoms. The aim of this study was to investigate the prevalence of SBS symptoms among residents in Suzhou, China, during the COVID-19 pandemic. A questionnaire survey was conducted online from February to May 2022. A total of 442 adults were included in this study. The prevalence of SBS symptoms was much higher during the COVID-19 pandemic, and the proportions of general, mucosal, and skin symptoms were 59.3%, 45.5% and 49.1%, respectively. The influences of building characteristics, occupant lifestyle, indoor environment and perception of indoor environment were analysed using a multivariate logistic regression model. Neighbourhood pollutant sources, older residences, non-ownership and humidity indicators were significant risk factors for SBS symptoms. Frequent use of air fresheners was significantly associated with general (adjusted odds ratio (AOR): 4.9, 95% confidence interval (CI: 2.4–10.0), mucosal (AOR: 5.3, 95% CI: 2.4–11.5), and skin symptoms (AOR: 4.6, 95% CI: 1.6–13.1), while the use of disinfectants was significantly correlated with skin symptoms (AOR: 4.0, 95% CI: 1.5–10.7). Residents’ perception of an uncomfortable indoor environment was a significant predictor of general (AOR: 2.2, 95% CI: 1.2–4.0) and mucosal symptoms (AOR: 3.1, 95% CI: 1.6–6.0). The use of air cleaners can reduce the prevalence of general symptoms. An important finding of this study is that the psychological impact of staying at home for a long time was associated with general symptoms (AOR: 1.3, 95% CI: 1.1–1.7), which means that attention should also be paid to the mental health of residents during the COVID-19 pandemic. Although the quarantine period has now ended, our results can still serve as a reference for the impact of the indoor environment on the health of people with SBS, especially for those who stay at home for a long time, such as the elderly.

Background/Objectives: High fructose has been implicated as an important trigger of kidney inflammation in patients and experimental models. Magnolol, isolated from Magnolia officinalis, has an anti-inflammatory effect, but its protective role in podocytes remains underexplored. This study explored the protective effects and underlying mechanism of magnolol against high fructose-induced podocyte inflammation. Methods: The effects of magnolol on high fructose-induced podocyte inflammation were assessed in male Sprague Dawley rats administered 10% (w/v) fructose water for 12 weeks and heat-sensitive human podocyte cell lines (HPCs) exposed to 5 mM fructose. Podocyte foot processes were examined using transmission electron microscopy. The expression levels of nephrin, podocin, tumor necrosis factor-α (TNF-α), Notch1 intracellular domain (NICD1), triokinase/FMN cyclase (TKFC), specificity protein 1 (Sp1) and histone deacetylase 4 (HDAC4) were determined by Western blot, immunofluorescence and real-time quantitative polymerase chain reaction (qRT-PCR). The chromatin immunoprecipitation (ChIP) assay was performed to evaluate the interaction between Sp1 and the promoter region of HDAC4. Results: Magnolol mitigated the impairment of glomerular filtration function in high fructose-fed rats. Besides, it significantly alleviated the inflammatory responses in glomeruli and HPCs, evidenced by decreased protein levels of TNF-α and NICD1. Increased protein levels of TKFC, Sp1 and HDAC4 were observed in high fructose-stimulated HPCs and rat glomeruli. TMP195, an HDAC4 inhibitor, reduced TNF-α and NICD1 protein levels in high fructose-exposed HPCs. The increased Sp1 was shown to associate with the promoter region of HDAC4, promoting HDAC4 protein expression in high fructose-exposed HPCs. The knockdown of TKFC in HPCs by TKFC siRNA decreased Sp1, HDAC4 and NICD1 protein levels, alleviating podocyte inflammatory response. Furthermore, magnolol inhibited TKFC/Sp1/HDAC4/Notch1 activation in vivo and in vitro. Conclusions: Magnolol attenuated high fructose-induced podocyte inflammation possibly through the suppression of TKFC/Sp1/HDAC4/Notch1 activation, providing new evidence for its potential role in podocyte protection.

Despite the availability of electronic navigators and automated vehicles, static wayfinding instructions remain widely used due to their resistance to signal disturbances, as well as their economic and environmental advantages over electronic signs. To investigate the impact of static wayfinding on the network cost and flow distribution, this paper presents an efficient method for updating the incident matrix and extends the stochastic user equilibrium (SUE) framework to incorporate static wayfinding instructions by using the generalized nested logit (GNL) choice model to represent user behavior. The SUE principle relaxes the assumption that users possess perfect knowledge of traffic conditions and always choose the optimal link to minimize their costs. The GNL model improves generalization performance of the cross-nested logit (CNL) model while solving the overlap problem of the multinomial logit (MNL) model. The disaggregate simplicial decomposition (DSD) algorithm is applied to solve proposed user equilibrium by iteratively finding decent directions through an auxiliary solution and determining step size using different methods. The results indicate that the self-regulated averaging (SRA) method can solve the proposed model efficiently. Additionally, increasing travel time cost on guided links and even outer links can be potential influences caused by static wayfinding instructions. The study results can assist decision-makers in quantitatively assessing the value of placing static wayfinding instructions at certain locations and choosing effective layout information.

Following publication, concerns were raised regarding the peer-review process related to the publication of this article [...]

