Modulation of alveolar macrophage and mitochondrial fitness by medicinal plant-derived nanovesicles to mitigate acute lung injury and viral pneumonia

Acute lung injury (ALI) is generally caused by severe respiratory infection and characterized by overexuberant inflammatory responses and inefficient pathogens-containing, the two major processes wherein alveolar macrophages (AMs) play a central role. Dysfunctional mitochondria have been linked with distorted macrophages and hence lung disorders, but few treatments are currently available to correct these defects. Plant-derive nanovesicles have gained significant attention because of their therapeutic potential, but the targeting cells and the underlying mechanism remain elusive. We herein prepared the nanovesicles from Artemisia annua, a well-known medicinal plant with multiple attributes involving anti-inflammatory, anti-infection, and metabolism-regulating properties. By applying three mice models of acute lung injury caused by bacterial endotoxin, influenza A virus (IAV) and SARS-CoV-2 pseudovirus respectively, we showed that Artemisia-derived nanovesicles (ADNVs) substantially alleviated lung immunopathology and raised the survival rate of challenged mice. Macrophage depletion and adoptive transfer studies confirmed the requirement of AMs for ADNVs effects. We identified that gamma-aminobutyric acid (GABA) enclosed in the vesicles is a major molecular effector mediating the regulatory roles of ADNVs. Specifically, GABA acts on macrophages through GABA receptors, promoting mitochondrial gene programming and bioenergy generation, reducing oxidative stress and inflammatory signals, thereby enhancing the adaptability of AMs to inflammation resolution. Collectively, this study identifies a promising nanotherapeutics for alleviating lung pathology, and elucidates a mechanism whereby the canonical neurotransmitter modifies AMs and mitochondria to resume tissue homeostasis, which may have broader implications for treating critical pulmonary diseases such as COVID-19.

Dynamic nonreversibility view of intrinsic brain organization and brain dynamic analysis of repetitive transcranial magnitude stimulation

Intrinsic neural activities are characterized as endless spontaneous fluctuation over multiple time scales. However, how the intrinsic brain organization changes over time under local perturbation remains an open question. By means of statistical physics, we proposed an approach to capture whole-brain dynamics based on estimating time-varying nonreversibility and k-means clustering of dynamic varying nonreversibility patterns. We first used synthetic fMRI to investigate the effects of window parameters on the temporal variability of varying nonreversibility. Second, using real test-retest fMRI data, we examined the reproducibility, reliability, biological, and physiological correlation of the varying nonreversibility substates. Finally, using repetitive transcranial magnetic stimulation-fMRI data, we investigated the modulation effects of repetitive transcranial magnetic stimulation on varying nonreversibility substate dynamics. The results show that: (i) as window length increased, the varying nonreversibility variance decreased, while the sliding step almost did not alter it; (ii) the global high varying nonreversibility states and low varying nonreversibility states were reproducible across multiple datasets and different window lengths; and (iii) there were increased low varying nonreversibility states and decreased high varying nonreversibility states when the left frontal lobe was stimulated, but not the occipital lobe. Taken together, these results provide a thermodynamic equilibrium perspective of intrinsic brain organization and reorganization under local perturbation.

Proteomic Analysis of Frozen-Thawed Spermatozoa with Different Levels of Freezability in Dairy Goats

The results of artificial insemination (AI) are adversely affected by changes in sperm motility and function throughout the cryopreservation procedure. The proteome alterations of frozen-thawed spermatozoa with various levels of freezability in dairy goats, however, remain largely unknown. To discover differentially expressed proteins (DEPs) and their roles in dairy goat sperm with high or low freezability (HF or LF), we conducted 4D-DIA quantitative proteomics analysis, the results of which are presented in this work. Additionally, we explored the underlying processes that may lead to the variations in sperm freezing resistance. A total of 263 DEPs (Fold Change > 2.0, p-value < 0.05) were identified between the HF group and LF group in frozen-thawed dairy goat spermatozoa. In our Gene Ontology (GO) enrichment analysis, the DEPs were mostly associated with the regulation of biological processes, metabolic processes, and responses to stress and cellular component biogenesis. Our Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis also revealed that the DEPs were predominantly engaged in oxidative phosphorylation, N-Glycan biosythesis, and cysteine and methionien metabolism. A protein-protein interaction (PPI) network analysis revealed 14 potential proteins (NUDFB8, SDHC, PDIA4, HSPB1, etc.) that might influence the freezability of dairy goat sperm. These findings shed light on the processes underlying alterations in the proteome and sperm freezability, aiding further research on sperm cryopreservation.

