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Astragalus polysaccharides (APSs), the compounds extracted from the common herb Astragalus membranaceus, have been extensively studied for their antitumor properties. In this study, we investigated the effect of APS on lung adenocarcinoma A549 cells. The effects of APS and the anti-diabetic drug metformin on apoptosis and ferroptosis were compared. Furthermore, the combination treatment of APS and metformin was also investigated. We found that APS not only reduced the growth of lung cancer cells but also had a synergistic effect with metformin on A549 cells. The study results showed that it may be promising to use APS and metformin as a combination therapy for the treatment of lung adenocarcinoma.

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk.

The transition from vernacular architectural patterns to current architecture in rural Iran has led to various socio-cultural and environmental problems in the last decade. This study explores the nature of this transition, which has been overlooked in the studies of vernacular architecture in Iran. Furthermore, this article contributes to the ongoing academic debate on the decline and transformation of vernacular architectural patterns in the context of modernization. It analyzes the forces behind the decline and rise of vernacular settlements in a case study area, the Salami region of the Khaf district in Iran’s Khorasan Razavi province, by exploring how it is possible to reinterpret vernacular architectural patterns in the context of current architecture to utilize the new developments in rural Iran not as an obstacle but as an opportunity for improvement. To this end, this article explores vernacular architectural patterns in a case study area in Iran, supported by socio-cultural aspects and the environmental conditions of the region. This study conducts architectural and anthropological fieldwork on three vernacular houses in a case study area and uses participant observation and informal interview methods to understand the people and their interaction with their built environment. The findings of this article thus contradict previous studies on learning from vernacular architecture by shedding light on vernacular architecture in Iran as a system by demonstrating the inextricable links between different vernacular architectural patterns. Therefore, this study argues that to draw lessons from vernacular architectural patterns for current architecture in rural Iran, it is necessary to limit its communication concerning people’s contemporary needs but not to neglect this communication completely.

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

To reduce the losses caused by insufficient preservation efforts during transportation, the preservation effort level has been the focus of research. In the fierce competition of online sales, it is particularly important to reduce the cost of damaged goods by improving the level of preservation efforts. Therefore, according to Stackelberg game theory, this article establishes five decision-making models and incorporates the damage rate and preservation effort level into the research. Finally, this article coordinates the online shipping supply chain (SC) through a joint contract. After comparing and analyzing the model results, research has found that: (1) in centralized model, the level of preservation effort reaches its optimal level and the system benefit is maximized; (2) under third-party logistics (TPL) leading decision-making, the different bearers of cargo damage costs will not affect the profits of both parties and the system; (3) among the four decentralized models, the level of preservation efforts and system profit are highest when the decision is led by online store and TPL bears the cost of damaged goods; and (4) under a given sharing ratio, when the logistics service quotation satisfies a certain range of condition, the online shopping SC can achieve Pareto improvement. This paper studies the differences and reasons for decision models in the supply and demand relationship between online stores and TPL, which provides fresh product e-commerce decision-makers with a theoretical basis.

Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

The study aims to characterize the immune cell landscape in convalescent Asian seabass (Lates calcarifer) blood samples after exposure to scale-drop disease virus (SDDV). Traditional immunophenotyping approaches used in human and mouse studies are impractical for non-model organisms like the Asian seabass due to the lack of specific antibody-based reagents. To overcome this challenge, 10x Genomics single-cell RNA sequencing was employed. The analysis of blood samples revealed 24 distinct leukocyte clusters, with elevated proportions of B cells, granulocytes, and T cells in the convalescent group compared to the uninfected group. While distinguishing granulocyte and macrophage subsets was challenging, the analysis of differential gene expression in the macrophage population indicated that the upregulated genes were linked to inflammatory processes. Specific T cell clusters showed notable expressions of cd4-1, cd8a, perforin-1 and il-2rβ, suggesting the presence of CD4+ T helper (Th), CD8+ cytotoxic T (Tc) cells, immature T cells, and naive T cells. Attempts to categorize CD4+ T cells into Th subtypes lacked clear distinctions, while CD8+ T cells exhibited three clusters, predominantly Tc1 cells. Furthermore, comparisons between convalescent and uninfected groups revealed increased percentages of activated and antibody-secreting B cells in the convalescent group. This single-cell analysis provides vital insights into the immune cell dynamics in convalescent and uninfected Asian seabass, providing valuable information on potential immune responses to SDDV infection.

Iron is essential for many physiological processes, and the dysregulation of its metabolism is implicated in the pathogenesis of various diseases. Recent advances in iron metabolism research have revealed multiple complex pathways critical for maintaining iron homeostasis. Molecular imaging, an interdisciplinary imaging technique, has shown considerable promise in advancing research on iron metabolism. Here, we comprehensively review the multifaceted roles of iron at the cellular and systemic levels (along with the complex regulatory mechanisms of iron metabolism), elucidate appropriate imaging methods, and summarize their utility and fundamental principles in diagnosing and treating diseases related to iron metabolism. Utilizing molecular imaging technology to deeply understand the complexities of iron metabolism and its critical role in physiological and pathological processes offers new possibilities for early disease diagnosis, treatment monitoring, and the development of novel therapies. Despite technological limitations and the need to ensure the biological relevance and clinical applicability of imaging results, molecular imaging technology’s potential to reveal the iron metabolic process is unparalleled, providing new insights into the link between iron metabolism abnormalities and various diseases.

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representation learning, FSFGIC methods can make better use of limited sample information, learn more discriminative feature representations, greatly improve the classification accuracy and generalization ability, and thus achieve better results in FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local and/or global deep feature representation learning, class representation learning, and task-specific feature representation learning). In addition, the existing popular datasets, current challenges and future development trends of feature representation learning on FSFGIC are also described.