Trichoderma species have been reported as masters in producing cellulolytic enzymes for the biodegradation of lignocellulolytic biomass and biocontrol agents against plant pathogens and pests. In our previous study, a novel Trichoderma strain LZ117, which shows potent capability in cellulase production, was isolated. Herein, we conducted multilocus phylogenetic analyses based on DNA barcodes and performed time-scaled phylogenomic analyses using the whole genome sequences of the strain, annotated by integrating transcriptome data. Our results suggest that this strain represents a new species closely related to T. atrobrunneum (Harzianum clade). Genes encoding carbohydrate-active enzymes (CAZymes), transporters, and secondary metabolites were annotated and predicted secretome in Trichoderma sp. LZ117 was also presented. Furthermore, genetic manipulation of this strain was successfully achieved using PEG-mediated protoplast transformation. A putative transporter gene encoding maltose permease (Mal1) was overexpressed, which proved that this transporter does not affect cellulase production. Moreover, overexpressing the native Cre1 homolog in LZ117 demonstrated a more pronounced impact of glucose-caused carbon catabolite repression (CCR), suggesting the importance of Cre1-mediated CCR in cellulase production of Trichoderma sp. LZ117. The results of this study will benefit further exploration of the strain LZ117 and related species for their applications in bioproduction.

Since the rural revival, the return of the rural labor force has become a trend. Different ways of returning to the rural labor force contain different policy implications. Based on the data of 8209 households in the China Labor Force Dynamic Survey, the probit model was used to explore the impact of long-term security in urban and rural areas on different return ways of the rural labor force. The results show that: (1) 18% of farmers choose to return, of which 3% and 10% choose active return and passive return, respectively, and 10% and 6% choose long-term return and short-term return, respectively. (2) The improvement in urban long-term security will inhibit the return of farmers (including active return and passive return), and the improvement in rural long-term security will promote farmers’ return behavior (including active return, passive return, long-term return, and short-term return). (3) The improvement of rural long-term security will promote the return of the first and second generations of rural households (including active return, passive return, long-term return, and short-term return), and the improvement of urban long-term security will restrain the return of the first generation of rural households (including active returns).

In order to address the problem of a high miss-seeding rate in mechanized potato planting work, a novel reseeding device is designed and analyzed. Based on dynamic and kinematic principles, the seed potato’s motion analysis model in the seed preparation process was constructed. The analysis results indicate that the seed preparation performance is positively related to the seed preparation opening length l1 and inclination angle of the seed-returning pipe θ. Then, the potato’s motion analysis model in the reseeding process was constructed. The analysis showed that the displacement of seeding potatoes in the horizontal direction ds is influenced by the initial seeding potato’s speed v0t, dropping height hs, and the angle between the seeding pipe and the horizontal ground βs. The horizontal moving distance xr of the reseeding potatoes is influenced by the angle between the bottom of the reseeding pipe and horizontal ground βs2, the distance from its centroid to the reseeding door d, and the dropping height of the potato hr. The analysis results indicated that the reseeding potato can be effectively discharged into the furrow. Then, a prototype of a reseeding control system was constructed based on the STM32 microcontroller, electric pushers, and through-beam laser sensors. The simulation analysis was conducted to verify the theoretical analysis by using EDEM2020 software. The simulation results indicated that with the increase in the seeding chain speed, the seed preparation success rate initially increased slowly and then decreased gradually. The seed preparation performance can be increased by increasing the seed preparation opening length or decreasing the seed-returning pipe inclination angle. The impact on the successful seed preparation rate is ranked by significance as follows: seed preparation opening length > seed-returning pipe inclination angle > chain speed. Then, the prototype reseeding device and the corresponding seed metering device were manufactured and a series of bench tests and field tests were conducted. The bench test results showed an average successful seed preparation rate of 93.6%. The average qualified-seeding rate, miss-seeding rate, and multi-seeding rate in the field test were 89.6%, 2.46%, and 7.94%, respectively. This study can provide a theoretical reference for the design of potato reseeding devices.

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.

Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is difficult to deploy, while a smaller one sacrifices accuracy. To address this challenge, the novel “LiOSR-SAR” lightweight recognizer is proposed for OSR in SAR imageries. It incorporates the compact attribute focusing and open-prediction modules, which collectively optimize its lightweight structure and high accuracy. To validate LiOSR-SAR, “fast image simulation using bidirectional shooting and bouncing ray (FIS-BSBR)” is exploited to construct the corresponding dataset. It enhances the details of targets for more accurate recognition significantly. Extensive experiments show that LiOSR-SAR achieves remarkable recognition accuracies of 97.9% and 94.1% while maintaining a compact model size of 7.5 MB, demonstrating its practicality and efficiency.

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.

