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Despite its rapid growth and economic success, the sustainability of the floriculture industry as it is presently conducted is debatable, due to the huge environmental impacts it initiates and incurs. Achieving sustainability requires joint efforts from all stakeholders, a fact that is often neglected in discussions that frequently focus upon economically driven management concerns. This review attempts to raise awareness and collective responsibility among the key practitioners in floriculture by discussing its sustainability in the context of soil health, as soil is the foundation of agriculture systems. Major challenges posed to soil health arise from soil acidification and salinization stimulated by the abusive use of fertilizers. The poisoning of soil biota by pesticide residues and plastic debris due to the excessive application of pesticides and disposal of plastics is another significant issue and concern. The consequence of continuous cropping obstacles are further elucidated by the concept of plant-soil feedback. Based on these challenges, we propose the adoption and implementation of several sustainable practices including breeding stress-resistant and nutrient-efficient cultivars, making sustainable soil management a goal of floriculture production, and the recycling of plastics to overcome and mitigate the decline in soil health. The problems created by flower waste materials are highlighted and efficient treatment by biochar synthesis is suggested. We acknowledge the complexity of developing and implementing the proposed practices in floriculture as there is limited collaboration among the research and operational communities, and the policymakers. Additional research examining the impacts the floriculture industry has upon soils is needed to develop more sustainable production practices that can help resolve the current threats and to bridge the understanding gap between researchers and stakeholders in floriculture.

Backgrounds: A contemporary public health challenge is the increase in the prevalence rates of herpes zoster (HZ) worldwide. Methods: In this work, the gE gene structure was analyzed using bioinformatics techniques, and three plasmids of varying lengths, tgE537, tgE200, and tgE350, were expressed in Chinese hamster ovary (CHO) cells. These proteins were used to immunize BALB/c mice with Al/CpG adjuvant; ELISPOT and FCM were used to evaluate cellular immunity; and ELISA, VZV microneutralization, and FAMA assays were performed to detect antibody titers. Results: Target protein concentrations of 1.8 mg/mL for tgE537, 0.15 mg/mL for tgE200 and 0.65 mg/mL for tgE350 were effectively produced. The ability of the three protein segments to stimulate CD4+ and CD8+ T cells, as well as to cause lymphocytes to secrete IFN-γ and IL-4, did not significantly differ from one another. Both tgE537 and tgE350 were capable of generating VZV-specific antibodies and neutralizing antibodies, while tgE350 had the highest neutralizing antibody titer (4388). There was no equivalent humoral immune response induced by tgE200. Conclusions: The results of this investigation provide the groundwork for the creation of HZ recombinant vaccines using truncated proteins as antigens.

The data envelopment analysis (DEA) models have been widely recognized and applied in various fields. However, these models have limitations, such as their inability to globally rank DMUs, the efficiency values are definite numerical values, they are unable to reflect potential efficiency changes, and they fail to adequately reflect the degree of the decision maker’s preference. In order to address these shortcomings, this paper combines possibility theory with self-interest and non-self-interest principles to improve the DEA model to provide a more detailed reflection of the differences between DMUs. First, the self-interest and non-self-interest principles are employed to establish the DEA evaluation model, and the determined numerical efficiency is transformed into efficiency intervals. Second, an attitude function is added to the common possible-degree formula to reflect the decision maker’s preference, and a more reasonable method for solving the attitude function is presented. Finally, the improved possible-degree formula proposed in this paper is used to rank and compare the interval efficiencies. This improved method not only provides more comprehensive ranking information but also better captures the decision maker’s preferences. This model takes preference issues into account and has improved stability and accuracy compared with existing models. The application of the improved model in airlines shows that the model proposed in this paper effectively achieved a full ranking. From a developmental perspective, the efficiency levels of Chinese airlines were generally comparable. Joyair and One Two Three performed poorly, exhibiting significant gaps compared with other airlines.

A self-degradable rubber plug (SDRP) was developed to address issues in existing crosslinked polymer temporary plugging technology, such as poor self-degradation properties. The synthesis formula was optimized using response surface analysis, resulting in an optimized composition of the SDRP: 13 wt% monomer, 0.02 wt% initiator, 0.7 wt% crosslinker, and 1.8 wt% degradation catalyst. Under the condition of 70–120 °C, the SDRP was transformed from a liquid to a solid gel in 30–110 min; the degradation time was 3–10 days, and the viscosity of the completely degraded solution was lower than 20 mPa·s. At an injection volume of 1 PV SDPR, a breakthrough pressure of 8.34 MPa was achieved. The hydrolysis of the unstable crosslinker was found to have caused the breakage of the SDRP. Over time, the functional groups within the unstable crosslinker underwent hydrolysis due to the combined effects of temperature and the degradation catalyst. This process led to the disruption of crosslinking points, resulting in a gradual deterioration of the network structure. As a consequence, some immobile water was converted into free water. The mobility of water molecules increased until the plug was completely degraded into a viscous liquid. This study enriches the temporary plugging gel system.

(1) Background: Discarded young fig fruits (DYFFs) result in a waste of resources, such as sparse fruits and residual fruits, and there has been no research on the relationship between phenolic compounds and biological activity in DYFFs (2) Methods: Different deep eutectic solvents (DESs) and 80% ethanol were used to prepare DYFF extracts, and polyphenol extraction efficiency and bioactivities in the DYFFs extracts were compared. (3) Results: More than 1700 phytochemicals were identified in DYFFs, and thirteen of these typical phenolic compounds were analyzed quantitatively; chlorogenic acid, rutin, luteolin 8-C-glucoside, and epicatechin are the main polyphenols in DYFFs, especially chlorogenic acid with 2720–7980 mg/kg. Ferulic acid, caffeic acid, epicatechin, (+)-catechin, luteolin 8-C-glucoside, rutin, hesperetin, and chlorogenic acid showed different degrees of correlation with in vitro antioxidant activity. Moreover, the highest total phenol content found in the extracts of ChCl-Ethylene glycol (Choline chloride:Ethylene glycol = 1:2) was 8.88 mg GAE/g DW, and all quantitatively analyzed phenolic compounds had high levels in various DESs and 80% ethanol. The 80% ethanol and Choline chloride (ChCl) solvent system showed the greatest antioxidant properties, and the Choline chloride-Urea (Choline chloride: Urea = 1:2) extract of DYFFs exhibited the highest inhibitory activity. (4) Conclusions: DESs have demonstrated potential as promising green solvents, especially the ChCl solvent system, which facilitates the extraction of polyphenols.