Vehicles often experience low tire pressures and high torques in off-road operations, making tire–rim slip likely. Tire–rim slip is undesirable relative rotation between the tire and rim, which, in this study, is measured by the relative tire–rim slip rate. There is little research on the effect of different terrains on tire–rim slip despite its significance for off-road driving; therefore, this topic was explored through Finite Element Analysis (FEA) simulations. An upland sandy loam soil was modelled and calibrated using Smoothed-Particle Hydrodynamics (SPH), and then a Regional Haul Drive (RHD) truck tire was simulated driving over this terrain, with a drawbar load added to increase drive torque. To examine their effects, five parameters were changed: tire–rim friction coefficient, longitudinal wheel speed, drawbar load, vertical load, and inflation pressure. The simulations showed that increasing the tire–rim friction coefficient and the inflation pressure decreased the tire–rim slip while increasing the vertical and drawbar loads increased the tire–rim slip. Varying the longitudinal wheel speed had no significant effect. Tire–rim slip was more likely to occur on the soil because it happened at lower drawbar loads on the soil than on the hard surface. These research results increased knowledge of tire–rim slip mechanics and provided a foundation for exploring tire–rim slip on other terrains, such as clays or sands.

This paper focuses on the modeling and analysis of a four-groove passenger car tire, size 235/55R19, using finite element analysis. The Mooney–Rivlin material model is employed to define the hyperelastic behavior of the tire rubber compounds for all solid elements. The tire rim is modeled as a rigid body using aluminum alloy material, and the beads are modeled as beam elements using steel material. The tire model is validated in both static and dynamic domains through several simulations and is compared to published measured data. The tire is validated using footprint and vertical stiffness tests in the static domain. In the static footprint test, a steady-state vertical load is applied, and the tire–road contact area is computed. In the vertical stiffness test, a ramp vertical load is applied, and the tire’s vertical displacement is measured to calculate the tire’s vertical stiffness. In the dynamic domain, the tire is validated using drum-cleat and cornering tests. In the drum-cleat test, a drum with a 2.5 m diameter and a cleat with a 15 mm radius is used to excite the tire structure and obtain the frequency of the vertical and longitudinal first modes of vibration, that is, by applying the fast Fourier transformation (FFT) of the vertical and longitudinal reaction forces at the tire center. In addition to this test, the tire model is pre-steered on a flat surface with a two-degree slip angle and subjected to a steady state linear speed of 10 km/h to predict the cornering force and compute the cornering stiffness. In addition, the effect of tire longitudinal speed on the rolling resistance coefficient is then predicted at zero slip angle using the ISO 28580 rolling resistance test. The findings of this research work provide insights into passenger car tire–road interaction analysis and will be further used to perform tire rubber compound material model sensitivity analysis.

Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing factors of China’s aquaculture mechanization were systematically analyzed. The triple bottom theory was selected, and three aspects were identified, including environmental, economic, and social aspects. Through the literature review, the Delphi method, and the analytic hierarchy process, the comprehensive evaluation indicator system, including 18 influencing factors, was proposed. Moreover, the fuzzy comprehensive evaluation method was combined with the model to solve the evaluation results. A case study in Liaoning Province was offered and, according to the analysis results, the economic aspect at the first level was the most critical factor; the financial subsidy for the purchase of aquaculture machinery, the energy consumption of the machinery and equipment, and the promotion and use of aquaculture technology were the most important factors and had the greatest impact on the development of aquaculture mechanization in China. The effective implementation paths and countermeasures were proposed, such as the promotion of mechanized equipment and the enhancement of the machinery purchase subsidies, in order to provide an important decision-making basis for the improvement of the level of aquaculture mechanization.

The removal of air pollutants is an important research topic in order to improve the environment. In addition, many common pollutants can affect human health to varying degrees. In this work, we investigate NO and SO2 conversion by reaction with a commonly used metal oxide catalyst, TiO2. Rutile TiO2(110) single crystals and industrial powder samples used in sunscreen are studied using near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) as a main tool. This allows in situ monitoring of the gas conversion process. We find Ti3+ defects (oxygen vacancies) or Mn oxides/cations (MnO) at the TiO2 surfaces can improve the conversion of NO and SO2 to surface-bound species. MnO and Ti3+ defects at the surface of rutile TiO2(110) exhibit a synergistic effect on the conversion of NO and SO2 that is significantly improved by nearly an order of magnitude. The by-products are mainly in the form of NO3−, SO32−, and SO42−. We find the main oxidation products formed on the single crystals are subtly different from those on the industrial powder samples. For TiO2 nanopowders (undoped and Mndoped), the presence of Mn also shows improvement in toxic gas adsorption capacity. Overall, it is believed that the outcome obtained from NAP-XPS in this research provides useful insights for the future use of TiO2 in pollutant gas capture.

A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, despite its few demerits. Sufficient progress in accuracy and depth computation speed has manifested during the period. Over the years, stereo depth estimation has been provided with various training modes, such as supervised, self-supervised, and unsupervised, before deploying it for real-time performance. These modes are to be used depending on the application and/or the availability of datasets for training. Deep learning, on the other hand, has provided the stereo depth estimation methods with a new life to breathe in the form of enhanced accuracy and quality of images, attempting to successfully reduce the residual errors in stages in some of the methods. Furthermore, depth estimation from a single RGB image has been intricate since it is an ill-posed problem with a lack of geometric constraints and ambiguities. However, this monocular depth estimation has gained popularity in recent years due to the development in the field, with appreciable improvements in the accuracy of depth maps and optimization of computational time. The help is mostly due to the usage of CNNs (Convolutional Neural Networks) and other deep learning methods, which help augment the feature-extraction phenomenon for the process and enhance the quality of depth maps/accuracy of MDE (monocular depth estimation). Monocular depth estimation has seen improvements in many algorithms that can be deployed to give depth maps with better clarity and details around the edges and fine boundaries, which thus helps in delineating between thin structures. This paper reviews various recent deep learning-based stereo and monocular depth prediction techniques emphasizing the successes achieved so far, the challenges acquainted with them, and those that can be expected shortly.

The COVID-19 pandemic precipitated drastic changes in cancer care. Its impact on the U.S. head and neck cancer population has yet to be fully understood. This study aims to understand the impact of pandemic-related changes on the head and neck cancer population. An observational study of head and neck cancer patients at a single institution during the spring of 2020 and 2019 was performed. Clinical characteristics and survival outcomes were analyzed. In 2020, 54 head and neck cancer patients were evaluated in the department of radiation oncology vs. 74 patients seen in 2019; 42% of the patients were female in 2019 versus 24% in 2020 (p = 0.036). The median follow-up time was 19.4 and 31 months for 2020 and 2019, respectively. After adjusting for stage, the relapse-free survival probability at 6 and 12 months was 79% and 69% in 2020 vs. 96% and 89% in 2019, respectively (p = 0.036). There was no significant difference in the overall survival, with 94% and 89% in 2020 and 2019, respectively (p = 0.61). Twenty-one percent of patients received induction chemotherapy in 2020 versus 5% in 2019 (p = 0.011); significantly more treatment incompletions occurred in 2020, 9% vs. 0% in 2019 (p = 0.012). Moreover, the stage-adjusted RFS differed between cohorts, suggesting head and neck cancer patients seen during the initial wave of COVID-19 may experience worse oncologic outcomes.

