A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online at https://github.com/ALEX95GOGO/CAGNN.

Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that publication of the guideline will facilitate institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of hNSCs for clinical development and therapeutic applications.

Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed. "Human midbrain dopaminergic progenitor" jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human mDAPs in China. This standard specifies the technical requirements, test methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for human mDAPs, which is applicable to the quality control for human mDAPs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that the publication of this guideline will facilitate the institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human mDAPs for clinical development and therapeutic applications.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources. To the best of our knowledge, however, past research has not fully uncovered the neurological characteristics of IB nor classified these characteristics in life-alike virtual situations. Therefore, the purpose of this study is to determine whether ERP dynamics in the brain may be utilised as a neural feature to predict the occurrence of IB using machine learning (ML) algorithms. In a virtual reality simulation of an IB experiment, 30 participants' behaviour and Electroencephalography (EEG) measurements were obtained. Participants were given a target detection task in the IB experiment without knowing the unattended shapes displayed on the background building. The targets were presented in three different sensory modalities (auditory, visual, and visual-auditory). On the post-experiment questionnaire, participants who claimed not to have noticed the unattended shapes were assigned to the IB group. Subsequently, the Aware group was formed from individuals who reported seeing the unattended shapes. Using EEGNet to classify IB and Aware groups demonstrated a high classification performance. According to the research, ERP brain dynamics are associated with the awareness of unattended shapes and have the potential to serve as a reliable indication for predicting the visual consciousness of unexpected objects.(p/)(p)Clinical relevance- This research offers a potential brain marker for the mixed-reality and BCI systems that will be used in the future to identify cognitive deterioration, maintain attentional capacity, and prevent disasters.

[Characteristics and Ecological Risk Assessment of Pharmaceutical Active Compounds in Guangdong Province].

Pharmaceutically active compounds(PhACs) have become a class of new pollutants in the environment after extensive production and use of PhACs in China. To investigate the pollution characteristics of PhACs in Guangdong Province, raw sewage was collected from 186 sewage treatment plants in 21 cities, including 178 townships and administrative districts in Guangdong Province. The pollution levels of ten typical PhACs in influent water of sewage treatment plants were analyzed using automatic solid phase extraction and high performance liquid chromatography-triple quadrupole mass spectrometry. The spatial distribution characteristics of PhACs in Guangdong Province were fully revealed, and the potential ecological risks of PhACs were evaluated. The results showed that PhACs were detected in all wastewater plants, and the mass concentration of PhACs ranged from 21.00 to 9558.25 ng·L-1. Metoprolo, acetaminophen, bezafibrate, and caffeine were the main pollutants. In terms of spatial distribution, the average mass concentration of ΣPhACs in various regions of Guangdong Province was in the following order:Pearl River Delta>North Guangdong>East Guangdong≈West Guangdong. When the mass concentration of ΣPhACs was over 2500 ng·L-1 in the influent water of sewage treatment plants, the concentration of PhACs in effluent was estimated according to the sewage disposal technology. The ecological risk of PhACs was carried out based on the effluent. The results revealed that the ecological risk of PhACs was low in Guangdong Province, and the risk of bezafibrate was moderate in the cities of Shaoguan, Jiangmen, and Shenzhen. The highest ecological risk of ΣPhACs was located in Shaoguan.

Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.

Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data. Inspired by the waveform characteristics and processing methods shared between EEG and speech signals, we propose Speech2EEG, a novel EEG recognition method that leverages pretrained speech features to improve the accuracy of EEG recognition. Specifically, a pretrained speech processing model is adapted to the EEG domain to extract multichannel temporal embeddings. Then, several aggregation methods, including the weighted average, channelwise aggregation, and channel-and-depthwise aggregation, are implemented to exploit and integrate the multichannel temporal embeddings. Finally, a classification network is used to predict EEG categories based on the integrated features. Our work is the first to explore the use of pretrained speech models for EEG signal analysis as well as the effective ways to integrate the multichannel temporal embeddings from the EEG signal. Extensive experimental results suggest that the proposed Speech2EEG method achieves state-of-the-art performance on two challenging motor imagery (MI) datasets, the BCI IV-2a and BCI IV-2b datasets, with accuracies of 89.5% and 84.07% , respectively. Visualization analysis of the multichannel temporal embeddings show that the Speech2EEG architecture can capture useful patterns related to MI categories, which can provide a novel solution for subsequent research under the constraints of a limited dataset scale.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1-51 Hz frequency range. In the preferred 8-30 Hz frequency band, known for motor imagery data classification (including α, µ, and ß waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, µ, and ß, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8-30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.

Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

Adaptive Initialization Method for K-Means Algorithm.

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding.