Accessibility is an important indicator for measuring the level of medical facility services in cities. Traditional studies on healthcare accessibility for the elderly have typically focused on the capabilities of emergency services while overlooking the daily independent healthcare needs of the elderly. Taking Wuhan City as an example, the Gaussian two-step floating catchment area method is used in this study to measure the accessibility of urban elderly residents with respect to general hospitals, specialized hospitals, and community health service centers. It also analyzes the appropriateness of the medical facility layout for the elderly in Wuhan City. The research findings indicate the following: (1) The accessibility evaluation method for urban medical facilities established with the Gaussian two-step floating catchment area model can accurately assess the supply–demand relationship between the elderly and various levels of medical facilities. (2) The overall coverage of medical service facilities in Wuhan City is relatively high, but there are significant differences in accessibility for elderly residents in different areas. (3) The appropriateness of daily healthcare access for elderly residents in the central urban areas of Wuhan City is mainly influenced by the layout of general hospitals, while, in the peripheral areas, it is mainly influenced by the layout of specialized hospitals and community health service centers. (4) Improving the appropriateness of urban medical facilities for elderly individuals requires differentiated improvement measures tailored to the characteristics of different zones.

With the development of commercialized planting of genetically modified crops, their ecological security risks remain a key topic of public concern. Insect-resistant genetically modified maize, Ruifeng125, which expresses a fusion Bt protein (Cry1Ab-Cry2Aj), has obtained the application safety certificate issued by the Chinese government. To determine the effects of Ruifeng125 on the diversity and dynamics of bacterial communities, the accumulation and degradation pattern of the fusion Bt protein in the rhizosphere soil of transgenic maize were detected. Results showed that the contents of Bt protein varied significantly at different developmental stages, but after straw was returned to the field, over 97% of Bt proteins were degraded quickly at the early stages (≤10 d) and then they were degraded at a relatively slow rate. In addition, the variations in bacterial community diversity in the rhizosphere soil were detected by 16S ribosomal RNA (Rrna) high-throughput sequencing technology. A total of 44 phyla, 435 families, and 842 genera were obtained by 16S rRNA sequencing, among which Proteobacteria, Actinobacia, Acidobacter Acidobacterium, and Chloroflexi were the dominant taxa. At the same developmental stage, no significant differences in soil bacterial diversity were detected between Ruifeng125 and its non-transgenic control variety. Further analysis revealed that developmental stage, rather than the transgenic event, made the greatest contribution to the changes in soil microbial diversity. This research provides important information for evaluating the impacts of Bt crops on the soil microbiome and establishes a theoretical foundation for their environmental safety assessment.

An alkali-soluble β-glucan (AHEP-A-b, 20 kDa) purified from Hericium erinaceus fruiting bodies, was structurally characterized and examined for antioxidant activity. Methylation analysis and NMR spectroscopy show that the backbone of AHEP-A-b is composed of (1→6)-linked-D-β-glucopyran residues, branched at O-3 of glucopyranose (Glcp) residues with [→3)-β-D-Glcp-(1→] oligosaccharides or single unit of β-Glcp. Periodate oxidation analysis and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) indicate that the degree of polymerization (DP) of [→3)-β-D-Glcp-(1→] side chains is 2 to 8. Functionally, AHEP-A-b is a relatively strong antioxidant as demonstrated by using 2, 2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) free radical (ABTS·+), 1,1-diphenyl-2-picrylhydrazyl (DPPH) radicals, and hydroxyl radicals scavenging assays. The present study lays the foundation for further studies into structure-activity relationships of polysaccharides from H. erinaceus.

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F1macro value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods.

The bit error rate (BER) in relation to the signal-to-noise ratio (SNR) serves as a widely recognized metric for assessing the performance of communication systems. The concept of SNR is so integral that many existing studies presume its definition to be understood, often omitting the specifics of its calculation in their simulations. Notably, the computation of SNR from the perspective of the transmitter yields distinct behaviors and outcomes compared to that from the receiver’s side, particularly when the channel encompasses more than mere noise. Typically, research papers utilize the transmitter-side (or ensemble-average) SNR to benchmark the BER performance across various methodologies. Conversely, the receiver-side (or short-term) SNR becomes pertinent when prioritizing the receiver’s performance. In the context of simulating the long-term evolution (LTE) downlink, applying both SNR calculation approaches reveals that the receiver-side SNR not only produces a significantly lower BER compared to the transmitter-side SNR but also alters the relative BER performance rankings among the channel models tested. It is deduced that while the transmitter-side SNR is apt for broad performance comparisons, it falls short in thoroughly examining the BER behavior of a receiver across varying SNR scenarios. Therefore, the transmitter-side SNR is useful when comparing the performance of the simulated system with other studies. Conversely, if the primary concern is the actual BER performance of the receiver, the receiver-side SNR could provide a more accurate performance assessment.

The tea tussock moth is a pest that damages tea leaves, affecting the quality and yield of tea and causing huge economic losses. The efficient asymmetric total synthesis of the sex pheromone of the tea tussock moth was achieved using commercially available starting materials with a 25% overall yield in 11 steps. Moreover, the chiral moiety was introduced by Evans’ template and the key C-C bond construction was accomplished through Julia–Kocienski olefination coupling. The synthetic sex pheromone of the tea tussock moth will facilitate the subsequent assessment and implementation of pheromones as environmentally friendly tools for pest management.