Accurate segmentation of the pulmonary airway tree is crucial for diagnosing lung diseases. To tackle the issues of low segmentation accuracy and frequent leaks in existing methods, this paper proposes a precise segmentation method using quasi-spherical region-constrained wavefront propagation with tracheal wall gap sealing. Based on the characteristic that the surface formed by seed points approximates the airway cross-section, the width of the unsegmented airway is calculated, determining the initial quasi-spherical constraint region. Using the wavefront propagation method, seed points are continuously propagated and segmented along the tracheal wall within the quasi-spherical constraint region, thus overcoming the need to determine complex segmentation directions. To seal tracheal wall gaps, a morphological closing operation is utilized to extract the characteristics of small holes and locate low-brightness tracheal wall gaps. By filling the CT values at these gaps, the method seals the tracheal wall gaps. Extensive experiments on the EXACT09 dataset demonstrate that our algorithm ranks third in segmentation completeness. Moreover, its performance in preventing airway leaks is significantly better than the top-two algorithms, effectively preventing large-scale leak-induced spread.

Rubble-pile asteroids may be the type of near-Earth object most likely to threaten Earth in a future collision event. Small-scale impact experiments and numerical simulations for large-scale impacts were conducted to clarify the size ratio of the boulder/projectile diameter effects on ejecta size–velocity distribution. A series of small-scale impact cratering experiments were performed on porous gypsum–basalt targets at velocities of 2.3 to 5.5 km·s−1. Three successive ejection processes were observed by high-speed and ultra-high-speed cameras. The momentum transfer coefficient and cratering size were measured. A three-dimensional numerical model reflecting the random distribution of the interior boulders of the rubble-pile structure asteroid is established. The size ratio (length to diameter) of the boulder size inside the asteroid to the projectile diameter changed from 0.25 to 1.7. We conducted a smoothed particle hydrodynamics numerical simulation in the AUTODYN software to study the boulder size effect on the ejecta size–velocity distribution. Simulation results suggest that the microscopic porosity on regolith affects the propagation of shock waves and reduces the velocity of ejecta. Experiments and numerical simulation results suggest that both excavation flow and spalling ejection mechanism can eject boulders (0.12–0.72 m) out of the rubble-pile asteroid. These experiments and simulation results help us select the potential impact site in a planetary defense scenario and reduce deflection risk. are comprised primarily of boulders of a range of sizes.

To explore the role of the membrane permease ⅡB (EⅡB) gene of Listeria pathogenicity island 4 (LIPI-4) in the virulence of Listeria monocytogenes, both an EⅡB deletion strain (∆EⅡB) and a complemented strain were constructed. In vitro experiments demonstrated that EⅡB deletion affected the biofilm formation ability of the wild-type strain (Lm928). Moreover, this deletion decreased the intracellular proliferation abilities of L. monocytogenes. Mice infected with ∆EⅡB survived longer and experienced less weight loss on days 1, 2, and 3 post-infection. The bacterial load in the liver tissue of ∆EⅡB-infected mice was significantly reduced, and a considerable decrease in the blood levels of inflammatory cytokines IL-β, IL-6, IL-10, and TNF-α were observed. Following EⅡB deletion, 65% (13/20) of genes were downregulated, 25% (5/20) were upregulated, and 10% (2/20) showed no change. These findings suggest that EⅡB deletion may reduce both the in vivo and in vitro virulence levels as well as the biofilm formation ability of Lm928 by downregulating the transcription levels of genes associated with virulence and biofilm formation. These findings provide a foundation for further examining the pathogenic mechanisms of LIPI-4 and EⅡB in L. monocytogenes.

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities.

Tea is a daily drink for most people, and one of its major ingredients, epigallocatechin-3-gallate (EGCG), has been widely recognized as a potent antioxidant with diverse biological activities. However, its low stability and bioavailability hinder its further clinical applications. In this study, we designed and synthesized a novel EGCG-valine derivative 4 by replacing the gallic acid with a valine moiety in four steps. The structural elucidation of derivative 4 was performed using NMR, IR, mass, and UV spectroscopies. Additionally, the physicochemical properties of 4 were predicted by SwissADME, showing improved drug-like parameters and intestinal absorption compared to the parent compound EGCG.

In this report, we discuss the synthesis of a compound obtained from the derivatization of the natural compound celastrol. This derivative is connected to PEG azide moiety through an amide linkage. The linkage was achieved through the activation of the carboxylic acid using HOBt/EDC. The compound was fully characterized by proton (1H), carbon-13 (13C), heteronuclear single quantum coherence (HSQC), correlation spectroscopy (1H-1H-COSY), and distortionless enhancement by polarization transfer (DEPT) NMR. Ultraviolet (UV), Fourier-transform infrared (FTIR), and high-resolution mass spectrometry (HRMS) were also adopted. Computational investigations were conducted to forecast the binding mode between the synthesized compound and sarco-endoplasmic reticulum (SR) Ca2+ transport ATPase (SERCA), a known target for the development of novel therapeutics for rheumatoid arthritis. Additionally, the drug-likeness of the synthesized compound was assessed by predicting its pharmacokinetic properties.

Lithium nickel–cobalt–aluminum oxide (NCA) is a promising cathode material for lithium-ion batteries due to its high energy density of more than 274 mAh/g. However, thermal runaway inhibits its practical applications. Lithium ferromanganese phosphate (LFMP), due to its olivine structure, can effectively stabilize the surface stability of NCA and reduce the exothermic reactions that occur during thermal runaway. LFMP can also inhibit cathode expansion and contraction during charging and discharging. To improve the conductivity of an NCM–LFMP composite electrode, three different conductive additives, namely carbon black, carbon nanotubes (CNTs), and graphene, were introduced into the electrode. Finally, battery safety tests were conducted on 1.1 Ah pouch cells fabricated in the present study. The energy density of the NCA–LFMP 1.1 Ah lithium-ion pouch cells with only 0.16% CNT content reached 224.8 Wh/kg. The CNT–NCA–LFMP pouch cell was also the safest among the cells tested. These results provide a strategy for designing high-energy-density and safe pouch cells for energy storage device applications.

