Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort

EEG-based brain-computer interfaces are vulnerable to backdoor attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it

The Prevalence of Immunologic Injury in Renal Allograft Recipients with De Novo Proteinuria

Post-transplant proteinuria is a common complication after renal transplantation; it is associated with reduced graft and recipient survival. However, the prevalence of histological causes has been reported with considerable variation. A clinico-pathological re-evaluation of post-transplant proteinuria is necessary, especially after dismissal of the term “chronic allograft nephropathy,” which had been considered to be an important cause of proteinuria. Moreover, urinary protein can promote interstitial inflammation in native kidney, whether this occurs in renal allograft remains unknown. Factors that affect the graft outcome in patients with proteinuria also remain unclear. Here we collected 98 cases of renal allograft recipients who developed proteinuria after transplant, histological features were characterized using Banff scoring system. Cox proportional hazard regression models were used for graft survival predictors. We found that transplant glomerulopathy was the leading (40.8%) cause of post-transplant proteinuria. Immunological causes, including transplant glomerulopathy, acute rejection, and chronic rejection accounted for the majority of all pathological causes of proteinuria. Nevertheless, almost all patients that developed proteinuria had immunological lesions in the graft, especially for interstitial inflammation. Intraglomerular C3 deposition was unexpectedly correlated with the severity of proteinuria. Moreover, the severity of interstitial inflammation was an independent risk factor for graft loss, while high level of hemoglobin was a protective factor for graft survival. This study revealed a predominance of immunological parameters in renal allografts with post-transplant proteinuria. These parameters not only correlate with the severity of proteinuria, but also with the outcome of the graft

PyTSK: A Python Toolbox for TSK Fuzzy Systems

This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent (MBGD) based algorithms. Several state-of-the-art MBGD-based optimization algorithms are implemented in the toolbox, which can improve the generalization performance of TSK fuzzy systems, especially for big data applications. PyTSK can also be easily extended and customized for more complicated algorithms, such as modifying the structure of TSK fuzzy systems, developing more sophisticated training algorithms, and combining TSK fuzzy systems with neural networks. The code of PyTSK can be found at https://github.com/YuqiCui/pytsk

User Identity Protection in EEG-Based Brain–Computer Interfaces

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs

Investigation of the Optimization of Unloading Mining Scheme in Large Deep Deposit Based on Vague Set Theory and Its Application

With the development of shallow surface mineral resources in metal mines, it is gradually turning to the stage of deep mining. According to the current mining depth and the average annual depth, during the period of “14th Five-Year Plan,” one-third of the underground metal mines will reach or exceed the mining depth of 1,000 m, with the deepest being 2,000 m. In the stage of deep mining, mines will face the conditions of high stress, high temperature, high well depth, and strong mining disturbance, which will greatly increase the difficulty of large-scale deep mining. Among them, the high ground stress environment is the principal problem of many technical problems in deep mining. The selection of mining method has become a prerequisite for solving the problem of efficient and safe mining of deep deposits. In this paper, the vague set theory was introduced into the selection of mining methods and a vague set model for deep unloading mining schemes was established. Taking the Jinchuan No. 2 mining area as the engineering background, four unloading schemes for deep mining were proposed, and the Vague set model was used for optimization. It is concluded that the mining approach with large-section unloading is the optimal unloading mining plan. The application shows that it has the advantages of high unloading efficiency, large production capacity, and low loss index. It has been fully promoted in the deep mining of the mining area. It is feasible and effective to use the vague set theory in the selection of deep unloading mining schemes, which provides a proper approach in the selection of deep unloading mining schemes

