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Daylight plays a crucial role in human health, and as research into its effects expands, it is essential for designers to estimate the non-image-forming impacts of various daylighting and lighting strategies. This allows them to create indoor environments that are both pleasant and comfortable. To support this, daylight measurements were taken in five Chinese cities, focusing on spectral power distribution, correlated color temperature, and illuminance. The study calculated the non-image-forming effects of daylight exposure using metrics like melanopic Equivalent Daylight Illuminance and Circadian Light. A key finding was the development of the action factor SAI, which estimates the potential non-image-forming effects of light in built environments. This factor serves as a proxy for understanding how non-image-forming effects relate to correlated color temperatures. Additionally, the research suggests the possibility of creating a time-variational daylighting and lighting strategy with four distinct periods of non-image-forming effects throughout a 24 h day. These insights could be valuable for architects and designers in optimizing indoor lighting systems.

The pig is the most widely consumed domestic animal in China, providing over half of the meat supply in food markets. For livestock, a key economic trait is the reproductive performance, which is significantly influenced by placental development. The placenta, a temporary fetal organ, is crucial for establishing maternal–fetal communication and supporting fetal growth throughout pregnancy. DNA methylation is an epigenetic modification that can regulate the gene expression by recruiting proteins involved in gene silencing or preventing transcription factor binding. To enhance our understanding of the molecular mechanisms underlying DNA methylation in porcine placental development, this review summarizes the structure and function of the porcine placenta and the role of DNA methylation in placental development.

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

Sacral spinal cord injury (SSCI) can disrupt bladder neuromodulation and impair detrusor function. Current studies provide limited information on the histologic and genetic changes associated with SSCI-related neurogenic lower urinary tract dysfunction (NLUTD), resulting in few treatment options. This study aimed to establish a simple animal model of SSCI to better understand the disease progression. Ninety 8-week-old Sprague-Dawley (SD) rats were randomly separated into sham operation and SSCI groups. The SSCI group underwent sacral spinal cord injury, while the sham group did not. Urodynamic and histological assessments were conducted at various intervals (1, 2, 3, 4, and 6 weeks) post-injury to elucidate the disease process. Urodynamic examinations revealed significant bladder dysfunction in the SSCI group compared to the sham group, stabilizing around 3–4 weeks post-injury. Histological examination, including hematoxylin–eosin and Masson’s trichrome staining, correlated these functional changes with bladder microstructural alterations. RNA-seq was performed on bladder tissues from the sham group and SSCI group at 6 weeks to identify differentially expressed genes and pathways. Selected genes were further analyzed using polymerase chain reaction (PCR). The findings indicated a pronounced inflammatory response in the first 2 weeks post-SSCI, progressing to bladder fibrosis at 3–4 weeks. In conclusion, this study presents a reliable, reproducible, and straightforward SSCI model, providing insights into bladder functional and morphological alterations post-SSCI and laying the groundwork for future therapeutic research.

Inspired by hummingbirds and certain insects, flapping wing micro aerial vehicles (FWMAVs) exhibit potential energy efficiency and maneuverability advantages. Among them, the bi-directional motor-driven tailless FWMAV with simple structure prevails in research, but it requires active pose control for hovering. In this paper, we employ deep reinforcement learning to train a low-level hovering strategy that directly maps the drone’s state to motor voltage outputs. To our knowledge, other FWMAVs in both reality and simulations still rely on classical proportional-derivative controllers for pose control. Our learning-based approach enhances strategy robustness through domain randomization, eliminating the need for manually fine-tuning gain parameters. The effectiveness of the strategy is validated in a high-fidelity simulation environment, showing that for an FWMAV with a wingspan of approximately 200 mm, the center of mass is maintained within a 20 mm radius during hovering. Furthermore, the strategy is utilized to demonstrate point-to-point flight, trajectory tracking, and controlled flight of multiple drones.

Core–shell nanostructures are powerful platforms for the development of novel nanoscale drug delivery systems with sustained drug release profiles. Coaxial electrospinning is facile and convenient for creating medicated core–shell nanostructures with elaborate designs with which the sustained-release behaviors of drug molecules can be intentionally adjusted. With resveratrol (RES) as a model for a poorly water-soluble drug and cellulose acetate (CA) and PVP as polymeric carriers, a brand-new electrospun core–shell nanostructure was fabricated in this study. The guest RES and the host CA molecules were designed to have a reverse gradient distribution within the core–shell nanostructures. Scanning electron microscope and transmission electron microscope evaluations verified that these nanofibers had linear morphologies, without beads or spindles, and an obvious core–shell double-chamber structure. The X-ray diffraction patterns and Fourier transform infrared spectroscopic results indicated that the involved components were highly compatible and presented in an amorphous molecular distribution state. In vitro dissolution tests verified that the new core–shell structures were able to prevent the initial burst release, extend the continuous-release time period, and reduce the negative tailing-off release effect, thus ensuring a better sustained-release profile than the traditional blended drug-loaded nanofibers. The mechanism underlying the influence of the new core–shell structure with an RES/CA reverse gradient distribution on the behaviors of RES release is proposed. Based on this proof-of-concept demonstration, a series of advanced functional nanomaterials can be similarly developed based on the gradient distributions of functional molecules within electrospun multi-chamber nanostructures.

Alginate is a natural polymer with good biocompatible properties and is a potential polymeric material for the sustainable development and replacement of petroleum derivatives. However, the non-spinnability of pure alginate solutions has hindered the expansion of alginate applications. With the continuous development of electrospinning technology, synthetic polymers, such as PEO and PVA, are used as co-spinning agents to increase the spinnability of alginate. Moreover, the coaxial, parallel Janus, tertiary and other diverse and novel electrospun fiber structures prepared by multi-fluid electrospinning have found a new breakthrough for the problem of poor spinning of natural polymers. Meanwhile, the diverse electrospun fiber structures effectively achieve multiple release modes of drugs. The powerful combination of alginate and electrostatic spinning is widely used in many biomedical fields, such as tissue engineering, regenerative engineering, bioscaffolds, and drug delivery, and the research fever continues to climb. This is particularly true for the controlled delivery aspect of drugs. This review provides a brief overview of alginate, introduces new advances in electrostatic spinning, and highlights the research progress of alginate-based electrospun nanofibers in achieving various controlled release modes, such as pulsed release, sustained release, biphasic release, responsive release, and targeted release.