Background: Nodal failure is a major failure pattern for patients with FIGO IIIC cervical cancer, which is further associated with worse survival. This study was designed to investigate risk factors for nodal failure in FIGO IIIC cervical cancer patients. Methods: The characteristics of positive lymph nodes (LNs) and relevant clinical factors of 162 FIGO IIIC cervical cancer patients were collected. The chi-square test and logistic regression model were used to identify risk factors for nodal failure. Results: In total, 368 positive LNs were identified, including 307 pelvic LNs and 61 para-aortic LNs. The nodal failure rates for all LNs, pelvic LNs, and para-aortic LNs were 9.2%, 7.8%, and 16.4%, respectively. After 20 fractions of RT, a nodal short diameter (D20F) ≥ 0.95 cm and a ratio of nodal shrinkage (ΔV20F) < 0.435 resulted; 20F < 0.435 and 20F < 0.435 was the only risk factor for nodal failure. Conclusions: Para-aortic LNs were more likely to experience nodal failure than pelvic LNs. Nodal shrinkage during radiotherapy and cycles of chemotherapy were associated with nodal failure in patients with FIGO IIIC cervical cancer.

Akebia trifoliata is an economically important, self-incompatible fruit tree in the Lardizabalaceae family. Asexual propagation is the main strategy used to maintain excellent agronomic traits. However, the generation of adventitious roots during asexual propagation is very difficult. To study the important role of the WUSCHEL-related homeobox (WOX) transcription factor in adventitious root growth and development, we characterized this transcription factor family in the whole genome of A. trifoliata. A total of 10 AktWOXs were identified, with the following characteristics: length (657~11,328 bp), exon number (2~5), isoelectric point (5.65~9.03), amino acid number (176~361 AA) and molecular weight (20.500~40.173 kDa), and their corresponding expression sequence could also be detectable in the public transcriptomic data for A. trifoliata fruit. A total of 10 AktWOXs were classified into modern (6), intermediate (2) and ancient clades (2) and all AktWOXs had undergone strong purifying selection during evolution. The expression profile of AktWOXs during A. trifoliata adventitious root formation indicated that AktWOXs play an important role in the regulation of adventitious root development. Overall, this is the first study to identify and characterize the WOX family in A. trifoliata and will be helpful for further research on A. trifoliata adventitious root formation.

DNA as an intriguing organic ligand has been widely employed for synthesizing metal nanoclusters and engineering their properties. This review aims to present recent progress on DNA-encoded metal (Ag, Cu, Au, Ag/Pt, Cu/Ag, etc.) nanoclusters (DNA-MNCs), focusing on their applications in the fields of analysis, logic operation, and therapy based on properties including fluorescence, electrochemiluminescence (ECL), and antibacterial and catalytic activity, and summarizes the attractive features of the latest research. The key points are briefly described as follows. (1) Analytical systems have been constructed based on fluorescence regulation, and nuclease-assisted and enzyme-free amplification strategies have been extensively adopted with fluorescent DNA-MNCs for amplified analysis. (2) DNA-MNCs may play more than one role (emitter, quencher, or catalyst) in ECL-based analytical systems. (3) Apart from antibacterial activity, DNA-MNCs also possess apparent catalytic capability, such as enzyme-like activity (i.e., nanozymes), which has been applied in colorimetric systems. (4) Reversibly regulating the catalytic activity of DNA-MNCs has been attained with DNA systems. It is believed that through in-depth investigation of the relationship between atomic structure and property, more novel DNA-MNCs will be explored and applied in the future.

We experience life and interact with others in a multitude of ways. The term ‘neurodivergence’ refers to variations from what is considered typical or normal. Neurodivergence influences an individual’s behaviour in social situations and is associated with atypical emotional responses. This can precipitate inequity and rejection. Neurodivergent females are especially prone to many physical and psychological health issues, and musculoskeletal disorders account for a significant proportion of these. Research and education into neurodivergent conditions in females should inform the reassessment of clinicians’ present approach to those who present with multiple unexplained symptoms. Obtaining official confirmation of a neurodivergent condition improves access to support services and helps them and their family better understand themselves and the challenges they face. This commentary highlights the increased risk of developing rheumatological disease for females with neurodivergent conditions and suggests how clinicians might increase their awareness of this.

Path planning is the most fundamental necessity for autonomous mobile robots. Traditionally, the path planning problem was solved using analytical methods, but these methods need perfect localization in the environment, a fully developed map to plan the path, and cannot deal with complex environments and emergencies. Recently, deep neural networks have been applied to solve this complex problem. This review paper discusses path-planning methods that use neural networks, including deep reinforcement learning, and its different types, such as model-free and model-based, Q-value function-based, policy-based, and actor-critic-based methods. Additionally, a dedicated section delves into the nuances and methods of robot interactions with pedestrians, exploring these dynamics in diverse environments such as sidewalks, road crossings, and indoor spaces, underscoring the importance of social compliance in robot navigation. In the end, the common challenges faced by these methods and applied solutions such as reward shaping, transfer learning, parallel simulations, etc. to optimize the solutions are discussed.

