Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

IntroductionWithin the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.MethodsInspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.Results and discussionAchievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications

Self-planning Code Generation with Large Language Models

Although large language models have demonstrated impressive ability in code generation, they are still struggling to address the complicated intent provided by humans. It is widely acknowledged that humans typically employ planning to decompose complex problems and schedule the solution steps prior to implementation. Thus we introduce planning into code generation to help the model understand complex intent and reduce the difficulty of problem solving. This paper proposes a self-planning code generation method with large language model, which consists of two phases, namely planning phase and implementation phase. Specifically, in the planning phase, the language model plans out the solution steps from the intent combined with in-context learning. Then it enters the implementation phase, where the model generates code step by step, guided by the solution steps. The effectiveness of self-planning code generation has been rigorously evaluated on multiple code generation datasets and the results have demonstrated a marked superiority over naive direct generation approaches with language model. The improvement in performance is substantial, highlighting the significance of self-planning in code generation tasks

Wheat and Rice Growth Stages and Fertilization Regimes Alter Soil Bacterial Community Structure, but Not Diversity

Maintaining soil fertility and the microbial communities that determine fertility is critical to sustainable agricultural strategies, and the use of different organic fertilizer regimes represents an important practice in attempts to preserve soil quality. However, little is known about the dynamic response of bacterial communities to fertilization regimes across crop growth stages. In this study, we examined microbial community structure and diversity across eight representative growth stages of wheat-rice rotation under four different fertilization treatments: no nitrogen fertilizer (NNF), chemical fertilizer (CF), organic-inorganic mixed fertilizer (OIMF) and organic fertilizer (OF). Quantitative PCR (QPCR) and high-throughput sequencing of bacterial 16S rRNA gene fragments revealed that growth stage as the best predictor of bacterial community abundance and structure. Additionally, bacterial community compositions differed between wheat and rice rotations. Relative to soils under wheat rotation, soils under rice rotation contained higher relative abundances (RA) of anaerobic and mesophilic microbes and lower RA of aerophilic microbes. With respect to fertilization regime, NNF plots had a higher abundance of nitrogen–fixing Cyanobacteria. OIMF had a lower abundance of ammonia-oxidizing Thaumarchaeota compared with CF. Application of chemical fertilizers (CF and OIMF treatments) significantly increased the abundance of some generally oligotrophic bacteria such those belonging to the Acidobacteria, while more copiotrophic of the phylum Proteobacteria increased with organic fertilizer application. A high correlation coefficient was found when comparing RA of Acidobacteria based upon QPCR versus sequence analysis, yet poor correlations were found for the Alpha- and Beta- Proteobacteria, highlighting the caution required when interpreting these molecular data. In total, crop, fertilization scheme and plant developmental stage all influenced soil microbial community structure, but not total levels of alpha diversity

Research on the Delimitation of Marine Spatial Pattern Based on the Goal of “Carbon Peaking and Carbon Neutrality”

In the context of carbon peaking and carbon neutrality (“double carbon”), it is urgent to clarify the effect of marine spatial planning (MSP) on carbon sink increases and emission reductions, since such planning acts as a spatial governance tool for the earth’s largest carbon pool. In this paper, a linkage model between marine spatial functional zones and carbon distribution is established. To explore the relationship between marine spatial functional zones and carbon, the study analyzed the carbon increase or reduction role of sea-use activities in each zone and considered the carbon sequestration function of the marine ecosystem itself. A marine spatial pattern of “Two Spaces and Four Carbon Areas” is proposed to present the linkage. A carbon distribution pattern in marine space is delimited using the linkage model and the current MSP in the case study of the city of Tangshan, Hebei, China. Some measures have been taken or planned to be taken in Tangshan to improve the carbon sink function of the ecosystem and the marine space. The supporting role of MSP in achieving the “double carbon” goal is studied, and the paths and suggestions for integrating the “double carbon” goal into MSP are explored

Distribution of multidrug-resistant bacterial infections in diabetic foot ulcers and risk factors for drug resistance: a retrospective analysis

Objective To investigate the distribution, drug resistance and risk factors of multi-drug resistant bacterias (MDROs) in patients with Type 2 diabetic foot ulcers (DFU). Method The clinical data, foot secretions, pathogenic microorganisms and drug sensitivity tests of 147 patients with type 2 diabetes admitted to our department from January 2018 to December 2021 were analyzed. Patients were divided into two groups according to whether they had been infected with MDROs or not. Seventy-one cases were infected with MDROs as the case group, and the remaining 76 cases were the control group. Chi-square test and t-test were used to analyze the results of MDROs infection and DFU, and logistic multivariate regression was used to evaluate the risk factors of MDROs infection. Results A total of 71 strains were isolated from the MDROs-positive group, with the top three being Staphylococcus aureus (46.48%), Escherichia coli (22.53%), and Pseudomonas aeruginosa (18.31%), respectively. Logistic multifactorial regression analysis showed that history of previous antimicrobial exposure, neuroischemic wound, Wagner grade 3–5, and combined osteomyelitis were associated with Type 2 diabetic foot infection MDROs (P < 0.05). Conclusion Previous history of antimicrobial exposure, neuroischemic wounds, Wagner grade 3–5, and combined osteomyelitis are independent risk factors for MDROs, which can identify the risk factors for MDROs at an early stage and help to identify people at high risk of MDROs infection and take relevant comprehensive treatment in time to slow down the development of the disease