An Enterprise Service Demand Classification Method Based on One-Dimensional Convolutional Neural Network with Cross-Entropy Loss and Enterprise Portrait

To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method-1D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected as the data source. Finally, the performance of 1D-CNN-CrossEntorpyLoss is compared with XGBoost, SVM, and logistic regression models. From the experimental results, it can be found that 1D-CNN-CrossEntorpyLoss has the best classification results with an accuracy of 72.44%. In addition, compared to the results without the enterprise-quality portrait, the enterprise-quality portrait improves the accuracy and recall of 1D-CNN-CrossEntorpyLoss model. It is also verified that the enterprise-quality portrait can further improve the classification ability of enterprise quality-service demand, and 1D-CNN-CrossEntorpyLoss is better than other classification methods, which can improve the precision service of the comprehensive quality service platform for MSMEs.

Effect of Visual Feedback on Behavioral Control and Functional Activity During Bilateral Hand Movement

Previous researches state vision as a vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated with various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. In the current study, we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-manual and unimanual motor tasks. The errors were controlled using visual feedback. To complete the unimanual tasks, the participant was asked to grip the strain gauge using the index finger and thumb of the right hand thereby exerting force on the connected visual feedback system. Whereas the bi-manual task involved finger abduction of the left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for the right hand. Primarily, the existence of visual feedback for the right hand significantly decreased brain network global and local efficiency in theta and alpha bands when compared with the elimination of visual feedback using twenty participants. Brain network activity in theta and alpha bands coordinates to facilitate fine hand movement. The findings may provide new neurological insight on virtual reality auxiliary equipment and participants with neurological disorders that cause movement errors requiring accurate motor training. The current study investigates task-dependent modulation by simultaneously measuring high time resolution electroencephalogram, electromyogram and force along with bi-manual and unimanual motor tasks. The findings show that visual feedback for right hand decreases the force root mean square error of right hand. Visual feedback for right hand decreases local and global efficiency of brain network in theta and alpha bands.

Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

[Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage]

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.

Transcriptome and Metabolome Dynamics Explain Aroma Differences between Green and Red Prickly Ash Fruit

Green prickly ash (Zanthoxylum armatum) and red prickly ash (Zanthoxylum bungeanum) fruit have unique flavor and aroma characteristics that affect consumers' purchasing preferences. However, differences in aroma components and relevant biosynthesis genes have not been systematically investigated in green and red prickly ash. Here, through the analysis of differentially expressed genes (DEGs), differentially abundant metabolites, and terpenoid biosynthetic pathways, we characterize the different aroma components of green and red prickly ash fruits and identify key genes in the terpenoid biosynthetic pathway. Gas chromatography-mass spectrometry (GC-MS) was used to identify 41 terpenoids from green prickly ash and 61 terpenoids from red prickly ash. Piperitone was the most abundant terpenoid in green prickly ash fruit, whereas limonene was most abundant in red prickly ash. Intergroup correlation analysis and redundancy analysis showed that HDS2, MVK2, and MVD are key genes for terpenoid synthesis in green prickly ash, whereas FDPS2 and FDPS3 play an important role in the terpenoid synthesis of red prickly ash. In summary, differences in the composition and content of terpenoids are the main factors that cause differences in the aromas of green and red prickly ash, and these differences reflect contrasting expression patterns of terpenoid synthesis genes.

