Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset.

Introduction: The volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system. Methods: Dataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction. Results: Experiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols. Discussion: Our work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.

Isolation, structural characterization of polysaccharide from Cephalosporium sinensis mycelia and its anti-nephritic effects in adenine-induced CKD rats.

In this study, a new polysaccharide (CSMP, Mw = 16,685 Da) was isolated and purified from Cephalosporium sinensis mycelia. Monosaccharide composition analysis indicated that CSMP consists of mannose, glucose and galactose. A detailed structural analysis revealed that CSMP has a backbone consisting of →2,6)-ß-D-Manp-(1→ and →3,6)-ß-D-Manp-(1→, as well as two branched chains including of α-D-Manp-(1→6)-α-D-Glcp-(1→ and α-D-Glcp-(1→4)-α-D-Glcp-(1→3)-ß-D-Galp-(1→2)-ß-D-Manp-(1→ attached to C6 of →2,6)-ß-D-Manp-(1→ and →3,6)-ß-D-Manp-(1→. Orally administrated CSMP showed renal protection function in adenine-induced chronic kidney disease (CKD) rats. Further analysis demonstrated that CSMP increased relative abundance of the genera Lactobacillus group, Clostridium coccoides group and Bifidobacterium, and decreased Echerichia subgroup. CSMP increased acetate, propionate and butyrate levels both in colon and cecum. The mechanisms behind these effects could be related to the down-regulation nuclear factor kappa-B (NF-κB) level by up-regulating expression of G protein-coupled receptor 41 (GPR41) and improvement regulatory T cells (Tregs) ratio by inhibiting histone deacetylase (HDAC) activity. These results indicated that CSMP could be developed as one of the potential drugs in the treatment of CKD.

Structural characterization and anti-inflammatory effect in hepatocytes of a galactoglucan from Antrodia camphorata mycelium.

The galactoglucan ACP2 was isolated from cultured Antrodia camphorata mycelium through anion-exchange column chromatography and Sephadex G-100 chromatography and shown to exhibit hepatoprotective function in L02 cells. Based on monosaccharide composition analysis, ACP2 was mainly composed of glucose, galactose, and 6-deoxyglucose in a molar ratio of 5 : 2 : 1. The average molecular weight of ACP2 was 1.93 × 104 Da. The primary structure of ACP2 was elucidated with Fourier-transform infrared spectroscopy, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy. The results indicated the following composition: →6)-linked-ß-d-Galp-(1→, →6)-linked-α-d-Glcp-(1→, →3)-linked-α-d-Glcp-(1→, and →2,4)-linked-ß-d-Glcp-(1→, with terminal 6-deoxy-α-d-Glcp and α-d-Glcp. ACP2 alleviated lipopolysaccharide-induced hepatocyte inflammation by down-regulating the expressions of COX-2, IL-1ß, TNF-α and IL-6. The decreased expressions of TLR4, MyD88, NF-κB, and phosphorylated p38 in ACP2-treated L02 cells indicated that ACP2 might ameliorate inflammation through the TLR4 and p38/NF-κB signaling pathways.