Although previous studies have demonstrated that methane (CH4) can mitigate the toxicity of cadmium (Cd) in alfalfa seedlings, the CH4-rich water used in these studies may create hypoxic conditions, potentially influencing the experimental outcomes. Therefore, this study aimed to investigate whether CH4 can reduce Cd toxicity in alfalfa seedlings without the interference of hypoxia and to analyze its underlying mechanisms. Here, it was observed that supplementing oxygen with saturated CH4-rich water can significantly alleviate the inhibition of 75 μM CdCl2 on the growth of alfalfa (Medicago sativa L.) seedlings. Less Cd accumulation was also observed in both root and shoot parts, which could be explained by the CH4-altered cell wall components in alfalfa seedling roots, including covalent and ionic soluble pectin, and the degree of demethylation in pectin, thus enabling a higher proportion of Cd binding to the cell walls and reducing the entry of Cd into the cells. The above actions of CH4 were accompanied by an increase in hydrogen peroxide (H2O2) content and NADPH oxidase activity, which could be blocked by the addition of the NADPH oxidase inhibitor diphenylene iodonium (DPI). Taken together, these results implied that exogenously applied CH4 could alleviate Cd toxicity in alfalfa seedlings by enhancing Cd chelation onto the root cell walls, which might be closely associated with NADPH oxidase-dependent H2O2 signals. These findings could provide insight into the mechanism through which CH4 alleviates Cd toxicity in alfalfa plants.

Given that two-phase closed thermosiphons (TPCT) are a prevalent and efficacious means of heat transfer. However, non-condensable gas (NCG) may potentially impact the heat transfer performance of the aforementioned devices. Nevertheless, the theoretical analysis of heat transfer in TPCT containing NCG is not fully comprehensive and therefore requires further supplementation by means of relevant experiments. This paper presents the development and experimental investigation of a theoretical heat transfer model for a TPCT containing NCG. The research encompasses the optimal fluid-filling ratio of R22 and R410a working fluid and the impact of NCG on heat transfer in the condenser section of TPCT. Experimental findings indicate that TPCT with R22 and R410a working fluids at a fluid-filling ratio of 60% and 50%, respectively, demonstrate excellent isotherm and heat transfer efficiency. The presence of NCG affects the condenser section heat transfer process of the vapor, leading to a 2 °C decrease in the average temperature of the condenser section of the TPCT (Tca). In comparison with the TPCT without NCG, it was observed that an increase in the mass of NCG from 0.0097 to 0.0197 mol resulted in a reduction in the effective length of the condenser section (La) and effective heat transfer rate (κ) of R22 TPCT. The decrease in La was 75.1 mm, while the decrease in κ was 15.02%. Furthermore, at the same NCG mass, the effective heat transfer rate of R410a TPCT is evidently superior to that of R22 TPCT. The NCG in the TPCT can be removed by using a check valve. Nevertheless, this will result in a reduction in the fluid-filling ratio of the TPCT. The temperature of the R410a TPCT containing 0.0197 mol NCG with a fluid-filling ratio of 50% is comparable to that observed in fluid-filling ratio of 40% after the NCG is exhausted.

This study investigated the effects of herbicide exposure on Navicula sp. (MASCC-0035) algae, focusing on growth density, chlorophyll content, antioxidant system, and lipid metabolism. Navicula cultures were exposed to different concentrations of atrazine (ATZ), glyphosate (Gly), and acetochlor (ACT) for 96 h. Results showed a significant decrease in cell numbers, with higher herbicide concentrations having the most noticeable impacts. For instance, Gly-G2 had reduced cell populations by 21.00% at 96 h. Chlorophyll content varied, with Gly having a greater impact on chlorophyll a compared to ATZ and ACT. Herbicide exposure also affected the antioxidant system, altering levels of soluble sugar, soluble protein, and reactive oxygen species (ROS). Higher herbicide rates increased soluble sugar content (e.g., ATZ, Gly, and ACT-G2 had increased by 14.03%, 19.88%, and 19.83%, respectively, at 72 h) but decreased soluble protein content, notably in Gly-G2 by 11.40%, indicating cellular stress. Lipid metabolism analysis revealed complex responses, with changes in free proline, fatty acids, and lipase content, each herbicide exerting distinct effects. These findings highlight the multifaceted impacts of herbicide exposure on Navicula algae, emphasizing the need for further research to understand ecological implications and develop mitigation strategies for aquatic ecosystems.

Enhancing urban ecological welfare performance is essential for achieving sustainable urban development and fostering a comprehensive regional green transformation. This study develops a quantitative assessment framework for urban ecological welfare performance, grounded in both the welfare of urban residents and their consumption of ecological resources. Employing the spatio-temporal Logarithmic Mean Divisia Index model to dissect the ecological welfare performance across 108 key prefecture-level cities within China’s Yangtze River Economic Belt, considering both temporal and spatial dimensions, the analysis reveals a “W”-shaped trajectory in the ecological welfare performance from 2006 to 2022, characterized by pronounced spatial disparities. Particularly in the downstream coastal regions and notably the Yangtze River Delta, advantages in social and economic structures, along with public fiscal outlays, contribute to a superior ecological welfare performance, exhibiting a notable spatial spillover effect. The study introduces six key factors—social benefit, economic benefit, population dispersion, population density in urban areas, urbanization scale, and ecological sustainability—to examine their influence on ecological welfare performance, uncovering substantial differences in the outcomes of temporal and spatial decomposition. Temporal decomposition indicates that economic benefit and urbanization scale are the primary drivers enhancing ecological welfare performance, whereas population dispersion is identified as the primary inhibitor. Spatial decomposition reveals that the determinants of above-average urban ecological welfare vary regionally and undergo dynamic shifts over time. Overall, a holistic understanding of the interplay among economic growth, ecological preservation, and the enhancement of residents’ welfare can inform the development and execution of tailored policies by local governments.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Background: New-onset diabetes (NOD) has been identified as a high-risk factor for the early detection of pancreatic ductal adenocarcinoma (PDAC). The role of tumor volume and remnant pancreas volume (RPV) in the progression from normal to NOD in PDAC patients is not fully illustrated yet. Methods: In this cross-sectional study, glycemic metabolism traits of 95 PDAC patients before pancreatic surgery were described and compared with chronic pancreatitis and type 2 diabetes mellitus patients based on the oral glucose tolerance test. The remnant RPV and tumor volume, calculated by three-dimensional reconstruction of radiological images, were included in the ordinal logistic regression models. Results: The prevalence of NOD was high among PDAC patients (38.9%). However, normal glucose tolerance (NGT) or prediabetes mellitus status were present as more than half (24/44) of advanced tumor stage patients. Indexes reflecting beta-cell function but not insulin sensitivity gradually worsened from NGT to NOD patients (all p < 0.05). The remnant pancreas volume (RPV) was identified as a potential protective factor for diabetes secondary to PDAC (odds ratio 0.95, 95% CI [0.92, 0.97], p < 0.001). Conclusions: Reduced RPV causing beta-cell dysfunction might be one of the mechanisms of NOD secondary to PDAC. Subjects with sufficient pancreas volume could not be detected earlier when regarding patients with NOD as the population at risk for PDAC.