Fatty Acid Profiling and Chemometric Analyses for Zanthoxylum Pericarps from Different Geographic Origin and Genotype

Zanthoxylum plants, important aromatic plants, have attracted considerable attention in the food, pharmacological, and industrial fields because of their potential health benefits, and they are easily accessible because of the wild distribution in most parts of China. The chemical components vary with inter and intraspecific variations, ontogenic variations, and climate and soil conditions in compositions and contents. To classify the relationships between different Zanthoxylum species and to determine the key factors that influence geographical variations in the main components of the plant, the fatty acid composition and content of 72 pericarp samples from 12 cultivation regions were measured and evaluated. Four fatty acids, palmitic acid (21.33-125.03 mg/g), oleic acid (10.66-181.37 mg/g), linoleic acid (21.98-305.32 mg/g), and linolenic acid (0.06-218.84 mg/g), were the most common fatty acid components in the Zanthoxylum pericarps. Fatty acid profiling of Zanthoxylum pericarps was significantly affected by Zanthoxylum species and geographical variations. Stearic acid and oleic acid in pericarps were typical fatty acids that distinguished Zanthoxylum species based on the result of DA. Palmitic acid, palmitoleic acid, trans-13-oleic acid, and linoleic acid were important differential indicators in distinguishing given Zanthoxylum pericarps based on the result of OPLS-DA. In different Zanthoxylum species, the geographical influence on fatty acid variations was diverse. This study provides information on how to classify the Zanthoxylum species based on pericarp fatty acid compositions and determines the key fatty acids used to classify the Zanthoxylum species.

Comparing the effect of electroacupuncture treatment on obese and non-obese women with stress urinary incontinence or stress-predominant mixed urinary incontinence: A secondary analysis of two randomised controlled trials

OBJECTIVE

To explore whether obesity patients with a body mass index (BMI) of ≥25 kg/m2 who suffer from stress urinary incontinence (SUI) or stress-predominant mixed urinary incontinence (S-MUI) show less improvement in urinary incontinence (UI) symptoms after electroacupuncture (EA) treatment compared with non-obese counterparts.

METHODS

This study was a secondary analysis of existing data. About 252 SUI patients and 250 S-MUI patients treated with the same EA regimen were assigned to one of the two groups: the obesity group for BMI ≥25 kg/m2 and the non-obesity group for BMI <25 kg/ m2 . The primary outcome was the proportion of treatment responders, defined as patients exhibiting a ≥50% reduction in 72-hours incontinence episode frequency, as measured by a 72-hours bladder diary at week 6 compared with baseline.

RESULTS

Of the 1004 randomised women, 129 obese women (86 SUI and 43 S-MUI) and 255 non-obese women (166 SUI and 89 S-MUI) treated with EA were included in a secondary analysis. The primary outcome was that 58.3% (74/127) of patients in the obesity group and 60.7% (150/247) of patients in the non-obesity group (difference 0.55%; 95% confidence interval, -10.01 to 11.11; P = .919) responded to treatment.

CONCLUSION

This study suggests that EA treatment may safely improve UI symptoms in both obese and non-obese patients, regardless of BMI category. Additionally, obesity status may not affect the efficacy of EA treatment on SUI or S-MUI among Chinese women.

Adsorption of Cd(II) in water by mesoporous ceramic functional nanomaterials

Mesoporous ceramic functional nanomaterials (MCFN) is a self-assembled environmental adsorbent with a monolayer molecular which is widely used in the treatment of industrial wastewater and contaminated soil. This work aimed to study the relationship between the adsorption behaviour of Cd(II) by MCFN and contact time, initial concentration, MCFN dosage, pH, oscillation rate and temperature through a batch adsorption method. The adsorption kinetic and isotherm behaviours were well described by the pseudo-second-order and Langmuir models. The batch characterization technique revealed that MCFN had several oxygen-containing functional groups. Using Langmuir model, the maximum adsorption capacity of MCFN for Cd(II) was 97.09 mg g-1 at pH 6, 25°C, dosage of 0.2 g and contact time of 180 min. Thermodynamic study indicated that the present adsorption process was feasible, spontaneous and exothermic at the temperature range of 25-55°C. The results of this study provide an important enlightenment for Cd removal or preconcentration of porous ceramic nanomaterial adsorbents for environmental applications.