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks.

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Vanilla (Vanilla planifolia Andrews) is a valuable orchid spice cultivated for its highly priced beans. Vanilla has been planted in Hainan province of China via cutting propagation for about 40 years. The yield has been decreasing annually for the past ten years due to pod numbers declining significantly even though it seems to grow normally without disease symptoms, while the reason is still unknown. In this study, we found that Cymbidium mosaic virus (CymMV), one of the most devastating viruses causing losses in the vanilla industry, massively presented within the pods and leaves of vanilla plants, so the virus infecting the vanilla seems to be a highly probable hypothesis of the main contributions to low yield via decreasing the number of pods. This represents the first speculation of CymMV possibly affecting the yield of vanilla in China, indicating the important role of virus elimination in restoring high yield in vanilla. This research can also serve as a warning to important economic crops that rely on cuttings for propagation, demonstrating that regular virus elimination is very important for these economically propagated crops through cuttings.

Electric vehicles are becoming a mainstay of road transport. However, the uneven distribution of the electric vehicle charging piles in cities has led to the problem of “mileage anxiety”. This has become a significant concern of consumers in purchasing electric vehicles. At the same time, it also hinders the development of the electric vehicle industry. For this reason, this paper designs an electric vehicle energy mutual aid device. In the event that the power battery of an electric vehicle is low on energy and there are no suitable charging piles around, the device can also seek energy supplementation from surrounding electric vehicles with sufficient energy. This device should satisfy the characteristics of wide gain, high power, small size, and light weight. Firstly, the quadratic boost converter and its state space model are constructed. It uses only one electrical switching element to realize the light weight of the device, and it also provides wide voltage gain to fulfill the needs of electric vehicle charging. Secondly, an improved voltage mode controller is proposed to address the shortcomings of the conventional voltage mode controller of the quadratic boost converter. It avoids the tradeoff between the transient and steady-state performance due to the integration action of the conventional voltage mode controller. Also, this controller uses less feedback to make the controlled system output the required DC power, reducing the weight of the device. Finally, the effectiveness of the proposed energy mutual aid device is verified by simulations and experiments.

Based on the actual situation of the project on the Weihai–Yanhai Expressway section of Rongwu Expressway, the effects of water content change and the dry–wet cycle on the mechanical behavior of unsaturated clayey sandy soil were analyzed in this study. In this study, ventilated undrained triaxial shear tests were carried out on unsaturated clayey sandy soils with different water contents (6%, 8%, 10%, 12%, 14% and 16%). Concurrently, the soil samples were subjected to three distinct wet and dry cycle pathways (2~22%, 2~12%, and 12~22%) to gain an understanding of how the mechanical features of the soil changed under the different conditions. The test findings demonstrate that when the water content increases, the unsaturated clayey sandy soil’s cohesiveness and shear strength diminish. The strength of shear decline exhibits a pattern of first being quick, followed by sluggish. The strength of shear and cohesiveness of clayey sandy soil declined under the influence of the dry and wet cycles, with the first cycle primarily affecting variations in cohesiveness and strength of shear. Furthermore, the strength of shear and cohesiveness of clayey sandy soil diminish more with increasing wet and dry cycle amplitude and upper water content limits. Lastly, the drying shrinkage and hygroscopic expansion of clay particles in clayey sandy soils during wet and dry cycles are not significant, resulting in less structural damage and deterioration of the mechanical properties of the soils. The study’s findings have a significant impact on the durability of roadbeds made of unsaturated clayey sandy soil in both wet and dry situations.

As a powerful imidazole antifungal drug, ketoconazole’s low solubility (0.017 mg/mL), together with its odor and irritation, limited its clinical applications. The inclusion complex of ketoconazole with randomly methylated β-cyclodextrin was prepared by using an aqueous solution method after cyclodextrin selection through phase solubility studies, complexation methods, and condition selection through single factor and orthogonal strategies. The complex was confirmed by FTIR (Fourier-transform infrared spectroscopy), DSC (differential scanning calorimetry), TGA (thermogravimetric analysis), SEM (scanning electron microscope images), and NMR (Nuclear magnetic resonance) studies. Through complexation, the water solubility of ketoconazole in the complex was increased 17,000 times compared with that of ketoconazole alone, which is the best result so far for the ketoconazole water solubility study. In in vitro pharmacokinetic studies, ketoconazole in the complex can be 100% released in 75 min, and in in vivo pharmacokinetic studies in dogs, through the complexation, the Cmax was increased from 7.56 μg/mL to 13.58 µg/mL, and the AUC0~72 was increased from 22.69 μgh/mL to 50.19 μgh/mL, indicating that this ketoconazole complex can be used as a more efficient potential new anti-fungal drug.

Using a combination of experimental and numerical methods, this study examines the stability of the slope of Waste Dump#1 in Ziluoyi Iron Mine. We conducted direct shear tests on soil samples taken from the waste dump, which provided important insights into slope stability. The tests identified key mechanical parameters, including an average cohesion of 4.80 kPa and an internal friction angle of 25.63°. By implementing GEO-SLOPE software, we could determine that the slope stability factor is 1.047, which is far from the required safety standards. To address this issue, we proposed an appropriate rectification strategy including the construction of safety platforms and reconfiguration of the slope structure. This approach effectively improved the slope stability factor to 1.219 and met the safety criteria. In addition, particle flow code (PFC) simulations were methodically performed to model the slope morphology and particle displacement before and after rectification. The obtained results revealed a remarkable reduction in sliding areas and particle displacement post-rectification, enhancing mine safety and efficiency. Our findings provide valuable insights into the application of combined experimental and numerical methods to assess and improve slope stability in open-pit mines, which will substantially contribute to the field of geotechnical engineering and mining safety.

