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To find out the bioelectric signal discriminative features of different acupoints at the same time and at the same acupoint, and then analyze these differences to determine the type of acupuncture points and assist in judging the patient's condition, a hardware system was designed that can collect subcutaneous potential information of acupoints. Then, the changes of ultrasonic images were analyzed after acupuncture at Zu San Li, as well as the influence of acupuncture at Zu San Li on vagal nerve activity. The hardware system is composed of 4 parts: an analog circuit module, a digital circuit module, a power module, and a host personal computer. The power module adopted dual-module power supply mode, which was composed of MC78M05CT and LM2576 and their related peripheral circuits to ensure the stability of the circuit. The analog circuit module collected the electric signals from the 2 acupoints of Zu San Li and Shang Ju Xu of the volunteers' calves through a multistage electrode probe. In the amplifier module, Butterworth filter and 50 HZ notch are used to reduce the noise of electric signals. The filtered and amplified electric signals are converted into A/D converter in the digital circuit module. Serial communication is used to transmit data to the upper computer of the personal computer to compare the noise reduction effect of acupoint information, multiple sequences of different acupoints, and multiple sequences of the same acupoint. The influence of acupuncture at the Zu San Li acupoint on the muscle tension around the acupoint was investigated by shear wave acoustic elastic imaging technology. The influence of acupuncture at this acupoint was explored on vagal nerve activity. The results show that the wavelet noise reduction method can greatly filter out the interference signals. In the case of artificial stimulation, the signal waveform of each twist shows a big increase, and the comparison shows that there is no significant change in the frequency of other bands. Through ultrasonic image evaluation, it was found that acupuncture at Zu San Li can significantly affect the mean value of Young modulus. After acupuncture at Zu San Li, the vagal activity was increased and the balance ratio of sympathetic vagal activity was reduced. Part of the potential frequency of the acupoint is extremely active, which is the response potential of the subcutaneous induction tissue at the acupoint, which proved the feasibility of collecting electric signals around the acupoints in this kind of hardware system. It is of great significance to diagnose the disease according to the characteristics of acupoint electric signals. [ABSTRACT FROM AUTHOR]

Driver fatigue is a major cause of traffic accidents, and electroencephalography (EEG) based driver fatigue classification is widely regarded as a future direction. In practical applications, different tasks may require different classification approaches. However, traditional fatigue classification approaches are task-specific, ignoring adaptive classification for different tasks. Achieving adaptive classification is challenging due to diversity of tasks, individual differences, etc. This paper proposes an adaptive fatigue classification framework to extend the applicability of EEG-based techniques. The proposed framework adjusts classification granularities based on requirements of different tasks. In order to capture information suitable for different granularities, the proposed framework includes an attention-based hybrid neural network (AHNN). AHNN combines 1D convolutions with attention mechanisms to enhance relationships between features and channels while suppressing the influence of insensitive information. To improve the generality of the proposed framework among individuals, an individual feature selection approach is added to generate required feature subsets for different individuals to weaken individual differences. We conduct subject-dependent and subject-independent experiments using the public SEED-VIG dataset to verify the feasibility of our proposed framework. In subject-dependent experiments, classification accuracy is 92.97% and 97.86% for fine-grained and coarse-grained classification, respectively, while it is 87.05% and 95.64% in subject-independent experiments. Experimental results show that the framework could be applied to different tasks, and outperforms some existing studies and classical models in specific tasks. AHNN could suit different classification granularities, and weaken individual differences, improving the generality of the framework. The proposed framework could provide a reference for future adaptive classification technique researches. [ABSTRACT FROM AUTHOR]

