Development of a miniaturized and modular probe for fNIRS instrument.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive and promising method for continuously monitoring hemodynamic and metabolic changes in tissues. However, the existing fNIRS equipment uses optical fiber, which is bulky, expensive, and time-consuming. We present a miniaturized, modular, novel silicon photomultiplier (SiPM) detector and develop a fNIRS instrument aimed at investigating the cerebral hemodynamic response for patients with epilepsy. Light emitting probe is a circle with a diameter of 5 mm. Independent and modular light source and detector are more flexible in placement. The system can be expanded to high-density measurement with 16 light sources, 16 detectors, and 52 channels. The sampling rate of each channel is 25 Hz. Instrument performance was evaluated using brain tissue phantom and in vivo experiments. High signal-to-noise ratio (60 dB) in source detector separation (SDS) of 30 mm, good stability (0.1%), noise equivalent power (0.89 pW), and system drift (0.56%) were achieved in the phantom experiment. Forearm blood-flow occlusion experiments were performed on the forearm of three healthy volunteers to demonstrate the ability to track rapid hemodynamic changes. Breath holding experiments on the forehead of healthy volunteers demonstrated the system can well detect brain function activity. The computer software was developed to display the original light signal intensity and the concentration changes of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in real time. This system paves the way for our further diagnosis of epilepsy.

A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila.

Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health. Drosophila melanogaster is a powerful model to study small-scale, temporally-transient social behavior. However, longer-term analysis of large groups of flies is hampered by the lack of effective and reliable tools. We built a new imaging arena and improved the existing tracking algorithm to reliably follow a large number of flies simultaneously. Next, based on the automatic classification of touch and graph-based social network analysis, we designed an algorithm to quantify changes in the social network in response to prior social isolation. We observed that isolation significantly and swiftly enhanced individual and local social network parameters depicting near-neighbor relationships. We explored the genome-wide molecular correlates of these behavioral changes and found that whereas behavior changed throughout the six days of isolation, gene expression alterations occurred largely on day one. These changes occurred mostly in metabolic genes, and we verified the metabolic changes by showing an increase of lipid content in isolated flies. In summary, we describe a highly reliable tracking and analysis pipeline for large groups of flies that we use to unravel the behavioral, molecular and physiological impact of isolation on social network dynamics in Drosophila.

[Analysis of epileptic seizure detection method based on improved genetic algorithm optimization back propagation neural network].

In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.

Qualitative and quantitative analysis of major constituents from Dazhu Hongjingtian capsule by UPLC/Q-TOF-MS/MS combined with UPLC/QQQ-MS/MS.

In this work, a sensitive and efficient method was established and validated for qualitative and quantitative analysis of major bioactive constituents in Dazhu Hongjingtian capsule by liquid chromatography tandem mass spectrometry. A total of 32 compounds were tentatively identified using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Furthermore, 12 constituents, namely gallic acid, 3,4-dihydroxybenzoic acid, salidroside, p-coumaric acid-4-O-ß-d-glucopyranoside, bergeninum, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, syringate, 6''-O-galloylsalidroside, rhodiosin, rhodionin and kaempferol-7-O-α-l-rhamnoside, were simultaneously quantified by the developed ultra-performance liquid chromatography coupled with a triple quadrupole mass spectrometry method in 9 min. All of them were analyzed on an Agilent ZorBax SB-C18 column (3.0 × 100 mm, 1.8 µm) with linear gradient elution of methanol-0.1% formic acid water. The proposed method was applied to analyze three batches of samples with acceptable linearity (R, 0.9979-0.9997), precision (RSD, 1.3-4.7%), repeatability (RSD, 1.7-4.9%), stability (RSD, 2.2-4.9%) and recovery (RSD, 0.6-4.4%) of the 12 compounds. As a result, the analytical method possessing high throughput and sensitivity is suitable for the quality control of Dazhu Hongjingtian capsule.

[Study on Indicator Densitometry Determination Method of Hemodynamic Parameters].