Optical 3D detection technology has a wide range of applications in industrial detection, agricultural production, and so on. Its advantages are non-contact, efficiency, and high precision. However, the specular reflection problem affects model coverage and measurement accuracy. An optimization algorithm for calculating the number and pose of sensors has been proposed to address this issue. First, the specular reflection problem is viewed as a multi-sensor position search problem. Then, an optimization algorithm is used to find the optimal number and bit positions of sensors to avoid specular reflection. The experiment shows that the optimization results of this algorithm can cover the area to be measured with the least number of sensor combinations while avoiding the influence of specular reflection.

TCP transcription factors play a key role in regulating various developmental processes, particularly in shoot branching, flower development, and leaf development, and these factors are exclusively found in plants. However, comprehensive studies investigating TCP transcription factors in pepper (Capsicum annuum L.) are lacking. In this study, we identified 27 CaTCP members in the pepper genome, which were classified into Class I and Class II through phylogenetic analysis. The motif analysis revealed that CaTCPs in the same class exhibit similar numbers and distributions of motifs. We predicted that 37 previously reported miRNAs target 19 CaTCPs. The expression levels of CaTCPs varied in various tissues and growth stages. Specifically, CaTCP16, a member of Class II (CIN), exhibited significantly high expression in flowers. Class I CaTCPs exhibited high expression levels in leaves, while Class II CaTCPs showed high expression in lateral branches, especially in the CYC/TB1 subclass. The expression profile suggests that CaTCPs play specific roles in the developmental processes of pepper. We provide a theoretical basis that will assist in further functional validation of the CaTCPs.

The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.

In response to the issues of high honeysuckle-picking costs and low efficiency in honeysuckle picking, this study has devised a comb-brush-type picking device, considering the unique characteristics of honeysuckle plants. We elucidated the device’s structure and operational principles and designed critical components within the picking mechanism. Subsequently, through theoretical analysis, we identified the primary factors influencing the device’s operational performance. We then used the honeysuckle picking rates, honeysuckle breakage rates, and impurity rates as assessment metrics. Utilizing a one-factor test, we determined the permissible ranges for each factor. Employing the response surface methodology, we analyzed the interactions among these factors and conducted model parameter optimization. This optimization identified the optimal parameter combination: a forward speed of 3.99 km/h, a driving shaft speed of 316.53 rpm, and a picking teeth length of 70 mm. Finally, we performed verification tests using these optimized parameters. The results demonstrated that the maximum relative error between test verification values and model-optimized predictions was 4.86%. This outcome confirms that the comb-brush-type honeysuckle-picking device can meet the operational requirements of mechanized harvesting and offers valuable insights for developing harvesting devices for vine plants.

Damage deterioration of rocks in cold regions under seasonal changes and daily cycles of freezing and thawing generate a series of engineering geological problems. These problems will seriously affect the safe and efficient production of open-pit mines. In this paper, a freeze–thaw cycle test and uniaxial compression test considering the natural conditions of the slope were carried out. Mechanical properties and damage mechanisms of open-pit mine mudstone under freeze–thaw conditions were investigated based on nuclear magnetic resonance (NMR) technology. The test results show that the microscopic internal pore structure of mudstone was changed under the superimposed effect of freeze–thaw damage and hydration damage. The internal pore size of mudstone increased with the number of freeze–thaw cycles, while the average pore size of the natural mudstone test increased more. Macroscopically, the compressive strength and modulus of elasticity of mudstone varied linearly with the number of freezing cycles, and the compressive strength and modulus of elasticity showed a decreasing trend. The strain-softening characteristics of mudstone samples were significant for more freeze–thaw cycles. The study explains the microscopic causes of mudstone deterioration in open-pit mines in cold regions and offers guidance for solving engineering disasters caused by mudstone deterioration.

Since griseofulvin was marketed as a non-polyene antifungal antibiotic drug in 1958, its poor water solubility has been an issue for its wide applications, and over the last sixty years, many attempts have been made to increase its water solubility; however, a significant result has yet to be achieved. Through supercritical carbon dioxide-assisted cyclodextrin complexation with the addition of a trace amount of water-soluble polymer surfactant, the griseofulvin inclusion complex with HP-γ-cyclodextrin was prepared and confirmed. The 1:2 ratio of griseofulvin and HP-γ-cyclodextrin in the complex was determined based on its NMR study. After complexation with HP-γ-cyclodextrin, griseofulvin’s water solubility was increased 477 times compared with that of griseofulvin alone, which is the best result thus far. The complex showed 90% of griseofulvin release in vitro in 10 min, in an in vivo dog pharmacokinetic study; the Cmax was increased from 0.52 µg/mL to 0.72 µg/mL, AUC0–12 was increased from 1.55 μg·h/mL to 2.75 μg·h/mL, the clearance was changed from 51.78 L/kg/h to 24.16 L/kg/h, and the half-life time was changed from 0.81 h to 1.56 h, indicating the obtained griseofulvin complex can be a more effective drug than griseofulvin alone.

Albendazole is the preferred deworming drug and has strong insecticidal effects on human and animal helminth parasites, showing remarkable activity against hepatocellular carcinoma and colorectal cancer cells. However, it is classified as being in class II in the Biopharmaceutics Classification System due to its poor water solubility (0.2 mg/L) and high permeability, which make the clinical application of albendazole impractical. Through complexation with methyl-β-cyclodextrin, as the best result so far, albendazole’s water solubility was increased by 150,000 times, and albendazole could be 90% released during the first 10 min. In an in vivo pharmacokinetic study, the Cmax and Tmax of the active metabolized sulfoxide were changed from 2.81 µg/mL at 3 h to 10.2 µg/mL at 6 h and the AUC0–48 was increased from 50.72 h⁎μg/mL to 119.95 h⁎μg/mL, indicating that the inclusion complex obtained can be used as a new oral therapeutic anti-anthelmintic and anti-tumor agent formulation.

Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple target load sequences. Existing data-driven methods for load forecasting in integrated energy systems use multi-task learning to address these challenges. When determining the input data for multi-task learning, existing research primarily relies on data correlation analysis and considers the influence of external environmental factors in terms of feature engineering. However, such feature engineering methods lack the utilization of the characteristics of multi-target sequences. In leveraging the characteristics of multi-target sequences, language generation models trained on textual logic structures and other sequence features can generate synthetic data that can even be applied to self-training to improve model performance. This provides an idea for feature engineering in data-driven time-series forecasting models. However, because time-series data are different from textual data, existing transformer-based language generation models cannot be directly applied to generating time-series data. In order to consider the characteristics of multi-target load sequences in integrated energy system load forecasting, this paper proposed a generative tractive network (GTN) model. By selectively utilizing appropriate autoregressive feature data for temporal data, this model facilitates feature mining from time-series data. This model is capable of analyzing temporal data variations, generating novel synthetic time-series data that align with the intrinsic temporal patterns of the original sequences. Moreover, the model can generate synthetic samples that closely mimic the variations in the original time series. Subsequently, through the integration of the GTN and autoregressive feature data, various prediction models are employed in case studies to affirm the effectiveness of the proposed methodology.

Background: Calcific aortic valve disease (CAVD) is a significant cause of morbidity and mortality among elderly people. However, no effective medications have been approved to slow or prevent the progression of CAVD. Here, we examined the effect of liraglutide on aortic valve stenosis. Methods: Male Apoe−/− mice were fed with a high-cholesterol diet for 24 weeks to generate an experimental CAVD model and randomly assigned to a liraglutide treatment group or control group. Echocardiography and immunohistological analyses were performed to examine the aortic valve function and morphology, fibrosis, and calcium deposition. Plasma Glucagon-like peptide-1 (GLP-1) levels and inflammatory contents were measured via ELISA, FACS, and immunofluorescence. RNA sequencing (RNA-seq) was used to identify liraglutide-affected pathways and processes. Results: Plasma GLP-1 levels were reduced in the CAVD model, and liraglutide treatment significantly improved aortic valve calcification and functions and attenuated inflammation. RNA-seq showed that liraglutide affects multiple myofibroblastic and osteogenic differentiations or inflammation-associated biological states or processes in the aortic valve. Those liraglutide-mediated beneficial effects were associated with increased GLP-1 receptor (GLP-1R) expression. Conclusions: Liraglutide blocks aortic valve calcification and may serve as a potential therapeutic drug for CAVD treatment.

The radiation effect of materials is very important and directly related to the safety and reliability of nuclear reactors. Polymer materials, one of the indispensable materials in nuclear power equipment, must withstand the ordeal of high-energy ionizing rays. In this work, through screening different γ-ray dose irradiation conditions, we systematically and comprehensively study the changes in the structure and properties of nitrile butadiene rubber (NBR) before and after γ-ray static irradiation at a high dose rate, and master the rule and mechanism of the γ-ray static irradiation effect of these polymer materials. The mapping relationship between the macroscopic properties, microstructure, and irradiation dose of NBR is accurately characterized. With an increase in total irradiation dose, the C=C double bond reaction occurs, and the C≡N bond, C=C, and C=O participate in the hyper crosslinking reaction. The glass transition temperature (Tg) increases with the cumulative irradiation amount. With the increased total irradiation amount, the degree of rubber cross-linking increases, causing an increased crystallinity and decomposition temperature. A growing amount of gamma irradiation causes the mechanical properties of the rubber to degrade simultaneously, increasing the shore hardness while decreasing the tensile strength and ultimate elongation at break. When the cumulative amount reaches 1 MGy, the ultimate elongation at break decreases significantly. A cumulative dose of radiation resistance of 4 MGy can be achieved by the samples. This work can provide theoretical and experimental support for the long-term stability of nitrile butadiene rubber and its derivatives in nuclear radiation fields and space radiation conditions.

Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network architecture search (NAS) serves as a solution to help experts discover new network structures that are suitable for different tasks and platforms. However, traditional NAS algorithms often consume time and many computational resources, especially when dealing with complex tasks and large-scale models, and the search process can become exceptionally time-consuming and difficult to interpret. In this paper, we propose a class activation graph-based neural structure search method (CAM-NAS) to address these problems. Compared with traditional NAS algorithms, CAM-NAS does not require full training of submodels, which greatly improves the search efficiency. Meanwhile, CAM-NAS uses the class activation graph technique, which makes the searched models have better interpretability. In our experiments, we tested CAM-NAS on an NVIDIA RTX 3090 graphics card and showed that it can evaluate a submodel in only 0.08 seconds, which is much faster than traditional NAS methods. In this study, we experimentally evaluated CAM-NAS using the CIFAR-10 and CIFAR-100 datasets as benchmarks. The experimental results show that CAM-NAS achieves very good results. This not only proves the efficiency of CAM-NAS, but also demonstrates its powerful performance in image classification tasks.

Some chronic diseases, including Parkinson’s disease (PD), diabetic foot, flat foot, stroke, elderly falling, and knee osteoarthritis (KOA), are related to orthopedic organs, nerves, and muscles. The interaction of these three parts will generate a comprehensive result: gait. Furthermore, the lesions in these regions can produce abnormal gait features. Therefore, monitoring the gait features can assist medical professionals in the diagnosis and analysis of these diseases. Nowadays, various insole systems based on different sensing techniques have been developed to monitor gait and aid in medical research. Hence, a detailed review of insole systems and their applications in disease management can greatly benefit researchers working in the field of medical engineering. This essay is composed of the following sections: the essay firstly provides an overview of the sensing mechanisms and parameters of typical insole systems based on different sensing techniques. Then this essay respectively discusses the three stages of gait parameters pre-processing, respectively: pressure reconstruction, feature extraction, and data normalization. Then, the relationship between gait features and pathogenic mechanisms is discussed, along with the introduction of insole systems that aid in medical research; Finally, the current challenges and future trends in the development of insole systems are discussed.

