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The data imbalance problem extensively exists in wind turbine fault diagnosis, resulting in the compromise between learning attention to majority and minority classes. In this paper, a deep neural network method is proposed to resolve the mentioned problem. Specifically, convolutional and recurrent neural networks are designed to extract spatial and temporal features within supervisory control and data acquisition (SCADA) measurements. To improve the reliability of fault diagnosis results by collective decision, a coarse learner and multiple fine learners are established. With the consideration of data imbalance and learning diversity, fault-related information can be revealed. Moreover, a learner selection scheme is designed to ensure high computational efficiency. The effectiveness of the proposed method is demonstrated by experiments based on simulated data and real-world SCADA measurements from a wind farm. Experimental results show that the accuracy in identifying health conditions can be improved by the proposed method regardless of the data imbalance. On the two datasets, the proposed method outperforms four benchmark approaches as the learning attention to all classes can be enhanced. Therefore, the proposed method is a promising solution to wind turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: Corni Fructus is one of the most famous traditional Chinese medicines (TCMs) for the treatment of various chronic kidney diseases. Wine-processed Corni Fructus (WCF) is the main processed form of Crude Corni Fructus (CCF). In this study, potential mechanisms of action of CCF and WCF on chronic renal failure (CRF) model were developed to explore wine-processed mechanism of Corni Fructus. Methods: An integrated strategy combining metabolomics, network analysis and bioinformatics analysis has been established to investigate the therapeutic mechanisms of WCF and CCF in rats with CRF. Key findings: The histopathological results showed that both WCF and CCF improved kidney injury and dysfunction of CRF rats, but WCF was more effective than CCF. Metabolic pathway analysis indicated that 24 metabolites and 5 major disturbed pathways associated with CCF, while WCF regulated 27 metabolites and 2 metabolic pathways. Bioinformatic analysis and network analysis revealed that 8 genes and 7 genes were regulated by CCF and WCF on CRF rats, respectively. The quantitative real-time polymerase chain reaction experiments verified the regulatory ability of CCF and WCF on the expression of 4 genes. Conclusions: An integrated strategy combined metabolomics, network analysis and bioinformatics was established to provide valuable holistic insight to explore the processing mechanism of TCMs. Graphical Abstract [ABSTRACT FROM AUTHOR]

Biomedical interaction prediction is essential for the exploration of relationships between biomedical entities. Predicted biomedical interactions can help researchers with drug discovery, disease treatment, and more. In recent years, graph neural networks have taken advantage of their natural structure to achieve great progress in biomedical interaction prediction. However, most of them use node embedding instead of directly using edge embedding, resulting in information loss. Moreover, they predict links based on node similarity correlation assumptions, which have poor generalization. In addition, they do not consider the difference in topological information between negative and positive sample links, which limits their performance. Therefore, in this paper, we propose an adaptive line graph contrastive (ALGC) method to convert negative and positive sample links into two kinds of line graph nodes. By adjusting the number of intra-class line graph edges and inter-class line graph edges, an augmented line graph is generated and, finally, the information of the two views is balanced by contrastive learning. Through experiments on four public datasets, it is proved that the ALGC model outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The actual role of coronavirus disease 2019 (COVID-19) in brain damage has been increasingly reported, necessitating a meta-analysis to collate and summarize the inconsistent findings from functional imaging and voxel-based morphometry (VBM) studies. A comprehensive voxel-wise meta-analysis of the whole brain was conducted to identify alterations in functional activity and gray matter volume (GMV) between COVID-19 patients and healthy controls (HCs) by using Seed-based d Mapping software. We included 15 functional imaging studies (484 patients with COVID-19, 534 HCs) and 9 VBM studies (449 patients with COVID-19, 388 HCs) in the analysis. Overall, patients with COVID-19 exhibited decreased functional activity in the right superior temporal gyrus (STG) (extending to the right middle and inferior temporal gyrus, insula, and temporal pole [TP]), left insula, right orbitofrontal cortex (OFC) (extending to the right olfactory cortex), and left cerebellum compared to HCs. For VBM, patients with COVID-19, relative to HCs, showed decreased GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (extending to the bilateral OFC), and left cerebellum, and increased GMV in the bilateral amygdala (extending to the bilateral hippocampus, STG, TP, MTG, and right striatum). Moreover, overlapping analysis revealed that patients with COVID-19 exhibited both decreased functional activity and increased GMV in the right TP (extending to the right STG). The multimodal meta-analysis suggests that brain changes of function and structure in the temporal lobe, OFC and cerebellum, and functional or structural alterations in the insula and the limbic system in COVID-19. These findings contribute to a better understanding of the pathophysiology of brain alterations in COVID-19. This first large-scale multimodal meta-analysis collates existing neuroimaging studies and provides voxel-wise functional and structural whole-brain abnormalities in COVID-19. Findings of this meta-analysis provide valuable insights into the dynamic brain changes (from infection to recovery) and offer further explanations for the pathophysiological basis of brain alterations in COVID-19. • Convergent functional and structural changes in the right superior temporal gyrus and temporal pole were found. • Dynamic brain functional and structural changes over time (from infection to recovery) were found after COVID-19 infection. • Older age was correlated with more serious functional changes in the insula and orbitofrontal cortex. [ABSTRACT FROM AUTHOR]