The emergence of deep learning methods has driven the widespread use of brain–machine interface motor imagery classification in machine control and medical rehabilitation, and has achieved classification accuracy superior to those of traditional machine learning methods. However, models trained using current mainstream deep learning methods show a maximum variation in accuracy of over 20% when using data from different subjects in the same dataset for classification. The large variation indicates the weak robustness of such models and the difficulties in feature extraction for some subjects. As motor imagery classification is aimed at individual users, it is not conducive to the diffusion of the technique if the results vary too much from one user to another. In our research, we have found the accuracy differences between different subjects are caused by the data differ in spatial characteristics and training difficulty. Therefore, exploring the differences between different subjects' data and weakening these differences can reduce the accuracy gap between subjects and ensure that the model can have good classification accuracy for each subject. We call this operation of reducing the accuracy gap individual differences weakening. To implement this operation, we propose a Double-branch Graph Convolutional Attention Neural Network (DGCAN), which uses a graph neural network to filter channels that are less disturbed by spatial location factors, and uses spatial–temporal domain convolution to focus on extracting features contained in the filtered channels, weakening the influence of spatial features contributes to individual differences weakening. We also design a loss function, EegLoss, which focuses on training hard samples and can effectively reduce the model-insensitive data contained in different subjects. We test model performance on the BCI Competition IV datasets 2a and 2b, achieving accuracies of 84% and 86%. We also compare the accuracy gap between subjects, showing that our model is effective in reducing the accuracy gap between subjects and has higher robustness than previous models. [ABSTRACT FROM AUTHOR]

Traffic prediction has drawn considerable attention due to its potential to optimize the operational efficiency of road networks. Existing methods commonly combine graph neural network (GNN) and recurrent neural network (RNN) to model spatio-temporal correlations. However, the above models still face challenges, including an inability to capture time-varying spatial correlations, inadequate consideration of spatio-temporal heterogeneity and inefficient iterative operations. To address the above challenges, in this paper, we propose a novel framework for traffic prediction, named time-based adaptive graph neural network (TAGNN). First, a novel graph learning module was developed to generate time-based adaptive graph dependency matrices, which capture hidden spatial correlations at different time steps. Second, two embedding matrices are proposed to assist the model in capturing spatio-temporal heterogeneity by attaching essential external features. Third, a temporal convolution module is proposed to capture temporal correlations by stacking grouped convolution. The receptive field expands exponentially with each additional layer, reducing parameters and improving prediction efficiency. Extensive experimental results demonstrate that our model adequately extracts the spatio-temporal correlation of nodes while ensuring prediction efficiency. • A time-based adaptive adjacency matrix capturing time-varying spatial correlations. • A novel temporal convolutional module with superior prediction efficiency. • Adding external features to capture spatio-temporal heterogeneity of traffic flows. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-branch hybrid neural network which is called MBHNN. Since we split the input signal into different frequency bands for processing, the EEG classification accuracy is improved compared to the single branch structure of our proposed model. • In order to make full use of the rich information in the forward and backward directions, we introduce a bidirectional attention mechanism into the BGRU block of the EEG feature decoding model, which improves the efficiency of decoding. • Since the EEG field lacks a large amount of training data, deep learning is a data research methodology that requires a large number of training samples, this paper proposes a novel data augmentation method for expanding EEG datasets, which is called frequency domain segmentation swap (Seg-Swap). • It is worth noting that comparing the classification performance of the state-of-the-art models with the proposed model, better results are obtained on the BCI Competition IV dataset 2a and HGD, respectively. As a typical spontaneous brain-computer interface system, motor imagery has been widely used in areas such as robot control and stroke rehabilitation. Recently, researchers have started to study and propose various Convolutional Neural Network (CNN) structures based on motor imagery signals and have obtained better decoding accuracy compared with traditional machine learning algorithms. In this paper, we focus on a four-class motor imagery task. Therefore, we propose an end-to-end novel multi-branch hybrid neural network for motor imagery EEG signal classification. Notably, we divide the input signal into four frequency bands associated with the motor imagery signal. Meanwhile, we also introduced a Bidirectional Gated Recurrent Unit (BGRU) to identify EEG features. Nevertheless, it is extremely difficult to collect high-quality EEG data and the classification accuracy is degraded due to the strict requirements of the subjects and the experimental environment. To address this issue, we propose a novel data augmentation approach for frequency domain segmentation swap (Seg-Swap) for improving EEG motor imagery signal classification accuracy. We make use of the publicly available BCI IV 2a dataset and High Gamma dataset to evaluate the decoding performance of the model. The proposed multi-branch hybrid neural network (MBHNN) achieves 86.15% and 95.04% on the both datasets. Compared with several state-of-the-art (SOA) algorithms, our proposed model has a slight improvement in classification accuracy. Experimental results show that our proposed MBHNN has higher decoding classification and stronger robustness. [ABSTRACT FROM AUTHOR]