Indoor Optical Wireless Communication (OWC) provides a promising solution for high-capacity, low-latency, and electromagnetic interference-resistant wireless communication. Over the past decade, there has been extensive research addressing key challenges in indoor OWC. This article provides an overview of the current development status, key technologies, and challenges faced in the field of indoor OWC. Furthermore, at the end of this overview, an experimental demonstration of an indoor non-line-of-sight (NLOS) OWC system utilizing a spatial light modulator (SLM) for beam steering is demonstrated, which is expected to inspire research on related technologies.

Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection speed. To overcome these problems, a novel learning-based algorithm is proposed; rather than detecting the sea–sky line first, the proposed algorithm directly provides SSLR, which mainly consists of three parts: Firstly, an IR sea–sky line region detection module (ISRDM) is proposed, which combines strip pooling and the connection mode of a cross-stage partial network to extract the features of the SSLR target, with an unbalanced aspect ratio, more specifically, thus improving the detection accuracy. Secondly, a lightweight backbone is presented to reduce the parameters of the model and, therefore, improve the inference speed. Finally, a Detection Head Based on the spatial-aware attention module (SAMHead) is designed to enhance the perception ability of the SSLR and further reduce the inference time. Extensive experiments conducted on three datasets with more than 26,000 frames show that the proposed algorithm achieved approximately 80% average precision (AP), outperforms the state-of-the-art algorithms in accuracy, and can realize real-time detection.

Regarding the existing models for feature extraction of complex similar entities, there are problems in the utilization of relative position information and the ability of key feature extraction. The distinctiveness of Chinese named entity recognition compared to English lies in the absence of space delimiters, significant polysemy and homonymy of characters, diverse and common names, and a greater reliance on complex contextual and linguistic structures. An entity recognition method based on DeBERTa-Attention-BiLSTM-CRF (DABC) is proposed. Firstly, the feature extraction capability of the DeBERTa model is utilized to extract the data features; then, the attention mechanism is introduced to further enhance the extracted features; finally, BiLSTM is utilized to further capture the long-distance dependencies in the text and obtain the predicted sequences through the CRF layer, and then the entities in the text are identified. The proposed model is applied to the dataset for validation. The experiments show that the precision (P) of the proposed DABC model on the dataset reaches 88.167%, the recall (R) reaches 83.121%, and the F1 value reaches 85.024%. Compared with other models, the F1 value improves by 3∼5%, and the superiority of the model is verified. In the future, it can be extended and applied to recognize complex entities in more fields.

Fisheries and bottom seawater data were collected in July in 2022 and 2023 from 15 sampling stations in the Huanghe River estuary and adjacent seas in China. The patterns of functional alpha and beta diversity of fish communities in this area and their relationships with environmental factors were studied using trait-based analysis. Five functional classifications, which included 16 functional traits, four functional alpha diversity indices (functional richness, functional evenness, functional divergence, and community-weighted mean), three functional beta diversity indices (functional beta diversity and its turnover and nestedness components), and 14 bottom environmental factors were considered. The dominant traits of fish communities were the following: benthivorous feeding habits, small and front-facing mouths, high trophic level, demersal, anguilliform, low growth coefficient, medium resilience, low vulnerability, and adapted to warm temperate conditions. The dominant migration traits and types of fish eggs varied by year, and fish abundance was the main factor affecting the dominant traits of the communities. The results of multiple regression on distance matrices and variance partitioning analysis indicated that ammonia nitrogen content, total phosphorus content, and pH were the main environmental factors that affected fish functional diversity. The N/P ratio had a bottom-up control effect on fish functional diversity. Our findings also revealed that high pH gradients and distant geographical distances can inhibit trait turnover in fish communities.

The Yellow River Estuary and its adjacent waters serve as crucial spawning, foraging, and nursery areas for marine organisms, possessing abundant biological resources. Monitoring fish communities provides a baseline for implementing the sustainable utilization of marine resources. In this study, data were collected from 15 spring surface and bottom sites and 17 summer surface sites using eDNA and trawl surveys. The results showed that 37, 40, and 35 fish species were detected using eDNA in the spring (surface and bottom) and summer (surface), respectively, with 38 fish species caught during summer trawling. The dominant species mainly belonged to Engraulidae of Clupeiformes in the spring and Gobiidae of Perciformes in the summer, characterized by smaller-sized, short-lived, and pelagic fish species. The summer surface communities exhibited higher diversity than the spring surface and bottom communities. NMDS analysis revealed a degree of seasonal differences in fish communities and that there may be a lack of vertical stratification in the spring communities. The pH and DO were identified as the key environmental factors affecting the fish community. Additionally, the combination of eDNA and trawl surveys was regarded as a superior survey method. Our study provides valuable information for understanding seasonal fish communities in the Yellow River Estuary and its adjacent waters, contributing to fishery resource management and conservation in the region.

In order to effectively protect and verify the copyright information of multimedia digital works, this paper proposes a zero-watermarking algorithm based on carrier image feature point descriptors. The constructed feature matrix of this algorithm consists of two parts: the feature descriptor vector calculated from scale-invariant feature reconstruction transform (SIFRT) and the multi-radius local binary pattern (MrLBP) descriptor vector. The algorithm performs a standardization, feature decomposition, and redundancy reduction on the traditional keypoint descriptor matrix, combines it with the texture feature matrix, and achieves the dimensional matching of copyright information. The advantage of this algorithm lies in its non-modification of the original data. Compared to computing global features, the local features computed from a subset of key points reduce the amount of attack interference introduced during copyright verification, thereby reducing the number of erroneous pixel values that are introduced. The algorithm introduces a timestamp mechanism when uploading the generated zero-watermarking image to a third-party copyright center, preventing subsequent tampering. Experimental data analysis demonstrates that the algorithm exhibits good discriminability, security, and robustness.