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.

miR-19a/b-3p promotes inflammation during cerebral ischemia/reperfusion injury via SIRT1/FoxO3/SPHK1 pathway.

BACKGROUND: Stroke affects 3-4% of adults and kills numerous people each year. Recovering blood flow with minimal reperfusion-induced injury is crucial. However, the mechanisms underlying reperfusion-induced injury, particularly inflammation, are not well understood. Here, we investigated the function of miR-19a/b-3p/SIRT1/FoxO3/SPHK1 axis in ischemia/reperfusion (I/R). METHODS: MCAO (middle cerebral artery occlusion) reperfusion rat model was used as the in vivo model of I/R. Cultured neuronal cells subjected to OGD/R (oxygen glucose deprivation/reperfusion) were used as the in vitro model of I/R. MTT assay was used to assess cell viability and TUNEL staining was used to measure cell apoptosis. H&E staining was employed to examine cell morphology. qRT-PCR and western blot were performed to determine levels of miR-19a/b-3p, SIRT1, FoxO3, SPHK1, NF-κB p65, and cytokines like TNF-α, IL-6, and IL-1ß. EMSA and ChIP were performed to validate the interaction of FoxO3 with SPHK1 promoter. Dual luciferase assay and RIP were used to verify the binding of miR-19a/b-3p with SIRT1 mRNA. RESULTS: miR-19a/b-3p, FoxO3, SPHK1, NF-κB p65, and cytokines were elevated while SIRT1 was reduced in brain tissues following MCAO/reperfusion or in cells upon OGD/R. Knockdown of SPHK1 or FoxO3 suppressed I/R-induced inflammation and cell death. Furthermore, knockdown of FoxO3 reversed the effects of SIRT1 knockdown. Inhibition of the miR-19a/b-3p suppressed inflammation and this suppression was blocked by SIRT1 knockdown. FoxO3 bound SPHK1 promoter and activated its transcription. miR-19a/b-3p directly targeted SIRT1 mRNA. CONCLUSION: miR-19a/b-3p promotes inflammatory responses during I/R via targeting SIRT1/FoxO3/SPHK1 axis.

The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System.

Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system's dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40-50% of max heart rate), vigorous-CE (RSVP in 71-85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can open avenues for real-time measurement of cognitive functions in athletes outside the laboratory.

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

Gender-related difference in altered fractional amplitude of low-frequency fluctuations after electroacupuncture on primary insomnia patients: A resting-state fMRI study.

BACKGROUND: Primary insomnia (PI) is defined as a sleep disorder with no definite cause or inducement. Electroacupuncture, a treatment of inserting needles into specific points on the body surface and applying electrical stimulation, has been proved effective in treating PI with minimal adverse effects. However, the influence of gender difference on the clinical treatment efficacy of electroacupuncture for PI patients remains unclear. Therefore, we designed a clinical trial to compare the clinical treatment efficacy of electroacupuncture for PI patients with different genders. The research on the mechanism of electroacupuncture suggested it could modulate the sleep and wakefulness by activating or deactivating brain regions via a needling/tactile somatosensory specific stimulus. Therefore, we also designed a resting-state functional magnetic resonance imaging (rs-fMRI) study to detect the spontaneous brain activity of PI patients before and after the electroacupuncture treatment. METHOD: Thirty PI patients were recruited to accept 5-week electroacupuncture treatment on HT-7. Athens Insomnia Scale (AIS) and Pittsburgh sleep quality index (PSQI) questionnaires were used to evaluate the clinical treatment efficacy. Rs-fMRI was employed to observe the spontaneous brain activity in the resting state at the baseline and after 5 weeks of electroacupuncture treatment, which was measured by the fractional amplitude of low-frequency fluctuations (fALFF). RESULT: The AIS and PSQI scores were significantly decreased both in the female PI group and the male PI group after treatment. The decreased PSQI of female patients was significantly more than that of male patients (p < .05). The gender-related difference in the cerebral response to electroacupuncture was mainly in posterior cingulate and supramarginal gyrus. CONCLUSION: There is a gender-related difference in the clinical treatment efficacy of electroacupuncture for PI patients, and female patients may benefit more from electroacupuncture. Gender-related differences in the cerebral response to electroacupuncture may be one of the factors affecting clinical treatment efficacy.

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.

Effectiveness and cerebral responses of multi-points acupuncture for primary insomnia: a preliminary randomized clinical trial and fMRI study.

