Maternal smoking, consumption of alcohol, and caffeinated beverages during pregnancy and the risk of childhood brain tumors: a meta-analysis of observational studies.

BACKGROUND: We conducted this meta-analysis to investigate the potential association between maternal smoking, alcohol and caffeinated beverages consumption during pregnancy and the risk of childhood brain tumors (CBTs). METHODS: A thorough search was carried out on PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Internet to identify pertinent articles. Fixed or random effects model was applied to meta-analyze the data. RESULTS: The results suggested a borderline statistically significant increased risk of CBTs associated with maternal smoking during pregnancy (OR 1.04, 95% CI 0.99-1.09). We found that passive smoking (OR 1.12, 95% CI 1.03-1.20), rather than active smoking (OR 1.00, 95% CI 0.93-1.07), led to an increased risk of CBTs. The results suggested a higher risk in 0-1 year old children (OR 1.21, 95% CI 0.94-1.56), followed by 0-4 years old children (OR 1.12, 95% CI 0.97-1.28) and 5-9 years old children (OR 1.11, 95% CI 0.95-1.29). This meta-analysis found no significant association between maternal alcohol consumption during pregnancy and CBTs risk (OR 1.00, 95% CI 0.80-1.24). An increased risk of CBTs was found to be associated with maternal consumption of caffeinated beverages (OR 1.16, 95% CI 1.07-1.26) during pregnancy, especially coffee (OR 1.18, 95% CI 1.00-1.38). CONCLUSIONS: Maternal passive smoking, consumption of caffeinated beverages during pregnancy should be considered as risk factors for CBTs, especially glioma. More prospective cohort studies are warranted to provide a higher level of evidence.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Motion states identification of underwater glider based on complex networks and graph convolutional networks.

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

Toxicity of decabromodiphenyl ethane on lettuce: Evaluation through growth, oxidative defense, microstructure, and metabolism.

Decabromodiphenyl ethane (DBDPE) as the most widely used novel brominated flame retardants (NBFRs), has become a ubiquitous emerging pollutant in the environment. However, its toxic effects on vegetable growth during agricultural production have not been reported. In this study, we investigated the response mechanisms of hydroponic lettuce to DBDPE accumulation, antioxidant stress, cell structure damage, and metabolic pathways after exposure to DBDPE. The concentration of DBDPE in the root of lettuce was significantly higher than that in the aboveground part. DBDPE induced oxidative stress on lettuce, which stimulated the defense of the antioxidative system of lettuce cells, and the cell structure produced slight plasma-wall separation. In terms of metabolism, metabolic pathway disorders were caused, which are mainly manifested as inhibiting amino acid biosynthesis and metabolism-related pathways, interfering with the biosyntheses of amino acids, organic acids, fatty acids, carbohydrates, and other substances, and ultimately manifested as decreased total chlorophyll content and root activity. In turn, metabolic regulation alleviated antioxidant stress. The mechanisms of the antioxidative reaction of lettuce to DBDPE were elucidated by IBR, PLS-PM analysis, and molecular docking. Our results provide a theoretical basis and research necessity for the evaluation of emerging pollutants in agricultural production and the safety of vegetables.

EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training.

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition.

Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.

RGD and Scutellarin Conjugate (WK001) Targeting Platelet Glycoprotein IIb/IIIa Receptor Protects from Myocardial Ischemia/Reperfusion Injury: Synthesis, Characterization, and Bioactivity Evaluation.

Myocardial ischemia/reperfusion (MI/R) injury is an unresolved clinical challenge. The blockade of binding fibrinogen by glycoprotein IIb/IIIa (GPIIb-IIIa) inhibitors has become a new therapeutic approach against MI/R injury. In this study, we modified the RGD structure to combine with scutellarin and synthesized a novel peptide, scutellarin-HomoArg-Gly-Asp-Trp-NH2 (WK001). Herein, reported experimental and docking evidence indicates that WK001 provides immediate and potent platelet inhibition, with stronger inhibition of platelet aggregation than eptifibatide and scutellarin. In particular, it is administered intravenously to prevent thrombus formation and attenuate myocardial fibrosis progression in vivo. Therefore, WK001 could be developed as an antiplatelet drug to treat thrombosis-associated diseases, such as stroke and myocardial infarction.

Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor.

Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.

Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition.

Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.

Prrx1 promotes resistance to temozolomide by upregulating ABCC1 and inducing vasculogenic mimicry in glioma.