Alternative splicing plays a key role in the regulation of the central dogma. Four major types of alternative splicing have been classified as intron retention, exon skipping, alternative 5 splice sites or alternative donor sites, and alternative 3 splice sites or alternative acceptor sites. A few algorithms have been developed to detect splice junctions from RNA-Seq reads. However, there are few tools targeting at the major alternative splicing types at the exon/intron level. This type of analysis may reveal subtle, yet important events of alternative splicing, and thus help gain deeper understanding of the mechanism of alternative splicing. This paper describes a user-friendly R package, extracting, annotating and analyzing alternative splicing types for sequence alignment files from RNA-Seq. SplicingTypesAnno can: (1) provide annotation for major alternative splicing at exon/intron level. By comparing the annotation from GTF/GFF file, it identifies the novel alternative splicing sites; (2) offer a convenient two-level analysis: genome-scale annotation for users with high performance computing environment, and gene-scale annotation for users with personal computers; (3) generate a user-friendly web report and additional BED files for IGV visualization. SplicingTypesAnno is a user-friendly R package for extracting, annotating and analyzing alternative splicing types at exon/intron level for sequence alignment files from RNA-Seq. It is publically available at https://sourceforge.net/projects/splicingtypes/files/ or http://genome.sdau.edu.cn/research/software/SplicingTypesAnno.html . [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new visualization software called pairheatmap, which is able to generate and compare two heatmaps so as to compare expression patterns of gene groups. It adds a conditioning variable such as time to the heatmap, and provides separate clustering for row groups in the first heatmap in order to visualize pattern changes between two heatmaps. pairheatmap is developed in R statistical environment. It provides: (1) the flexible framework for comparing two heatmaps; and (2) high-quality figures based on R package grid. The general architecture can be efficiently incorporated into bioinformatics pipeline. The package and user documentation are free to download at http://cran.r-project.org/web/packages/pairheatmap/index.html. [Copyright &y& Elsevier]

The inherent interfacial bonding formation of layers determines the quality of complex 3D parts fabricated using fused deposition modeling (FDM). The inferior and anisotropic mechanical properties of FDM parts prevent their industrial application. This research is aimed at enhancing the mechanical properties and reducing the mechanical anisotropy of FDM parts through an innovative auxiliary heating treatment. Polylactide (PLA), which is a distinct semicrystalline polymer commonly used in 3D printing, was selected to examine the enhancement of mechanical properties by implementing auxiliary heating treatment. When the auxiliary heating treatment is applied, the tensile strength of the FDM-printed PLA part increases from 38.4 MPa to 63.6 MPa, and the degree of mechanical anisotropy decreases from 0.51 to 0.22. The auxiliary heating treatment yields superior mechanical properties compared to those attained through post-thermal annealing. Auxiliary heating represents a kind of "in-situ" annealing strategy during FDM printing. Moreover, the auxiliary heating temperature is similar to the cold crystallization temperature (Tcc) of PLA and is the optimal processing condition to obtain superior mechanical properties. As indicated by the SEM and DSC methods, the interfacial bonding of adjacent strands and crystallinity considerably influence the mechanical properties of FDM-printed PLA parts. Therefore, the auxiliary heating approach has the potential to be applied in the fabrication of large FDM parts. • An enhanced interface of bond zone between adjacent strands can be obtained through auxiliary heating treatment. • Auxiliary heating treatment increased tensile property and mechanical isotropy of FDM-printed PLA parts. • Both the interfacial bonding and crystallinity contribute to the mechanical properties of FDM-printed PLA parts. [ABSTRACT FROM AUTHOR]

Abstract: Population pharmacokinetic (PopPK) modeling has become increasing important in drug development because it handles unbalanced design, sparse data and the study of individual variation. However, the increased complexity of the model makes it more of a challenge to diagnose the fit. Graphics can play an important and unique role in PopPK model diagnostics. The software described in this paper, PKgraph, provides a graphical user interface for PopPK model diagnosis. It also provides an integrated and comprehensive platform for the analysis of pharmacokinetic data including exploratory data analysis, goodness of model fit, model validation and model comparison. Results from a variety of modeling fitting software, including NONMEM, Monolix, SAS and R, can be used. PKgraph is programmed in R, and uses the R packages lattice, ggplot2 for static graphics, and rggobi for interactive graphics. [Copyright &y& Elsevier]