BACKGROUND: Primary insomnia (PI) is characterized by difficulties in initiating sleep or maintaining sleep, which lead to many serious diseases. Acupuncture for PI has drawn attention with its effectiveness and safety. However, the operation of choosing acupoints lacks scientific suggestion. Our trial aims to provide reference and scientific basis for the selection of acupoints and to explore its possible mechanism. METHODS: A patient-assessor-blinded, randomized and sham controlled trial was designed to compare the efficacy of 5-weeks acupuncture at a single acupoint, the combination of multi-acupoints, and a sham point. The Pittsburgh sleep quality index and Athens Insomnia Scale questionnaire were used for the primary clinical outcomes, while polysomnography was performed for the secondary clinical outcomes. The resting state functional MRI was employed to detect the cerebral responses to acupuncture. The brain activity in resting state was measured by calculating the fractional amplitude of low-frequency fluctuations (fALFF), which reflected the idiopathic activity level of neurons in the resting state. These results were analyzed by two factorial ANOVA test and post-hoc t-tests. RESULTS: The clinical outcomes suggest that acupuncture could improve clinical symptoms, and the combination of multi-acupoints might lead to a better clinical efficacy. The rs-fMRI results suggested that the brain activity of certain regions was related to the sleep experience, and acupuncture could regulate the activity of these regions. Furthermore, the combination of multi-acupoints could impact more regions which were influenced by the sleep experience. CONCLUSIONS: Acupuncture has been proven to be beneficial for PI patients, and the combination of multi-acupoints might improve its efficacy. TRIAL REGISTRATION: This trial has been registered on the U.S. National Library of Medicine (https://clinicaltrials.gov) ClinicalTrials.gov Identifier: NCT02448602 . Registered date: 14/04/2015.

Characteristics of the 24-h ambulatory blood pressure monitoring in patients with Parkinson's disease - the SFC BP multicentre study in China.

OBJECTIVES: Cardiovascular dysautonomia can be present at early, late and even prodromal stages of Parkinson's disease. This study aimed to describe the characteristics of 24-h ambulatory blood pressure (BP) monitoring and investigate the frequency of cardiovascular dysautonomia in Parkinson's disease without an abnormal BP history. METHODS: Parkinson's disease patients without history of abnormal BP were consecutively enrolled from three Chinese centres, on whom office BP measurement, neurological evaluations and 24-h ambulatory BP monitoring were performed. RESULTS: Totally, 101 Parkinson's disease patients (42.6% women) with an average age of 66.6 ±â€Š8.2 years were included in our cohort, and data analysis revealed that 26 (25.74%) patients suffered from orthostatic hypotension, among whom 18 (69.23%) were symptomatic. Patients with orthostatic hypotension compared with those without had significantly higher nocturnal SBP level, and more severe nonmotor symptoms, autonomic dysfunction and cognitive impairment. Further, 54 out of 101 (53.47%) individuals had a reverse dipping pattern in SBP and/or DBP. Reverse dippers had more cases of orthostatic hypotension (P < 0.001), and more severe nonmotor symptoms. SBP dipping ratio of less than -2.98% generated 76.9% of sensitivity, 69.3% of specificity, 46.5% of positive predictive value (PPV), 89.7% of negative predictive value (NPV) and 77.4% of accuracy, while diastolic dipping ratio of less than -1.80% generated 76.9% of sensitivity, 70.7% specificity, 47.6% of PPV, 89.8% of NPV and 77.8% of accuracy for suspecting orthostatic hypotension. CONCLUSION: Orthostatic hypotension can occur in one-fourth Parkinson's disease patients without abnormal BP history, and reverse dipping was present in more than half of patients with Parkinson's disease. Reverse dipping pattern was helpful to suspect orthostatic hypotension.

Facile and Reliable Thickness Identification of Atomically Thin Dichalcogenide Semiconductors Using Hyperspectral Microscopy.

Although large-scale synthesis of layered two-dimensional (2D) transition metal dichalcogenides (TMDCs) has been made possible, mechanical exfoliation of layered van der Waals crystal is still indispensable as every new material research starts with exfoliated flakes. However, it is often a tedious task to find the flakes with desired thickness and sizes. We propose a method to determine the thickness of few-layer flakes and facilitate the fast searching of flakes with a specific thickness. By using hyperspectral wild field microscopy to acquire differential reflectance and transmittance spectra, we demonstrate unambiguous recognition of typical TMDCs and their thicknesses based on their excitonic resonance features in a single step. Distinct from Raman spectroscopy or atomic force microscopy, our method is non-destructive to the sample. By knowing the contrast between different layers, we developed an algorithm to automatically search for flakes of desired thickness in situ. We extended this method to measure tin dichalcogenides, such as SnS2 and SnSe2, which are indirect bandgap semiconductors regardless of the thickness. We observed distinct spectroscopic behaviors as compared with typical TMDCs. Layer-dependent excitonic features were manifested. Our method is ideal for automatic non-destructive optical inspection in mass production in the semiconductor industry.