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Eucommia ulmoides Oliver (E. ulmoides Oliver), a multipurpose woody plant, holds great economic significance due to its expansive medicinal, food and industrial applications. The rapid advancement of E. ulmoides in various fields has resulted in the inadequacy of existing breeding methods to meet its growth and annual production demands. Consequently, there is an urgent need for innovative propagation strategies. This study introduces an optimized micropropagation protocol for E. ulmoides, facilitating direct shoot organogenesis from nodal segments with axillary buds. We systematically examined the impact of basal medium composition, plant growth regulators, photosynthetic photon flux density, and sucrose concentration on bud sprouting. Employing cuttings with axillary buds as propagation material, we achieved a shortened cultivation period of merely 4 weeks for bud elongation and proliferation, marking a substantial enhancement in propagation efficiency. Notably, the Driver Kuniyuki Walnut medium, supplemented with 20.0 g L−1 sucrose and 2.0 mg L−1 trans-zeatin, induced shoots sprouting with a 100% success rate and an average length of 5.18 cm per nodal segment, equating to a great bud propagation rate of approximately 500%. Furthermore, a light source with an intensity of 80 μmol m−2 s−1 was shown the most economical choice. To address the primary challenge of inducing roots in regenerated plants, we employed a refined two-step rooting technique. This method yielded the optimal rooting frequency of 93.02%, producing an average of 5.90 adventitious roots per plantlet, each with an average length of 2.77 cm. The micropropagation program developed in this work will be the cornerstone for the preservation of the germplasm of E. ulmoides and its long-term use in medicinal and industrial applications.

Synthetic nitrogen (N) fertilizer has made a great contribution to the improvement of soil fertility and productivity, but excessive application of synthetic N fertilizer may cause agroecosystem risks, such as soil acidification, groundwater contamination and biodiversity reduction. Meanwhile, organic substitution has received increasing attention for its ecologically and environmentally friendly and productivity benefits. However, the linkages between manure substitution, crop yield and the underlying microbial mechanisms remain uncertain. To bridge this gap, a three-year field experiment was conducted with five fertilization regimes: i) Control, non-fertilization; CF, conventional synthetic fertilizer application; CF1/2M1/2, 1/2 N input via synthetic fertilizer and 1/2 N input via manure; CF1/4M3/4, 1/4 N input synthetic fertilizer and 3/4 N input via manure; M, manure application. All fertilization treatments were designed to have equal N input. Our results showed that all manure substituted treatments achieved high soil fertility indexes (SFI) and productivities by increasing the soil organic carbon (SOC), total N (TN) and available phosphorus (AP) concentrations, and by altering the bacterial community diversity and composition compared with CF. SOC, AP, and the soil C:N ratio were mainly responsible for microbial community variations. The co-occurrence network revealed that SOC and AP had strong positive associations with Rhodospirillales and Burkholderiales, while TN and C:N ratio had positive and negative associations with Micromonosporaceae, respectively. These specific taxa are implicated in soil macroelement turnover. Random Forest analysis predicted that both biotic (bacterial composition and Micromonosporaceae) and abiotic (AP, SOC, SFI, and TN) factors had significant effects on crop yield. The present work strengthens our understanding of the effects of manure substitution on crop yield and provides theoretical support for optimizing fertilization strategies.

Silver nanoparticles (AgNPs) synthesized from plant extracts have recently emerged as a rapidly growing field with numerous applications in pharmaceutical and clinical contexts. The purpose of this research is to come up with a novel method for the biosynthesis of silver nanoparticles that use Eucommia ulmoides leaf extract as a reducing agent. The synthesis of AgNPs was confirmed using UV-vis spectroscopy, and the properties of AgNPs were characterized using Transmission Electron Microscope, Fourier Infrared Spectrometer, X-ray diffraction, Thermogravimetric Analysis, and Zeta potential. The results showed that the AgNPs exhibited a characteristic absorption peak at 430 nm, their diameter ranged from 4 nm to 52 nm, and C, O, and Cl elements, which might represent flavonoids and phenolic components absorbed on the surface of AgNPs. The zeta potential of AgNPs was found to be −30.5 mV, which indicates repulsion among AgNPs and they have good dispersion stability. AgNPs have been found to suppress the tyrosinase activity both in mushroom tyrosinase and A375 cells, as well as diminish ROS formation in HaCat cells. According to this study, AgNPs is a novel material that can enhance skin health by preventing melanin development.