Undesirable meteorological conditions are typical aviation flight safety threats. Although most meteorological radar and flight augmentation computers have avionic system wind shear alarms, the preferred approach is that pilots avoid unsafe wind shear events. Therefore, effective pilot competency evaluations are needed to assess pilots’ abilities to deal with these events. This study developed a wind shear operation competency model that includes observable behavior indicators, sub-task decomposition, and competency check items. An adapted competency model and a quantitative data-driven competency evaluation criteria optimization method were then developed using three-dimensional competency feature modeling, after which wind shear simulation flight training data were used to verify the effectiveness of the proposed method. The competency assessment grades were significantly correlated with the results from experienced examiners with a 93.33% evaluation accuracy. The proposed quantitative data-driven competency assessment method can provide effective pilot competency assessments for a range of aviation meteorological threats.

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system’s transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system’s transient-state model can reduce the errors between day-ahead scheduling and rolling correction.

The purpose of this study was to investigate how students’ disabilities impacted their learning at the university and to measure their perceptions of universal design for learning (UDL). An online survey was administered at a large public research university in the south-central United States; 160 students with disabilities completed the survey. These students with disabilities described how their disabilities influenced their learning in the following aspects: attention issues, slow processing, absence, accessibility issues, reading and writing challenges, mental health challenges, and social interaction challenges. The results also revealed that some UDL teaching practices were not being fully utilized by instructors based on the perceptions of students with disabilities. We discussed those UDL practices which were not being used by instructors and highlighted the impact of using the practices on students’ learning. These practices include effective teaching methods such as helping students organize and summarize learning content, using technology to increase accessibility, providing flexibility in assessments and assignments, providing meaningful feedback, and recruiting students’ attention and engagement.

Duckweed growing in paddy fields (DGP) has substantially increased because of the effects of climate warming and/or eutrophication in irrigated water. Previous studies have primarily focused on investigating the effects of DGP as a nonchemical agent for enhancing rice productivity on nitrogen utilization in rice paddy fields. However, how DGP impacts rice yield remains poorly understood. Therefore, a field experiment with three representative rice cultivars was conducted to determine the effects of DGP on rice yield, considering ecological factors, photosynthetic capacity, spectral changes, and plant growth. The results showed that DGP significantly reduced the pH value by 0.6 and the daily water temperature by 0.6 °C, accelerated rice heading by 1.6 days and increased the soil and plant analyzer development (SPAD) and photosynthetic rate of leaves by 10.8% and 14.4% on average, respectively. DGP also markedly enhanced the values of various vegetation indices such as RARSc, MTCI, GCI, NDVI705, CI, CIrededge, mND705, SR705, and GM, and the first derivative curve of the rice canopy reflectance spectrum exhibited a ‘red shift’ phenomenon upon DGP treatment. Changes in the aforementioned factors may lead to average increases of 4.7% in plant height, 15.0% in dry matter weight, 10.6% in panicles m−2, 2.3% in 1000-grain weight, and ultimately a 10.2% increase in grain yield. The correlation observed suggested that the DGP-induced enhancement in grain yield can be achieved by reducing the pH and temperature of the paddy water, thus enhancing the SPAD value and photosynthesis of leaves and stimulating rice plant growth. These results could offer valuable theoretical support for the future sustainable development of agriculture and the environment through the biological synergy between rice and duckweed.

As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

Flower color is a major feature of ornamental plants, and the rich flower color of plants is an important factor in determining their ornamental and economic values, so flower color is an important research target for gardening and horticulture breeders at home and abroad. Our research group collected four colors of Impatiens uliginosa (white, pink, red, and deep red) during the collection of germplasm resources in the field. In this study, we analyzed the transcriptomes of the four flower colors of I. uliginosa by using RNA-Seq technology. The transcriptomes were screened to identify candidate genes related to flower color, and the coloring mechanisms of four flower colors were revealed at the molecular level. The main findings were as follows: (1) The number of the four different transcripts ranged from 64,723 to 93,522 and contained a total of 100,705 unigenes. (2) The analysis of differentially expressed genes revealed structural genes including C4H, FLS, PAL, and ANS and transcription factors including MYB, MYB-related, AP2-EREBP, and bHLH. (3) Among the four flower colors of I. uliginosa, the C4H1 gene had the highest expression in pink flowers, and the C4H2 gene had the highest expression in red flowers. This indicated that C4H genes positively regulated the red flower color of I. uliginosa. However, FLS expression was the highest in white flowers, and with deepening flower color, FLS gene expression gradually weakened, acting as a negative regulator. The results of this study could lay the theoretical foundation for investigating the mechanism of coloration and flower color variation in I. uliginosa.

Federated learning has become a prevalent distributed training paradigm, in which local devices collaboratively train learning models without exchanging local data. One of the most dominant frameworks of federated learning (FL) is FedAvg, since it is efficient and simple to implement; here, the first-order information is generally utilized to train the parameters of learning models. In practice, however, the gradient information may be unavailable or infeasible in some applications, such as federated black-box optimization problems. To solve the issue, we propose an innovative zeroth-order adaptive federated learning algorithm without using the gradient information, referred to as ZO-AdaFL, which integrates the zeroth-order optimization algorithm into the adaptive gradient method. Moreover, we also rigorously analyze the convergence behavior of ZO-AdaFL in a non-convex setting, i.e., where ZO-AdaFL achieves convergence to a region close to a stationary point at a speed of O(1/T) (T represents the total iteration number). Finally, to verify the performance of ZO-AdaFL, simulation experiments are performed using the MNIST and FMNIST datasets. Our experimental findings demonstrate that ZO-AdaFL outperforms other state-of-the-art zeroth-order FL approaches in terms of both effectiveness and efficiency.