Wind turbine blades are critical components in wind energy generation, and blade health management is a challenging issue for the operation and maintenance of wind turbines. In this paper, an adaptive method is developed to identify blade damages based on the microphone array and compressive beamforming, and global and remote health assessment can be accomplished. In this method, the generalized minimax-concave penalty function is employed to enhance sparse recovery capacities, and step-sizes in computation processes are adjusted adaptively to adapt to variational conditions. Besides, potential damage locations are extracted in coarse acoustic maps to improve convergence rates. Numerical simulations show that high spatial resolutions can be achieved by the proposed method, and the computation time for solving acoustic inverse problems is less than using existing algorithms, especially with low-frequency sources. Moreover, experiments are conducted with a small-scale wind turbine. Results demonstrate that several damages in operating blades can be precisely recognized with high efficiencies, and the deterioration of acoustic maps induced by improper step-sizes can be avoided. The proposed method provides a promising way for in-situ health monitoring of wind turbine blades. [Display omitted] • An adaptive method for compressive beamforming is proposed to examine blade damages. • The generalized minimax-concave penalty function is utilized in the method. • Step-sizes in iterations are adjusted adaptively to adapt to variational conditions. • Special initial solutions are arranged based on potential damage locations. • Experimental validations are conducted with a small-scale wind turbine. [ABSTRACT FROM AUTHOR]

CRISPR/Cas9 system has emerged as a powerful tool in genome editing; however, generation of CRISPR-edited DNA-free plants is still challenging. In this study, Betula platyphylla (birch) was used to build a method to generate CRISPR-edited plant without foreign DNA integration using Agrobacterium-mediated transformation (CPDAT method). This technique utilizes transient genetic transformation to introduce T-DNA coding gRNA and Cas9 into birch cells, and T-DNA will express to synthesize gRNA and Cas9 protein, which will form a complex to cleave the target DNA site. The genome may be mutated due to DNA repair, and these mutations will be preserved and accumulated not dependent on whether T-DNA is integrated into the genome or not. After transient transformation, birch plants were cut into explants to induce adventitious buds without antibiotic selection pressure. Each adventitious bud can be considered as an independent potentially CRISPR-edited line for mutation detection. CRISPR-edited birch plants without foreign DNA integration are further selected by screening CRISPR-edited lines without T-DNA integration. Among 65 randomly chosen independent lines, the mutation rate was 80.00% including 40.00% of lines with both alleles mutated. In addition, 5 lines out of 65 studied lines (7.69%) were CRISPR-edited birch plants without DNA integration. In conclusion, this innovative method presents a novel strategy for generating CRISPR-edited birch plants, thereby significantly enhancing the efficiency of generating common CRISPR-edited plants. These findings offer considerable potential to develop plant genome editing techniques further. [ABSTRACT FROM AUTHOR]