Incorporating silver wires into propellant has emerged as a highly effective strategy for enhancing propellant burning rates, a technique extensively deployed in the construction of numerous fielded sounding rockets and tactical missiles. Our research, employing a multi-faceted approach encompassing thermogravimetric-differential scanning calorimetry measurements (TG-DSC), combustion diagnoses, burning rate tests, and meticulous collection of condensed combustion products, sought to elucidate how variations in silver wire quantity and winding configuration impact the combustion properties of propellants. Our findings underscore the remarkable efficacy of double tightly twisted silver wire in significantly boosting propellant burning rates under ambient conditions. Moreover, at lower temperatures, the reduced gap between the propellant and silver wire further magnifies the influence of silver wire on burning rates. However, it is noteworthy that the relationship between burning speed and combustion efficiency is not deterministic. While a smaller cone angle of the burning surface contributes to heightened burning rates, it concurrently exacerbates the polymerization effect of vapor phase aluminum particles, consequently diminishing propellant combustion efficiency. Conversely, propellants configured with sparsely twinned silver wires exhibit notable enhancements in combustion efficiency, despite a less pronounced impact on the burning rate attributed to the larger cone angle of the burning surface. Remarkably, these trends persist at lower temperatures. Based on the principle of heat transfer balance, a theoretical model for the combustion of propellants with wire inserts is developed. The reliability of this theoretical model is validated through a comparison of calculated values with experimental data. Our research outcomes carry significant implications for guiding the application and advancement of the silver wire method in solid propellants for solid rocket motors, offering valuable insights to inform future research and development endeavors in this domain.

The accurate prediction of blade loading is crucial to designing efficient turbomachinery, but traditional methods often neglect the impact of compressibility, leading to inaccuracies at high speeds. This study investigates the effect of compressibility on the blade loading parameters, particularly the Zweifel coefficient (Zw) and lift coefficient (CL), in turbine cascades. A novel intermediate method (IM), with averaged flow properties derived from both inlet and outlet conditions, is proposed to enhance the accuracy of Zw and CL calculations in compressible flow regimes. This method is based on the extended Kutta–Joukowski theorem for compressible flow and incorporates the Mach number directly into the modified definitions of Zw and CL. The analysis reveals that the averaged flow angle (αm), calculated by using a velocity-weighted approach, serves as a crucial parameter for blade similarity studies. The proposed correction method is applied and validated based on CFD simulations of the VKI-RG turbine cascade. The IM and modified definitions provide a robust framework for accurately predicting blade loading at high speeds, enabling improved design and analysis of turbomachinery.

The perch landing maneuver of a wing-deforming unmanned aerial vehicle (UAV) was investigated through a framework that uses the free, open-source OpenFOAM with 6 degrees of freedom (6DOF) simulations. The framework uses a moving grid to follow the trajectory of the UAV, reducing computational resources. Together with the ability to allow internal grid deformation, sliding mesh, and algorithm addition, it can accurately mimic the entire landing process. Different wing deformation speeds, additional elevator rotation and emulated propeller lift were added to the 6DOF simulations to investigate their effects on the landing maneuver. The results showed that the wing deformation retraction speed has a considerable effect on the trajectory and velocity of the UAV. The wing deformation reduced the forward velocity of the UAV by 32%, from 13.89 to 9 m/s. With the elevator control, the velocity was reduced to 5 m/s. Lastly, and an activation time of 1 s for the emulated propeller lift can further decrease the velocity to around 4.2 m/s. A better algorithm for the emulated propeller lift may be able to give a superior performance. This framework allows us to understand the underlying perch landing maneuver aerodynamics. It can also be used on problems like fast-turning agile and flapping wing flight.

The traditional Satcom-on-the-Move (SOTM) mechanical structure consists of a dual-axis configuration with an azimuth axis and a pitch axis. In this structure, when the pitch angle is 90 degrees, the rotation of the azimuth axis cannot change the antenna’s direction. To solve this issue, a three-axis SOTM mechanical structure has been developed. The traditional three-axis SOTM servo control system adopts a closed-loop control scheme. In this scheme, due to the difficulty in directly obtaining the antenna’s rotation angle, the angles of rotation for each axis are typically selected to represent the antenna’s rotation angle. The closed-loop feedback includes the angles and angular velocities of the axes, which cannot completely capture the antenna’s motion state, essentially constituting an indirect closed-loop control. Addressing the shortcomings of this indirect closed-loop control, this paper first establishes the kinematic relations between the axes of the three-axis SOTM antenna using the Denavit–Hartenberg (DH) method. Subsequently, the relationship between antenna pointing and the rotational states of the three axes was derived using the Jacobian operator. Building upon this foundation, a direct closed-loop control structure for a three-axis SOTM antenna was designed. To enable the control system to achieve rapid convergence with minimal overshoot, an Active Disturbance Rejection Control (ADRC) algorithm based on smooth continuous functions is introduced as the inner and outer loop controller algorithms within the direct closed-loop control structure. To address the nonlinearity in the design scheme, a piecewise linearization method is proposed to reduce the demands on the microprocessor’s performance and enhance the engineering feasibility of the solution. Finally, the effectiveness of the proposed approach is validated through experiments. The experimental results demonstrate that compared to traditional indirect closed-loop control methods, utilizing the direct closed-loop control method for the three-axis SOTM antenna presented in this paper can lead to higher precision in pointing the antenna towards satellites and enhance communication effectiveness.

This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing a supersonic flat plate as the main structure, the research integrates various flight conditions into the aeroelastic equation. The resulting structural vibration data create a large-scale database for training the models. The dataset, divided into training, validation, and test sets, includes input features such as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM, and LSTM-NN all showed improved training with more neurons and layers, excessive numbers beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN model achieved the highest accuracy in classification tasks, effectively capturing sequential features and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling long-term dependencies and complex non-linear relationships, making it ideal for predicting flutter Mach numbers. Despite LSTM’s higher accuracy, it required longer training times due to increased computational complexity, necessitating a balance between accuracy and training duration. The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing model architecture and training processes, deep learning models can achieve high accuracy in predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs.

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Inspired by the maxillary mouthparts of longicorn beetles, four types of bionic cutters were designed in this research to address the prevalent issues of high cutting resistance and severe stubble damage encountered during alfalfa harvesting. Finite element simulation was utilized to assess the structural integrity and cutting performance of these bionic cutters. Additionally, bench tests were conducted on a homemade stem-cutting force measurement and control rig to evaluate their effectiveness. The results indicated: (1) the bionic cutters achieved a reduction in maximum equivalent force ranging from 20.9% to 49.2% and a decrease in maximum deformation from 31.4% to 64.1% compared to conventional cutters; (2) the maximum cutting resistance of alfalfa stems was reduced by 28.6%, 43.9%, 52.4%, and 38.6%, significantly enhancing the flatness of the cut surfaces; (3) orthogonal bench tests demonstrated that the type of cutter and the slip-cutting angle significantly influenced the maximum cutting resistance of the stems (p < 0.01), with the optimal configuration being bionic cutter c, a slip-cutting angle of 10°, and a rotational speed of 2600 rpm. In conclusion, bionic cutters demonstrate substantial advantages in reducing maximum cutting resistance and improving the flatness of alfalfa stubble, suggesting their potential for widespread application and adoption.

Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management.

Multi-rotor unmanned aerial vehicles (UAVs) are increasingly prevalent due to technological advancements. During rapeseed’s seedling stage, UAV-generated airflow, known as wind-induced response, affects leaf movement, tied to airflow speed and distribution. Understanding wind-induced response aids early rapeseed lodging prediction. Determining airflow distribution at various UAV heights is crucial for wind-induced response study, yet lacks theoretical guidance. In this study, Computational Fluid Dynamics (CFD) was employed to analyze airflow distribution at different UAV heights. Fluid–solid coupling simulation assessed 3D rapeseed model motion and surface pressure distribution in UAV downwash airflow. Validation occurred via wind speed experiments. Optimal uniform airflow distribution was observed at 2 m UAV height, with a wind speed variation coefficient of 0.258. The simulation showed greater vertical than horizontal leaf displacement, with elastic modulus inversely affecting displacement and leaf area directly. Discrepancies within 10.5% in the 0.5–0.8 m height range above the rapeseed canopy validated simulation accuracy. This study guides UAV height selection, leaf point determination, and wind-induced response parameter identification for rapeseed seedling stage wind-induced response research.

The cleaning system is a critical component of the sugarcane chopper harvester, facing challenges such as high impurity rate, elevated power consumption, and an inadequate understanding of the cleaning mechanism. This study aims to simulate the process of removing extraneous matter (represented by sugarcane leaves) from the cleaning system by employing a coupling approach of computational fluid dynamics (CFD) and the discrete element method (DEM) to determine the speed of the extractor fan. Initially, a CFD model was established to analyze the airflow field within the extractor, and its accuracy was verified on a test bench for the cleaning system. Subsequently, a DEM model was developed for sugarcane billets and leaves, which was then integrated with the CFD model to form a gas–solid coupling model. The efficacy of this integrated model was confirmed through experimental measurements of impurity rate. Furthermore, a ternary quadratic regression orthogonal combination design was utilized in the gas–solid coupling simulation to assess the impacts of feed rate, leaf–stalk ratio, and extractor fan speed on impurity rate. Finally, the extractor fan speeds were obtained for various feed rates and leaf–stalk ratios under impurity rates of 5%, 6%, 7%, and 8%. This research can guide in controlling the extractor fan speed during sugarcane chopper harvester field operations and can serve as a foundation for extractor fan design.

The arid and semi-arid region of Northwest China plays a significant role in potato production, yet yields are often hampered by drought due to limited precipitation and irrigation water. The ridge–furrow rainwater-harvesting technology is an efficient and widely used technique to relieve drought impact and improve crop yield by changing the micro-topography to harvest rainwater to meet the water demand of crops. An analysis of precipitation, water demand, and runoff data spanning 30 years guided the selection of suitable rainwater-harvesting methods tailored to meteorological conditions. The results showed that potato water demand exceeded precipitation in the region. The mulching approach performed best in the western arid region with the most significant increase in yield and water use efficiency (WUE) and was suitable for the western semi-arid region and the agro-pastoral ecotone. In the potato dryland farming areas, the water deficit increased from southeast to northwest. Specifically, northern Gansu, northern Ningxia, and midwestern Inner Mongolia experienced a water deficit of over 200 mm, and rainwater harvesting combined with irrigation was recommended. Conversely, regarding deficits below 200 mm in southern Gansu, Ningxia, and central Inner Mongolia, a 1:1 or 2:1 pattern of ridges could be applied, and mulching was needed only in the necessary areas. For the southern Qinghai, Shaanxi, and eastern Inner Mongolia regions, ridge–furrow rainwater harvesting could be replaced by flat potato cropping. In summary, rainwater harvesting addresses water deficits, aiding climate adaptation in Northwest China’s arid and semi-arid regions. The implementation of mulching and ridge–furrow technology must be location-specific.

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young “Okubo” peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model’s lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the “Okubo” peach fruit-thinning robot and scientific management of orchards.

Plant–microbe interactions are key to nutrient-use efficiency. Root microbes are influenced by rhizosphere soil and plant cultivars. The impact of cultivar-by-nitrogen (N) interactions on the maize-root microbiome remains unclear, yet it is crucial for understanding N use efficiency in maize. This study evaluated the effects of maize cultivars and N forms, along with their interactions, on the diversity and composition of root bacteria and fungi. Additionally, we examined correlations between soil microbes and root metabolites. The maize cultivar Zhengdan 958 (ZD958) showed a positive response to the mixture of nitrate and ammonium N, resulting in increased in biomass, grain yield, shoot N content, grain N content, and root area. In contrast, the cultivar Denghai605 (DH605) did not exhibit a similar response. The diversity and composition of root bacteria and fungi differed between ZD958 and DH605. The N form primarily affected the community structure of rhizospheric fungi in ZD958 and rhizospheric bacteria in DH605, rather than endophytic microbes. A mixed N supply increased the relative abundance of Basidiomycota, which was positively correlated with ZD958 yield. For DH605, a mixed N treatment enhanced nitrification functions involving Bacteroidetes and Proteobacteria, while it reduced the effects of ammonium N supply. The dominant rhizospheric microbes in DH605 showed a stronger response to changes in root metabolites compared to those in ZD958. A mixed N supply increased the content of palmitoleic acid in ZD958 root exudates, facilitating the recruitment of beneficial rhizospheric microbes, which promotes maize growth. In DH605, a mixed N supply decreased the concentration of sphinganine, which is significantly correlated with Acidobacteria (negatively), Proteobacteria (negatively), Bacteroidetes (positively), and TM7 (positively). Our findings suggest that different maize cultivars respond differently to N forms, causing distinct rhizospheric microbial effects, and that root metabolites send metabolic signals to regulate and recruit key bacterial and fungal communities.