The integration of a multidimensional treatment dominated by active ingredients of traditional Chinese medicine (TCM), including enhanced chemotherapy and synergistically amplification of oxidative damage, into a nanoplatform would be of great significance for furthering accurate and effective cancer treatment with the active ingredients of TCM. Herein, in this study, we designed and synthesized four matrine-proteolysis-targeting chimeras (PROTACs) (depending on different lengths of the chains named LST-1, LST-2, LST-3, and LST-4) based on PROTAC technology to overcome the limitations of matrine. LST-4, with better anti-tumor activity than matrine, still degrades p-Erk and p-Akt proteins. Moreover, LST-4 NPs formed via LST-4 self-assembly with stronger anti-tumor activity and glutathione (GSH) depletion ability could be enriched in lysosomes through their outstanding enhanced permeability and retention (EPR) effect. Then, we synthesized LST-4@ZnPc NPs with a low-pH-triggered drug release property that could release zinc(II) phthalocyanine (ZnPc) in tumor sites. LST-4@ZnPc NPs combine the application of chemotherapy and phototherapy, including both enhanced chemotherapy from LST-4 NPs and the synergistic amplification of oxidative damage, through increasing the reactive oxygen species (ROS) by photodynamic therapy (PDT), causing an GSH decrease via LST-4 mediation to effectively kill tumor cells. Therefore, multifunctional LST-4@ZnPc NPs are a promising method for killing cancer cells, which also provides a new paradigm for using natural products to kill tumors.

As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduce a transformer-based multi-task framework that concurrently leverages both labeled and unlabeled volumes by encoding shared representation patterns. We first integrate transformers into YOLOv5 to enhance segmentation capabilities and adopt a multi-task approach spanning shadow region detection and boundary localization. Subsequently, we leverage the mean teacher model to simultaneously learn from labeled and unlabeled inputs alongside orthogonal view representations, enabling our approach to harness all available annotations. Our network can improve the learning ability and attain superior performance. Extensive experiments demonstrate that the transformer-powered architecture encodes robust inter-sample relationships, unlocking substantial performance gains by capturing shared information between labeled and unlabeled data. By treating both data types concurrently and encoding their shared patterns, our framework addresses the limitations of existing semi-supervised approaches, leading to improved segmentation accuracy and robustness.

Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.

Automatic image segmentation is vital for the computer-aided determination of treatment directions, particularly in terms of labelling lesions or infected areas. However, the manual labelling of disease regions is inconsistent and a time-consuming assignment. Meanwhile, radiologists’ comments are exceedingly subjective, regularly impacted by personal clinical encounters. To address these issues, we proposed a transformer learning strategy to automatically recognize infected areas in medical images. We firstly utilize a parallel partial decoder to aggregate high-level features and then generate a global feature map. Explicit edge attention and implicit reverse attention are applied to demonstrate boundaries and enhance their expression. Additionally, to alleviate the need for extensive labeled data, we propose a segmentation network combining propagation and transformer architectures that requires only a small amount of labeled data while leveraging fundamentally unlabeled images. The attention mechanisms are integrated within convolutional networks, keeping their global structures intact. Standalone transformers connected straightforwardly and receiving image patches can also achieve impressive segmentation performance. Our network enhanced the learning ability and attained a higher quality execution. We conducted a variety of ablation studies to demonstrate the adequacy of each modelling component. Experiments conducted across various medical imaging modalities illustrate that our model beats the most popular segmentation models. The comprehensive results also show that our transformer architecture surpasses established frameworks in accuracy while better preserving the natural variations in anatomy. Both quantitatively and qualitatively, our model achieves a higher overlap with ground truth segmentations and improved boundary adhesion.

Severe acute pancreatitis (SAP), a widespread inflammatory condition impacting the abdomen with a high mortality rate, poses challenges due to its unclear pathogenesis and the absence of effective treatment options. Isorhamnetin (ISO), a naturally occurring flavonoid, demonstrates robust antioxidant and anti-inflammatory properties intricately linked to the modulation of mitochondrial function. However, the specific protective impact of ISO on SAP remains to be fully elucidated. In this study, we demonstrated that ISO treatment significantly alleviated pancreatic damage and reduced serum lipase and amylase levels in the mouse model of SAP induced by sodium taurocholate (STC) or L-arginine. Utilizing an in vitro SAP cell model, we found that ISO co-administration markedly prevented STC-induced pancreatic acinar cell necrosis, primarily by inhibiting mitochondrial ROS generation, preserving ATP production, maintaining mitochondrial membrane potential, and preventing the oxidative damage and release of mitochondrial DNA. Mechanistically, our investigation identified that high-temperature requirement A2 (HtrA2) may play a central regulatory role in mediating the protective effect of ISO on mitochondrial dysfunction in STC-injured acinar cells. Furthermore, through an integrated approach involving bioinformatics analysis, molecular docking analysis, and experimental validation, we uncovered that ISO may directly impede the histone demethylation activity of KDM5B, leading to the restoration of pancreatic HtrA2 expression and thereby preserving mitochondrial function in pancreatic acinar cells following STC treatment. In conclusion, this study not only sheds new light on the intricate molecular complexities associated with mitochondrial dysfunction during the progression of SAP but also underscores the promising value of ISO as a natural therapeutic option for SAP.

Seed storability has a significant impact on seed vitality and is a crucial genetic factor in maintaining seed value during storage. In this study, RNA sequencing was used to analyze the seed transcriptomes of two rice thermo-sensitive genic male sterile (TGMS) lines, S1146S (storage-tolerant) and SD26S (storage-susceptible), with 0 and 7 days of artificial accelerated aging treatment. In total, 2658 and 1523 differentially expressed genes (DEGs) were identified in S1146S and SD26S, respectively. Among these DEGs, 729 (G1) exhibited similar regulation patterns in both lines, while 1924 DEGs (G2) were specific to S1146S, 789 DEGs (G3) were specific to SD26S, and 5 DEGs (G4) were specific to contrary differential expression levels. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that “translation”, “ribosome”, “oxidative phosphorylation”, “ATP-dependent activity”, “intracellular protein transport”, and “regulation of DNA-templated transcription” were significantly enriched during seed aging. Several genes, like Os01g0971400, Os01g0937200, Os03g0276500, Os05g0328632, and Os07g0214300, associated with seed storability were identified in G4. Core genes Os03g0100100 (OsPMEI12), Os03g0320900 (V2), Os02g0494000, Os02g0152800, and Os03g0710500 (OsBiP2) were identified in protein–protein interaction (PPI) networks. Seed vitality genes, MKKK62 (Os01g0699600), OsFbx352 (Os10g0127900), FSE6 (Os05g0540000), and RAmy3E (Os08g0473600), related to seed storability were identified. Overall, these results provide novel perspectives for studying the molecular response and related genes of different-storability rice TGMS lines under artificial aging conditions. They also provide new ideas for studying the storability of hybrid rice.