Corni Fructus, derived from the fruit of Cornus officinalis Sieb. et Zucc , is a widely utilized traditional Chinese medicine (TCM) with established efficacy in the treatment of diverse chronic kidney diseases. Crude Corni Fructus (CCF) and wine-processed Corni Fructus (WCF) are the main processed forms of Corni Fructus. Generally, TCM is often used after processing (paozhi). Despite the extensive use of processed TCM, the underlying mechanisms of processing for most TCMs have been unclear so far. In this study, an integrated strategy combined renal metabolomics with proteomics was established and investigated the potential processing mechanisms of CCF or WCF on chronic renal failure (CRF) models. Firstly, the differences in biochemical parameters and pathological histology were compared to evaluate the effects of CCF and WCF on CRF model rats. Then, the tissue differential metabolites and proteins between CCF and WCF on CRF model rats were screened based on metabolomics and proteomics technology. Concurrently, a combined approach of metabolomics and proteomics was employed to investigate the underlying mechanisms associated with these marker metabolic products and proteins. Compared to the MG group, there were 27 distinct metabolites and 143 different proteins observed in the CCF-treatment group, while the WCF-treatment group exhibited 24 distinct metabolites and 379 different proteins. Further, the integration interactions analysis of the protein and lipid metabolite revealed that both WCF and CCF improved tryptophan degradation and LPS/IL-1-mediated inhibition of RXR function. WCF inhibited RXR function more than CCF via the modulation of LPS/IL-1 in the CRF model. Experimental results were validated by qRT-PCR and western blotting. Notably, the gene expression amount and protein levels of FMO3 and CYP2E1 among 8 genes influenced by WCF were higher compared to CCF. The results of this study provide a theoretical basis for further study of Corni Fructus with different processing techniques in CRF. The findings also offer guidance for investigating the mechanism of action of herbal medicines in diseases employing diverse processing techniques. [Display omitted] • The implementation of a comprehensive strategy that amalgamates metabolomics and proteomics methodologies. • The LPS/IL-1 inhibition of RXR function is crucial for treating CRF-induced rats by modulating WCF and CCF pathways. • Exploring the mechanisms of wine-processed Corni Fructus through differences in disease treatment. • This integrated strategy provides a novel and valuable approach to explain the processing mechanism of TCMs. [ABSTRACT FROM AUTHOR]

Blade breakages in wind turbines are serious threats to reliable power generation. However, blade breakage detection is still challenging owing to varying circumstances and the questionable assumption that distinct health conditions result in inherently separable measurements. To address these challenges, a novel method is proposed for blade breakage detection based on environment-adapted contrastive learning. Specifically, nonparametric regression models between operational and environmental variables are established, and the variable adjustment is conducted to suppress the influence of variational circumstances on SCADA measurements. In addition, an improved strategy of contrastive learning is developed to learn valid data representations, guaranteeing the assumed separability of different samples. Furthermore, normal models of healthy operations are constructed, and online monitoring is implemented based on likelihood estimation and control charts. Actual data from commercial wind farms have been employed to demonstrate the effectiveness and superiority of the proposed method. Experimental results confirm that the correlation degree between operational and environmental variables can be reduced by 28.7 %, and blade breakages can be detected more than 23 h in advance. [ABSTRACT FROM AUTHOR]

• We design a novel contrastive learning framework based on line graph to be suitable for link prediction on sparse and dense graphs. • We propose a cross-scale contrastive learning strategy to maximize the mutual information between subgraph and line graph. • The dual perspectives contrastive progress to some extent avoids the problem of inconsistent prediction on the similarity based methods with a single view. • Our comprehensive experiments on six datasets from diverse areas demonstrate that our model has better performance on generalization and robustness than the SOTA methods. Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning (LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h -hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph to maximize the mutual information of them, so that fuses the structure and feature information. The experimental results demonstrate that the proposed LGCL outperforms the state-of-the-art methods and has better performance on generalization and robustness. [ABSTRACT FROM AUTHOR]

