Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.

OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

Application of 18ß-glycyrrhetinic acid in the structural modification of natural products: a review.

18ß-Glycyrrhetinic acid (GA) is an oleane-type pentacyclic triterpene saponin obtained from glycyrrhizic acid by removing 2 glucuronic acid groups. GA and its analogues are active substances of glycyrrhiza aicd, with similar structure and important pharmacological effects such as anti-inflammatory, anti-diabetes, anti-tumor and anti-fibrosis. Although GA combined compounds are in the clinical trial stages, its application potential is severely restricted by its low bioavailability, water solubility and membrane permeability. In this article, synthetic methods and structure-activity relationships (SARs) of GA derivatives from 2018 to present are reviewed based on pharmacological activity. It is hoped that this review can provide reference for the future development of potential GA preclinical candidate compounds, and furnish ideas for the development of pentacyclic triterpenoid lead compounds.

Laparoscopic Sigmoid Vaginoplasty for the Treatment of Mayer-Rokitansky-Kuster-Hauser Syndrome in a Single Center: 20 years' Experience.

INTRODUCTION AND HYPOTHESIS: We investigate the feasibility, safety, and clinical therapeutic effect of laparoscopic sigmoid vaginoplasty in women with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. METHODS: We performed a retrospective case review cohort study of 56 patients with MRKHs undergoing laparoscopic sigmoid vaginoplasty in Wuhan Union Hospital between 2000 and 2020, and all patients were followed up. RESULTS: The median operating time was 165 min (120-420 min). The median hospital stay was 10 days (rang 7-15 days). A functional neovagina was created 11-15 cm in length and two fingers in breadth in all patients. No introitus stenosis was observed. No intra- or post-operative complications occurred. Two patients were lost to follow-up after 3 months of outpatient visits. Six patients had no intercourse and were required to wear a vaginal mold occasionally. None of the patients had complained of local irritation or dyspareunia. Patients who had post-surgery sexual intercourse were satisfied with their sexual life and the mean total Female Sexual Function Index (FSFI) score was 25.17 ± 0.63. The cosmetic results were excellent. CONCLUSIONS: The laparoscopic sigmoid vaginoplasty can achieve the goal of making a functional neovagina. The main advantage of this surgical technique is that it is minimally invasive and that there are fewer complications post-operation. It is an acceptable procedure for patients with MRKH syndrome.

Intelligent ankle-foot prosthesis based on human structure and motion bionics.

The ankle-foot prosthesis aims to compensate for the missing motor functions by fitting the motion characteristics of the human ankle, which contributes to enabling the lower-limb amputees to take care of themselves and improve mobility in daily life. To address the problems of poor bionic motion of the ankle-foot prosthesis and the lack of natural interaction among the patient, prosthesis, and the environment, we developed a complex reverse-rolling conjugate joint based on the human ankle-foot structure and motion characteristics, the rolling joint was used to simulate the rolling-sliding characteristics of the knee joint. Meanwhile, we established a segmental dynamics model of the prosthesis in the stance phase, and the prosthetic structure parameters were obtained with the optimal prosthetic structure dimensions and driving force. In addition, a carbon fiber energy-storage foot was designed based on the human foot profile, and the dynamic response of its elastic strain energy at different thicknesses was simulated and analyzed. Finally, we integrated a bionic ankle-foot prosthesis and experiments were conducted to verify the bionic nature of the prosthetic joint motion and the energy-storage characteristics of the carbon fiber prosthetic foot. The proposed ankle-foot prosthesis provides ambulation support to assist amputees in returning to social life normally and has the potential to help improve clinical viability to reduce medical rehabilitation costs.

Quantitative evaluation of anisometropic amblyopia treatment efficacy by coupling multiple visual functions via CRITIC algorithm.

