Phenolic Acids and Flavonoids Play Important Roles in Flower Bud Differentiation in Mikania micrantha: Transcriptomics and Metabolomics.

Mikania micrantha is a highly invasive vine, and its ability to sexually reproduce is a major obstacle to its eradication. The long-distance dissemination of M. micrantha depends on the distribution of seeds; therefore, inhibiting M. micrantha flowering and seed production is an effective control strategy. The number of blooms of M. micrantha differs at different altitudes (200, 900, and 1300 m). In this study, we used a combination of metabolomics and transcriptomics methods to study the patterns of metabolite accumulation in the flower buds of M. micrantha. Using LC-MS/MS, 658 metabolites were found in the flower buds of M. micrantha at three different altitudes (200, 900, and 1300 m). Flavonoids and phenolic acids were found to be the main differential metabolites, and their concentrations were lower at 900 m than at 200 m and 1300 m, with the concentrations of benzoic acid, ferulic acid, and caffeic acid being the lowest. The biosynthesis pathways for flavonoids and phenolic compounds were significantly enriched for differentially expressed genes (DEGs), according to the results of transcriptome analysis. The production of flavonoid and phenolic acids was strongly linked with the expressions of phenylalanine ammonia-lyase (PAL), caffeoyl-CoA O-methyltransferase (COMT), and 4-coumarate-CoA ligase (4CL), according to the results of the combined transcriptome and metabolome analysis. These genes' roles in the regulation of distinct phenolic acids and flavonoids during M. micrantha bud differentiation are still unknown. This study adds to our understanding of how phenolic acids and flavonoids are regulated in M. micrantha flower buds at various altitudes and identifies regulatory networks that may be involved in this phenomenon, offering a new approach for the prevention and management of M. micrantha.

Transcriptome analysis of critical genes related to flowering in Mikania micrantha at different altitudes provides insights for a potential control.

BACKGROUND: Mikania micrantha is a vine with strong invasion ability, and its strong sexual reproduction ability is not only the main factor of harm, but also a serious obstacle to control. M. micrantha spreads mainly through seed production. Therefore, inhibiting the flowering and seed production of M. micrantha is an effective strategy to prevent from continuing to spread. RESULT: The flowering number of M. micrantha is different at different altitudes. A total of 67.01 Gb of clean data were obtained from nine cDNA libraries, and more than 83.47% of the clean reads were mapped to the reference genome. In total, 5878 and 7686 significantly differentially expressed genes (DEGs) were found in E2 vs. E9 and E13 vs. E9, respectively. Based on the background annotation and gene expression, some candidate genes related to the flowering pathway were initially screened, and their expression levels in the three different altitudes in flower bud differentiation showed the same trend. That is, at an altitude of 1300 m, the flower integration gene and flower meristem gene were downregulated (such as SOC1 and AP1), and the flowering inhibition gene was upregulated (such as FRI and SVP). Additionally, the results showed that there were many DEGs involved in the hormone signal transduction pathway in the flower bud differentiation of M. micrantha at different altitudes. CONCLUSIONS: Our results provide abundant sequence resources for clarifying the underlying mechanisms of flower bud differentiation and mining the key factors inhibiting the flowering and seed production of M. micrantha to provide technical support for the discovery of an efficient control method.

An Efficient Model-Compressed EEGNet Accelerator for Generalized Brain-Computer Interfaces With Near Sensor Intelligence.

Brain-computer interfaces (BCIs) is promising in interacting with machines through electroencephalogram (EEG) signal. The compact end-to-end neural network model for generalized BCIs, EEGNet, has been implemented in hardware to get near sensor intelligence, but without enough efficiency. To utilize EEGNet in low-power wearable device for long-term use, this paper proposes an efficient EEGNet inference accelerator. Firstly, the EEGNet model is compressed by embedded channel selection, normalization merging, and product quantization. The customized accelerator based on the compressed model is then designed. The multilayer convolutions are achieved by reusing multiplying-accumulators and processing elements (PEs) to minimize area of logic circuits, and the weights and intermediate results are quantized to minimize memory sizes. The PEs are clock-gated to save power. Experimental results in FPGA on three datasets show the good generalizing ability of the proposed design across three BCI diagrams, which only consumes 3.31% area and 1.35% power compared to the one-to-one parallel design. The speedup factors of 1.4, 3.5, and 3.7 are achieved by embedded channel selection with negligible loss of accuracy (-0.80%). The presented accelerator is also synthesized in 65 nm CMOS low power (LP) process and consumes 0.23M gates, 24.4 ms/inference, 0.267 mJ/inference, which is 87.22% more efficient than the implementation of EEGNet in a RISC-V MCU realized in 40 nm CMOS LP process in terms of area, and 20.77% more efficient in terms of energy efficiency on BCIC-IV-2a dataset.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification.

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

[Efficacy of nasal packing, septal suture technique and vacuum sealing drainage after nasal septum surgery].

OBJECTIVE: The objective of this study was to evaluate the effect of nasal packing, septal suture technique and vacuum sealing drainage (VSD) after septoplasty. METHOD: Ninety patients of nasal septal deviation in Combination with outfracture of the inferior turbinates who had received septoplasty were selected in this study. The patients were allocated into three groups, with thirty in each: for packing group, marcel materials were used for nasal packing after septoplasty; for suturing group, septal suture technique was performed after septoplasty; for VSD group, one drainage tube was used for negative pressure sucking after septoplasty without nasal packing. Postoperative signs and symptoms were compared between three groups. The comfort degree assessment included headache and nasal obstruction were evaluated by using visual analogue scale (VAS) at the 12th hour and 24 hour after operation. The edema in nasal cavity, hemorrhage. abscess,adhesive and healing rates after operation were compared among three groups. RESULT: The VAS score of headache and nasal obstruction and the severity of patient's conditions were significantly less in septal suture group and VSD group than that in packing group at the 12th and 24th hour after operation. The mucosa edema of nasal cavity was significantly slighter in septal suture group and VSD group than that in packing group at the third day after operation. The healing rates and number of complications are better in septal suture group and VSD group than those in packing group at the 7th day after operation. There were no hemorrhage or abscess in VSD group. CONCLUSION: Septal suture technique and VSD after septoplasty can significantly relieve the distress of patients and reduce the healing time of mucosa in nasal cavity without increasing the risk of complications.