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ObjectiveThe objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets.MethodsTwenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared.ResultsPMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area.ConclusionLeft-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Objective: To determine the extent to which Lycium barbarum polysaccharide (LBP) improves 60Co γ-ray radiation-induced brain injury (RIBI) by regulating the gut microbiota. Methods: The RIBI model of mice was established with the appropriate dose of 60Co γ-ray to identify the changes in the body weight, behaviors, gut microbiota, and inflammatory reactions of mice. Mice were randomly divided into healthy, RIBI model, and LBP groups. The related inflammatory cytokines were determined using an enzyme linked immunosorbent assay kit. Then, 16S rRNA sequencings of feces were carried out to evaluate the differences in intestinal flora. Results: Compared with the spontaneous activity and exploratory spirit of the healthy group, those traits in the RIBI model mice in the open field significantly decreased, the freezing time in the elevated plus maze (EPM) significantly increased, and the number of times the mice discriminated the novel object was significantly lower. Hematoxylin-eosin slides showed that the main histopathological changes of RIBI occurred in the hippocampus. In addition, the diversity and relative abundances ratio of the gut bacterial phylum, order, family, and genus in the model group varied widely. Changes in Bacteroidetes, Firmicutes, and Proteobacteria were the most obvious after head radiation exposure. In comparison, LBP could accelerate the recovery of weight loss in RIBI mice. The frequency that mice entered the center of the open field, facing the open arm in the EPM, and the number of times they discriminated the novel object were significantly increased with LBP administration. LBP could also reduce the levels of inflammatory factor caused by RIBI. LBP increased the diversity and abundance of gut microbiota in RIBI model mice. In addition, LBP increased the relative abundance of Bacteroidetes but decreased the levels of Firmicutes and Proteobacteria for irradiated mice. Conclusion: LBP can improve depression and tension by regulating the composition of gut microbiota, including lowering the relative abundance of Clostridia and Burkholderiales and raising that of Lactobacillales. Thus, LBP provides a new strategy for improving the protective effects of RIBI.

Abstract Few‐shot learning is to distinguish novel concepts with few annotated data, which has attracted much attention due to its requirement of limited training data for target classes. Recent few‐shot learning methods usually pretrain a feature extractor with images from the base set to boost the performance of few‐shot tasks and classify novel categories in this prior feature space. However, it is difficult for the pretrained feature extractor to extract accurate representations for novel categories, resulting in large amounts of overlapping areas between new classes. To address these issues, the prior feature space with a triplet network to learn a more discriminative space is refined, where features belonging to same class are pulled together and that from different classes are pushed apart. Specifically, the authors first follow recent paradigm of pretraining to obtain a prior feature space. Then, a triplet network with contrastive learning is trained to project the features from this space into a low‐dimensional one. The main difference lies in that the authors’ model is based on Maximum A Posteriori (MAP) and the triplet network with hallucinated features is finetuned from it to make them generalise well to novel categories. Finally, the authors conduct classification tasks in the finetuned space. The authors’ intuition is that the overlapping areas in novel categories can be separated by finetuning the triplet network pretrained on base set with contrastive learning. Experimental results on four few‐shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09% ∼ 13.09% than the best results in each dataset on both 1‐ and 5‐shot tasks.

Radiation-induced brain injury (RIBI) is a serious adverse effect of radiotherapy. RIBI has garnered considerable clinical attention owing to its powerful effects on brain function and cognition; however, no effective treatment is available. The microbiota–gut–brain axis theory is a novel concept of treating RIBI by regulating gut microbiota. Quercetin, a particularly common flavonoid compound, has a wide range of biological activities and can regulate gut microbiota; however, it has poor solubility and dispersibility. In the present study, oral gels of inclusion complex comprising quercetin and HP-β-CD were prepared, which increased quercetin dispersion and extended its release time in the intestinal tract. First, the relative abundance and diversity of gut microbiota in RIBI mice changed after oral administration of quercetin inclusion complex gels (QICG). Second, the spontaneous activity behavior and short-term memory ability as well as anxiety level were improved. Third, changes in physical symptoms were observed, including a decrease in TNF-α and IL-6 levels. H&E staining revealed that gut epithelial injury and intestinal inflammation as well as hippocampal inflammation were ameliorated. Antibiotics treatment (Abx) mice were developed to disrupt the mice's original gut microbiota composition. No significant improvement was observed in behavior or histopathology after oral administration of QICG in Abx mice of RIBI, indicating that the effect of QICG on improving RIBI was regulated by intestinal microbiota. Finally, the QICG preparation is efficient, exerting a protective effect on RIBI by regulating gut microbiota via the microbiota–gut–brain axis, which provides a novel idea for RIBI treatment.