Rockfalls are an important factor affecting underground engineering safety. However, there has been limited progress in understanding and predicting these disasters in the past few years. Therefore, a large-scale three-dimensional experimental simulation apparatus to study failure mechanisms of rockfalls occurring during underground engineering was developed. This apparatus, measuring 4 m × 4 m × 3.3 m in size, can achieve vertical and horizontal symmetric loading. It not only simulates the structure and stress environment of a rock mass but also simulates the stepwise excavation processes involved in underground engineering. A complete simulation experiment of rockfalls in an underground engineering context was performed using this apparatus. Dynamic evolution characteristics of block displacement, temperature, natural vibration frequency, and acoustic emissions occurring during rockfalls were studied during the simulation. These data indicate there are several indicators that could be used to predict rockfalls in underground engineering contexts, leading to better prevention and control.

Using metal additive manufacturing processes can make up for traditional forging technologies when forming complex-shaped parts. At the same time, metal additive manufacturing has a fast forming speed and excellent manufacturing flexibility, so it is widely used in the aerospace industry and other fields. The fatigue strength of metal additive manufacturing is related to the microstructure of the epitaxially grown columnar grains and crystallographic texture. The crystal plasticity finite element method is widely used in the numerical simulation of the microstructure and macro-mechanical response of materials, which provides a strengthening and toughening treatment and can reveal the inner rules of material deformation. This paper briefly introduces common metal additive manufacturing processes. In terms of additive manufacturing fatigue, crystal plasticity simulations are summarized and discussed with regard to several important influencing factors, such as the microstructure, defects, surface quality, and residual stress.

Nitrogen (N) is one of the most crucial elements for plant growth. However, a deficiency of N affects plant growth and development. Wedelia trilobata is a notorious invasive plant species that exhibits superior tolerance to adapt to environmental stresses. Yet, research on the growth and antioxidant defensive system of invasive Wedelia under low N stress, which could contribute to understanding invasion mechanisms, is still limited. Therefore, this study aims to investigate and compare the tolerance capability of invasive and native Wedelia under low and normal N conditions. Native and invasive Wedelia species were grown in normal and low-N conditions using a hydroponic nutrient solution for 8 weeks to assess the photosynthetic parameters, antioxidant activity, and localization of reactive oxygen species (ROS). The growth and biomass of W. trilobata were significantly (p < 0.05) higher than W. chinensis under low N. The leaves of W. trilobata resulted in a significant increase in chlorophyll a, chlorophyll b, and total chlorophyll content by 40.2, 56.2, and 46%, respectively, compared with W. chinensis. W. trilobata significantly enhanced antioxidant defense systems through catalase, peroxidase, and superoxide dismutase by 18.6%, 20%, and 36.3%, respectively, providing a positive response to oxidative stress caused by low N. The PCA analysis showed that W. trilobata was 95.3% correlated with physiological traits by Dim1 (79.1%) and Dim2 (16.3%). This study provides positive feedback on W. trilobata with respect to its comprehensive invasion mechanism to improve agricultural systems via eco-friendly approaches in N deficit conditions, thereby contributing to the reclamation of barren land.

Symbiotic systems are intimately integrated at multiple levels. Host–endosymbiont metabolic complementarity in amino acid biosynthesis is especially important for sap-feeding insects and their symbionts. In weevil–Nardonella endosymbiosis, the final step reaction of the endosymbiont tyrosine synthesis pathway is complemented by host-encoded aminotransferases. Based on previous results from other insects, we suspected that these aminotransferases were likely transported into the Nardonella cytoplasm to produce tyrosine. Here, we identified five aminotransferase genes in the genome of the red palm weevil. Using quantitative real-time RT-PCR, we confirmed that RfGOT1 and RfGOT2A were specifically expressed in the bacteriome. RNA interference targeting these two aminotransferase genes reduced the tyrosine level in the bacteriome. The immunofluorescence-FISH double labeling localization analysis revealed that RfGOT1 and RfGOT2A were present within the bacteriocyte, where they colocalized with Nardonella cells. Immunogold transmission electron microscopy demonstrated the localization of RfGOT1 and RfGOT2A in the cytosol of Nardonella and the bacteriocyte. Our data revealed that RfGOT1 and RfGOT2A are transported into the Nardonella cytoplasm to collaborate with genes retained in the Nardonella genome in order to synthesize tyrosine. The results of our study will enhance the understanding of the integration of host and endosymbiont metabolism in amino acid biosynthesis.

Medium additives have been shown to affect the synthesis of active products in fungi. This study investigated the effects of corn stalk, poplar sawdust, Tween-80, and oleic acid on mycelial biomass and physicochemical properties, as well as the bioactivity of polysaccharides, including exopolysaccharides (EPS) and intracellular polysaccharides (IPS), in the submerged culture of Bjerkandera fumosa. Results showed that the addition of corn stalk or poplar sawdust increased the production of EPS but decreased the production of IPS; Tween-80 had less effect on the production of EPS and IPS; and oleic acid stimulated polysaccharide production significantly. Polysaccharide property analysis showed that the addition of corn stalk or poplar sawdust promoted the production of high-molecular-weight components in polysaccharides and changed the monosaccharide composition of polysaccharides, as well as increased the mannose, glucuronic acid, and xylose contents of IPS. Tween-80 and oleic acid also changed the molecular weight distribution of polysaccharides but only slightly affected the composition of monosaccharides. The bioactivity assay indicated that the polysaccharides obtained by adding corn stalk possessed high hydroxyl radical scavenging and antitumor activities. The effect of poplar sawdust was slightly weaker than that of corn stalk. EPS and IPS obtained from a culture with Tween-80 and oleic acid possessed low antioxidant activity. Moreover, their antitumor activity was improved and lost, respectively. The results obtained in this work are useful for improving the understanding of the optimization and regulation of bioactive polysaccharide production in the submerged culture of B. fumosa.