Data-driven intelligent systems provide a possible solution to condition-based maintenance of wind turbines without experts' knowledge or mechanism models. However, the accuracy of fault diagnosis results is easily impaired by the data imbalance in real-world applications. In this paper, a novel method is proposed to address the mentioned problem. Specifically, spatial and temporal information of supervisory control and data acquisition (SCADA) data is extracted, and a contrastive learning strategy is developed to obtain the data representations correlated with the health conditions. Additionally, the bollards of the data distributions are determined in the representation space, and they are matched with the data centers of each health state. Under this circumstance, the unexpected effects of data imbalance on data representations are relieved, leading to effective decision boundaries regardless of the sample numbers. Then, a classifier is trained on the learned representations to recognize the faults in wind turbines. Compared with several baselines and state-of-the-art approaches, the impressive performance of the proposed method is demonstrated with simulated data and actual measurements from a utility-scale wind turbine. This research provides useful insights into the efficient development of wind energy with data-driven intelligent systems. [ABSTRACT FROM AUTHOR]

• A novel method is proposed for short-term multi-step wind power forecasting. • Spatial information is evaluated to guarantee the effectiveness of data fusion. • Two metrics based on the distance and correlation are developed for the evaluation. • Transformer neural networks are established for wind power modeling. • Sequential dependencies can be learned regardless of the contextual distance. Spatio-temporal wind power forecasting is significant to the stability of electric power systems. However, the accuracy of power forecasting results is easily impaired by the insufficient capacity of sequence modeling and misleading information from distinct wind turbines. In this paper, a novel method is proposed to resolve the mentioned problem. Specifically, the reliability of wind condition knowledge is enhanced by considering the spatial information of surrounding wind turbines. Moreover, two metrics based on the distance and correlation are developed to evaluate the quality of spatial information. To learn sequential dependencies regardless of the distance, wind power modeling is achieved by transformer neural networks based on the multi-head attention mechanism. Furthermore, experiments are conducted to assess the performance of the proposed method with real-world measurements. Results show that the proposed method outperforms several baseline and state-of-the-art approaches, and the superiority is particularly prominent with large steps. In two experiments, the average values of mean absolute error of forecasting results generated by the proposed method are only 0.0914 and 0.0911, respectively, which is significantly better than other approaches. With accurate results of short-term multi-step forecasting, this work makes contributions to the effective utilization of wind energy resources. [ABSTRACT FROM AUTHOR]

[Display omitted] • 48 flavonoids of HJ and 28 potential biomarkers of serum samples were identified. • Biomarker LysoPC(20:3) was positive correlation with biochemical indices of ALI. • PLA2G4A and PLA2G2A were the most significant targets in metabolomics. • Sphingolipid and glycerophospholipid metabolism were deemed to key signaling pathways. • Molecular docking was used to validate 8 potential active ingredients in HJ. Hangju (HJ), the dried flower heads of Chrysanthemum morifolium Ramat., has a significant hepatoprotective effect. However, its underlying protection mechanism against acute liver injury (ALI) has been unclear. An integrated strategy based on metabolomics with network analysis and network pharmacology was developed to explore the potential molecular mechanism of HJ on ALI protection. Firstly, differential endogenous metabolites were screened and identified by metabolomics approach and metabolic pathway analysis was performed by MetaboAnalyst. Secondly, marker metabolites were used to construct metabolite-response-enzyme-gene networks and discover hub metabolites and potential gene targets in network analysis. Thirdly, hub genes through the protein-protein interaction (PPI) network were acquired by the aid of network pharmacology. Finally, the gene targets were taken to intersect with the relevant active ingredients for validation by molecular docking. In total, 48 flavonoids were identified in HJ, which were associated with 8 potential therapeutic targets in network pharmacological analysis. Biochemistry and histopathology analysis demonstrated that HJ exerted hepatoprotective effects. 28 biomarkers were successfully identified as possible biomarkers for the prevention of ALI. The sphingolipid metabolic pathway and the glycerophospholipid metabolic pathway was considered a crucial signaling pathway by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, phosphatidylcholine and sphingomyelin were considered as hub metabolites. Twelve enzymes and 38 genes were considered as potential targets in the network analysis. Based on the combined analysis above, HJ was shown to modulate 2 key upstream targets, including PLA2G2A and PLA2G4A. Molecular docking showed that active compounds of HJ had high binding affinity with these key targets. In conclusion, the flavonoid components of HJ can inhibit PLA2 and regulate glycerophospholipid and sphingolipid metabolism pathway to delay the pathological process of ALI, which may be a potential mechanism of HJ against ALI. [ABSTRACT FROM AUTHOR]