Radiation-induced brain injury (RBI) is a common neurological disease caused by ionizing radiation (IR). Edaravone (EDA) is a free radical scavenger, has the potential to treat RBI. EDA loaded temperature-sensitive gels (TSGs) were prepared for subcutaneous injection to improve inconvenient administration of intravenous infusion. RBI mice model was established by irradiation of 60Co γ-ray on head. EDA TSGs could improve spontaneous behavior, learning and memory and anxiety of RBI mice by behavior tests, including the open field test, the novel object recognition test, the elevated plus maze test and the fear conditioning test. The therapeutic effects were enhanced with the assistance of ultrasound. Alleviative pathological changes, decreased the expression of Molondialdehyde (MDA) and Interleukin-6 (IL-6) in the hippocampus of brain, indicated reduced oxidative stress and inflammatory response with the treatment of EDA TSGs and ultrasound. Moreover, ultrasound was superior to the use of EDA TSGs. Safe and effective EDA TSGs were prepared for RBI, and the feasibility of brain-targeted drug delivery enhanced by ultrasound was preliminarily demonstrated in this study.

Vision based depth estimation plays a significant role in Intelligent Transportation Systems (ITS) because of its low cost and high efficiency, which can be used to analyze driving environment, improve driving safety, etc. Although recently proposed approaches abandon time consuming pre-processing or post-processing steps and achieve an end-to-end prediction manner, fine details may be lost through max-pooling based encode modules. To tackle this problem, we propose Multi-Scale Dilated Convolution Network (MSDC-Net), a dilated convolution based deep network. For the feature encoding and decoding part, dilated layers maintain the scale of original image and reduce lost details. After that, a pyramid dilated feature extraction module is added to integrate the knowledge learned through forward steps with different receptive fields. The proposed approach is evaluated on KITTI dataset, and achieves a state-of-the-art result on the dataset.

During the outbreak of the COVID-19 (2019 coronavirus disease), misinformation related to the virus spread rapidly online and have led to serious difficulties in controlling the disease. The term infodemic is coined to outline the bad effect from the extensive dissemination of misinformation during the outbreak. With regards to this phenomenon, the World Health Organization emphasized the need to fight against infodemic and asked all countries not only to make efforts in slowing down the spread of the COVID-19 but also in countering the risk caused by infodemic. Due to its negative impact, this paper analyzes infodemic on Chinese social media at the initial stage of the COVID-19 outbreak and presents a 4P framework standing for the four features of Chinese infodemic: Prevention Attention, Problem Orientation, Patterns Interaction and Points Globalization. Furthermore, a selective review of existing datasets in the neural networks domain is synthesized based on the 4P framework. Finally, research directions, including recommendations, about constructing a large-scale dataset for Chinese infodemic automatic detection are proposed.

(1) Background: When the body is exposed to microwave radiation, the brain is more susceptible to damage than other organs. However, few effective drugs are available for the treatment of microwave-induced brain injury (MIBI) because most drugs are difficult to cross the blood–brain barrier (BBB) to reach the brain. (2) Methods: Nasal cinnarizine inclusion complexes with thermo-and ion-sensitive hydrogels (cinnarizine ISGs) were prepared to treat MIBI and the characteristics of the inclusion complexes and their thermo-and ion-sensitive hydrogels were evaluated. (3) Results: Due to high viscosity, cinnarizine ISGs can achieve long-term retention in the nasal cavity to achieve a sustained release effect. Compared with the model, the intranasal thermo-and ion-sensitive cinnarizine ISGs significantly improved the microwave-induced spatial memory and spontaneous exploration behavior with Morris water maze and open field tests. Cinnarizine ISGs inhibited the expression of calcineurin and calpain 1 in the brain, which may be related to the inhibition of calcium overload by cinnarizine. (4) Conclusion: Intranasal thermo- and ion-sensitive cinnarizine ISGs are a promising brain-targeted pharmaceutical preparation against MIBI.