Hydrogen sulphide (H2S), a crucial gas signal molecule, has been reported to be involved in various processes related to development and adversity responses in plants. However, the effects and regulatory mechanism of H2S in controlling Fusarium head blight (FHB) in wheat have not been clarified. In this study, we first reported that H2S released by low concentrations of sodium hydrosulphide (NaHS) could significantly alleviate the FHB symptoms caused by Fusarium graminearum (F. graminearum) in wheat. We also used coleoptile inoculation to investigate the related physiological and molecular mechanism. The results revealed that FHB resistance was strongly enhanced by the H2S released by NaHS, and 0.3 mM was confirmed as the optimal concentration. H2S treatment dramatically reduced the levels of hydrogen peroxide (H2O2) and malondialdehyde (MDA) while enhancing the activities of antioxidant enzymes. Meanwhile, the relative expressions levels of defence-related genes, including PR1.1, PR2, PR3, and PR4, were all dramatically upregulated. Our results also showed that H2S was toxic to F. graminearum by inhibiting mycelial growth and spore germination. Taken together, the findings demonstrated the potential value of H2S in mitigating the adverse effects induced by F. graminearum and advanced the current knowledge regarding the molecular mechanisms in wheat.

Aflatoxin B1 (AFB1) is the most harmful mycotoxin produced by filamentous fungi and presents a serious threat to human and animal health. Therefore, it is essential to protect humans and animals from AFB1-induced acute and chronic toxicity. In this study, Pseudomonas strain m29 having a high efficiency of AFB1 transformation was isolated from soil. The transformation ratio by m29 was more than 97% within 24 h, and the optimum temperature for transformation was 37°C. Moreover, the AFB1 transforming activity was mainly attributed to the cell-free supernatant of strain m29. The metabolite that plays a crucial role in AFB1 transformation is likely 1,2-dimethylhydrazine or 1,1-dimethylhydrazine, as identified by GC-MS and LC-MS analysis. AFB1 was transformed into a product with molecular formula C17H14O7. To the best of our knowledge, this is the first study of non-enzymatic AFB1 transformation by bacteria. Importantly, this AFB1 transformation mechanism could be universal to various microorganisms.

Invasive fungal otitis media is clinically rare and diagnosis often delayed. We report a 65-year-old female with Aspergillus fumigatus otitis media complicated by mastoiditis and peripheral facial paralysis. Complete mastoidectomy and type II tympanoplasty, with oral administration of voriconazole for more than 3 months resulted in a successful outcome.

Abstract Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease’s progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.

It is now clear that the intercellular transport on microtubules by dynein and kinesin-1 motors has an important role in the replication and spread of many viruses. Porcine epidemic diarrhea virus (PEDV) is an enveloped, single-stranded RNA virus of the Coronavirus family, which can infect swine of all ages and cause severe economic losses in the swine industry. Elucidating the molecular mechanisms of the intercellular transport of PEDV through microtubule, dynein and kinesin-1 will be crucial for understanding its pathogenesis. Here, we demonstrate that microtubule, dynein, and kinesin-1 are involved in PEDV infection and can influence PEDV fusion and accumulation in the perinuclear region but cannot affect PEDV attachment or internalization. Furthermore, we adopted a single-virus tracking technique to dynamically observe PEDV intracellular transport with five different types: unidirectional movement toward microtubule plus ends; unidirectional movement toward microtubule minus ends; bidirectional movement along the same microtubule; bidirectional movement along different microtubules and motionless state. Among these types, the functions of dynein and kinesin-1 in PEDV intercellular transport were further analyzed by single-virus tracking and found that dynein and kinesin-1 mainly transport PEDV to the minus and plus ends of the microtubules, respectively; meanwhile, they also can transport PEDV to the opposite ends of the microtubules different from their conventional transport directions and also coordinate the bidirectional movement of PEDV along the same or different microtubules through their cooperation. These results provided deep insights and references to understand the pathogenesis of PEDV as well as to develop vaccines and treatments.