In the solar reflective band, an on-board calibration method based on a solar diffuser (SD) can realize full aperture, full field of view, and end-to-end absolute radiometric calibration of optical remote sensors. The SD’s bidirectional reflectance distribution function (BRDF) is a key parameter that affects the accuracy of the on-board calibration. High-accuracy measurement of the SD BRDF is required in the laboratory before launch. Due to the uncertainty of the goniometer system, polarization effects, and other factors, the measurement uncertainty of the SD BRDF at large incident angles is much higher than that at a 0° incident zenith angle and 45° reflection zenith angle. In this paper, an absolute BRDF measurement facility is reported. The goniometric system consists of a high-brightness integrating sphere as a radiation source, a six-axis robot arm, and a large rotation stage. The measurement wavelength range was from 350 nm to 2400 nm. An improved data processing method based on the reciprocity theorem was proposed to reduce the measurement uncertainty of the SD BRDF at large incident angles. At an incident zenith angle of 75°, the improved data processing method reduced the measurement uncertainty of the SD BRDF by 52% at 410 nm to 480 nm, by 70% at 480 nm to 1000 nm, and by 20% at other bands compared to the absolute measurement method. The influence of the radiation source, goniometer system, detection system, and other factors on the measurement uncertainty are analyzed in this paper. The results show that the measurement uncertainty (coverage factor k = 2) of the SD BRDF was better than 1.04% at 350 nm to 410 nm, 0.60% at 410 nm to 480 nm, 0.43% at 480 nm to 1000 nm, and 0.86% at 1000 nm to 2400 nm.

Platycodonis Radix (PR), a widely consumed herbal food, and its bioactive constituents, platycodins, have therapeutic potential for lung inflammation. Transient Receptor Potential Ankyrin 1 (TRPA1), which is essential for the control of inflammation, may be involved in the development of inflammation in the lungs. The aim of this study was to determine the TRPA1-targeted effects of PR against pulmonary inflammation and to investigate the affinity of PR constituents for TRPA1 and their potential mechanisms of action. Using a C57BL/6J mouse lipopolysaccharides (LPS) intratracheal instillation pneumonia model and advanced analytical techniques (UPLC-Q-TOF-MS/MS, molecular docking, immuno-fluorescence), five platycodins were isolated from PR, and the interaction between these platycodins and hTRPA1 was verified. Additionally, we analyzed the impact of platycodins on LPS-induced TRPA1 expression and calcium influx in BEAS-2B cells. The results indicated that PR treatment significantly reduced the severity of LPS-triggered inflammation in the mouse model. Interestingly, there was a mild increase in the expression of TRPA1 caused by PR in healthy mice. Among five isolated platycodins identified in the PR extract, Platycodin D3 (PD3) showed the highest affinity for hTRPA1. The interaction between platycodins and TRPA1 was verified through molecular docking methods, highlighting the significance of the S5–S6 pore-forming loop in TRPA1 and the unique structural attributes of platycodins. Furthermore, PD3 significantly reduced LPS-induced TRPA1 expression and calcium ion influx in BEAS-2B cells, substantiating its own role as an effective TRPA1 modulator. In conclusion, PR and platycodins, especially PD3, show promise as potential lung inflammation therapeutics. Further research should explore the precise mechanisms by which platycodins modulate TRPA1 and their broader therapeutic potential.

Some high-index facets of BiVO4, such as (012), (210), (115), (511), (121), (132) and (231), exhibit much better photocatalytic performance than conventional (010) and (110) surfaces for water splitting. However, the detailed mechanisms and stability of improved photocatalytic performance for these high-index BiVO4 surfaces are still not clear, which is important for designing photocatalysts with high efficiency. Here, based on first principle calculation, we carried out a systematic theoretical research on BiVO4 with different surfaces, especially high-index facets. The results show that all of the high-index facets in our calculated systems show an n-type behavior, and the band edge positions indicate that all of the high-index facets have enough ability to produce O2 without external bias. Electronic structures, band alignments and formation enthalpy indicate that (012), (115) and (132) could be equivalent to (210), (511) and (231), respectively, in the calculation. Oxidation and reduction potential show that only (132)/(231) is stable without strongly oxidative conditions, and the Gibbs free energy indicates that (012)/(210), (115)/(511), (121) and (132)/(231) have lower overpotential than (010) and (110). Our calculation is able to unveil insights into the effects of the surface, including electronic structures, overpotential and stability during the reaction process.

As one of the most important components of the innate immune system, neutrophils are always at the forefront of the response to diseases. The immune functions of neutrophils include phagocytosis, degranulation, production of reactive oxygen species, and the production of neutrophil extracellular traps (NETs). NETs are composed of deconcentrated chromatin DNA, histones, myeloperoxidase (MPO) and neutrophil elastase (NE), playing an important role in the resistance to some pathogenic microbial invasions. Until recent years, when NETs were found to play a critical role in cancer. NETs play bidirectional regulation both positive and negative roles in the development and progression of cancer. Targeted NETs may provide new therapeutic strategies for the treatment of cancer. However, the molecular and cellular regulatory mechanisms underlying the formation and role of NET in cancer remain unclear. This review just summarizes the recent progress in regulatory mechanisms about the formation of NETs and their role in cancers.

In this study, we investigated the role of ferroptosis in the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC), the leading cause of renal cancer-related death. We analyzed single-cell data from seven ccRCC cases to determine cell types most correlated with ferroptosis and performed pseudotime analysis on three myeloid subtypes. We identified 16 immune-related ferroptosis genes (IRFGs) by analyzing differentially expressed genes between cell subgroups and between high and low immune infiltration groups in the TCGA-KIRC dataset and the FerrDb V2 database. Using univariate and multivariate Cox regression, we identified two independent prognostic genes, AMN and PDK4, and constructed an IRFG score model immune-related ferroptosis genes risk score (IRFGRs) to evaluate its prognostic value in ccRCC. The IRFGRs demonstrated excellent and stable performance for predicting ccRCC patient survival in both the TCGA training set and the ArrayExpress validation set, with an AUC range of 0.690–0.754, outperforming other commonly used clinicopathological indicators. Our findings enhance the understanding of TME infiltration with ferroptosis and identify immune-mediated ferroptosis genes associated with prognosis in ccRCC.