Male immune infertility is a kind of disease that damages family life and happiness. The development of novel methods treating male immune infertility is of great significance. This study aimed to investigate the therapeutic effects of Chinese medicine Xiaokang Liuwei Dihuang decoction on immune infertility of male rats and explored the involved mechanisms. Model rats were established by lipopolysaccharide (LPS) injection. Anti-sperm antibody (AsAb) was detected by ELISA assay and testicular cell apoptosis was evaluated by TUNEL staining to verify the successful model establishment and screen suitable doses of Xiaokang Liuwei Dihuang decoction. Thirty rats were then divided into five groups (n = 6 per group): Control, LPS, Xiaokang Liuwei Dihuang decoction (15.12 g/kg, 30.24 g/kg and 45.36 g/kg). Results of HE staining showed that compared with LPS group, Xiaokang Liuwei Dihuang decoction treatments gradually improved the morphology of seminiferous tubules and elevated the number of spermatogenic cells as the doses increased. The sperm number and the levels of testosterone, luteinizing hormone (LH) and follicle stimulating hormone (FSH) in the serum of 15.12 g/kg, 30.24 g/kg and 45.36 g/kg Xiaokang Liuwei Dihuang decoction groups were much higher than those in LPS group. Results of TUNEL staining, ELISA assay and western blot showed that compared with LPS group, the testicular cell apoptosis and the levels of interleukin 1β (IL-1β), tumor necrosis factor α (TNF-α), AsAb, malondialdehyde (MDA) and toll-like receptor 2 (TLR2) in the testicular tissue significantly decreased in three Xiaokang Liuwei Dihuang decoction groups. Compared with LPS group, Bax expression in the 30.24 g/kg and 45.36 g/kg Xiaokang Liuwei Dihuang decoction groups was significantly down-regulated as well. In conclusion, Xiaokang Liuwei Dihuang decoction might ameliorate the immune infertility of male rats induced by LPS through regulating the levels of sex hormones, reactive oxygen species, pro-apoptotic and immune factors. [Display omitted] • Xiaokang Liuwei Dihuang decoction ameliorates the immune infertility of male rats. • Xiaokang Liuwei Dihuang decoction regulates sex hormones and ROS. • Xiaokang Liuwei Dihuang decoction regulates pro-apoptotic and immune factors. [ABSTRACT FROM AUTHOR]

Fused Deposition Modeling (FDM) technology offers opportunities for high-efficiency design and low-cost manufacturing of 3D complex parts in additive manufacturing (AM). The warpage remains a big problem which prevents the industrial application of FDM parts, especially parts fabricated by fiber-reinforced composite. In this study, an auxiliary heating plate was developed and mounted on the printing head of an FDM 3D printer to suppress the warpage of the parts made with short carbon fiber (CF)-reinforced acrylonitrile-butadiene-styrene copolymers (ABS) composites. It is found that auxiliary heating and raster angle of FDM process are the two dominating parameters on the tensile properties and the warpage of FDM parts. For the best parameter combination, 160 oC for auxiliary heating temperature and 0 degree for raster angle, the warpage was completely suppressed and the tensile strength got 31% enhancement, ductility got 439% enhancement compared to the FDM part without auxiliary heating and 0 degree in raster angle. Cyclic fatigue behavior and anisotropy of CF/ABS specimen was also significantly improved by auxiliary heating treatment. The reduced stain caused by the annealing ability of auxiliary heating during the 3D printing, is responsible for this enhancement. Compared with auxiliary heating and post thermal annealing treatment, the tensile property and cyclic fatigue behavior of CF/ABS specimen was equivalent. Notably, auxiliary heating is the "in-situ" annealing during the process of 3D printing, which can break through the existing dimension constraints of post thermal annealing treatment. Therefore, the approach broadens the design for CF/ABS composites through temperature-specific optimization and makes it possible to fabricate huge-size FDM parts. Unlabelled Image • An innovative FDM 3D printer with an auxiliary heating plate was developed to fabricate CF/ABS composite parts; • Auxiliary heating process can reduce the warpage of CF/ABS composite parts; • Auxiliary heating process can increase the mechanical properties of CF/ABS composite parts. [ABSTRACT FROM AUTHOR]