Federated Learning (FL) is a newly emerged federated optimization technique for distributed data in a federated network. The participants in FL that train the model locally are classified into client nodes. The server node assumes the responsibility to aggregate local models from client nodes without data moving. In this regard, FL is an ideal solution to protect data privacy at each node of the network. However, the raw data generated on each node are unlabeled, making it impossible for FL to apply these data directly to train a model. The large volume of data annotating work prevents FL from being widely applied in the real world, especially for online scenarios, where the data are generated continuously. Meanwhile, the data generated on different nodes tend to be differently distributed. It has been proved theoretically and experimentally that non-independent and identically distributed (non-IID) data harm the performance of FL. In this article, we design a semi-federated active learning (semi-FAL) framework to tackle the annotation and non-IID problems jointly. More specifically, the server node can provide (i) a pre-trained model to help each client node annotate the local data uniformly and (ii) an estimation of the global gradient to help correct the local gradient. The evaluation results demonstrate our semi-FAL framework can efficiently handle unlabeled online network data and achieves high accuracy and fast convergence.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer’s disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects’ features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs’ input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph’s imaging features and edge-assigning functions can both significantly affect classification accuracy.

Aiming to obtain the acoustic attenuation performance of exhaust muffler of diesel engine and the influence of main structural parameters on its acoustic attenuation characteristics, the finite element analysis method and acoustic theory were adopted to numerically investigate the acoustic attenuation performance under the boundary condition of acoustic adiabatic propagation and muffler wall. It suggested that the noise cancellation effect of muffler was poor at the middle and low frequency in range of 0–3000 Hz, and the transfer loss of muffler was basically 0 dB pass frequency at 1100 Hz. According to previous single-factor study experience, the structural factors, such as the expansion ratio, insertion length of outlet perforated pipe, the distance between the diaphragm and the front part of muffler, have influences on the acoustic performance of muffler at low frequency. Thus, they were taken as the starting point to study the influence of multiple interaction factors on the muffling performance by using orthogonal design method combined with the finite element analysis method. The influence degree of different structure parameters on the acoustic performance of muffler and the optimized structure parameters were obtained. Through the analysis on the acoustic characteristic of the optimized muffler, it indicated that the transmission loss of the improved muffler had significant increase in other frequency range except the range of 650–800 Hz and 2500–2700 Hz, especially at frequency of 1100 Hz compared with the original muffler. In the range of 0–3000 Hz, the mean of transmission loss of the improved muffler was about 9.8 dB larger than that of original muffler, which indicated that better noise cancellation effect was achieved. The improved muffler also provided a certain reference for the structural improvement of similar muffler.

Many previous works only focused on the cascading failure of global coupling of one-to-one structures in interdependent networks, but the local coupling of dual coupling structures has rarely been studied due to its complex structure. This will result in a serious consequence that many conclusions of the one-to-one structure may be incorrect in the dual coupling network and do not apply to the smart grid. Therefore, it is very necessary to subdivide the dual coupling link into a top-down coupling link and a bottom-up coupling link in order to study their influence on network robustness by combining with different coupling modes. Additionally, the power flow of the power grid can cause the load of a failed node to be allocated to its neighboring nodes and trigger a new round of load distribution when the load of these nodes exceeds their capacity. This means that the robustness of smart grids may be affected by four factors, i.e., load redistribution, local coupling, dual coupling link and coupling mode; however, the research on the influence of those factors on the network robustness is missing. In this paper, firstly, we construct the smart grid as a two-layer network with a dual coupling link and divide the power grid and communication network into many subnets based on the geographical location of their nodes. Secondly, we define node importance ( N I ) as an evaluation index to access the impact of nodes on the cyber or physical network and propose three types of coupling modes based on N I of nodes in the cyber and physical subnets, i.e., Assortative Coupling in Subnets (ACIS), Disassortative Coupling in Subnets (DCIS), and Random Coupling in Subnets (RCIS). Thirdly, a cascading failure model is proposed for studying the effect of local coupling of dual coupling link in combination with ACIS, DCIS, and RCIS on the robustness of the smart grid against a targeted attack, and the survival rate of functional nodes is used to assess the robustness of the smart grid. Finally, we use the IEEE 118-Bus System and the Italian High-Voltage Electrical Transmission Network to verify our model and obtain the same conclusions: (I) DCIS applied to the top-down coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or ACIS, (II) ACIS applied to a bottom-up coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or DCIS, and (III) the robustness of the smart grid can be improved by increasing the tolerance α . This paper provides some guidelines for slowing down the speed of the cascading failures in the design of architecture and optimization of interdependent networks, such as a top-down link with DCIS, a bottom-up link with ACIS, and an increased tolerance α .