Involvement of GJA1 and Gap Junctional Intercellular Communication between Cumulus Cells and Oocytes from Women with PCOS

Polycystic ovary syndrome (PCOS) is a common female endocrine system disease that affects 17.8% of women of reproductive age and leads to infertility, obesity, glucose metabolic disorders, cardiovascular disease, and body-mind problems. However, the etiology of PCOS remains unclear. Follicular growth is disrupted as a result of ovarian hyperandrogenism and distorted intraovarian paracrine signaling in women with PCOS. Microcommunication between oocytes and cumulus cells plays a critical role in folliculogenesis. Gap junction alpha 1 (GJA1) plays a crucial role in the developing follicles by forming communication channels between cumulus cells and oocytes, but this has not yet been reported in women with PCOS. Therefore, we aimed to study the role of GJA1 in the microcommunication between oocytes and cumulus cells in women with PCOS. In our study, cumulus cell-oocyte complexes (COCs) from women were isolated via ultrasound-guided vaginal puncture, and oocytes were selected from COCs and categorized based on 3 oocyte maturation stages. Then, RT-qPCR and immunofluorescence analysis were performed to detect both the gene expression and protein of GJA1 in oocytes from women with and without PCOS. There was no statistically significant difference in age and BMI (body mass index), but patients with PCOS had a higher ratio of basic LH/FSH (luteinizing hormone/follicle-stimulating hormone), androstenedione, and total ovarian volume. The qRT-PCR results showed higher gene expression of GJA1 in oocytes without PCOS at the germinal vesicle (GV) stage compared with that of oocytes from women with PCOS. Immunofluorescence analysis showed that the expression level of GJA1 in oocytes from women with PCOS was very weak compared with that of oocytes from women without PCOS. In conclusion, GJA1 may play a critical role in the development of oogenesis arrest in women with PCOS throughout the oogenesis processes, including oogenesis and oocyte maturation

Analyzing the Transcriptome Profile of Human Cumulus Cells Related to Embryo Quality via RNA Sequencing

Selecting excellent oocytes is required to improve the outcomes of in vitro fertilization (IVF). Cumulus cells (CCs) are an integral part of the oocyte maturation process. Therefore, we sought to identify differentially expressed genes in CCs to assess oocyte quality and embryo development potential. We divided the participants’ embryos into the high-quality embryo group and low-quality embryo group by the information including age, body mass index, and the levels of luteinizing hormone, follicle-stimulating hormone, estradiol, and progesterone. We analyzed a total of 7 CC samples after the quality control in RNA sequencing. We found that 2499 genes were unregulated and 5739 genes were downregulated in high-quality embryo group compared to the low-quality embryo group (Padj < 0.05). Interestingly, MSTN, CTGF, NDUFA1, VCAN, SCD5, and STAR were significantly associated with the quality of embryo. In accordance with the results of RNA sequencing, the association of the expression levels of MSTN, CTGF, NDUFA1, VCAN, SCD5, and STAR with the embryo quality was verified by quantitative reverse-transcription polymerase chain reaction (RT-qPCR) in 50 CC samples. Despite the small sample size and lack of validation in animal models, our study supports the fact that differential gene expression profile of human CCs, including MSTN, CTGF, NDUFA1, VCAN, SCD5, and STAR, can serve as potential indicator for embryo quality

通过光学相干断层扫描血管成像检测发现袖状胃切除术改善肥胖队列的微血管表型

Abstract Aims To examine how metabolic status is associated with microvascular phenotype and to identify variables associated with vascular remodeling after bariatric surgery, using noninvasive optical coherence tomography angiography (OCTA). Methods The study included 136 obese subjects scheduled for bariatric surgery and 52 normal‐weight controls. Patients with obesity were divided into metabolically healthy obesity (MHO) and metabolic syndrome (MetS) groups according to the diagnosis criteria of the Chinese Diabetes Society. Retinal microvascular parameters were measured by OCTA, including superficial capillary plexus (SCP) and deep capillary plexus (DCP) vessel densities. Follow‐ups were performed at the baseline and 6 months after bariatric surgery. Results Fovea SCP, average DCP, fovea DCP, parafovea DCP, and perifovea DCP vessel densities were significantly lower in the MetS group, compared to controls (19.91% vs. 22.49%, 51.60% vs. 54.20%, 36.64% vs. 39.14%, 56.24% vs. 57.65% and 52.59% vs. 55.58%, respectively, all p < .05). Parafovea SCP, average DCP, parafovea DCP, and perifovea DCP vessel densities significantly improved in patients with obesity 6 months after surgery, compared to baseline (54.21% vs. 52.97%, 54.43% vs. 50.95%, 58.29% vs. 55.54% and 55.76% vs. 51.82%, respectively, all p < .05). Multivariable analyses showed that baseline blood pressure and insulin were independent predictors of vessel density changes 6 months after surgery. Conclusions Retinal microvascular impairment occurred mainly in MetS rather than MHO patients. Retinal microvascular phenotype improved 6 months after bariatric surgery and baseline blood pressure and insulin status may be key determinants. OCTA may be a reliable method to evaluate the microvascular complications associated with obesity