Blades are critical components in wind turbines (WTs) for power generation, and condition monitoring (CM) of WT blades is a crucial and challenging issue under operating conditions. Several methods have been developed to assess blade health status, and some of them have the potential to achieve in-situ CM. Nevertheless, there is a lack of critical and comprehensive survey concerning CM techniques for operational WT blades, as well as unsolved problems and research prospects. In this paper, typical causes and types of blade damage that deserve to be considered during the running time are presented. Besides, in-situ CM techniques for WT blades are reviewed in terms of four sub-targets: damage detection, localization, classification, and evaluation. Further, the investigation and development timeline of in-situ CM methods and types of damage that have been verified to be recognizable in field tests are concluded for the first time. The paper ends with discussions on research impediments and prospects that indicate promising development tendencies. • In-situ condition monitoring methods for wind turbine blades are reviewed. • Typical causes and types of blade damage are discussed. • Damage detection, localization, classification, and evaluation are considered. • Relationships between techniques and detectable damage are revealed. • Research challenges and development tendencies are summarized. [ABSTRACT FROM AUTHOR]

• An integrated model is developed to reconstruct the sound field. • Both acoustic source locations and strengths are considered in the model. • A novel sparsity-based method is proposed to identify off-grid acoustic sources. • Group minimax concave penalty is employed to promote the group sparsity. • Remarkable performance of the proposed method is examined with several cases. Acoustic source identification based on the microphone array is an attractive technique, and the effective identification of off-grid sources is still a challenging issue. To improve the accuracy and efficiency of source identification, a novel method based on the off-grid model and group sparsity is proposed. Specifically, both source locations and strengths are considered in an integrated model to reconstruct the sound field with the microphone array at arbitrary positions, and the group minimax concave penalty is employed to promote the group sparsity of identification results. Then, the performance of the proposed method is examined with numerical simulations. Results indicate that the proposed method can identify off-grid sources with higher accuracy than existing methods within a broad range of noise levels and source frequencies, and less computing time is needed than using on-grid approaches. Benefit from the capacity to approximate propagation functions, the computational efficiency can be further enhanced by utilizing coarser search grids. Moreover, experimental results demonstrate the satisfactory performance and application potential of the proposed method. [ABSTRACT FROM AUTHOR]

• A data-driven method is developed for health management of wind turbine blades. • The proposed method is based on self-supervised health representation learning. • Both vibration and SCADA data are applicable for the developed method. • Better performance can be achieved than using traditional methods. • The developed method has excellent generalization and anti-noise ability. To improve the efficiency and reliability of wind power generation, condition monitoring of wind turbines has drawn extensive attention worldwide. However, blade health monitoring is still challenging because of volatile operating conditions and the dependence on the assumption that healthy and unhealthy measurements can be naturally separated after the training stage. In this paper, a self-supervised health representation learning method is proposed to address these problems, and only healthy measurements are required in training. Specifically, data representations related to blade health conditions are learned by neural networks though data augmentation and an auxiliary task. In this case, the interference of operating circumstances and noise can be eliminated, and the volatility of measurements can be suppressed to establish accurate models of healthy operations. Moreover, the separability assumption is guaranteed by imposing constraints on the representation distributions of unhealthy samples, improving the reliability of decision making based on the learned knowledge. Blade health conditions are recognized using kernel density estimation. The satisfactory performance of the proposed method is demonstrated through laboratory and field measurements, achieving higher accuracy than existing approaches for online health monitoring. This work contributes to the economy of clean energy utilization. [ABSTRACT FROM AUTHOR]

