PlantCircRNA: a comprehensive database for plant circular RNAs.

Circular RNAs (circRNAs) represent recently discovered novel regulatory non-coding RNAs. While they are present in many eukaryotes, there has been limited research on plant circRNAs. We developed PlantCircRNA (https://plant.deepbiology.cn/PlantCircRNA/) to fill this gap. The two most important features of PlantCircRNA are (i) it incorporates circRNAs from 94 plant species based on 39 245 RNA-sequencing samples and (ii) it imports the original AtCircDB and CropCircDB databases. We manually curated all circRNAs from published articles, and imported them into the database. Furthermore, we added detailed information of tissue as well as abiotic stresses to the database. To help users understand these circRNAs, the database includes a detection score to measure their consistency and a naming system following the guidelines recently proposed for eukaryotes. Finally, we developed a comprehensive platform for users to visualize, analyze, and download data regarding specific circRNAs. This resource will serve as a home for plant circRNAs and provide the community with unprecedented insights into these mysterious molecule.

Exonic Circular RNAs Are Involved in Arabidopsis Immune Response Against Bacterial and Fungal Pathogens and Function Synergistically with Corresponding Linear RNAs.

Circular RNAs (circRNAs) are a group of covalently closed RNAs, and their biological function is largely unknown. In this study, we focused on circRNAs that are generated from exon back-splicing (exonic circRNAs). The linear RNA counterparts encode functional proteins so that we can compare and investigate the relationship between circular and linear RNAs. We compared circRNA expression profiles between untreated and Pseudomonas syringae-infected Arabidopsis and identified and experimentally validated differentially expressed exonic circRNAs by multiple approaches. We found that exonic circRNAs are preferentially enriched in biological processes that associate with biotic and abiotic stress responses. We discovered that circR194 and circR4022 are involved in plant response against P. syringae infection, whereas circR11208 is involved in response against Botrytis cinerea infection. Intriguingly, our results indicate that these exonic circRNAs function synergistically with their corresponding linear RNAs. Furthermore, circR4022 and circR11208 also play substantial roles in Arabidopsis tolerance to salt stress. This study extends our understanding of the molecular functions of plant circRNAs.

Monitoring Wheat Lodging at Various Growth Stages.

Lodging is one of the primary factors that reduce wheat yield; therefore, rapid and accurate monitoring of wheat lodging helps to provide data support for crop loss and damage response and the subsequent settlement of agricultural insurance claims. In this study, we aimed to address two problems: (1) calculating the wheat lodging area. Through comparative experiments, the SegFormer-B1 model can achieve a better segmentation effect of wheat lodging plots with a higher prediction rate and a stronger generalization ability. This model has an accuracy of 96.56%, which realizes the accurate extraction of wheat lodging plots and the relatively precise calculation of the wheat lodging area. (2) Analyzing wheat lodging areas from various growth stages. The model established, based on the mixed-stage dataset, generally outperforms those set up based on the single-stage datasets in terms of the segmentation effect. The SegFormer-B1 model established based on the mixed-stage dataset, with its mIoU reaching 89.64%, was applicable to wheat lodging monitoring throughout the whole growth cycle of wheat.

AtCircDB: a tissue-specific database for Arabidopsis circular RNAs.

Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.

Modulation Classification Using Compressed Sensing and Decision Tree-Support Vector Machine in Cognitive Radio System.

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree-support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO.

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm.

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Digital inventory of Arabidopsis transcripts revealed by 61 RNA sequencing samples.

Alternative splicing is an essential biological process to generate proteome diversity and phenotypic complexity. Recent improvements in RNA sequencing accuracy and computational algorithms have provided unprecedented opportunities to examine the expression levels of Arabidopsis (Arabidopsis thaliana) transcripts. In this article, we analyzed 61 RNA sequencing samples from 10 totally independent studies of Arabidopsis and calculated the transcript expression levels in different tissues, treatments, developmental stages, and varieties. These data provide a comprehensive profile of Arabidopsis transcripts with single-base resolution. We quantified the expression levels of 40,745 transcripts annotated in The Arabidopsis Information Resource 10, comprising 73% common transcripts, 15% rare transcripts, and 12% nondetectable transcripts. In addition, we investigated diverse common transcripts in detail, including ubiquitous transcripts, dominant/subordinate transcripts, and switch transcripts, in terms of their expression and transcript ratio. Interestingly, alternative splicing was the highly enriched function for the genes related to dominant/subordinate transcripts and switch transcripts. In addition, motif analysis revealed that TC motifs were enriched in dominant transcripts but not in subordinate transcripts. These motifs were found to have a strong relationship with transcription factor activity. Our results shed light on the complexity of alternative splicing and the diversity of the contributing factors.

A synthetic TLR3 ligand mitigates profibrotic fibroblast responses by inducing autocrine IFN signaling.