The floral color phenotypes of Osmanthus fragrans cultivars range from light yellow to orange yellow, with ‘Yanzhi Hong’ being the only reported cultivar with a red color. However, the underlying reason for this unique floral coloration remains unclear. The study conducted targeted metabolomics and transcriptomics analyses on the petals of ‘Yanzhi Hong’ at both initial and peak flowering stages. Candidate gene expression was validated, and expression levels of the petals of three cultivars were compared using RT-qPCR. The results revealed the presence of 27 components in the petals of ‘Yanzhi Hong’, including 5 carotenoids, 8 xanthophylls, and 14 xanthophyll esters. Notably, lycopene was detected in abundance for the first time in O. fragrans cultivars. Carotenes accounted for 78.82 ± 3.17% and 91.19 ± 1.69% of the total carotenoid content in petals during the initial and peak flowering stages, respectively, with all carotene contents increasing during the peak flowering period. β-carotene, lycopene, and γ-carotene were identified as the top three carotene components in petals during both initial and full flowering stages. The unique blush red color of ‘Yanzhi Hong’ petals could be attributed to the low content of α-carotene and the rich accumulation of lycopene. Furthermore, a total of 1550 differentially expressed genes (DEGs) were identified in petals at the peak flowering stage relative to the initial flowering stage, with 1003 genes being downregulated and 547 genes being upregulated during the full flowering stage. There are 926 differentially expressed genes (DEGs) annotated in the Gene Ontology (GO) database. Among these DEGs, those that were downregulated and upregulated during the peak flowering period showed significant enrichment in carbohydrate metabolism and oxidation–reduction processes, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 14 structural genes associated with phenylpropanoid biosynthesis and 7 structural genes linked to carotenoid biosynthesis. Expression levels of candidate genes involved in carotenoid biosynthesis were examined in the petals of three cultivars (‘Yanzhi Hong’, ‘Liuye Jingui’, and ‘Gecheng Dangui’) at both the initial and peak flowering stages. The results indicated that the decreased expression of LYG009054 (LYCE) and LYG018651 (LYCB) in ‘Yanzhi Hong’ resulted in higher lycopene accumulation and lower α-carotene content in the petals. This study offers valuable insights into the mechanisms underlying the unique flower color phenotype of O. fragrans, proving a basis for further research on carotenoid metabolism pathways and the breeding of new cultivars with a variety of flower colors in O. fragrans.

Accurately measuring drug and its release kinetics in both in vitro and in vivo environments is crucial for enhancing therapeutic effectiveness while minimizing potential side effects. Nevertheless, the real-time visualization of drug release from microspheres to monitor potential overdoses remains a challenge. The primary objective of this investigation was to employ fluorescence imaging for the real-time monitoring of drug release from microspheres in vitro, thereby simplifying the laborious analysis associated with the detection of drug release. Two distinct varieties of microspheres were fabricated, each encapsulating different drugs within PLGA polymers. Cy5 was selected as the donor, and Cy7 was selected as the acceptor for visualization and quantification of the facilitated microsphere drug release through the application of the fluorescence resonance energy transfer (FRET) principle. The findings from the in vitro experiments indicate a correlation between the FRET fluorescence alterations and the drug release profiles of the microspheres.

We conducted an experiment of planting a dead cow and a metal-framed cage with cameras on the 1629 m deep sea floor off the southeast coast of Hainan Island in the northwestern South China Sea, using ROV diving and setting up a video camera on the cage to observe animals who came to eat the bait. The deep-sea cameras captured footage of eight Pacific sleeper sharks (Somniosus pacificus) swimming and feeding around the dead cow. To our knowledge, this is the first time the occurrence of such a shark species has been reported in the South China Sea. Eight individuals were differentiated based on the characteristic differences displayed in the images, with lengths of 1.9 to 5.1 m estimated. The video camera also recorded the predators’ behavior of tearing at the dead cow on the seabed. It was discovered that Pacific sleeper sharks are not strictly solitary and exhibit queue-feeding behavior. This study is significant as it documents a record of a data-scarce shark species, for which little information is available in the literature. It also documents an expansion of the species’ known habitat from the north Pacific Ocean into the South China Sea. Such sharks diving into the deep sea to predate on dead animals also suggests that occurrences of large chunks of dead organic bodies falling onto the deep sea might have been more frequent than we previously thought in the South China Sea. The findings have implications for understanding the geographic connectivity of large swimming animals between the South China Sea and the Pacific Ocean and provide scientific evidence for formulating conservation and management strategies for sharks and other large animals in the oceans.