A potential biomarker hsa-miR-200a-5p distinguishing between benign thyroid tumors with papillary hyperplasia and papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common endocrine cancer with a significantly increase of the incidence recently. Several cytokines, such as thyroid peroxidase (TPO), cluster of differentiation 56 (CD56), Galectin-3, mesothelial cell (MC), cytokeratin 19 (CK19) and BRAF (B-raf) were recommended to be tested by immunohistochemistry (IHC) for a definitive diagnosis, but were still limited in clinical use because of their relative lower sensitivity and specificity. MicroRNA (miRNA), as a new molecular biomarkers, however, has not been reported yet so far. To address this, hsa-miR-200a-5p, a miRNA, was selected and detected in PTC patients by in situ hybrization with benign thyroid tumor with papillary hyperplasia as a control, and the differential expression of hsa-miR-200a-5p between fresh PTC tissues and control was detected by qRT-PCR. Expressive levels of cytokines of TPO, CD56, Galectin-3, MC, CK19 and B-raf were also detected by immunohistochemistry. The correlation was analyzed by SPSS software using Spearman methods. As expected, the hsa-miR-200a-5p expressive level was significantly increased in PTC patients, compared to that of control, and was consistent with that of TPO, CD56, Galectin-3, MC, CK19 and B-raf. In addition, expression of hsa-miR-200a-5p showed negative correlation to that of TPO (rs = - 0.734; **: P < 0.01) and CD56 (rs = - 0.570; **: P < 0.01), but positive correlation to that of Galectin-3 (rs = 0.601; **: P < 0.01), MC (rs = 0.508; **: P < 0.01), CK19 (rs = 0.712; **: P < 0.01) and B-raf (rs = 0.378; **: P < 0.01). PTC and papillary benign thyroid papillary hyperplasia are difficult to distinguish in morphology, so requiring immunohistochemistry to further differentiate the diagnosis, however, for the existing clinical common diagnostic marker for immunohistochemistry, the sensitivity and accuracy are low, it is easy to miss diagnosis. Therefore, there is an urgent need for a rapid and sensitive molecular marker. So miR-200a-5p can be used to assist in the diagnosis of PTC at the molecular level, and as a biomarker, can be effectively used to distinguish between PTC and benign thyroid tumor with papillary hyperplasia

Impact of Environmental Microbes on the Composition of the Gut Microbiota of Adult BALB/c Mice.

To investigate the impact of microbes within the living environment on the gut microbiota of adults, we raised three groups of BALB/c mice from 3-4 weeks age in the same specific-pathogen-free animal room for 8 weeks. The control group lived in cages with sterilized bedding (pelletized cardboard), the probiotics group had three probiotics added to the sterilized bedding, and the intestinal microbes (IM) group had the intestinal microbes of a healthy goat added to the bedding. All other variables such as diet, age, genetic background, physiological status, original gut microbiota, and living room were controlled. Using high-throughput sequencing of the 16S rRNA gene, we observed that the control and probiotics groups had similar diversity and richness of gut microbiota. The two groups had significantly lower diversity than the IM group. We also observed that the IM group had a specific structure of gut microbial community compared with the control and probiotics groups. However, the dominate bacteria changed slightly upon exposure to intestinal microbes, and the abundance of the non-dominate species changed significantly. In addition, exposure to intestinal microbes inhibited DNFB-induced elevation of serum IgE levels. Our results provide new evidence in support of the microflora and hygiene hypotheses

Impaired Gal-9 Dysregulates the PBMC-Induced Th1/Th2 Imbalance in Abortion-Prone Matings

Recurrent miscarriage is defined as the loss of 3 or more consecutive pregnancies; however, the underlying immunologic mechanisms that trigger pregnancy loss remain largely unelucidated. Galectin-9 (Gal-9) may modulate a variety of biologic functions and play an important role in Th1/Th2 immune deviation. To analyze the mechanism of Gal-9 in abortion, we used the classical abortion-prone mouse model (DBA/2-mated CBA/J mice) to detect the expression of Gal-9 at the maternal-fetal interface. We also mimicked the immune environment of pregnancy by culturing trophoblast cells with peripheral blood mononuclear cells (PBMCs) to explore how Gal-9 might be involved in the pathogenesis of abortion. We found that the expression levels of Gal-9 in abortion-prone matings were lower than that for controls. Using a coculture system, we detected a Th1 preponderance in the coculture from abortion-prone matings. Furthermore, Gal-9 blockade augmented the imbalance of Th1/Th2 immunity in abortion-prone matings by promoting the secretion of Th1-derived cytokines in coculture, while there was a Th2 preponderance when we administered recombinant Gal-9. In conclusion, our results suggest that the Gal-9 signal is important for the regulation of PBMC function toward a Th2 bias at the maternal-fetal interface, which is beneficial for the maintenance of a normal pregnancy