Gliomas are the most common primary brain tumors with dismal prognoses. Temozolomide (TMZ), the frontline therapeutic agent for gliomas, has shown limited clinical benefit primarily due to the acquired chemoresistance. Although growing evidence has suggested that the multi-drug resistance phenotype and abnormal vascular microenvironment are responsible for the intrinsic and extrinsic TMZ resistance, the molecular mechanism of TMZ resistance remains to be elucidated. In this study, we found Paired-related homeobox 1 (Prrx1) was an independent prognostic factor for the efficacy of chemotherapy-based postoperative treatment. Silencing Prrx1 markedly enhanced the TMZ-induced cytotoxicity both in vitro and in vivo. We also demonstrated that Prrx1 increased the expression of ABCC1, a member of ATP-Binding Cassette (ABC) transporter protein family, through binding to the promoter region of ABCC1 gene and initiating its transcription. Silencing ABCC1 mitigated the TMZ resistance induced by Prrx1. Furthermore, Prrx1 facilitates the formation of vasculogenic mimicry (VM), a critical extrinsic mechanism for glioma TMZ resistance. Collectively, our findings supported the critical role of Prrx1 in TMZ resistance via intrinsic and extrinsic mechanism. Targeting Prrx1 might represent a feasible strategy to overcome therapeutic resistance in glioma.

Paradoxical role of ß8 integrin on angiogenesis and vasculogenic mimicry in glioblastoma.

Glioblastoma multiforme (GBM) is the most aggressive and highly vascularized brain tumor with poor prognosis. Endothelial cell-dependent angiogenesis and tumor cell-dependent Vasculogenic mimicry (VM) synergistically contribute to glioma vascularization and progression. However, the mechanism underlying GBM vascularization remains unclear. In this study, GBM stem cells (GSCs) were divided into high and low ß8 integrin (ITGB8) subpopulations. Co-culture assays followed by Cell Counting Kit-8 (CCK-8), migration, Matrigel tube formation, and sprouting assays were conducted to assess the proliferative, migratory and angiogenic capacity of GBM cells and human brain microvascular endothelial cells (hBMECs). An intracranial glioma model was constructed to assess the effect of ITGB8 on tumor vascularization in vivo. Our results indicated that ITGB8 expression was elevated in GSCs and positively associated with stem cell markers in glioma tissues, and could be induced by hypoxia and p38 activation. ITGB8 in GSCs inhibited the angiogenesis of hBMECs in vitro, while it promoted the ability of network formation and expression of VM-related proteins. The orthotopic GBM model showed that ITGB8 contributed to decreased angiogenesis, meanwhile enhanced invasiveness and VM formation. Mechanistic studies indicated that ITGB8-TGFß1 axis modulates VM and epithelial-mesenchymal transition (EMT) process via Smad2/3-RhoA signaling. Together, our findings demonstrated a differential role for ITGB8 in the regulation of angiogenesis and VM formation in GBM, and suggest that pharmacological inhibition of ITGB8 may represent a promising therapeutic strategy for treatment of GBM.

Immortalized Mesenchymal Stem Cells: A Safe Cell Source for Cellular or Cell Membrane-Based Treatment of Glioma.

Mesenchymal stem cells (MSCs) have emerged as putative therapeutic tools due to their intrinsic tumor tropism, and anti-tumor and immunoregulatory properties. The limited passage and self-differentiation abilities of MSCs in vitro hinder preclinical studies on them. In this study, we focused on the safety of immortalized mesenchymal stem cells (im-MSCs) and, for the first time, studied the feasibility of im-MSCs as candidates for the treatment of glioma. The im-MSCs were constructed by lentiviral transfection of genes. The proliferative capacity of im-MSCs and the proliferative phenotype of MSCs and MSCs co-cultured with glioma cells (U87) were measured using CCK-8 or EdU assays. After long-term culture, karyotyping of im-MSCs was conducted. The tumorigenicity of engineered MSCs was evaluated using soft agar cloning assays. Next, the engineered cells were injected into the brain of female BALB/c nude mice. Finally, the cell membranes of im-MSCs were labeled with DiO or DiR to detect their ability to be taken up by glioma cells and target in situ gliomas using the IVIS system. Engineered cells retained the immunophenotype of MSC; im-MSCs maintained the ability to differentiate into mesenchymal lineages in vitro; and im-MSCs showed stronger proliferative capacity than unengineered MSCs but without colony formation in soft agar, no tumorigenicity in the brain, and normal chromosomes. MSCs or im-MSCs co-cultured with U87 cells showed enhanced proliferation ability, but did not show malignant characteristics in vitro. Immortalized cells continued to express homing molecules. The cell membranes of im-MSCs were taken up by glioma cells and targeted in situ gliomas in vivo, suggesting that im-MSCs and their plasma membranes can be used as natural drug carriers for targeting gliomas, and providing a safe, adequate, quality-controlled, and continuous source for the treatment of gliomas based on whole-cell or cell membrane carriers.