Acoustic source identification using compressive beamforming has been extensively investigated and applied in numerous fields. In this paper, a generalized minimax-concave (GMC) penalty based compressive beamforming method is proposed. The method solves acoustic inverse problems using the GMC penalty, by which the capacity of sound field reconstruction in low-frequency and low-signal-to-noise ratio environments can be enhanced. In addition, the problem of systematic underestimation of source strength can be relieved with the benefit of penalty homogeneity, and a balance can be reached between the sparsity-induced capacity and computing complexity. Moreover, the convergence of the proposed method and parameter influences were studied through numerical simulations. The results show that the method is robust within a relatively wide range of parameters, and the computational efficiency is within the limits of acceptability. Furthermore, the performance of the proposed method was compared with two other methods using simulated and experimental data. The results indicate that the proposed method performs better than existing algorithms under low-frequency and strong-noise conditions, and the spatial resolution in acoustic maps and the capacity of source strength estimation can be improved. [ABSTRACT FROM AUTHOR]

Background: This meta-analysis aimed to explore the most robust findings across numerous existing resting-state functional imaging and voxel-based morphometry (VBM) studies on the functional and structural brain alterations in individuals with autism spectrum disorder (ASD). Methods: A whole-brain voxel-wise meta-analysis was conducted to compare the differences in the intrinsic functional activity and gray matter volume (GMV) between individuals with ASD and typically developing individuals (TDs) using Seed-based d Mapping software. Results: A total of 23 functional imaging studies (786 ASD, 710 TDs) and 52 VBM studies (1728 ASD, 1747 TDs) were included. Compared with TDs, individuals with ASD displayed resting-state functional decreases in the left insula (extending to left superior temporal gyrus [STG]), bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), left angular gyrus and right inferior temporal gyrus, as well as increases in the right supplementary motor area and precuneus. For VBM meta-analysis, individuals with ASD displayed decreased GMV in the ACC/mPFC and left cerebellum, and increased GMV in the left middle temporal gyrus (extending to the left insula and STG), bilateral olfactory cortex, and right precentral gyrus. Further, individuals with ASD displayed decreased resting-state functional activity and increased GMV in the left insula after overlapping the functional and structural differences. Conclusions: The present multimodal meta-analysis demonstrated that ASD exhibited similar alterations in both function and structure of the insula and ACC/mPFC, and functional or structural alterations in the default mode network (DMN), primary motor and sensory regions. These findings contribute to further understanding of the pathophysiology of ASD. [ABSTRACT FROM AUTHOR]

Abstract Railway plays an indispensable role in long-distance passenger transport. Here, we investigate train exposure time to bad weather and its influence on an important railway operation problem, train delay. We find that train delay is more likely to occur in snow and rain weather and the total train delay time is in a good correlation with the train exposure time to bad weather. We also develop a train delay network to identify the communities of railway stations from the perspective of train delay co-occurrence. The obtained community structure can be potentially useful in designing regional management strategy for delayed trains. Highlights • Trains tend to follow their historical behavior from the perspective of delays. • The average train delay time is prominently larger in the typical bad weather. • Train delay has good positive correlation with train exposure to bad weather. • Train exposure time to the snow and rain weather follows power-law distribution. [ABSTRACT FROM AUTHOR]

Drought stress is a common adverse environment that plants encounter, and many drought-tolerant genes have been characterized. The gene regulatory network (GRN) is important in revealing the drought tolerance mechanism. Here, to investigate the regulatory mechanism of Shanxin poplar (Populus davidiana × P. bolleana) responding to drought stress, a three-layered GRN was built, and the regulatory relationship between genes in the GRN were predicted from expression correlation using a partial correlation coefficient-based algorithm. The GRN contains 1869 regulatory relationships, and includes 11 and 19 transcription factors (TFs) in the first and second layers, respectively, and 158 structural genes in the bottom layers involved in eight enriched biological processes. ChIP-PCR and qRT-PCR based on transient transformation were performed to validate the reliability of the GRN. About 88.0% of predicted interactions between the first and second layers, and 82.0% of predicted interactions between the second and third layers were correct, suggesting that the GRN is reliable. Six TFs were randomly selected from the top layer for characterizing their function in drought, and all of these TFs can confer drought tolerance. The important biological processes related to drought tolerance were identified, including "response to jasmonic acid", "response to oxidative stress", and "response to osmotic stress". In this GRN, PdbERF3 is predicted to play an important role in drought tolerance. Our data revealed the key regulators, TF-DNA interactions, and the main biological processes involved in adaption of drought stress in Shanxin poplar. [ABSTRACT FROM AUTHOR]