The precise quantification of soil salinity and the spatial distribution are paramount for proficiently managing and remediating salinized soils. This study aims to explore a pioneering methodology for forecasting soil salinity by combining the spectroscopy of soda saline–alkali soil with crack characteristics, thereby facilitating the ground-based remote-sensing inversion of soil salinity. To attain this objective, a surface cracking experiment was meticulously conducted under controlled indoor conditions for 57 soda saline–alkali soil samples from the Songnen Plain of China. The quantitative parameters for crack characterization, encompassing the length and area of desiccation cracks, together with the contrast texture feature were methodically derived. Furthermore, spectral reflectance of the cracked soil surface was measured. A structural equation model (SEM) was then employed for the estimation of soil salt parameters, including electrical conductivity (EC1:5), Na+, pH, HCO3−, CO32−, and the total salinity. The investigation unveiled notable associations between different salt parameters and crack attributes, alongside spectral reflectance measurements (r = 0.52–0.95), yet both clay content and mineralogy had little effect on the cracking process due to its low activity index. In addition, the presence of desiccation cracks accentuated the overall spectral contrast of salt-affected soil samples. The application of SEMs facilitated the concurrent prediction of multiple soil salt parameters alongside the regression analysis for individual salt parameters. Nonetheless, this study confers the advantage of the swift synchronous observation of multiple salt parameters whilst furnishing lucid interpretation and pragmatic utility. This study helps us to explore the mechanism of soil salinity on the surface cracking of soda saline–alkali soil in the Songnen Plain of China, and it also provides an effective solution for quickly and accurately predicting soil salt content using crack characteristics, which also provides a new perspective for the hyperspectral measurement of saline–alkali soils.

Fermented chopped chili pepper (FCCP) is the most important fermented and processed chili pepper product, and most pepper varieties can be processed with obvious flavor differences. Many studies have tried to explain these differences using the results of component analysis, processing technology, the microbes involved, etc., but few have examined the characteristics of these varieties along with the flavor of products. We conducted a physiochemical characteristic analysis of 35 kinds of fresh chili peppers with different genotypes and made fermented products from 17 varieties, using principal component analysis (PCA) and cluster analysis (CA) to correlate their components with FCCP products. The results were as follows: (1) Protein, total acids, a* (representation of the object’s red and green color), protopectin, and fruit thickness are the five key indexes that affect the quality of fresh chili peppers the most. (2) Protein, total acids, a*, protopectin, and fruit thickness are also the key indexes that affect the quality of FCCP. (3) Suitable ranges of fresh chili pepper to manufacture FCCP are 1.3–2.0 g/100 g for protein, 2.5–4.0 g/kg for total acids, 10–15 g/100 g for protopectin, 30–39 for a*, and 1.2–2.0 mm for fruit thickness. (4) Sensory appraisal shows crispness is the most important mouthfeel sensation and can decrease during the process. The varieties with the top three scores were A12 (21G675), A13 (20Z663), and A14 (21Z698) with scores of 87.92, 74.08, and 74.15, respectively. The varieties in the top three scores are pod peppers. The PCA and CA showed that pod peppers were the most suitable materials for making FCCP. Our results will benefit others in selecting fresh chili peppers for making FCCP and provide data support for directing processable variety breeding.

Urea supplements, such as humic acids, could enhance fertilizer nitrogen use effectiveness. Melting is superior to mixing for humate urea application; however, the effects of diverse humate ureas from various coal sources on soil N supply remain unclear. This study compared the properties of two humic acids from different coal sources (HA1, weathered coal; HA2, lignite coal), and their impact on soil mineral N supply and the nitrate–ammonium ratio under flooded and 60% water-filled pore space (WFPS) over a 14-day incubation. Humate ureas stimulated soil mineral N accumulation and balanced the soil nitrate–ammonium ratio at 1:1; however, no significant difference existed between the two humate ureas under 60% WFPS. Humate urea enhanced soil ammonium nitrogen (NH4+-N) retention and delayed nitrate nitrogen (NH4−-N) release, leading to soil mineral N retention, especially in lignite humic acid urea (H2AU) treatments from lignite under flooding. Structural equation modeling (SEM) and linear regression revealed that humic acids elevated soil redox potential (Eh) and electrical conductivity (EC), stimulating soil N mineralization and adjusting the optimal nitrate–ammonium ratio. Humate urea improved soil mineral N supply compared to traditional urea treatments, and humic acids from lignite were more beneficial for crop cultivation from a mineral soil N supply perspective. These findings enhance our understanding of humate urea benefits and aid in optimizing humic acids application for N management.

Recent studies have shown a correlation between piglet diarrhea and the gut microbiota. However, the precise mechanism by which intestinal microorganisms and their metabolites influence diarrhea in weaned piglets remains unclear. This study explored differences in the gut microbiota and associated metabolites between healthy and diarrheic-weaned piglets using macrogenomic and metabolomic analyses. The histomorphological results showed that diarrheic piglets had shorter jejunal and ileal villi, some of which were shed, compared to healthy piglets. Substantial differences in gut microbial diversity and metabolites were also observed, with Bacteroidaceae bacterium and Caudoviricetes being the main differential organisms that were strongly correlated with host status. Microbial functions, mainly the metabolism of carbohydrates, glycans, lipids, and amino acids, as well as related enzyme activities, were substantially different. The major differential metabolites were carnosine, pantothenic acid (vitamin B5), pyridoxal, methylimidazoleacetic acid, indole-3-acetaldehyde, and 5-hydroxyindoleacetic acid. These metabolites were enriched in beta-alanine, histidine, tryptophan, and vitamin B6 metabolism, and in the pantothenate and CoA biosynthesis pathways. Combined macrogenomic and metabolomic analyses revealed that carnosine, vitamin B5, and pyridoxal were negatively correlated with Caudoviricetes; methylimidazoleacetic acid, indole-3-acetaldehyde, and 5-hydroxyindoleacetic acid were positively correlated with Caudoviricetes. Whereas carnosine and vitamin B5 were positively correlated with Bacteroidaceae bacterium, 5-hydroxyindoleacetic acid was negatively correlated. The decreased abundance of Bacteroidaceae bacterium and the increased abundance of Caudoviricetes and related metabolites likely contribute to post-weaning diarrhea in piglets. Therefore, the abundance of Bacteroidaceae bacterium and Caudoviricetes can likely serve as potential markers for identifying and preventing diarrhea in post-weaning piglets.