Dual role of WNT5A in promoting endothelial differentiation of glioma stem cells and angiogenesis of glioma derived endothelial cells.

Glioma is a devastating cancer with a rich vascular network. No anti-angiogenic treatment is available for prolonging the overall survival of glioma patients. Recent studies have demonstrated that the endothelial differentiation of glioma stem cells (GSCs) into glioma-derived endothelial cells (GDECs) may be a novel target for anti-angiogenic therapy in glioma; however, the underlying mechanisms of this process remain unknown. Here, we report that wingless-related integration site (WNT) family member 5A (WNT5A) plays significant roles in GSC endothelial differentiation and GDECs angiogenesis. WNT5A is preferentially secreted by GDECs, and inhibition of WNT5A suppresses angiogenesis and tumorigenesis in GDECs. Silencing of WNT5A in GDECs also disrupts the impact of GDECs on stimulating GSC endothelial differentiation. Frizzled-4 is a receptor that mediates the effect of WNT5A on GSC endothelial differentiation and angiogenesis of GDECs via GSK3ß/ß-catenin/epithelial-mesenchymal transition signalling. The shWNT5A@cRGD-DDD liposomes, targeting WNT5A, exert anti-angiogenic effects in vivo. In this study, we identified that WNT5A has a dual functional role in modulating the endothelial differentiation of GSCs and angiogenesis of GDECs, indicating that WNT5A is a potential target for anti-angiogenesis-based therapeutics in glioma.

Prrx1 promotes stemness and angiogenesis via activating TGF-ß/smad pathway and upregulating proangiogenic factors in glioma.

Glioma is one of the most lethal cancers with highly vascularized networks and growing evidences have identified glioma stem cells (GSCs) to account for excessive angiogenesis in glioma. Aberrant expression of paired-related homeobox1 (Prrx1) has been functionally associated with cancer stem cells including GSCs. In this study, Prrx1 was found to be markedly upregulated in glioma specimens and elevated Prrx1 expression was inversely correlated with prognosis of glioma patients. Prrx1 potentiated stemness acquisition in non-stem tumor cells (NSTCs) and stemness maintenance in GSCs, accompanied with increased expression of stemness markers such as SOX2. Prrx1 also promoted glioma angiogenesis by upregulating proangiogenic factors such as VEGF. Consistently, silencing Prrx1 markedly inhibited glioma proliferation, stemness, and angiogenesis in vivo. Using a combination of subcellular proteomics and in vitro analyses, we revealed that Prrx1 directly bound to the promoter regions of TGF-ß1 gene, upregulated TGF-ß1 expression, and ultimately activated the TGF-ß/smad pathway. Silencing TGF-ß1 mitigated the malignant behaviors induced by Prrx1. Activation of this pathway cooperates with Prrx1 to upregulate the expression of stemness-related genes and proangiogenic factors. In summary, our findings revealed that Prrx1/TGF-ß/smad signal axis exerted a critical role in glioma stemness and angiogeneis. Disrupting the function of this signal axis might represent a new therapeutic strategy in glioma patients.

Discovery, SAR, and putative mode of action of N-benzyl-2-methoxybenzamides as potential bleaching herbicides.

BACKGROUND: Herbicides acting on biosynthesis of plant pigments have contributed greatly to weed control in recent years. In our previous studies, 2-methoxybenzamides were discovered as a novel type of lead compound for the development of bleaching herbicides. RESULTS: A total of 67 benzamide analogues were synthesized and evaluated for herbicidal activity. The structure-activity relationship (SAR) revealed that a methoxyl substitution at the 2-position of the benzoyl moiety is essential for the herbicidal activity of benzamide derivatives, and introduction of small substituents at the meta- or para-position of the benzylamine moiety is also beneficial. Compounds 4, 43 and 44 showed 100% inhibition against Abutilon theophrasti and Amaranthus retroflexus at an application rate of 150 g a.i. ha-1 . CONCLUSION: The relationship between the structure and herbicidal activity of 2-methoxybenzamides was discussed intensively. Compounds 4, 43 and 44 may serve as novel candidates with a bleaching effect. © 2021 Society of Chemical Industry.

Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification.

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Selective exosome exclusion of miR-375 by glioma cells promotes glioma progression by activating the CTGF-EGFR pathway.