Numerous neuroimaging studies have reported that Alzheimer's disease (AD) spectrum have been linked to alterations in intrinsic functional activity and cortical thickness (CT) of some brain areas. However, the findings have been inconsistent and the correlation with the transcriptional profile and neurotransmitter systems remain largely unknown. We conducted a meta-analysis to identify multimodal differences in the amplitude of low-frequency fluctuation (ALFF)/fractional ALFF (fALFF) and CT in patients with AD and preclinical AD compared to healthy controls (HCs), using the Seed-based d Mapping with Permutation of Subject Images software. Transcriptional data were retrieved from the Allen Human Brain Atlas. The atlas-based nuclear imaging-derived neurotransmitter maps were investigated by JuSpace toolbox. We included 26 ALFF/fALFF studies comprising 884 patients with AD and 1,020 controls, along with 52 studies comprising 2,046 patients with preclinical AD and 2,336 controls. For CT, we included 11 studies comprising 353 patients with AD and 330 controls. Overall, compared to HCs, patients with AD showed decreased ALFF/fALFF in the bilateral posterior cingulate gyrus (PCC)/precuneus and right angular gyrus, as well as increased ALFF/fALFF in the bilateral parahippocampal gyrus (PHG). Patients with peclinical AD showed decreased ALFF/fALFF in the left precuneus. Additionally, patients with AD displayed decreased CT in the bilateral PHG, left PCC, bilateral orbitofrontal cortex, sensorimotor areas and temporal lobe. Furthermore, gene sets related to brain structural and functional changes in AD and preclincal AD were enriched for G protein-coupled receptor signaling pathway, ion gated channel activity, and components of biological membrane. Functional and structural alterations in AD and preclinical AD were spatially associated with dopaminergic, serotonergic, and GABAergic neurotransmitter systems. The multimodal meta-analysis demonstrated that patients with AD exhibited convergent functional and structural alterations in the PCC/precuneus and PHG, as well as cortical thinning in the primary sensory and motor areas. Furthermore, patients with preclinical AD showed reduced functional activity in the precuneus. AD and preclinical AD showed genetic modulations/neurotransmitter deficits of brain functional and structural impairments. These findings may provide new insights into the pathophysiology of the AD spectrum. • Convergent functional and structural alterations in the posterior cingulate gyrus/precuneus and parahippocampal gyrus were demonstrated in patients with Alzheimer's disease (AD) through the multimodal meta-analysis. • Reduced functional activity in the precuneus was observed in patients with preclinical AD. • Genetic modulations and neurotransmitter deficits of brain functional and structural impairments were noted in AD and preclinical AD. [ABSTRACT FROM AUTHOR]

Recently, sodium percarbonate (SPC) is widely applied as a potential alternative of liquid H 2 O 2 for advanced oxidation processes in waste treatment. Compared with H 2 O 2 , SPC could be used in a wider pH range and acted as a buffer for neutralizing the acidity in Fenton oxidation. Herein, we synthesized the two-dimension graphene oxide-supported CoO x nanoparticles (CoO x /GO), via the stabilization of CoO x nanoparticles at the surface of graphene oxide (GO), in SPC activation for degrading tetracycline. The detailed characterizations have confirmed CoO x /GO nanohybrid exhibits a two-dimensional thin nanosheet structure, and CoO x , which is a mixture of cobalt oxides and cobalt hydroxides, are successfully immobilized onto the surface of GO. Compared with other carriers, CoO x /GO presents the superior catalytic activity, with 80% degradation efficiency and 37.86% mineralization rate, in TC degradation via SPC activation. These results have lighted the significant role of GO in CoO x /GO nanohybrid for TC degradation via SPC activation. It seems that the electrons were transferred from GO to Co atoms, and enriching the electron cloud density of Co atoms, which was favour of SPC activation. Free radical quenching tests and EPR results demonstrated that OH• and •CO 3 - were dominating reactive oxygen species in CoO x /GO/SPC system. This study not only proposes an efficient method for the synthesis of Co-based nanocatalysts, but also provides a high-efficiency catalytic system for TC degradation. [Display omitted] ● CoO x /GO is developed by immobilization of CoO x nanoparticles onto GO. ● CoO x /GO is found to be an efficient catalyst for TC removal via percarbonate activation. ● OH• and •CO 3 - are major reactive oxygen species in CoO x /GO/percarbonate system. ● This work provides a cobalt catalyst for wastewater treatment via percarbonate activation. [ABSTRACT FROM AUTHOR]