Activation of TLR3 by exogenous microbial ligands or endogenous injury-associated ligands leads to production of type I IFN. Scleroderma patients with progressive skin fibrosis display an IFN-regulated gene signature, implicating TLR3 signaling in the disease. In this study, we show that TLR3 expression was detected on foreskin, adult skin, and lung fibroblasts, and TLR3 levels were significantly elevated in a subset of scleroderma skin biopsies. In explanted skin and lung fibroblasts, the synthetic TLR3 ligand polyinosinic-polycytidylic acid (poly(I:C)), a dsRNA analog, caused dose- and time-dependent stimulation of IFN-ß production and generation of an IFN-response gene signature that was accompanied by substantial downregulation of collagen and α-smooth muscle actin gene expression. Furthermore, poly(I:C) abrogated TGF-ß-induced fibrotic responses and blocked canonical Smad signaling via upregulation of inhibitory Smad7. Surprisingly, the inhibitory effects of poly(I:C) in fibroblasts were independent of TLR3 and were mediated by the cytosolic receptors retinoic acid-inducible gene 1 and melanoma differentiation-associated gene 5, and involved signaling via the IFN receptor. Taken together, these results demonstrate that induction of a fibroblast IFN response gene signature triggered by dsRNA is associated with potent TLR3-independent anti-fibrotic effects. The characteristic IFN response gene signature seen in scleroderma lesions might therefore signify a tissue-autonomous protective attempt to restrict fibroblast activation during injury.

Genome-wide study of NAGNAG alternative splicing in Arabidopsis.

NAGNAG alternative splicing is one type of alternative splicing in mammals and plants. There are two opposite arguments regarding the mechanism of this NAGNAG event, i.e. whether splice variation is controllable by the cell or is just biological noise. In this paper, we systematically investigated NAGNAG acceptors in Arabidopsis thaliana using both cDNA/EST and RNA-Seq data. We identified 9,473 NAGNAG motifs, including 529 cDNA/EST-confirmed NAGNAG acceptors. A nomenclature tree for this type of alternative splicing was defined based on the cDNA/EST validation, location in the exon, sequence and expression level. Low expression of some NAGNAG motifs was observed in various tissues or pathogen-infected samples, indicating the existence of background splicing. Tissue-specific or treatment-specific differences in the dynamic profiles suggest that some NAGNAG acceptors are highly regulated.

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition. COMPARISON WITH EXISTING METHODS: This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data. NEW METHOD: To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize. RESULTS: The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models. CONCLUSIONS: The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.

PlantIntronDB: a database for plant introns that host functional elements.

Although more and more attention has been focused on introns and the important role of plant introns in plant growth and development has been discovered, there is still a lack of an open and comprehensive database on plant introns with functional elements in current research. In order to make full use of large-scale sequencing data and help researchers in related fields to achieve high-throughput functional verification of identified plant introns with functional elements, we designed a database containing five plant species, PlantIntronDB and systematically analyzed 358, 59, 185, 210 and 141 RNA-seq samples from Arabidopsis thaliana (Arabidopsis), Gossypium raimondii (cotton), Zea mays (maize), Brassica napus (oilseed rape) and Oryza sativa Japonica Group (rice). In total, we found 100 126 introns that host functional elements in these five species. Specifically, we found that among all species, the number of introns with functional elements on the positive and negative strands is similar, with a length mostly smaller than 1500 bp, and the Adenine/Thymine (A/T) content is much higher than that of Guanine/Cytosine (G/C). In addition, the distribution of functional elements in introns varies among different species. All the above data can be downloaded for free in this database. This database provides a concise, comprehensive and user-friendly web interface, allowing users to easily retrieve target data based on their needs, using relevant organizational options. The database operation is simple and convenient, aiming to provide strong data support for researchers in related fields to study plant introns that host functional elements, including circular RNAs, lncRNAs, etc. Database URL:  http://deepbiology.cn/PlantIntronDB/.

MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification.

BACKGROUND AND OBJECTIVE: The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual's EEG signal is recognized with precision. METHODS: We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data. RESULTS: We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49% and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71%, 85.83%, and 86.99%, respectively. CONCLUSIONS: The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification.

Intron-capture RNA-seq reveals the landscape of intronic RNAs in Arabidopsis.

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.

Clinical Efficacy Analysis of Biofeedback Electrical Stimulation Combined with Doxycycline in the Treatment of Type IIIA Chronic Prostatitis.