Asiatic acid (AA) is a pentacyclic triterpene derived from the traditional medicine Centella asiatica. It is known for its anti-inflammatory, antioxidant, and lipid-regulating properties. Though previous studies have suggested its potential therapeutic benefits for atherosclerosis, its pharmacological mechanism is unclear. The objective of this study was to investigate the molecular mechanism of AA in the treatment of atherosclerosis. Therefore, network pharmacology was employed to uncover the mechanism by which AA acts as an anti-atherosclerotic agent. Furthermore, molecular docking, molecular dynamics (MD) simulation, and in vitro experiments were performed to elucidate the mechanism of AA’s anti-atherosclerotic effects. Molecular docking analysis demonstrated a strong affinity between AA and PPARγ. Further MD simulations demonstrated the favorable stability of AA-PPARγ protein complexes. In vitro experiments demonstrated that AA can dose-dependently inhibit the expression of inflammatory factors induced by lipopolysaccharide (LPS) in RAW264.7 cells. This effect may be mediated through the PPARγ/NF-κB signaling pathway. This research underscores anti-inflammation as a crucial biological process in AA treatments for atherosclerosis, with PPARγ potentially serving as a key target.

Many studies have consistently demonstrated that the near-surface phytoplankton chlorophyll (Chl) levels in anticyclonic eddies (AEs) are higher than in cyclonic eddies (CEs) in the South Pacific Ocean (SPO), using remote sensing data, which is attributed to higher phytoplankton biomass or physiological adjustments in AEs. However, the characteristics of the Chl profile induced by mesoscale eddies and their underlying dynamic mechanism have not been comprehensively studied by means of field measurement, and the influence mechanism of environmental factors at different depths on Chl has not been investigated. To fill this gap, we utilized Biogeochemical-Argo (BGC-Argo) data to investigate the relationships between Chl concentration and environmental factors at different water layers and the underlying dynamic mechanisms of mesoscale eddies in the SPO. Our findings indicate that the same environmental factor can have different effects on Chl at different depths. Within a mixed layer (ML), the elevated Chl levels in AEs result from both physiological adjustments and increased phytoplankton biomass, and the former plays a more dominant role, which is induced by enhanced nutrient availability and weakened light, due to the deepening ML in AEs. At depths ranging from 50 m to 110 m, and between 110 m and 150 m (near the depth of pycnocline or the bottom of the euphotic zone), the dominant factor contributing to higher Chl levels in CEs is phytoplankton physiological adaptation driven by reduced temperature and light. At depths exceeding 150 m (beyond the euphotic zone), higher Chl in AEs is primarily caused by high phytoplankton biomass as a result of downwelling by eddy pumping. This work should advance our comprehensive understanding of the physical–biological interactions of mesoscale eddies and their impacts on primary productivity throughout the water column, and it should provide some implications for understanding the biogeochemical processes.

The emergence of SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and partial resistance to neutralization by antibodies has been observed globally. There is an urgent need for an effective vaccine to combat these variants. Our study demonstrated that the B.1.351 variant inactivated vaccine candidate (B.1.351V) generated strong binding and neutralizing antibody responses in BALB/c mice against the B.1.351 virus and other SARS-CoV-2 variants after two doses within 28 days. Immunized K18-hACE2 mice also exhibited elevated levels of live virus-neutralizing antibodies against various SARS-CoV-2 viruses. Following infection with these viruses, K18-hACE2 mice displayed a stable body weight, a high survival rate, minimal virus copies in lung tissue, and no lung damage compared to the control group. These findings indicate that B.1.351V offered protection against infection with multiple SARS-CoV-2 variants in mice, providing insights for the development of a vaccine targeting SARS-CoV-2 VOCs for human use.

Gastroesophageal reflux disease (GERD), a prevalent clinical condition, is often attributed to aberrant esophageal motility, leading to gastric content reflux and associated symptoms or complications. The rising incidence of GERD presents an escalating healthcare challenge. Endoscopic and esophageal reflux monitoring can provide a basis for the diagnosis of patients with gastroesophageal reflux disease, but when the diagnostic basis is at an inconclusive value, some additional supportive evidence will be needed. Advanced technology is the key to improving patient diagnosis, accurate assessment, and the development of effective treatment strategies. High-resolution esophageal manometry (HREM) and endoscopic functional lumen imaging probe (EndoFLIP) represent the forefront of esophageal motility assessment. HREM, an evolution of traditional esophageal manometry, is considered the benchmark for identifying esophageal motility disorders. Its widespread application in esophageal dynamics research highlights its diagnostic significance. Concurrently, EndoFLIP’s emerging clinical relevance is evident in diagnosing and guiding the treatment of coexisting esophageal motility issues. This review integrates contemporary research to delineate the contributions of HREM, EndoFLIP, and novel technologies in GERD. It examines their efficacy in facilitating an accurate diagnosis, differentiating similar gastrointestinal disorders, quantifying the extent of reflux, assessing the severity of the disease, forecasting patient responsiveness to proton pump inhibitor therapy, and guiding decisions for surgical interventions. The overarching aim is to deepen the understanding of GERD’s underlying mechanisms and advance the formulation of holistic, efficacious treatment approaches.