We developed self-microemulsifying drug-delivery systems (SMEDDS), including bile salts, to improve the oral bioavailability of pueraria flavones (PFs). The physical properties of the SMEDDS using Cremophor RH 40, and bile salts as mixed surfactants at weight ratios of 10:0–0:10 were determined. The particle sizes of PFs-SMEDDSNR containing sodium taurocholate (NaTC) and Cremophor RH 40, and PFs-SMEDDSR containing Cremophor RH 40 were measured upon dilution with deionized water and other aqueous media. Dilution volume presented no remarkable effects on particle size, whereas dilution media slightly influenced particle size. PFs-SMEDDSNR and PFs-SMEDDSR provided similar release rates in pH-1.2 hydrochloride solution. However, the release rate of PFs-SMEDDSNR was faster than that of PFs-SMEDDSR in pH-6.8 phosphate buffer containing 20 mM NaTC and 500 U/mL porcine pancreas lipase. The pharmacokinetics and bioavailability were measured in rats. The oral bioavailability of PFs-SMEDDSNR was 2.57- and 2.28-fold that of a suspension of PFs (PFs-suspension) before and after the blockade of the lymphatic transport route by cycloheximide, respectively. These results suggested PFs-SMEDDSNR could significantly improve the oral relative absorption of PFs via the lymphatic uptake pathway. SMEDDS containing NaTC may provide an effective approach for enhancing the oral bioavailability of PFs. [ABSTRACT FROM AUTHOR]

Currently, many surfactants used in self-emulsifying drug delivery systems (SMEDDS) can cause gastrointestinal mucosal irritation and systemic toxicity. In the present study, SMEDDS were loaded with pueraria flavones, using sodium taurocholate to replace polyoxyl 40 dydrogenated castor oil (Cremophor® RH 40) as the surfactant (PF-SMEDDS NR ) to reduce the toxicity of SMEDDS using Cremophor® RH 40 as the surfactant (PF-SMEDDS R ). The absorption rate constants ( K a ) and intestinal permeability coefficients ( P eff ) were measured. The effects of P-glycoprotein inhibitor (verapamil), adenosine triphosphate (ATP) inhibitor (2,4-dinitrophenol), and carrier inhibitor on K a and P eff values in the ileum were determined. Biological safety was also evaluated. The K a and P eff values increased for PF-solution concentrations of 200 μg/ml > 100 μg/ml > 400 μg/ml in individual segments of the intestines. The results indicated that P eff values of PF-SMEDDS NR were distinctly higher than those of SMEDDS loaded with pueraria flavones using Cremophor®RH 40 as the surfactant (PF-SMEDDS R ) and PF-solution in four intestinal segments. However, the K a values of PF-SMEDDS NR were higher only in the jejunum and ileum segments compared with those of PF-SMEDDS R and PF-solution. The K a and P eff values without verapamil were significantly lower than those with verapamil. 2,4-Dinitrophenol had no effect on K a and P eff values. The K a and P eff values of PF-SMEDDS NR significantly decreased after perfusing B-SMEDDS NR for 1 h prior to the study. The cell viabilities after exposure to SMEDDS NR were higher than those of SMEDDS R in the range of 81–324 μg/ml. Lactate dehydrogenase release from cells treated with PF-SMEDDS NR or B-SMEDDS NR was significantly lower than that from cells treated with PF-SMEDDS R or B-SMEDDS R at surfactant concentrations of 243 and 324 μg/ml. However, there were no differences with SMEDDS treatment at surfactant concentrations of 0–162 μg/ml. Hence, we conclude that SMEDDS using sodium taurocholate as the surfactant can reduce the toxicity of SMEDDS, meanwhile, maintain the characteristics of SMEDDS, and enhance intestinal absorption. [ABSTRACT FROM AUTHOR]