As the first caprine enterovirus identified from goat herds characterized by severe diarrhea with a high morbidity and mortality rate, the underlying pathogenesis and tissue tropism for CEV-JL14 remains largely unknown. Here, we reported the establishment of a neonatal murine model for caprine enterovirus and the unveiling of the tissue tropism and underlying pathogenesis for CEV-JL14 enterovirus. Susceptible murine strains, the infective dose, the infective routes, viral loads, and tissue tropism for CEV-JL14 infection were determined. The findings showed that ICR mice were susceptible to CEV-JL14 infection via all infection routes. Tissue viral load analysis showed that CEV-JL14 was detected in almost all tissues including the heart, liver, spleen, lung, kidney, intestine, brain, and muscle, with significantly higher viral loads in the heart, liver, lung, kidney, and intestine. These results revealed the pattern of viral load and tropism for CEV-JL14 and provided a model system for elucidating the pathogenesis of CEV-JL14 viruses.

Scorias spongiosa, a type of edible fungus, is beneficial for intestinal health. However, the mechanisms by which polysaccharides derived from S. spongiosa contribute to the integrity of the intestinal barrier have been little investigated. In the present study, 40 C57BL/6J mice were assigned into five groups: (1) Normal; (2) Dextran sulfate sodium (DSS)Administration; (3) DSS + Uncapped polysaccharides; (4) DSS + Low microcapsules; (5) DSS + High microcapsules. After one week of administration of S. spongiosa polysaccharides, all mice, excluding the Normal group, had free access to the drinking water of 3.5% DSS for seven days. Serum and feces were then taken for analysis. Scanning electron microscopy analysis indicated the structure of the micro-capped polysaccharides with curcumin was completed with a rough surface, which differs from the uncapped polysaccharides. Noticeably, S. spongiosa polysaccharides enhanced intestinal barrier integrity as evidenced by increasing the protein levels of Claudin-1, ZO-1 and ZO-2. Low-capped polysaccharides mitigated the DSS-induced oxidative stress by increasing catalase (CAT) concentration and decreasing malondialdehyde (MDA) and myeloperoxidase (MPO) concentrations. Besides, DSS treatment caused a disturbance of inflammation and the contents of IL-1β, IL-6, TNF-α and CRP were downregulated and the contents of IL-4, IL-10 and IFN-γ were upregulated by S. spongiosa polysaccharides. Research on the potential mechanisms indicated that S. spongiosa polysaccharides inhibited the DSS-triggered activation of NF-κB signaling. Moreover, the JAK/STAT1 and MAPK pathways were suppressed by S. spongiosa polysaccharides in DSS-challenged mice, with Lcap showing the strongest efficacy. 16S rDNA amplicon sequencing revealed that the richness and diversity of the microbial community were reshaped by S. spongiosa polysaccharide ingestion. Therefore, our study substantiated that S. spongiosa polysaccharides exhibited protective effects against colitis mice by reshaping the intestinal microbiome and maintaining the balance of intestinal barrier integrity, antioxidant capacity and colonic inflammation through regulation of the NF-κB–STAT1–MAPK axis.

Water lilies (Nymphaea L.) are ancient angiosperms that can be cultivated in both fresh and brackish water. Water lily plants have adapted morphologically and physiologically to the aqueous environment. Nonetheless, little is known about the regulatory mechanisms that enable water lily to acclimate to saline conditions, restricting its production and distribution. To illustrate the role of roots in water lily salinity tolerance, we investigated the adaptive regulation of the water lily root system under high salinity. Aspects of its root architecture, including root length, surface area, volume, and tip number, were significantly reduced by salt stress. Transcriptome sequencing showed that 120 genes were upregulated and 1214 genes were downregulated under salt stress. The differentially expressed genes were mainly enriched in oxidoreductase activity, structural molecule activity, and transmembrane transporter activity. Most ion transporter genes were downregulated, suggesting that water lily may partially close ion channels and/or transporters to avoid excessive ion accumulation or ion imbalance under long-term salt stress. Genes related to NO3− transport were both up- and downregulated, whereas genes related to ammonium transport were uniformly downregulated, suggesting that transcriptional changes may play a role in balancing nitrogen metabolism under long-term saline conditions. The roots showed relatively high concentrations of Na+ and had the ability to hyper-accumulate Na+ under salt stress. These findings provide insight into the regulatory mechanisms that enable water lily roots to tolerate salinity and lay a foundation for the breeding of salt-tolerant cultivars.

The free-piston engine is a type of none-crank engine that could be operated under variable compression ratio, and this provides it flexible fuel applicability and low engine emission potential. In this work, several 1-D engine models, including conventional gasoline engines, free-piston gasoline engines and free-piston hydrogen engines, have been established. Both engine performance and emission performance under engine speeds between 5–11 Hz and with different equivalent ratios have been simulated and compared. Results indicated that the free-piston engine has remarkable potential for NOx reduction, and the largest reduction is 57.37% at 6 Hz compared with a conventional gasoline engine. However, the figure of NOx from the hydrogen free-piston engine is slightly higher than that of the gasoline free-piston engine, and the difference increases with the increase of engine speed. In addition, several factors and their relationships related to hydrogen combustion in the free-piston engine have been investigated, and results show that the equivalent ratio φ=0.88 is a vital point that affects NOx production, and the ignition advance timing could also affect combustion duration, the highest in-cylinder temperature and NOx production to a large extent.

Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.

An important principle in rational manufacturing design is matching the properties of composites to their intended uses. Herein, six laminated composites (LCs) were manufactured using fibrous moso bamboo and poplar veneer units, and their pore structure, water resistance, and mechanical properties were evaluated. The LC density (640–1290 kg/m3) increased significantly with increasing bamboo veneer unit content. The LC surface texture and roughness depended on the density and type of surface layer. With increasing LC density, the water absorption rate (WAR), width swelling rate (WSR), and thickness swelling rate (TSR) decreased exponentially and the mechanical properties increased linearly. This behavior was closely related to the changes in pore structure caused by density. Notably, the water resistance and mechanical properties of the LCs with densities higher than 910 kg/m3 were superior to the highest levels specified in GB/T 20241–2006 for ‘‘laminated veneer lumber’’ and GB/T 30364–2013 for “bamboo scrimber flooring”. Thus, these engineered materials are promising for outdoor structures and flooring.