The backflow of high-temperature products in an engine’s combustion chamber is a key issue which can significantly reduce combustion efficiency. This is particularly problematic for hypergolic propellants, as the high-temperature products may still contain fuel or an oxidizer. If either the fuel or the oxidizer backflows into the manifold of the other, it can easily lead to micro-explosions, thereby creating a threat. To address this problem, this paper proposes a new design of an anti-backflow injector, aimed at effectively preventing the backflow of combustion products to the propellant manifold. A steady-state, non-reactive Computational Fluid Dynamics (CFD) model is employed to evaluate the steady internal flow characteristics of the proposed anti-backflow injector. Additionally, a complementary transient, multiphase fluid dynamics simulation is carried out to assess the response characteristics of the anti-backflow injector. Our analysis focuses on the response characteristics of the concave valve core. The study also explores the impact of different expansion port angles on the injection effect, finding that the vortex diameter at the injector outlet is positively related to the expansion port angle. It is also shown that the injection angle becomes more stable as the expansion angle increases. When the expansion port angles are 10° and 20°, the injection angles show similar trends. In terms of anti-backflow effect, taking the injection stiffness of 150% and a time range of 300 µs as examples, the response time of the anti-backflow models with expansion ports of 10°, 15°, and 20° is increased by 67 µs, 99 µs, and 213 µs, respectively, compared to the base models with the same expansion angles. Meanwhile, when the injection stiffness is 50%, the response time of the anti-backflow models with expansion ports of 10°, 15°, and 20° is increased by 207 µs, 210 µs, and 207 µs, respectively. When the injection stiffness is 20%, the response recovery speed of the anti-backflow models with expansion ports of 10° and 15° is increased by 41 µs and 96 µs, respectively. However, the performance of the anti-backflow model with a 20° expansion port is 216 µs of the base model. The optimized design of the anti-backflow injector has potential applications in solving the propellant backflow problem and contributes to the advancement of combustors.

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm2 with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively.

Litter size is a significant economic trait during animal reproduction. This current study attempted to decipher whether MTHFR promotes the apoptosis of granulosa cells (GCs) and inhibits their proliferation by investigating the effects of the MTHFR gene using flow cytometry and a Cell Counting Kit-8 (CCK-8) assay. MTHFR is linked with ovarian follicle development in the reproductive performance of 104 female New Zealand rabbits. We observed that MTHFR could regulate the mRNA of follicular development-related genes (TIMP1, CITED1, FSHR, GHR, HSD17B1, and STAR) with a qRT-PCR, and we observed the protein expression of CITED1 and GHR using a western blot (WB) analysis. The dual luciferase activity assays helped identify the core promoter region of the MTHFR gene, and the polymorphism of the MTHFR promoter region was studied using Sanger sequencing. The results indicated four single nucleotide polymorphisms (SNPs) within the core promoter region, among which the g.-680C>A locus was significantly associated with both the total and alive litter sizes. Additionally, the CC genotype was associated with the largest total and alive litter sizes, compared to the CA and AA genotypes (p < 0.05). In conclusion, this study investigated the effects of MTHFR on ovarian granulosa cells and its association with selected reproductive parameters in rabbits. The results provide a theoretical foundation for the use of MTHFR as a molecular marker in rabbits.

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

Fruit storage is optimized at the lowest safe temperature to maximize storage life whilst avoiding chilling or freezing injury. The few published studies of freezing in kiwifruit (Actinidia spp.) have been conducted with A. chinensis var. deliciosa ‘Hayward’ fruit, with freezing temperatures reported in the range of −1.5 °C to −2.5 °C. In China, a large number of kiwifruit cultivars have been commercialized recently, with at least ten being commonly planted. In this research, freshly harvested fruits from 45 kiwifruit germplasm accessions were measured for freezing point, soluble solids content (SSC) and water content (WC). The difference in freezing point of different tissue zones within a fruit and after different periods of cold storage were determined for the fruit of three representative main cultivars: the green-fleshed ‘Hayward’, the red-fleshed A. chinensis var. chinensis ‘Donghong’, and the yellow-fleshed interspecific hybrid A. eriantha × A. chinensis ‘Jinyan’. The the freezing point of kiwifruit was found to be highly correlated with the SSC. This relationship was found irrespective of whether the measurement was made at harvest, at different places in the fruit, or after ripening during storage. These findings agree with previous reports for kiwifruit and other fruits, although it appears that the relationship may differ among kiwifruit cultivars, something not previously reported and which requires further confirmation.

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a “prediction-scheduling” search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm’s parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations.