Wuxiang virus (WUXV) is the first sandfly-borne Phlebovirus isolated from Phlebotomus chinensis collected in China and has been established as a consistent viral presence in the local sandfly populations of both Wuxiang County and Yangquan City. However, its distribution in the Shanxi Province remains unclear. In this study, three novel WUXV strains were isolated from sandflies collected from Jiexiu City, Shanxi Province, China, in 2022. Subsequently, whole-genome sequences of these novel strains were generated using next-generation sequencing. The open reading frame (ORF) sequences of the WUXV strains from the three locations were subjected to gene analysis. Phylogenetic analysis revealed that WUXV belongs to two distinct clades with geographical differences. Strains from Wuxiang County and Yangquan City belonged to clade 1, whereas strains from Jiexiu City belonged to clade 2. Reassortment and recombination analyses indicated no gene reassortment or recombination between the two clades. However, four reassortments or recombination events could be detected in clade 1 strains. By aligning the amino acid sequences, eighty-seven mutation sites were identified between the two clades, with seventeen, sixty, nine, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Additionally, selection pressure analysis identified 17 positively selected sites across the entire genome of WUXV, with two, thirteen, one, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Notably, sites M-312 and M-340 in the M segment not only represented mutation sites but also showed positive selective pressure effects. These findings highlight the need for continuous nationwide surveillance of WUXV.

As the largest coastal city in China, Shanghai’s rapid development in transportation, tourism, trade, and commerce has facilitated the spread and invasion of non-native aquatic organisms. Aquatic organisms are highly elusive, and once established, eradicating them becomes a challenging task. Currently, our understanding of the invasion risk posed by non-native aquatic species in Shanghai is limited. Therefore, it is imperative to investigate the pathways of introduction, distribution, and dispersion and the invasion risk and impacts of non-native aquatic organisms in Shanghai. This study investigated aquatic organisms in Shanghai’s primary water bodies, including Huangpu River, Suzhou River, and Dianshan Lake. The risk assessment was conducted using the Aquatic Species Invasiveness Screening Kit (AS-ISK), and field monitoring was performed with environmental DNA (eDNA) technology. Results of the risk assessment indicate that among the 21 evaluated species, 9 fall into the medium-to-high-risk category with scores ≥26, while 12 are classified as low-risk with scores Gambusia affinis, Pomacea canaliculata, Lepomis macrochirus, and Coptodon zillii. This study identified 54 fish species belonging to seven orders, 16 families, and 42 genera at 16 sampling sites in Shanghai, among which Channa maculata, Micropterus salmoides, and Misgurnus bipartitus are non-native. The results suggest that Shanghai faces a high invasion risk of aquatic species, necessitating enhanced scientific prevention and control measures. Early monitoring is essential for species with medium-to-high invasion risk, and a further evaluation and analysis of the risks associated with introduced fish species already present in Shanghai are recommended for aquaculture practices.

Polarimetric synthetic aperture radar (PolSAR) image classification, a field crucial in remote sensing, faces significant challenges due to the intricate expertise required for accurate annotation, leading to susceptibility to labeling inaccuracies. Compounding this challenge are the constraints posed by limited labeled samples and the perennial issue of class imbalance inherent in PolSAR image classification. Our research objectives are to address these challenges by developing a novel label correction mechanism, implementing self-distillation-based contrastive learning, and introducing a sample rebalancing loss function. To address the quandary of noisy labels, we proffer a novel label correction mechanism that capitalizes on inherent sample similarities to rectify erroneously labeled instances. In parallel, to mitigate the limitation of sparsely labeled data, this study delves into self-distillation-based contrastive learning, harnessing sample affinities for nuanced feature extraction. Moreover, we introduce a sample rebalancing loss function that adjusts class weights and augments data for small classes. Through extensive experiments on four benchmark PolSAR images, our approach demonstrates its effectiveness in addressing label inaccuracies, limited samples, and class imbalance. Through extensive experiments on four benchmark PolSAR images, our research substantiates the robustness of our proposed methodology, particularly in rectifying label discrepancies in contexts marked by sample paucity and imbalance. The empirical findings illuminate the superior efficacy of our approach, positioning it at the forefront of state-of-the-art PolSAR classification techniques.

This study aims to explore the correlation between the activity trajectory of Crested Ibis and meteorological changes. The trajectory data of Crested Ibis were obtained using the HQBG3621L backpack-style tracker, and the spatiotemporal characteristics of the trajectory data were analyzed to obtain information on the activity range and habitat of Crested Ibis. The Scheirer–Ray–Hare test and Kruskal–Wallis test were used to investigate the impact of meteorological factors on the activity of Crested Ibis. The study found that meteorological factors have a certain influence on the habitat selection and activity patterns of Crested Ibis. Through this research, a better understanding of the interaction between Crested Ibis and the meteorological environment can be achieved, providing a scientific basis for the conservation and ecological management of Crested Ibis.