BACKGROUND: Exosomes are membrane-bound extracellular vesicles of 40-150 nm in size, that are produced by many cell types, and play an important role in the maintenance of cellular homeostasis. Exosome secretion allows for the selective removal of harmful substances from cells. However, it remains unclear whether this process also takes place in glioma cells. METHODS: Herein, the role of the tumour-suppressor miR-375 was explored in human glioma cells. Immunoblotting and qRT-PCR experiments demonstrated a functional link between miR-375 and its target, connective tissue growth factor (CTGF), which led to the identification of the underlying molecular pathways. The exosomes secreted by glioma cells were extracted by ultracentrifugation and examined by transmission electron microscopy. Exosomal expression of miR-375 was then analysed by qRT-PCR; while the exosome secretion inhibitor, GW4869, was used to examine the biological significance of miR-375 release. Moreover, the dynamics of miR-375 release by glioma cells was investigated using fluorescently labelled exosomes. Finally, exosomal miR-375 release was examined in an orthotopic xenograft model in nude mice. RESULTS: MiR-375 expression was downregulated in gliomas. MiR-375 suppressed glioma proliferation, migration, and invasion by inhibiting the CTGF-epidermal growth factor receptor (EGFR) signalling pathway. MiR-375-containing exosomes were also identified in human peripheral blood samples from glioma patients, and their level correlated with disease progression status. Exosomal miR-375 secretion impacted the CTGF-EGFR pathway activity. Once secreted, exosomal miR-375 was not taken back up by glioma cells. CONCLUSIONS: Exosomal miR-375 secretion allowed for sustained activation of the CTGF-EGFR oncogenic pathway, promoting the proliferation and invasion of glioma cells. These findings enhance our understanding of exosome biology and may inspire development of new glioma therapies.

Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue.

Fatigue driving has attracted a great deal of attention for its huge influence on automobile accidents. Recognizing driving fatigue provides a primary but significant way for addressing this problem. In this paper, we first conduct the simulated driving experiments to acquire the EEG signals in alert and fatigue states. Then, for multi-channel EEG signals without pre-processing, a novel rhythm-dependent multilayer brain network (RDMB network) is developed and analyzed for driving fatigue detection. We find that there exists a significant difference between alert and fatigue states from the view of network science. Further, key sub-RDMB network based on closeness centrality are extracted. We calculate six network measures from the key sub-RDMB network and construct feature vectors to classify the alert and fatigue states. The results show that our method can respectively achieve the average accuracy of 95.28% (with sample length of 5 s), 90.25% (2 s), and 87.69% (1 s), significantly higher than compared methods. All these validate the effectiveness of RDMB network for reliable driving fatigue detection via EEG.

Design, Synthesis, and Herbicidal Activity of N-Benzyl-5-cyclopropyl-isoxazole-4-carboxamides.

Based on the structures of isoxaflutole (IFT) and N-isobutyl-N-(4-chloro-benzyl)-4-chloro-2-pentenamide, a series of N-benzyl-5-cyclopropyl-isoxazole-4-carboxamides was designed by connecting their pharmacophores (i.e., a multitarget drug design strategy). A total of 27 N-benzyl-5-cyclopropyl-isoxazole-4-carboxamides were prepared from 5-cyclopropylisoxazole-4-carboxylic acid and substituted benzylamines, and their structures were confirmed by NMR and MS. Laboratory bioassays indicated that I-26 showed 100% inhibition against Portulaca oleracea and Abutilon theophrasti at a concentration of 10 mg/L, better than the positive control butachlor (50% inhibition for both weeds). A strong growth inhibition was observed, but a typical bleaching phenomenon of IFT could not be observed in the Petri dish assay. I-05 displayed excellent postemergence herbicidal activity against Echinochloa crusgalli and A. theophrasti at a rate of 150 g/ha, and bleaching symptoms were observed in the leaves of treated weeds. The bleaching effect of Chlamydomonas reinhardtii treated by I-05 could be reversed by adding homogentisate. Enzymatic bioassays indicated that I-05 could not inhibit 4-hydroxyphenylpyruvate dioxygenase (HPPD) activity, but II-05, an isoxazole ring-opening product of I-05, could inhibit HPPD activity with an EC50 value of 1.05 µM, similar to that of mesotrione (with an EC50 value of 1.35 µM). Detailed discussion about observed herbicidal symptoms is provided in the Results and Discussion section. This investigation provided a proof-of-concept foundation that a multitarget drug design strategy could be applied in agrochemical research.

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.