BACKGROUND: The evaluation of amblyopia treatment efficacy is essential for amblyopia prevention, control, and rehabilitation. METHODS: To evaluate the amblyopia treatment efficacy more precisely and quantitatively, this study recorded four visual function examination results, i.e., visual acuity, binocular rivalry balance point, perceptual eye position, and stereopsis before and after amblyopia treatment. RESULTS: We found that all these four results had a significant difference between before and after treatment, and the relationship between visual acuity improvement and the difference of BRBP, PEP, and stereoacuity cannot show a fitting correlation regarding the widely used index of visual acuity as the standard of treatment efficacy. By using the Criteria Importance Through Inter-criteria Correlation (CRITIC) method, a more comprehensive and quantitative index by coupling the selected four indexes with objective weights was obtained for further training efficacy representation, and the validation dataset also showed a good performance. CONCLUSIONS: This study proved that our proposed coupling method based on different visual function examination results via the CRITIC algorithm is a potential means to quantify the amblyopia treatment efficacy.

A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images.

An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.

Tenofovir Alafenamide for Pregnant Chinese Women With Active Chronic Hepatitis B: A Multicenter Prospective Study.

BACKGROUND & AIMS: Data on long-term tenofovir alafenamide (TAF) therapy for pregnant women with active chronic hepatitis B (CHB) (immune clearance and reactivation phases, currently and previously diagnosed) and their infants are lacking. METHODS: Pregnant women with active CHB treated with TAF and tenofovir disoproxil fumarate (TDF) were enrolled in this multicenter prospective study, and infants received immunoprophylaxis. The primary outcomes were rates of adverse (safety) events in pregnant women and defects in infants and fetuses. The secondary outcomes were virologic responses in pregnant women, infants' safety, hepatitis B surface antigen (HBsAg) status, and growth conditions. RESULTS: One hundred three and 104 pregnant women were enrolled and 102 and 104 infants were born in the TAF and TDF groups, respectively. In the TAF group, the mean age, gestational age, alanine aminotransferase level, and viral loads at treatment initiation were 29.3 years, 1.3 weeks, 122.2 U/L, and 5.1 log10 IU/mL, respectively. TAF was well-tolerated, and the most common adverse event was nausea (29.1%) during a mean of 2 years of treatment. Notably, 1 (1.0%) TAF-treated pregnant woman underwent induced abortion due to noncausal fetal cleft lip and palate. No infants in either group had birth defects. In the TAF group, the hepatitis B e antigen seroconversion rate was 20.7% at postpartum month 6, infants had normal growth parameters, and no infants were positive for HBsAg at 7 months. The TDF group had comparable safety and effectiveness profiles. CONCLUSIONS: TAF administered throughout or beginning in early pregnancy is generally safe and effective for pregnant women with active CHB and their infants.

Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition.

Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify performed activities in this work. Additionally, a dataset including 18 activities, performed by 14 subjects in 10 repetitions, is generated. The Smart-Sleeve is evaluated over six classical machine learning classifiers (support vector machine, k-nearest neighbor, logistic regression, random forest, decision tree and naive Bayes) and a convolutional neural network model. For classical machine learning, a new normalization approach is proposed to overcome signal differences caused by different body sizes and statistical, geometric, and symmetry features are used. All classification techniques are compared in terms of classification accuracy, precision, recall, and F-measure. Average accuracies of 82.02% (support vector machine) and 82.30% (convolutional neural network) can be achieved in 10-fold cross-validation, and 72.66% (support vector machine) and 74.84% (convolutional neural network) in leave-one-subject-out validation, which shows that the Smart-Sleeve and the proposed data processing method are suitable for human activity recognition.

Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series.

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment.

Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain−computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index­the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.

Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions.

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing.

A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions.

Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor's three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time-frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.

Quantitative and objective diagnosis of color vision deficiencies based on steady-state visual evoked potentials.