Abstract Background Alternative splicing (AS) is an important post-transcriptional process. It has been suggested that most AS events are subject to tissue-specific regulation. However, the global dynamics of AS in different tissues are poorly explored. Results To analyse global changes in AS in multiple tissues, we identified the AS events and constructed a comprehensive catalogue of AS events within each tissue based on the genome-wide RNA-seq reads from ten tissues in cucumber. First, we found that 58% of the multi-exon genes underwent AS. We further obtained 565 genes with significantly more AS events compared with random genes. These genes were found significant enrichment in biological processes related to the regulation of actin filament length. Second, significantly different AS event profiles among ten tissues were found. The tissues with the same origin of development are more likely to have a relatively similar AS profile. Moreover, 7370 genes showed tissue-specific AS events and were highly enriched in biological processes related to the positive regulation of cellular component organization. Root-specificity AS genes were related to the cellular response to DNA damage stimulus. Third, the genes with different intron retention (IR) patterns among the ten tissues showed significant difference in GC percentages of the retained intron, and the number of exons and FPKM of the major transcripts. Conclusions Our study provided a comprehensive view of AS in multiple tissues. We revealed novel insights into the patterns of AS in multiple tissues and the tissue-specific AS in cucumber.

Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually. Therefore, instead of developing techniques for infodemic detection, this paper aims at constructing a Chinese infodemic dataset, “infodemic 2019”, by collecting widely spread Chinese infodemic during the COVID-19 outbreak. Each record is labeled as true, false or questionable. After a four-time adjustment, the original imbalanced dataset is converted into a balanced dataset by exploring the properties of the collected records. The final labels achieve high intercoder reliability with healthcare workers’ annotations and the high-frequency words show a strong relationship between the proposed dataset and pandemic diseases. Finally, numerical experiments are carried out with RNN, CNN and fastText. All of them achieve reasonable performance and present baselines for future works.

Background: Although the cucumber reference genome and its annotation were published several years ago, the functional annotation of predicted genes, particularly protein-coding genes, still requires further improvement. In general, accurately determining orthologous relationships between genes allows for better and more robust functional assignments of predicted genes. As one of the most reliable strategies, the determination of collinearity information may facilitate reliable orthology inferences among genes from multiple related genomes. Currently, the identification of collinear segments has mainly been based on conservation of gene order and orientation. Over the course of plant genome evolution, various evolutionary events have disrupted or distorted the order of genes along chromosomes, making it difficult to use those genes as genome-wide markers for plant genome comparisons.Results: Using the localized LASTZ/MULTIZ analysis pipeline, we aligned 15 genomes, including cucumber and other related angiosperm plants, and identified a set of genomic segments that are short in length, stable in structure, uniform in distribution and highly conserved across all 15 plants. Compared with protein-coding genes, these conserved segments were more suitable for use as genomic markers for detecting collinear segments among distantly divergent plants. Guided by this set of identified collinear genomic segments, we inferred 94,486 orthologous protein-coding gene pairs (OPPs) between cucumber and 14 other angiosperm species, which were used as proxies for transferring functional terms to cucumber genes from the annotations of the other 14 genomes. In total, 10,885 protein-coding genes were assigned Gene Ontology (GO) terms which was nearly 1,300 more than results collected in Uniprot-proteomic database. Our results showed that annotation accuracy would been improved compared with other existing approaches.Conclusions: In this study, we provided an alternative resource for the functional annotation of predicted cucumber protein-coding genes, which we expect will be beneficial for the cucumber's biological study, accessible from http://cmb.bnu.edu.cn/functional_annotation. Meanwhile, using the cucumber reference genome as a case study, we presented an efficient strategy for transferring gene functional information from previously well-characterized protein-coding genes in model species to newly sequenced or “non-model” plant species.

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.