Piezo1 was originally identified as a mechanically activated, nonselective cation ion channel, with significant permeability to calcium ions, is evolutionally conserved, and is involved in the proliferation and development of various types of cells, in the context of various types of mechanical or innate stimuli. Recently, our study and work by others have reported that Piezo1 from all kinds of immune cells is involved in regulating many diseases, including infectious inflammation and cancer. This review summarizes the recent progress made in understanding the immunoregulatory role and mechanisms of the mechanical receptor Piezo1 in inflammation and cancer and provides new insight into the biological significance of Piezo1 in regulating immunity and tumors.

In the Bering Sea slope, ocean eddies are essential physical processes that carry nutrients to the shelf. The development of the satellite altimeter has facilitated the observation of oceanic eddies. Attention networks are used as the core algorithm for eddy detection to suppress feature responses in irrelevant non-eddy areas, which can address the issue of sample imbalance in high-latitude ocean eddies. Furthermore, data from both the sea surface height (SSH) and geostrophic velocity were employed as model inputs to integrate more eddy-related properties. The results of ocean eddy detection using this method and the dataset allowed more eddies to be detected than with traditional vector geometry-based methods and only SSH-based models. This study also incorporated the results of multiple deep learning models to increase both the overall and single-day eddy detection efficiency. As a result, the algorithms in this paper show that attention networks and geostrophic velocity data are both appropriate for high-latitude ocean eddy identification. This makes a contribution to the further application of deep learning methods to satellite altimetry data.

Traditional methods of quantifying epidemic spread are based on surveillance data. The most widely used surveillance data are normally incidence data from case reports and hospital records, which are normally susceptible to human error, and sometimes, they even can be seriously error-prone and incomplete when collected during a destructive epidemic. In this manuscript, we introduce a new method to study the spread of infectious disease. We gave an example of how to use this method to predict the virus spreading using the HIV gene sequences data of China. First, we applied Bayesian inference to gene sequences of two main subtypes of the HIV virus to infer the effective reproduction number (GRe(t)) to trace the history of HIV transmission. Second, a dynamic model was established to forecast the spread of HIV medication resistance in the future and also obtain its effective reproduction number (MRe(t)). Through fitting the two effective reproduction numbers obtained from the two separate ways above, some crucial parameters for the dynamic model were obtained. Simply raising the treatment rate has no impact on lowering the infection rate, according to the dynamics model research, but would instead increase the rate of medication resistance. The negative relationship between the prevalence of HIV and the survivorship of infected individuals following treatment may be to blame for this. Reducing the MSM population’s number of sexual partners is a more efficient strategy to reduce transmission per the sensitivity analysis.

Fractionation of stable isotopes in precipitation runs through the water cycle, and deuterium excess is a second-order parameter linking water-stable oxygen and hydrogen isotopes. It is strongly influenced by under-cloud evaporation in unsaturated air, especially in arid climates. Based on the improved Stewart model, this study used 670 precipitation stable isotope data and measured meteorological element data from 11 sampling points from January 2018 to September 2019 to verify the existence of sub-cloud secondary evaporation in the Shiyang river basin and quantitatively calculate the intensity of sub-cloud secondary evaporation and its influence on precipitation stable isotopes. The study used the vapor flux and the improved Lagrangian model to track the moisture source of precipitation and analyze the influence of the moisture source of different paths on the stable isotopes of precipitation. Therefore, this study is helpful to understand the evapotranspiration loss mechanism and recharge mechanism of moisture in the watershed. The results showed that there is sub-cloud secondary evaporation in the Shiyang River Basin, and from the seasonal scale, the sub-cloud secondary evaporation is stronger in spring and summer, but weaker in autumn and winter, which makes heavy isotopes enriched in spring and summer and depleted in autumn and winter. From the perspective of spatial distribution, the sub-cloud secondary evaporation is stronger in the midstream and downstream of the Shiyang river, resulting in more enrichment of heavy isotopes. In the vertical direction, the sub-cloud secondary evaporation at 850–700 hPa is the strongest, which enriches the heavy isotope in this layer and reduces the deuterium excess. In addition, the main moisture source of precipitation in the Shiyang River Basin is the westerly air mass, and the mid and high-latitude land sources contribute more moisture to the precipitation. However, the supply of the sea source is very limited, which makes the deuterium excess of precipitation higher and does not show regional consistency and seasonality well.

Insole plantar stress detection (PSD) techniques play an important role in gait monitoring. Among the various insole PSD methods, piezoelectric- and piezoresistive-based architectures are broadly used in medical scenes. Each year, a growing number of new research outcomes are reported. Hence, a deep understanding of these two kinds of insole PSD sensors and state-of-the-art work would strongly benefit the researchers in this highly interdisciplinary field. In this context, this review article is composed of the following aspects. First, the mechanisms of the two techniques and corresponding comparisons are explained and discussed. Second, advanced materials which could enhance the performance of current piezoelectric and piezoresistive insole prototypes are introduced. Third, suggestions for designing insole PSD prototypes/products for different diseases are offered. Last, the current challenge and potential future trends are provided.

Antibodies in response to antigens are related to the immune repertoire of T- and B-cell receptors. However, some patients with chronic hepatitis B (CHB) have coexisting HBsAg and anti-HBsAg antibodies (anti-HBs) that cannot neutralize HBV. We attempted to investigate the repertoires that produce this response in CHB patients. The T-cell receptor β chain (TRB) and B-cell receptor (BCR) repertoires of peripheral blood genomic DNA were analyzed using MiXCR. T-cell receptor (TCR) cluster analysis was carried out by clusTCR, and motifs prediction was selected by Multiple Em for Motif Elicitation (MEME). A total of 76 subjects were enrolled, including 26 HBsAg and anti-HBs coexisting patients with CHB (DP group), 25 anti-HBs single-positive healthy people (SP group), and 25 CHB patients (CHB group). The clone length of BCR in 39, 90 was significantly different among these groups (p = 0.005, 0.036). The motif “CASSLG” in the DP group was significantly higher than SP and CHB groups and may relate to coexistence, and the motif “GAGPLT” was only shown in the SP group and may relate to anti-HB expression. These provide important insights into vaccine development and CHB treatment.