PURPOSE: Traditional color vision tests depend on subjective judgments and are not suitable for infant children and subjects with cognitive dysfunction. We aimed to explore an objective and quantitative color vision testing method based on sweep steady-state visual evoked potentials (sweep SSVEPs) and compare the results with subjective Farnsworth-Munsell (FM) 100-hue test results. METHODS: A red-green SSVEP pattern reversal checkboard paradigm at different luminance ratios was used to induce visual evoked potentials (VEPs) from 15 color vision deficiencies (CVDs) and 11 normal color vision subjects. After electroencephalography signals were processed by canonical correlation analysis, an equiluminance turning curve corresponding to the activation of the L-cones and M-cones at different levels of color vision was established. Then, we obtained different equiluminance T and proposed the SSVEP color vision severity index (ICVD) to quantify color vision function and the severity of CVDs. In addition, the FM 100-hue test was used to obtain subjective data for the diagnosis of color vision. RESULTS: The value of ICVD can be an indicator of the level of color vision. Both the total error scores (TES) and confusion index (C-index) of the FM 100-hue test were significantly correlated with ICVD values (P < 0.001, respectively). ICVD also had a good classification effect in detecting normals, anomalous trichromats and dichromats. Moreover, equiluminance T had a good effect on classifying protans and deutans in subjects with CVDs. CONCLUSION: Color vision evaluation with sweep SSVEPs showed a good correlation with subjective psychophysical methods. SSVEPs can be an objective and quantitative method to test color vision and diagnose CVDs.

Associations of polymorphisms in interleukins with susceptibility to breast cancer: Evidence from a meta-analysis.

BACKGROUND: Associations between polymorphisms in interleukins and breast cancer (BC) were already investigated by many studies, yet with controversial findings. The aim of this meta-analysis was to better clarify associations between polymorphisms in interleukins and BC by combing the results of all relevant articles. METHODS: Eligible articles were searched from Pubmed, Embase, Web of Science and CNKI. We used Review Manager to combine the results of eligible studies. RESULTS: Fifty-seven studies were included in this meta-analysis. We found that IL-6 rs1800796 (dominant comparison: OR = 0.70, 95% CI 0.53-0.92), IL-8 rs4073 (dominant comparison: OR = 0.74, 95% CI 0.61-0.89; over-dominant comparison: OR = 1.16, 95% CI 1.05-1.29; allele comparison: OR = 0.82, 95% CI 0.69-0.89), IL-10 rs1800896 (recessive comparison: OR = 1.28, 95% CI 1.12-1.47) and IL-18 rs1946518 (dominant comparison: OR = 0.80, 95% CI 0.65-0.97; allele comparison: OR = 0.74, 95% CI 0.59-0.93) polymorphisms were all significantly associated with BC in overall combined analyses. In subgroup analyses, we noticed that IL-6 rs1800796, IL-8 rs4073, IL-10 rs1800896, IL-18 rs1946518 and rs187238 polymorphisms were all significantly associated with susceptibility to BC in East Asians from China. CONCLUSIONS: Collectively, this meta-analysis demonstrated that IL-6 rs1800796, IL-8 rs4073, IL-10 rs1800896, IL-18 rs1946518 and rs187238 polymorphisms may confer susceptibility to BC for East Asians from China.

Comparison of the performance of six stimulus paradigms in visual acuity assessment based on steady-state visual evoked potentials.

PURPOSE: There are several stimulus paradigms used in objective visual acuity assessment based on steady-state visual evoked potentials (SSVEPs). The aim of this study was to explore the difference and performance of common used six stimulus paradigms (reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, brief-onset vertical sinusoidal gratings, reversal checkerboards and oscillating expansion-contraction concentric-rings) of SSVEP acuity assessment. METHODS: We tested subjective visual acuity both by tumbling E and Freiburg Visual Acuity and Contrast Test (FrACT) in 11 subjects. SSVEPs were induced by 11 spatial frequencies for each paradigm, and then a threshold determination criterion was used to define the objective SSVEP visual acuity. RESULTS: After SSVEP signal analysis, we found there was difference in SSVEP response of harmonic components and no difference in sensitive electrode placement for the six paradigms. We selected six electrodes (PO3, POz, PO4, O1, Oz and O2) as the sensitive electrodes to use in data processing for each paradigm. The results showed that except for brief-onset vertical sinusoidal gratings, the correlation and agreement between objective SSVEP and subjective FrACT acuity were all quite good, demonstrating good performance in acuity detection for the rest five paradigms. CONCLUSION: Except for brief-onset vertical sinusoidal gratings, all the five stimulus paradigms of reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, reversal checkerboards and oscillating expansion-contraction concentric-rings performed quite well in objective SSVEP visual acuity assessment.

Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.

As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fréchet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired t-tests were used to assess the significance of the results. The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (p < 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. Improvements in the classification accuracies of 17% and 21% (p < 0.01) were observed after DA for the two datasets. In addition, the hybrid network CNN-DCGAN outperformed the other classification methods, with average kappa values of 0.564 and 0.677 for the two datasets.

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.

Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review.

Visual evoked potential (VEP) has been used as an alternative method to assess visual acuity objectively, especially in non-verbal infants and adults with low intellectual abilities or malingering. By sweeping the spatial frequency of visual stimuli and recording the corresponding VEP, VEP acuity can be defined by analyzing electroencephalography (EEG) signals. This paper presents a review on the VEP-based visual acuity assessment technique, including a brief overview of the technique, the effects of the parameters of visual stimuli, and signal acquisition and analysis of the VEP acuity test, and a summary of the current clinical applications of the technique. Finally, we discuss the current problems in this research domain and potential future work, which may enable this technique to be used more widely and quickly, deepening the VEP and even electrophysiology research on the detection and diagnosis of visual function.

Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm.

PURPOSE: The traditional assessment of visual acuity and contrast sensitivity depends more on subjective judgments. Steady-state motion visual evoked potentials (SSMVEPs) can provide an objective and quantitative method to evaluate visual functions such as visual acuity and contrast sensitivity. Here, we explored the possibility of objective SSMVEP visual acuity and contrast sensitivity testing, and compared its performance with that of psychophysical methods. METHODS: In this study, we designed a specific concentric ring with oscillating expansion and contraction SSMVEP paradigm to assess visual acuity and contrast sensitivity. By changing the parameters of the paradigm, the SSMVEP paradigm with different contrasts and spatial frequencies corresponding to different visual acuity and contrast sensitivity was designed. Moreover, we proposed a threshold determination criterion to define the corresponding objective SSMVEP visual acuity and contrast sensitivity. RESULTS: We tested visual acuity and contrast sensitivity of sixteen healthy adults utilizing this paradigm with an electroencephalography system. Our data suggested that there was no significant difference between objective visual acuity and contrast sensitivity measurements based on the SSMVEPs and subjective psychophysical ones. CONCLUSION: Our study proved that SSMVEPs can be an objective and quantitative method to measure visual acuity and contrast sensitivity.

Identification of a potent MAR element from the human genome and assessment of its activity in stably transfected CHO cells.

Low-level and unstable transgene expression are common issues using the CHO cell expression system. Matrix attachment regions (MARs) enhance transgene expression levels, but additional research is needed to improve their function and to determine their mechanism of action. MAR-6 from CHO chromosomes actively mediates high and consistent gene expression. In this study, we compared the effects of two new MARs and MAR-6 on transgene expression in recombinant CHO cells and found one potent MAR element that can significantly increase transgene expression. Two MARs, including the human CSP-B MAR element and DHFR intron MAR element from CHO cells, were cloned and inserted downstream of the poly(A) site in a eukaryotic vector. The constructs were transfected into CHO cells, and the expression levels and stability of eGFP were detected by flow cytometry. The three MAR sequences can be ranked in terms of overall eGFP expression, in decreasing order, as follows: human CSP-B, DHFR intron MAR element and MAR-6. Additionally, as expected, the three MAR-containing vectors showed higher transfection efficiencies and transient transgene expression in comparison with those of the non-MAR-containing vector. Bioinformatics analysis indicated that the NFAT and VIBP elements within MAR sequences may contribute to the enhancement of eGFP expression. In conclusion, the human CSP-B MAR element can improve transgene expression and its effects may be related to the NFAT and VIBP elements.