In recent years, numerous viruses have been identified from ticks, and some have been linked to clinical cases of emerging tick-borne diseases. Chinese northeast frontier is tick infested. However, there is a notable lack of systematic monitoring efforts to assess the viral composition in the area, leaving the ecological landscape of viruses carried by ticks not clear enough. Between April and June 2017, 7101 ticks were collected to perform virus surveillance on the China–North Korea border, specifically in Tonghua, Baishan, and Yanbian. A total of 2127 Ixodes persulcatus were identified. Further investigation revealed the diversity of tick-borne viruses by transcriptome sequencing of Ixodes persulcatus. All ticks tested negative for tick-borne encephalitis virus. Transcriptome sequencing expanded 121 genomic sequence data of 12 different virus species from Ixodes persulcatus. Notably, a new segmented flavivirus, named Baishan Forest Tick Virus, were identified, closely related to Alongshan virus and Harz mountain virus. Therefore, this new virus may pose a potential threat to humans. Furthermore, the study revealed the existence of seven emerging tick-borne viruses dating back to 2017. These previously identified viruses included Mudanjiang phlebovirus, Onega tick phlebovirus, Sara tick phlebovirus, Yichun mivirus, and three unnamed viruses (one belonging to the Peribunyaviridae family and the other two belonging to the Phenuiviridae family). The existence of these emerging tick-borne viruses in tick samples collected in 2017 suggests that their history may extend further than previously recognized. This study provides invaluable insights into the virome of Ixodes persulcatus in the China–North Korea border region, enhancing our ongoing efforts to manage the risks associated with tick-borne viruses.

In the environment, degradable plastics are decomposed into biodegradable microplastics (Bio-MPs), but there is limited study on the impact on plant growth and development. Therefore, this study aimed to investigate biodegradable polylactic acid microplastics (PLA-MPs) and nonbiodegradable polystyrene microplastics (PS-MPs) with different concentrations (0.02%, 0.2%, and 2% w/w) to explore their short-term toxic effects on cucumbers. The results of this study showed that PLA-MPs significantly (p < 0.05) reduced the aboveground and belowground biomass of cucumber seedlings compared to the control. At the level of 2% MPs, the chlorophyll content and PRI vegetation index of cucumber plants decreased significantly, anthocyanin content increased, and the photosynthetic system was disturbed. Likewise, the antioxidant defensive system of cucumber was affected after exposure to MPs stress, especially under 2% levels. The hyperspectral image is a novel technique which analyzed the chlorophyll content and absorption under MPs treatment; there was still a high correlation between chlorophyll content, anthocyanin content, and MCARI vegetation index, so a single vegetation index could be used for rapid detection of plant physiological status. Our study suggests that Bio-MPs have potential ecological toxicity that could affect the growth of cucumber seedlings through deactivation of the PSII reaction center. Therefore, biodegradable plastics do not seem to be the optimal solution, and there is an urgent need for long-term monitoring and evaluation of the biological toxicity of biodegradable MPs.

Diabetic kidney disease is a common complication of diabetes and remains the primary cause of end-stage kidney disease in the general population. Schisandrin B (Sch B) is an active ingredient in Schisandra chinensis. Our study illustrates that Sch B can mitigate renal tubular cell (RTC) epithelial–mesenchymal transition (EMT) and mitochondrial dysfunction in db/db mice, accompanied by the downregulation of TGF-β1 and the upregulation of PGC-1α. Similarly, Sch B demonstrated a protective effect by reducing the expression of TGF-β1, α-SMA, fibronectin, and Col I, meanwhile enhancing the expression of E-cadherin in human RTCs (HK2 cells) stimulated with high glucose. Moreover, under high glucose conditions, Sch B effectively increased mitochondrial membrane potential, lowered ROS production, and increased the ATP content in HK2 cells, accompanied by the upregulation of PGC-1α, TFAM, MFN1, and MFN2. Mechanistically, the RNA-seq results showed a significant increase in KCP mRNA levels in HK2 cells treated with Sch B in a high glucose culture. The influence of Sch B on KCP mRNA levels was confirmed by real-time PCR in high glucose-treated HK2 cells. Depletion of the KCP gene reversed the impact of Sch B on TGF-β1 and PGC-1α in HK2 cells with high glucose level exposure, whereas overexpression of the KCP gene blocked EMT and mitochondrial dysfunction. Furthermore, the PI3K/Akt pathway was inhibited and the AMPK pathway was activated in HK2 cells exposed to a high concentration of glucose after the Sch B treatment. Treatment with the PI3K/Akt pathway agonist insulin and the AMPK pathway antagonist compound C attenuated the Sch B-induced KCP expression in HK2 cells exposed to a high level of glucose. Finally, molecular autodock experiments illustrated that Sch B could bind to Akt and AMPK. In summary, our findings suggested that Sch B could alleviate RTC EMT and mitochondrial dysfunction by upregulating KCP via inhibiting the Akt pathway and activating the AMPK pathway in DKD.

Aconitum carmichaelii is a herbaceous herb indigenous to China that has been cultivated for traditional medicine for centuries. Virus-like symptoms of A. carmichaelii plants were observed on leaves in some A. carmichaelii plantations in Zhanyi and Wuding Counties, Yunnan Province, southwest China. High-throughput sequencing (HTS) was performed on 28 symptomatic plants, and the results revealed infection with 11 viruses, including 2 novel viruses and 9 previously described viruses: Aconitum amalgavirus 1 (AcoAV-1), aconite virus A (AcVA), cucumber mosaic virus (CMV), currant latent virus (CuLV), apple stem grooving virus (ASGV), chilli veinal mottle virus (ChiVMV), tomato spotted wilt orthotospovirus (TSWV), tobacco vein distorting virus (TVDV), and potato leafroll virus (PLRV). Two novel viruses tentatively named Aconitum potyvirus 1 and Aconitum betapartitivirus 1, were supported by sequence and phylogenetic analysis results of their genomes. We proposed the names Potyvirus aconiti and Betapartitivirus aconiti. RT-PCR assays of 142 plants revealed the predominance and widespread distribution of CMV, AcVA, and AcoPV-1 in plantations. The detection of isolates of CuLV, ASGV, ChiVMV, TSWV, TVDV, and PLRV infections for the first time in A. carmichaelii expands their known host ranges.