As a basic method of spatial data operation, spatial keyword query can provide meaningful information to meet user demands by searching spatial textual datasets. How to accurately understand users’ intentions and efficiently retrieve results from spatial textual big data are always the focus of research. Spatial textual big data and their complex correlation between textual features not only enrich the connotation of spatial objects but also bring difficulties to the efficient recognition and retrieval of similar spatial objects. Because there are a lot of many-to-many relationships between massive spatial objects and textual features, most of the existing research results that employ tree-like and table-like structures to index spatial data and textual data are inefficient in retrieving similar spatial objects. In this paper, firstly, we define spatial textual concept (STC) as a group of spatial objects with the same textual keywords in a limited spatial region in order to present the many-to-many relationships between spatial objects and textual features. Then we attempt to introduce the concept lattice model to maintain a group of related STCs and propose a hybrid tree-like spatial index structure, the lattice-tree, for spatial textual big data. Lattice-tree employs R-tree to index the spatial location of objects, and it embeds a concept lattice structure into specific tree nodes to organize the STC set from a large number of textual keywords of objects and their relationships. Based on this, we also propose a novel spatial keyword query, named Top-k spatial concept query (TkSCQ), to answer STC and retrieve similar spatial objects with multiple textual features. The empirical study is carried out on two spatial textual big data sets from Yelp and Amap. Experiments on the lattice-tree verify its feasibility and demonstrate that it is efficient to embed the concept lattice structure into tree nodes of 3 to 5 levels. Experiments on TkSCQ evaluate lattice from results, keywords, data volume, and so on, and two baseline index structures based on IR-tree and Fp-tree, named the inverted-tree and Fpindex-tree, are developed to compare with the lattice-tree on data sets from Yelp and Amap. Experimental results demonstrate that the Lattice-tree has the better retrieval efficiency in most cases, especially in the case of large amounts of data queries, where the retrieval performance of the lattice-tree is much better than the inverted-tree and Fpindex-tree.

The aim of this paper is to optimize the working parameters of the arc-shaped nail-tooth roller-type recovery machine for sowing layer residual film. Firstly, the tooth roller device of the residual film recovery machine is designed, and the main working parameters affecting the operation of the machine and the value range of each parameter are determined through the analysis of the operation process. Secondly, virtual simulation technology is used to establish a virtual simulation model of the interaction process between the tooth roller device and soil. At the same time, taking the soil-hilling quantity as the index, we build a quadratic regression mathematical model with three factors—the forward speed, rotation speed, and working depth—using the Box–Behnken method. Consequently, the analysis of the simulation results show that the order of the most significant factors is working depth, rotation speed, and forward speed. The optimal combination of working parameters are as follows: a forward speed of 4.5 km/h, a rotation speed of 43.2 r/min, and a working depth of 100.0 mm. Meanwhile, the predicted value of the soil-hilling quantity is 23.1 kg. Finally, we carried out field tests using the optimal combination parameters; the results show that the normal residual film collection rate is 66.8%, the soil-hilling quantity is 24.2 kg, and the relative error between the test value and the predicted value is 4.8%. This indicates that the devised DEM simulation model can be used to predict the operational performance of the tooth roller device in the working process. This study provides a reference that can be used in the planning and boundary enhancement of agricultural machinery and equipment.

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.

Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions.

There is a strong need to eliminate batch-specific differences when integrating single-cell RNA-sequencing (scRNA-seq) datasets generated under different experimental conditions for downstream task analysis. Existing batch correction methods usually transform different batches of cells into one preselected “anchor” batch or a low-dimensional embedding space, and cannot take full advantage of useful information from multiple sources. We present a novel framework, called IMGG, i.e., integrating multiple single-cell datasets through connected graphs and generative adversarial networks (GAN) to eliminate nonbiological differences between different batches. Compared with current methods, IMGG shows excellent performance on a variety of evaluation metrics, and the IMGG-corrected gene expression data incorporate features from multiple batches, allowing for downstream tasks such as differential gene expression analysis.

Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the interval values were measured through angle of repose tests of fallen jujube fruit (FJF), and the simulation angle of repose tests for FJF were established with EDEM software (DEM Solutions Ltd. Edinburgh, Scotland, UK). Then, the Plackett-Burman design, steepest ascent search experiment, and center composite design experimental methods were utilized to obtain the specific values of the simulation parameters from the interval values. The results showed that significant influencing factors in the simulation angle of repose include the Poisson’s ratio, the static friction coefficient between FJF, and the static friction coefficient between FJF and the steel plate, for which the optimal values were 0.248, 0.480, and 0.309, respectively. The angle of repose tests’ results showed that the error was 0.53% between the simulation angle of repose (29.69°) and the angle of repose (29.85°). In addition, the flow rate test results showed that the average error was 5.84% between the physical and simulation tests. This indicated that the calibrated parameters were accurate and reliable, and that the simulation model can accurately represent the physical tests. Consequently, this study provides an EDEM model of FJF that was essential in designing machinery and equipment through the EDEM simulation method.

Despite the interaction between bone marrow mesenchymal stem cells (BMSCs) and macrophages has been found to play a critical role in repairing bone defects, it remains a challenge to develop a desirable tissue engineering scaffold for synchronous regulation of osteogenic differentiation and macrophage polarization. Herein, this study proposed a novel strategy to treat bone defects based on three-dimensional Gelatin Methacryloyl Inverted Colloidal Crystal (3D GelMA ICC) scaffold and an active 15-hydroxyprostaglandin dehydrogenase (15-PGDH) inhibitor SW033291. Specifically, the 3D GelMA ICC scaffolds were firstly prepared by colloidal templating method, which displayed good cell attachment and promoted intercellular interaction among macrophage and BMSCs due to its uniform pore interconnectivity. By combined use of SW033291, the release of Prostaglandin E2 (PGE2) from BMSCs on the GelMA ICC scaffold was significantly upregulated and macrophages M2 polarization was markedly increased. In turn, BMSCs proliferation and osteogenic differentiation was further enhanced by paracrine regulation of M2 macrophage, and thus finally caused more in vivo new bone formation by shaping up a pro-regenerative local immune microenvironment surrounding GelMA ICC scaffold. Our findings demonstrate the potential of 3D GelMA ICC scaffolds combined with SW033291 to become an effective tissue engineering strategy for bone regeneration.