Ram sperm undergo a sequence of physiological and biochemical changes collectively termed as capacitation to perform oocyte fertilization. However, the protein changes induced by capacitation remain in need of further exploration. Thus, the present study investigated the comparative proteomic profiling in ram spermatozoa under non-capacitating (NC) and capacitating (CAP) conditions in vitro using a liquid chromatography–tandem mass spectrometry combined with tandem mass tag labeling strategy. As a results, 2050 proteins were identified and quantified; 348 of them were differentially abundant, with 280 of the proteins upregulated and 68 of the proteins downregulated between the CAP and NC spermatozoa, respectively. Functional enrichment analysis indicated that the differentially abundant proteins Prune Exopolyphosphatase 1, Galactose-1-Phosphate Uridylyltransferase, and ATP Citrate Lyase were strictly related to energy production and conversion, and Phosphoglycolate phosphatase, Glucosamine-6-Phosphate Deaminase 1 and 2 were related to metabolism, RNA processing, and vesicular transport pathways. Furthermore, the networks of protein–protein interaction indicated a strong interaction among these differential proteins in annotated pathways such as ubiquitin and transport metabolism. Our findings indicate that capacitation progress might be regulated through different pathways, providing insights into mechanisms involved in ram sperm capacitation and fertility.

Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

This study aims to investigate the dynamic changes in daily step counts under different housing systems and further explore the effects of housing system on the body conformation, carcass traits, meat quality, and serum biochemical parameters of a Chinese indigenous chicken breed. At 60 d of age, 300 Jiuyuan Black male chickens with similar body weights in each housing system were further raised until the age of 150 d. At 90, 120, and 150 d of age, in both cage-reared and free-range systems, the top 20 chickens with the highest step counts measured using pedometers and the bottom 20 chickens with the lowest step counts were designated as the cage high-steps group (CHS), the cage low-steps group (CLS), the free-range high-steps group (FHS), and the free-range low-steps group (FLS), respectively. The results show that, at any age stage, the average daily steps (ADS) and total steps (TS) of the FHS group are significantly higher than the other three groups (p < 0.05). The TS of almost all groups showed an overall downward trend as the age increased. Increased exercise volume results in reduced shank length (90 d), breast width (90 d), and keel length (150 d) (p < 0.05). Only birds at 90 d of age from the FHS and FLS groups exhibited lower live body weight, carcass weight, half-eviscerated weight, eviscerated weight, breast muscle weight, leg muscle weight, and percentage of eviscerated weight than the CLS group (p < 0.05). Birds from the FHS group showed the highest heart weight values but the lowest abdominal fat weight values among these four groups (p < 0.05). Both the breast and leg muscle samples from the FHS group displayed higher dry matter and shear force than those from the CHS and CLS groups (p < 0.05). The FHS group displayed the lowest intramuscular fat among the four groups (p < 0.05). The creatine kinase (CK) and lactate dehydrogenase (LDH) levels in chickens of all age stages were almost observed to rise with increased physical activity. In conclusion, free-range chickens with more exercise volume exhibited an elevated heart weight and reduced abdominal fat but showed negative effects on some body measurements and carcass traits. These results can provide a theoretical basis for the selection of different housing systems for Chinese indigenous chickens.

In recent years, genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS) have emerged as crucial methods for investigating complex traits in animals and plants. These have played pivotal roles in research on livestock and poultry breeding, facilitating a deeper understanding of genetic diversity, the relationship between genes, and genetic bases in livestock and poultry. This article provides a review of the applications of GWAS and mGWAS in animal genetic breeding, aiming to offer reference and inspiration for relevant researchers, promote innovation in animal genetic improvement and breeding methods, and contribute to the sustainable development of animal husbandry.

The balanced crosstalk between miRNAs and autophagy is essential in hypertensive nephropathy. Hydrogen sulfide donors have been reported to attenuate renal injury, but the mechanism is unclear. We aimed to identify and verify the miRNAs and autophagy regulatory networks in hypertensive nephropathy treated with hydrogen sulfide donors through bioinformatics analysis and experimental verification. From the miRNA dataset, autophagy was considerably enriched in mice kidney after angiotensin II (AngII) and combined hydrogen sulfide treatment (H2S_AngII), among which there were 109 differentially expressed miRNAs (DEMs) and 21 hub ADEGs (autophagy-related differentially expressed genes) in the AngII group and 70 DEMs and 13 ADEGs in the H2S_AngII group. A miRNA–mRNA–transcription factors (TFs) autophagy regulatory network was then constructed and verified in human hypertensive nephropathy samples and podocyte models. In the network, two DEMs (miR-98-5p, miR-669b-5p), some hub ADEGs (KRAS, NRAS), and one TF (RUNX2) were altered, accompanied by a reduction in autophagy flux. However, significant recovery occurred after treatment with endogenous or exogenous H2S donors, as well as an overexpression of miR-98-5p and miR-669b-5p. The miR/RAS/RUNX2 autophagy network driven by H2S donors was related to hypertensive nephropathy. H2S donors or miRNAs increased autophagic flux and reduced renal cell injury, which could be a potentially effective medical therapy.

The geomagnetic field (GMF) is crucial for the survival and evolution of life on Earth. The weakening of the GMF, known as the hypomagnetic field (HMF), significantly affects various aspects of life on Earth. HMF has become a potential health risk for future deep space exploration. Oxidative stress is directly involved in the biological effects of HMF on animals or cells. Oxidative stress occurs when there is an imbalance favoring oxidants over antioxidants, resulting in cellular damage. Oxidative stress is a double-edged sword, depending on the degree of deviation from homeostasis. In this review, we summarize the important experimental findings from animal and cell studies on HMF exposure affecting intracellular reactive oxygen species (ROS), as well as the accompanying many physiological abnormalities, such as cognitive dysfunction, the imbalance of gut microbiota homeostasis, mood disorders, and osteoporosis. We discuss new insights into the molecular mechanisms underlying these HMF effects in the context of the signaling pathways related to ROS. Among them, mitochondria are considered to be the main organelles that respond to HMF-induced stress by regulating metabolism and ROS production in cells. In order to unravel the molecular mechanisms of HMF action, future studies need to consider the upstream and downstream pathways associated with ROS.