The green production behaviors of farmers in protected areas (PAs) can reduce environmental disturbances and contribute to the effectiveness of PAs. Based on a survey of 708 farmers in Wuyishan National Park (WNP) and Crested Ibis Nature Reserve (CINR), we studied the influence of perceived benefit and policy incentive on farmers’ organic fertilizer application. Results: (1) Perceived benefit, subsidies policy, and propaganda policy significantly promoted farmers’ organic fertilizer application, with perceived benefit having the strongest effect, followed by subsidies policy. The influence of restriction policy was not significant. (2) Perceived benefit, subsidies policy, and propaganda policy indirectly influenced organic fertilizer application through ecological awareness. (3) In WNP, perceived benefit had the strongest effect on organic fertilizer application, and subsidies policy significantly enhanced farmers’ ecological awareness and influenced their behaviors. In contrast, subsidies policy had the strongest effect on organic fertilizer application, but did not significantly enhance farmers’ ecological awareness in CINR. These results offer valuable insights for promoting green production behaviors of farmers in PAs. This study implies that there must be a pathway for the realization of ecological value in ecological protection. Ecological value realization is the endogenous motivation for farmers’ sustainable green production behaviors. However, government subsidies and guidance are also essential in the early stage.

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10−4 and 6.94×10−5. Based on our selected model, the significance of TNSC’s effect in predicting the risk of IM is justified with a p-value less than 10−6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic changes and driving factors of nine vegetation net primary productivities (NPPs) in regions divided by the Hu Line using remote sensing data, trend analysis, and the Geodetector model. Findings reveal that from 2001 to 2022, 38.22% of regional vegetation NPP in China increased, especially in the Loess Plateau, Sichuan Basin, and Northeast Plains, while 2.39% decreased, primarily in the southeastern region and southern Tibet. Grasslands contributed 39.71% to NPP north of the Hu Line, and cultivated vegetation contributed 50.58% south. The driving explanatory power of factors on vegetation NPP on the north side of the Hu Line is generally greater than that on the south side. Natural factors primarily drive NPP changes, with human activities having less impact. Combined factors, particularly climate and elevation, significantly enhance the driving explanatory power (q, 0–1). The joint effects of elevation and precipitation on grassland NPP dynamics (q = 0.602) are notable. GDP’s influence on broadleaf forests north of the Hu Line (q = 0.404) is significant. Grasslands respond strongly to land use changes and population density, with a combined effect of q = 0.535. Shrubs, alpine vegetation, and meadows show minimal response to individual factors (q < 0.2). These findings offer insights for devising ecological protection measures tailored to local conditions.

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.

Rosacea is a chronic inflammatory skin disease whose late manifestations have not yet been clearly reported in animal models. The objective of this study is to describe the skin lesions and major histopathological changes in a rosacea-like phenotype in mice induced by prolonged LL-37 administration and furthermore, to assess the potential of long-term LL-37 administration in inducing irreversible rosacea-like skin lesion models. Balb/c mice were continuously injected intradermally with LL-37 every 12 h to induce a rosacea-like phenotype. After LL-37 injections were administered for 20 consecutive days, the area of rosacea-like lesions gradually expanded in the first 13 days, then entered a stable phase. Haematoxylin and eosin (H&E) and Van Gieson’s staining showed a high degree of inflammatory cell aggregation, thickening of the epidermis and dermis, and collagen deposition in large quantities. The results of immunofluorescence staining and Western blotting showed that the expression of α-SMA, TNF-α, vimentin, and COL1 in the skin of mice was significantly upregulated. Short-term LL-37 administration induced rosacea-like lesions that only featured the aggregation of inflammatory factors and thickening of the epidermis, whereas no collagen hyperplasia was observed, and a full recovery was noticed. However, rosacea-like skin lesions induced by long-term LL-37 administration did not completely recover. Our study compares rosacea-like lesions induced by short-term versus long-term LL-37 administration, and the results suggest that irreversible rosacea-like lesions can be induced by long-term LL-37 administration.

A female phenotype with strip-like gonads, 46, XY pure gonadal dysgenesis (PGD) has a high tendency to develop into gonadal germ cell tumors. We described one patient with 46, XY PGD, who had a gonadal mixed germ cell tumor (GCT) and acute lymphoblastic leukemia (ALL). This is a unique case because two malignancies developed and relapsed in one person with chromosome abnormality, and the patient is the youngest reported so far. There is an association between her GCT and ALL, as the two malignancies may share a common clonal origin and the NRAS mutation likely plays a role in tumor genesis. We organized MDT to formulate a suitable plan of treatment. We completed the surgery and full cycles of chemotherapy for GCT and controlled ALL by chemotherapy and bone marrow transplantation. However, unfortunately, the young life finally ended following a rare transplant rejection. We concluded that ALL likely shares common clonal origin with GCT and that gene mutations may play a role in neoplasia, which requires further exploration. In the face of such complex conditions, we need to balance the treatment of both diseases to prolong survival and improve the patients’ quality of life.

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model’s hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production.

The enterovirus A71 (EV71) inactivated vaccine is an effective intervention to control the spread of the virus and prevent EV71-associated hand, foot, and mouth disease (HFMD). It is widely administered to infants and children in China. The empty particles (EPs) and full particles (FPs) generated during production have different antigenic and immunogenic properties. However, the antigen detection methods currently used were established without considering the differences in antigenicity between EPs and FPs. There is also a lack of other effective analytical methods for detecting the different particle forms, which hinders the consistency between batches of products. In this study, we analyzed the application of sedimentation velocity analytical ultracentrifugation (SV-AUC) in characterizing the EPs and FPs of EV71. Our results showed that the proportions of the two forms could be quantified simultaneously by SV-AUC. We also determined the repeatability and accuracy of this method and found that both parameters were satisfactory. We assessed SV-AUC for bulk vaccine quality control, and our findings indicated that SV-AUC can be used effectively to analyze the percentage of EPs and FPs and monitor the consistency of the process to ensure the quality of the vaccine.