Intronic RNAs have been overlooked for a long time: They are functional, but treated as "junk." In this work, we designed a new sequencing strategy to investigate intronic RNAs. By using intron-capture RNA-seq, we systematically analyzed the intronic RNAs in Arabidopsis by zooming into the intronic regions an order of magnitude deeper than in previous work. Our key findings include: (1) Intron-capture RNA-seq is a much more efficient approach to analyze intronic RNAs than total RNA-seq and mRNA-seq. (2) We identified three types of intronic RNAs, and found that the GC pattern differs significantly between the introns with and without intronic RNAs. (3) We detected many hidden elements in introns, including circular RNAs, splice junctions, and transcripts that have previously been overlooked. (4) The expression of these intronic RNAs varies during the time course of pathogen infection, which indicates that an unknown mechanism may exist for these RNAs. (5) We also demonstrated that most of intronic RNAs are detectable in both Arabidopsis and rice, suggesting that these non-coding molecules are conserved. Taken together, this work proposes an efficient strategy to analyze intronic RNAs, and provides an unprecedented view of this essential component in biological pathways. • Intron-capture RNA-seq is a much more efficient approach to analyze intronic RNAs than total RNA-seq and mRNA-seq. • Three types of intronic RNAs are identified, and GC pattern of all types differs significantly between the introns with and without intronic RNAs. • Intronic RNAs are detectable in both Arabidopsis and rice, suggesting that these non-coding molecules are conserved. [ABSTRACT FROM AUTHOR]

Orthonormal basis function (OBF) decomposition method and minimum entropy (ME) detector are two typical methods of magnetic anomaly detection. The OBF detector works effectively only when the assumptions of the target signal and noise are appropriate, and the ME detector is limited by low signal-to-noise ratio (SNR). In order to improve magnetic anomaly detection performance in the case of low SNR and no prior information of the target signal, we proposed a novel detector by using stochastic resonance (SR) method and named it SR detector in the paper. The SR detector consists of a bistable SR system and a corresponding receiver. Firstly, the noise is used to enhance the magnetic anomaly signal with the help of SR system, instead of being suppressed in a traditional method; then the anomaly signal can be detected more effectively by the receiver. Experimental results show that the SR detector did work well, its detection probability approximated to 70% even if the input SNR was −3 dB, and was about 100% if the input SNR was 0dB, when the false alarm rate (FAR) was 1.5%. Furthermore, the SR detector provided higher detection probability than the ME detector under the same basis. In a word, due to the good detection performance and simple implementation, the SR detector would be more attractive in practice. [ABSTRACT FROM AUTHOR]

• A parallel NLOS identification method is proposed using the multiple input neural network model. • The raw CIR and its time-frequency diagram are effectively employed. • Five different typical obstacles are tested to validate the identification method within 1–4 m. • The UWB-based positioning results of LS and WLS algorithms are compared using the proposed NLOS identification method. Ultra wide band (UWB) radio positioning technology is widely used in indoor high-precision positioning scenes, while obstacles in the wireless signal propagation path will cause the non-line of sight (NLOS) propagation of UWB signal and the reduction of positioning reliability. In this paper, an efficient NLOS identification scheme based on multiple input learning (MIL) neural network model with channel impulse response (CIR) and time-frequency diagram of CIR (TFDOCIR) is proposed by direct detection in UWB positioning system. It is experimentally demonstrated that the average NLOS identification accuracies reach 86.82%, 92.53%, 91.61%, 92.91%, 92.02% corresponding to five different obstacles including wooden door, concrete wall, metal plate, human body and glass window, respectively. Additionally, the overall NLOS identification accuracy achieves 91.74%. Through the proposed NLOS identification scheme with weight least squares (WLS), the indoor UWB-based positioning tests are performed with the average error 7.35 cm, thereby proving its ability of ranging error preventation. [ABSTRACT FROM AUTHOR]