Defining the Inflammation Biomarkers of Inflammatory Bowel Diseases and Colorectal Carcinomas

Ulcerative colitis (UC) and Crohn’s disease (CD) are the two common forms of inflammatory bowel disease (IBD). They share similar clinical and demographic features as well as harbor key differences in tissue damage and prognosis. Previous studies indicated that they contributed to the increased rick to Colorectal cancer (CRC). However, whether UC and CD share inflammatory signatures still remains controversial. In addition, no inflammatory signatures have been reported on CRC. To answer these questions, a comprehensive study has been conducted on collected microarray datasets. Our analysis suggests that although CD and UC share common inflammatory pathways, they also present difference. Especially, CD patients are likely to have type I response, while UC patients are inclined to undergo type II response. Pathway enrichment analysis on CRC uncovered two potential CRC-specific inflammatory pathways

Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipments. PD direction of arrival (DOA) algorithms using conventional beamforming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, which make them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then according to the positioning requirements, the antenna array and its arrangement are developed, optimized, and simulation results suggested that the algorithm has reliable direction-finding performance in the form of 6 elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.Comment: 8 pages,13 figures,24 reference

Multiple bombesin-like peptides with opposite functions from skin of Odorrana grahami

AbstractBombesin-like peptides (BLPs) are a family of neuroendocrinic peptides that mediate a variety of biological activities. Three mature BLPs from the skin secretions of the frog Odorrana grahami were purified. Several bombesin-like peptide cDNA sequences encoding precursors of BLPs were identified from the skin cDNA library of O. grahami. This is the maximal diversity of BLPs ever found in animals. Five mature BLPs (B1–B5) based on the amino acid sequences derived from the cDNA cloning were synthesized. In the in vitro myotropic contraction experiment, all synthesized BLPs displayed a stimulating effect toward rat stomach strips, except B4 and B5 which showed the opposite effect, suggesting that certain BLPs may act as antagonists of bombesin receptors while most other BLPs act as agonists. This finding will facilitate the finding of novel bombesin receptors and novel ligands of bombesin receptors. The diversity of amphibian BLPs and their precursors were also analyzed and results suggest that amphibian BLPs and corresponding precursors of various sizes and processing patterns can be used as markers of taxonomic and molecular phylogenetics. The remarkable similarity of preproregions gives rise to very different BLPs and 3′-terminal regions in distantly related frog species, suggesting that the corresponding genes form a multigene family originating from a common ancestor. The diversification of BLP loci could thus be part of an evolutionary strategy developed by amphibian species as a result of shifts to novel ecological niches when environmental factors change rapidly

Fault Identification of Rotating Machinery Based on Dynamic Feature Reconstruction Signal Graph

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain multiple subbands including coefficient matrix. Then, with originally defined two feature extraction factors MDD and DDD, a dynamic feature selection method based on L2 energy norm (DFSL) is proposed, which can dynamically select the feature coefficient matrix of WPD based on the difference in the distribution of norm energy, enabling each sub-signal to take adaptive signal reconstruction. Next the coefficient matrices of the optimal feature sub-bands are reconstructed and reorganized to obtain the feature signal graphs. Finally, deep features are extracted from the feature signal graphs by 2D-Convolutional neural network (2D-CNN). Experimental results on a public data platform of a bearing and our laboratory platform of robot grinding show that this method is better than the existing methods under different noise intensities

Addressing rural–urban income gap in China through farmers’ education and agricultural productivity growth via mediation and interaction effects

Narrowing the rural–urban income gap is an important challenge in achieving sustained and stable economic and social development in China. The present study investigates the role of farmers’ education and agricultural productivity growth in influencing the rural–urban income gap by applying mediation, interaction, and quantile regression models to provincial panel data of China from 2003 to 2017. Results show that, first of all, China’s agricultural productivity (TFP) continues to improve, and it is mainly driven by technical change (TC), with no significant role of technical efficiency change (TEC) or stable scale change (SC). Improving farmers’ education not only directly narrows the rural–urban income gap but also indirectly improves agricultural productivity to further narrow the rural–urban income gap. Due to differences in income sources of farmers, the corresponding impacts of farmers’ education and agricultural productivity growth on the rural–urban income gap also differ. Policy recommendations include continued investments in farmers’ education and training as well as modernization of agricultural for higher productivity growth

AAU-Net: an Adaptive Attention U-Net for breast lesions segmentation in ultrasound images

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net

Development of a Non-invasive Deep Brain Stimulator With Precise Positioning and Real-Time Monitoring of Bioimpedance

Methods by which to achieve non-invasive deep brain stimulation via temporally interfering with electric fields have been proposed, but the precision of the positioning of the stimulation and the reliability and stability of the outputs require improvement. In this study, a temporally interfering electrical stimulator was developed based on a neuromodulation technique using the interference modulation waveform produced by several high-frequency electrical stimuli to treat neurodegenerative diseases. The device and auxiliary software constitute a non-invasive neuromodulation system. The technical problems related to the multichannel high-precision output of the device were solved by an analog phase accumulator and a special driving circuit to reduce crosstalk. The function of measuring bioimpedance in real time was integrated into the stimulator to improve effectiveness. Finite element simulation and phantom measurements were performed to find the functional relations among the target coordinates, current ratio, and electrode position in the simplified model. Then, an appropriate approach was proposed to find electrode configurations for desired target locations in a detailed and realistic mouse model. A mouse validation experiment was carried out under the guidance of a simulation, and the reliability and positioning accuracy of temporally interfering electric stimulators were verified. Stimulator improvement and precision positioning solutions promise opportunities for further studies of temporally interfering electrical stimulation

An improved model using convolutional sliding window-attention network for motor imagery EEG classification

IntroductionThe classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.MethodsTo solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.ResultsThe model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.DiscussionThe experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation

A neutralizing bispecific single-chain antibody against SARS-CoV-2 Omicron variant produced based on CR3022

IntroductionThe constantly mutating SARS-CoV-2 has been infected an increasing number of people, hence the safe and efficacious treatment are urgently needed to combat the COVID-19 pandemic. Currently, neutralizing antibodies (Nabs), targeting the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein are potentially effective therapeutics against COVID-19. As a new form of antibody, bispecific single chain antibodies (BscAbs) can be easily expressed in E. coli and exhibits broad-spectrum antiviral activity.MethodsIn this study, we constructed two BscAbs 16-29, 16-3022 and three single chain variable fragments (scFv) S1-16, S2-29 and S3022 as a comparison to explore their antiviral activity against SARS-CoV-2. The affinity of the five antibodies was characterized by ELISA and SPR and the neutralizing activity of them was analyzed using pseudovirus or authentic virus neutralization assay. Bioinformatics and competitive ELISA methods were used to identify different epitopes on RBD.ResultsOur results revealed the potent neutralizing activity of two BscAbs 16-29 and 16-3022 against SARS-CoV-2 original strain and Omicron variant infection. In addition, we also found that SARS-CoV RBD-targeted scFv S3022 could play a synergistic role with other SARS-CoV-2 RBD-targeted antibodies to enhance neutralizing activity in the form of a BscAb or in cocktail therapies.DiscussionThis innovative approach offers a promising avenue for the development of subsequent antibody therapies against SARSCoV-2. Combining the advantages of cocktails and single-molecule strategies, BscAb therapy has the potential to be developed as an effective immunotherapeutic for clinical use to mitigate the ongoing pandemic