The Tibetan Plateau is located in southwestern China. It has many important ecological functions, such as biodiversity protection, and is an important grassland agroecosystem in China. With the development of modern agriculture and animal husbandry, antibiotics are widely used to treat humans and livestock, and antibiotics cannot be fully metabolised by both. Antibiotics eventually find their way into the environment, affecting other parts of grassland agroecosystems. Triticale (Triticosecale wittmack) is an artificial hybrid forage that can be used for both grain and forage. This study revealed the diversity of seedborne bacteria in triticale on the Tibetan Plateau and the resistance of the bacteria to nine antibiotics. It identified 37 representative strains and successfully obtained the spliced sequences of 36 strains of the bacteria, which were clustered into 5 phyla and 16 genera. Among them, 18 strains showed resistance to at least one of the 9 antibiotics, and the colony-forming unit (CFU) abundance of antibiotic-resistant bacteria (ARB) accounted for 45.38% of the total samples. Finally, the bacterial motility and biofilm formation ability were measured, and their correlation with bacterial resistance was analysed. The results showed that the bacterial resistance did not have an absolute positive correlation with the motility or biofilm formation ability.

Multi-drug resistance of bacteria producing extended-spectrum β-lactamase (ESBL) is a public health challenge. Thus, this study aimed to investigate the antimicrobial susceptibility of ESBL-producing Escherichia coli (ESBL-EC) in Hunan Province, China. A total of 1366 fecal samples were collected from pig, chicken, and cattle farms over a six-year period, which were assessed using strain isolation, 16S rRNA identification, polymerase chain reaction, drug sensitivity testing, whole-genome sequencing, and bioinformatics analysis. The results showed an overall prevalence of 6.66% for ESBL-EC strains, with ESBL positivity extents for pigs, chickens, and cattle isolates at 6.77%, 6.54%, and 12.5%, respectively. Most ESBL-EC isolates were resistant to cefotaxime, tetracycline, and trimethoprim-sulfamethoxazole; however, all the isolates were susceptible to meropenem, with relatively low resistance to amikacin and tigecycline. Various multi-locus sequence types with different origins and similar affinities were identified, with ST155 (n = 16) being the most common subtype. Several types of resistance genes were identified among the 91 positive strains, with beta-lactamase blaCTX-M-55 being the most common ESBL genotype. IncFIB was the predominant plasmid type. Widespread use of antibiotics in animal farming may increase antibiotic resistance, posing a serious threat to the health of farmed animals and, thus, to human food security and health.

Emergency and critical illnesses refer to severe diseases or conditions characterized by rapid changes in health that may endanger life within a short period [...]

The distributed leader-follower control of multi-agent systems is discussed. Each agent is expressed in a discrete-time and non-linear dynamic model with an unknown parameter and can be affected by its neighbors’ history information. For each agent, to identify the parameter, one switching set of the parameter estimates is constructed and the optimal parameter estimate is chosen based on the index switching function. Using the given desired reference signal, the leader agent’s control law is designed, and relying on the neighbors’ history information, each follower agent’s local control law is designed. With the designed distributed tracking adaptive control laws, the whole system tracks the given desired reference signal, and in the face of strong couplings the closed-loop system ultimately reaches an agreement. Finally, by comparing simulations of the control strategy with a normal projection algorithm, the results indicate that the adaptive control method with a switching set of the parameter estimates is effective in improving the control performance.

Histone acetylation and methylation, governed by various histone modification (HM) gene families, are vital for plant biological processes. However, there are limited studies that have explored HMs in ornamental horticultural trees, including sweet osmanthus (Osmanthus fragrans). We performed genome-wide search and identified 208 OfHMs, encompassing 81 histone methyltransferases (OfHMTs), 51 histone demethylases (OfHDMs), 49 histone acetyltransferases (OfHATs) and 27 histone deacetylases (HDACs). Our comprehensive analysis covered chromosome locations, gene structures, conserved domains, cis-acting elements, phylogenetic comparisons, protein interaction networks and functional enrichment pathways for these gene families. Additionally, tandem and fragment replications were unveiled as contributors to the expansion of OfHMs, with some genes exhibiting positive selection. Furthermore, we examined OfHM expression profiles across various tissues and flowering stages, and under 5′-azacytidine (Aza) and ethylene treatments. Most OfHMs displayed heightened expression in leaves, and were downregulated during the flower opening and senescence stages, including OfPRMTs, OfHDTs, OfHDAs, OfSRTs, OfJMJs and OfHAGs; 75.86% and 80.77% of the differentially expressed OfHMs were upregulated after Aza and ethylene treatments, including OfHAGs, OfHDAs and OfSDGs. This study offers a comprehensive analysis of the OfHM gene family, which indicated their potential involvement in ethylene and Aza responses, and in the flowering process. These findings provide valuable insights into the role of OfHMs in flowering and stress responses.