Cholesterol homeostasis is vital for proper cellular and systemic functions. Disturbed cholesterol balance underlies not only cardiovascular disease but also an increasing number of other diseases ...such as neurodegenerative diseases and cancers. The cellular cholesterol level reflects the dynamic balance between biosynthesis, uptake, export and esterification - a process in which cholesterol is converted to neutral cholesteryl esters either for storage in lipid droplets or for secretion as constituents of lipoproteins. In this Review, we discuss the latest advances regarding how each of the four parts of cholesterol metabolism is executed and regulated. The key factors governing these pathways and the major mechanisms by which they respond to varying sterol levels are described. Finally, we discuss how these pathways function in a concerted manner to maintain cholesterol homeostasis.
In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in ...real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.
A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to ...noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications-motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks-are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.
Cholesterol metabolism produces essential membrane components as well as metabolites with a variety of biological functions. In the tumour microenvironment, cell-intrinsic and cell-extrinsic cues ...reprogram cholesterol metabolism and consequently promote tumourigenesis. Cholesterol-derived metabolites play complex roles in supporting cancer progression and suppressing immune responses. Preclinical and clinical studies have shown that manipulating cholesterol metabolism inhibits tumour growth, reshapes the immunological landscape and reinvigorates anti-tumour immunity. Here, we review cholesterol metabolism in cancer cells, its role in cancer progression and the mechanisms through which cholesterol metabolites affect immune cells in the tumour microenvironment. We also discuss therapeutic strategies aimed at interfering with cholesterol metabolism, and how the combination of such approaches with existing anti-cancer therapies can have synergistic effects, thus offering new therapeutic opportunities.
To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and ...negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08%, 83.99%, 82.70%, and 72.60%, respectively.
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns ...associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.
In the present study, we employed a
19
F NMR approach to study the association of telomere RNA and DNA
in vitro
and in living human cells. We successfully characterized the DNA-RNA hybrid ...G-quadruplex (HQ) structure formed by telomeric DNA and RNA. We further demonstrated for the first time that an HQ conformation can exist in the environmental conditions of HeLa cells.
In the present study, we employed a
19
F NMR approach to study the association of telomere RNA and DNA
in vitro
and in living human cells.
Cholesterol metabolism has been linked to immune functions, but the mechanisms by which cholesterol biosynthetic signaling orchestrates inflammasome activation remain unclear. Here, we have shown ...that NLRP3 inflammasome activation is integrated with the maturation of cholesterol master transcription factor SREBP2. Importantly, SCAP-SREBP2 complex endoplasmic reticulum-to-Golgi translocation was required for optimal activation of the NLRP3 inflammasome both in vitro and in vivo. Enforced cholesterol biosynthetic signaling by sterol depletion or statins promoted NLPR3 inflammasome activation. However, this regulation did not predominantly depend on changes in cholesterol homeostasis controlled by the transcriptional activity of SREBP2, but relied on the escort activity of SCAP. Mechanistically, NLRP3 associated with SCAP-SREBP2 to form a ternary complex which translocated to the Golgi apparatus adjacent to a mitochondrial cluster for optimal inflammasome assembly. Our study reveals that, in addition to controlling cholesterol biosynthesis, SCAP-SREBP2 also serves as a signaling hub integrating cholesterol metabolism with inflammation in macrophages.
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•NLRP3 inflammasome activation couples SREBP2 maturation•SCAP-SREBP2 translocation and S1P are required for optimal NLRP3 inflammasome activity•SCAP escorts both NLRP3 and SREBP2 by forming a ternary complex•SCAP-SREBP2 inhibition protects mice from systemic inflammation
The metabolic-inflammatory crosstalk plays a key role in host defense against pathogens and inflammation. Guo and colleagues demonstrate that SCAP-SREBP2 complex integrates NLRP3 inflammasome activation and cholesterol biosynthetic signaling during inflammation.
Unprecedented measures have been adopted to control the rapid spread of the ongoing COVID-19 epidemic in China. People's adherence to control measures is affected by their knowledge, attitudes, and ...practices (KAP) towards COVID-19. In this study, we investigated Chinese residents' KAP towards COVID-19 during the rapid rise period of the outbreak. An online sample of Chinese residents was successfully recruited via the authors' networks with residents and popular media in Hubei, China. A self-developed online KAP questionnaire was completed by the participants. The knowledge questionnaire consisted of 12 questions regarding the clinical characteristics and prevention of COVID-19. Assessments on residents' attitudes and practices towards COVID-19 included questions on confidence in winning the battle against COVID-19 and wearing masks when going out in recent days. Among the survey completers (n=6910), 65.7% were women, 63.5% held a bachelor degree or above, and 56.2% engaged in mental labor. The overall correct rate of the knowledge questionnaire was 90%. The majority of the respondents (97.1%) had confidence that China can win the battle against COVID-19. Nearly all of the participants (98.0%) wore masks when going out in recent days. In multiple logistic regression analyses, the COVID-19 knowledge score (OR: 0.75-0.90, P<0.001) was significantly associated with a lower likelihood of negative attitudes and preventive practices towards COVID-2019. Most Chinese residents of a relatively high socioeconomic status, in particular women, are knowledgeable about COVID-19, hold optimistic attitudes, and have appropriate practices towards COVID-19. Health education programs aimed at improving COVID-19 knowledge are helpful for Chinese residents to hold optimistic attitudes and maintain appropriate practices. Due to the limited sample representativeness, we must be cautious when generalizing these findings to populations of a low socioeconomic status.
Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of ...brain–computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To improve the accuracy of EEG-based emotion classification and visualize the changes of emotional states with time, this paper systematically compares three kinds of existing EEG features for emotion classification, introduces an efficient feature smoothing method for removing the noise unrelated to emotion task, and proposes a simple approach to tracking the trajectory of emotion changes with manifold learning. To examine the effectiveness of these methods introduced in this paper, we design a movie induction experiment that spontaneously leads subjects to real emotional states and collect an EEG data set of six subjects. From experimental results on our EEG data set, we found that (a) power spectrum feature is superior to other two kinds of features; (b) a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning.
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