Purpose: To analyse the clinical efficacy of biofeedback electrical stimulation combined with doxycycline in the treatment of type IIIA chronic prostatitis. Methods: Eighty patients who met the diagnostic criteria of type IIIA chronic prostatitis in our hospital between February 2020 and February 2022 were selected and equally divided into the drug group and electrical stimulation group according to the random number table method. The drug group was treated with medication alone for 4 weeks; the electrostimulation group was treated with biofeedback electrostimulation on top of medication for 12 weeks. The expressed prostatic secretious (EPS) routine (lecithin bodies, white blood cells) and the maximum urinary flow rate (Q max) and mean urinary flow rate (Q ave) were measured before and after treatment in both groups, and the National Institutes of Health chronic prostatitis symptom index (NIH-CPSI) was used to score the urinary symptom, pain or discomfort, and quality of life and determine the efficacy of the treatment in both groups. Results: After treatment, the number of lecithin bodies and white blood cells in EPS improved significantly in both groups compared to before, and both the electrical stimulation group was better than the drug group (P < 0.05). After treatment, the Q max and Q ave were significantly higher in both groups than before, and both the electrical stimulation groups were higher than the drug group (P < 0.05). After treatment, the urinary symptom scores, pain or discomfort scores, quality of life scores, and total NIH-CPSI scores were significantly lower in both groups than before, and all were lower in the electrical stimulation group than in the drug group (P < 0.05). After treatment, the overall efficiency of patients in the electrical stimulation group was significantly higher than that of the drug group (P < 0.05). Conclusion: Biofeedback electrical stimulation combined with doxycycline in the treatment of type IIIA chronic prostatitis can synergistically improve the patient's inflammation level, urinary dysfunction, relieve pelvic floor tension myalgia, and improve their quality of life, opening up new avenues for the rehabilitation of patients with type IIIA chronic prostatitis.

PKreport: report generation for checking population pharmacokinetic model assumptions.

BACKGROUND: Graphics play an important and unique role in population pharmacokinetic (PopPK) model building by exploring hidden structure among data before modeling, evaluating model fit, and validating results after modeling. RESULTS: The work described in this paper is about a new R package called PKreport, which is able to generate a collection of plots and statistics for testing model assumptions, visualizing data and diagnosing models. The metric system is utilized as the currency for communicating between data sets and the package to generate special-purpose plots. It provides ways to match output from diverse software such as NONMEM, Monolix, R nlme package, etc. The package is implemented with S4 class hierarchy, and offers an efficient way to access the output from NONMEM 7. The final reports take advantage of the web browser as user interface to manage and visualize plots. CONCLUSIONS: PKreport provides 1) a flexible and efficient R class to store and retrieve NONMEM 7 output, 2) automate plots for users to visualize data and models, 3) automatically generated R scripts that are used to create the plots; 4) an archive-oriented management tool for users to store, retrieve and modify figures, 5) high-quality graphs based on the R packages, lattice and ggplot2. The general architecture, running environment and statistical methods can be readily extended with R class hierarchy. PKreport is free to download at http://cran.r-project.org/web/packages/PKreport/index.html.

Xiaokang Liuwei Dihuang decoction ameliorates the immune infertility of male rats induced by lipopolysaccharide through regulating the levels of sex hormones, reactive oxygen species, pro-apoptotic and immune factors.

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 1ß (IL-1ß), 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.

Protective Effect of Ursolic Acid in Prunella vulgaris L. on LPS-Induced Asthenozoospermia via Bcl-2/Bax Apoptosis Signaling Pathway.

BACKGROUND: Asthenozoospermia, also known as lack of sperm motility, accounts for about 27.8% of male infertility as a separate factor, and is often associated with abnormal quantity and morphology of spermatozoa. Therefore, oligozoospermia has become one of the most important factors affecting male infertility. METHODS: Ursolic Acid (UA), also known as wusu acid, is the main active component isolated from Prunella vulgaris L. and has a variety of pharmacological effects. However, the protective effect of UA on asthenozoospermia disease has not been reported. In the current study, the purpose of this study was to investigate the regulatory effect of UA in rats with LPS-induced asthenozoospermia disease. SD rats were treated with 5 mg/kg LPS, respectively. RESULTS: After different concentrations of UA were infused into the stomach of SD rats, microscopy, flow cytometry, Enzyme-Linked Immunosorbent Assay (ELISA), qRT-PCR and western blot were used to detect sperm motility, apoptosis, the levels of TNF-α, IL-1ß and IL-6, and Bcl-2/Bax apoptosis pathway related proteins in rat serum and epididymis tissues. DISCUSSION: Compared with the normal group, the sperm motility and Bcl-2 level in LPS group decreased significantly, while the expression of inflammatory factors and Bax proteins increased significantly (P<0.05). Compared with LPS group, UA intervention group has the opposite result and dose dependence. CONCLUSION: This study shows that UA can protect LPS-induced asthenozoospermia of rats by increasing sperm density and motility, regulating Bcl-2/Bax apoptosis pathway and reducing inflammatory apoptosis response. This experiment provides ideas for improving the clinical treatment of infertile patients with oligoasthenospermia.

Analysis of Human Acupoint Biological Information and Neural Electric Activity Based on Ultrasonographic Image.

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.

Deciphering extrachromosomal circular DNA in Arabidopsis.

Extrachromosomal circular DNA (eccDNA) is independent of the chromosome and exists in many eukaryotes. However, the nature and origin of eccDNA in plants remains unclear. In this study, we sequenced 12 samples from four tissues (leaf, flower, stem and root) with three biological replicates. In total, we found 743 eccDNAs found in at least two samples. Most of eccDNA have inverted repeats ranging from 4 to 12 bp in the boundaries. Interestingly, eccDNA is not only related to transposon activity, but also hosts tRNA genes, suggesting that the eccDNAs may be associated with tRNA abundance which controls protein synthesis under conditions of stress. Our results provide an unprecedented view of eccDNA, which is still naïve in scope.