Measurement for hemodynamic parameters has always been a hot spot of clinical research. Methods for measuring hemodynamic parameters clinically have the problems of invasiveness, complex operation and being unfit for repeated measurement. To solve the problems, an indicator densitometry analysis method is presented based on near-infrared spectroscopy (NIRS) and indicator dilution theory, which realizes the hemodynamic parameters measured noninvasively. While the indocyanine green (ICG) was injected into human body, circulation carried the indicator mixing and diluting with the bloodstream. Then the near-nfrared probe was used to emit near-infrared light at 735, 805 and 940 nm wavelengths through the sufferer's fingertip and synchronously capture the transmission light containing the information of arterial pulse wave. By uploading the measured data, the computer would calculate the ICG concentration, establish continuous concentration curve and compute some intermediate variables such as the mean transmission time (MTT) and the initial blood ICG concentration (c(t0)). Accordingly Cardiac Output (CO) and Circulating Blood Volume (CBV) could be calculated. Compared with the clinical "gold standard" methods of thermodilution and I-131 isotope-labelling method to measure the two parameters by clinical controlled trials, ten sets of data were obtained. The maximum relative errors of this method were 8.88% and 4.28% respectively, and both of the average relative errors were below 5%. The result indicates that this method can meet the clinical accuracy requirement and can be used as a noninvasive, repeatable and applied solution for clinical hemodynamnic parameters measurement.

[Fingerprint of Dazhu Hongjingtian capsule by HPLC-DAD-QTOF-MS].

A method was established to analyze the fingerprint of Dazhu Hongjingtian capsule by HPLC-DAD.The separation was performed on Agilent Eclipse Plus-C18(4.6 mm×250 mm, 5 µm) with methanol-0.1% formic acid solution as the mobile phase for gradient elution at a flow rate of 1.0 mL•min⁻¹; the detection wavelength was set at 276 nm and column temperature was set at 35 ℃. A total of 10 batches of samples were detected by the above method, and based on their fingerprint by using Similarity Evaluation System for Chromatographic Fingerprint of TCM (2004A), 21 common chromatographic peaks were determined and after the individual common peak whose peak area was greater than 50% of the total peak area was deducted, the similarity results of these samples were analyzed and compared. The results showed that the similarity of 10 batches of samples was all higher than 0.940. HPLC/Q-TOF-MS was used to identify the common chromatographic peaks in the fingerprint and determine the molecular formulas of twenty-one common chromatographic peaks. The structures of 11 fingerprint peaks were tentatively identified based on the control products and mass spectrometry information. This was the first time to establish fingerprint by using HPLC method and identify fingerprint peaks by using HPLC/Q-TOF-MS. This method has good precision, stability and repeatability, and could provide basis for quality evaluation of Dazhu Hongjingtian capsule.

[Research on Detection Method with Wearable Respiration Device Based on the Theory of Bio-impedance].

Considering the importance of the human respiratory signal detection and based on the Cole-Cole bio-impedance model,we developed a wearable device for detecting human respiratory signal.The device can be used to analyze the impedance characteristics of human body at different frequencies based on the bio-impedance theory.The device is also based on the method of proportion measurement to design a high signal to noise ratio(SNR)circuit to get human respiratory signal.In order to obtain the waveform of the respiratory signal and the value of the respiration rate,we used the techniques of discrete Fourier transform(DFT)and dynamic difference threshold peak detection.Experiments showed that this system was valid,and we could see that it could accurately detect the waveform of respiration and the detection accuracy rate of respiratory wave peak point detection results was over 98%.So it can meet the needs of the actual breath test.

[A Measurement Method for the Hepatic Functional Reserves Anti Oxygen Fluctuations].

Hepatic functional reserves parameters are the key indictors to assess if the hepatic metabolic function is normal, they are also the important basis to a successful hepatectomy. Currently clinical hepatic functional reserves parameters are achieved through Indocyanine Green (ICG) concentration measurement in the method of pulse dye spectrophotometry, with the assumption that blood oxygen saturation is 100%, this hypothetical bias leads to an error in the calculated value of the hepatic functional reserves parameters. In order to solve this problem, hepatic functional reserves parameters measurement that resist fluctuation from blood is presented. The method is based on the modified Lambert Beer's law and realize the correction of ICG concentration measurement in the method of pulse dye spectrophotometry. While the ICG is injected into the patient's body by the cubital veins, using the data acquisition unit that developed by project team to collect 805 nm, 940 nm wavelengths of transmission signals and 730 nm, 805 nm and 890 nm wavelengths of reflected signals in the fingertip skin synchronously, and then upload 5 sets of data to the computer. Draw the ICG concentration curve according the collected data and blood oxygen saturation before injecting ICG to the human body and then calculate the characteristic parameters, according to the characteristic parameters calculate the hepatic reserves functional parameters. Taking the measurement of effective hepatic blood flow as an example, the relative error was obviously decreased when comparing this method and the pulse dye concentration method with the electromagnetic flowmeter (EMF) measurement which is the most accurate method to measure effective hepatic blood flow (EHBF) respectively. The results demonstrate that this method can improve the accuracy of hepatic reserves parameters, and it can also provide a more accurate detection method of hepatic functional reserves parameters for clinical application.

[EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy].

Currently, functional near-infrared spectroscopy (fNIRS) is widely used in the field of Neuroimaging. To solve the signal-noise frequency spectrum aliasing in non-linear and non-stationary fNIRS characteristic signal extraction, a new joint multi-resolution algorithm, EEMD-ICA, is proposed based on combining Independent Component Analysis with Ensemble Empirical Mode Decomposing. After functional brain imaging instrument detected the multi-channel and multi-wavelength NIR optical density signals, EEMD was performed to decompose measurement signals into multiple intrinsic mode function according to the signal frequency component. Then ICA was applied to extract the interest data from IMFs into ICs. Finally, reconstructed signals were obtained by accumulating the ICs set. EEMD-ICA was applied in de-noising Valsalva test signals which were considered as original signals and compared with Empirical Mode Decomposing and Ensemble Empirical Mode Decomposing to illustrate validity of this algorithm. It is proved that useful information loss during de-noising and invalidity of noise elimination are completely solved by EEMD-ICA. This algorithm is more optimized than other two de-noising methods in error parameters and signal-noise-ratio analysis.

DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps.

BACKGROUND: Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. NEW METHODS: We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. RESULTS: On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. COMPARISON WITH EXISTING METHODS: In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. CONCLUSIONS: DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.

[A dye densitometry analysis method for noninvasive measurement of cardiac output based on NIRS].

Currently, there exist technology problems in cardiac output (CO) parameter detection clinically, such as invasive and complex operation, as well as possibility of infection and death for patients. In order to solve these problems, a noninvasive and continuous method based on NIRS for CO detection was presented. In this way, the concentration changing of indocyanine green (ICG) dye in the patient's arterial blood was dynamically measured and analyzed, so that the CO could be noninvasively and continuously measured according to the characteristic parameters of dye densitometry curve. While the ICG dye was injected into the patient's body by the median cubital vein, block of photoelectric pulse dye densitometry measurement system as the lower machine acquired pulse wave data and uploaded the data to upper computer. In the scheme, two specialized light sources of LED at 940 and 805 nm were used to capture the signals of sufferer's fingertip pulse wave synchronously and successively. The CO value could then be successfully calculated through drawing complete ICG concentration variation of dye dilution and excretion process and computing mean transmission time (MTT) by upper computer. Compared with the "gold standard" method of thermodilution, the maximum relative error of this method was below 9. 76%, and the mean relative error was below 4. 39%. The result indicates that the method can be used as a kind of convenient operation, noninvasive and continuous solution for clinical CO measurement.

Design and Performance Analysis of a Micro-Displacement Worktable Based on Flexure Hinges.

The flexure hinge is a kind of micro-displacement adjustment device with application prospects because of its high displacement resolution, positioning accuracy and repeatability. In this study, a micro-displacement worktable with four degrees of freedom (X→, Z→, X︵, Z︵) was designed. The micro-displacement worktable was composed of three different flexure hinges. The adjustment ranges and adjustment accuracy of flexure hinges in terms of their respective degrees were improved. The micro-displacement worktable performance was examined by FEA (Finite Element Method). The maximum displacement that was adjusted in X→ and Z→ was 1.67 µm and 1.74 µm. The maximum angle adjusted in the X︵ and Z︵ direction was 14.90° and 18.58°. A test platform was developed for micro-displacement worktable performance tests. The simulation results showed a good agreement with the experimental results.

Application of Surface Electromyography in Exercise Fatigue: A Review.

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

Circulation times of prostate cancer and hepatocellular carcinoma cells by in vivo flow cytometry.

In metastasis, the cancer cells that travel through the body are capable of establishing new tumors in locations remote from the site of the original disease. To metastasize, a cancer cell must break away from its tumor and invade either the circulatory or lymphatic system, which will carry it to a new location, and establish itself in the new site. Once in the blood stream, the cancer cells now have access to every portion of the body. Here, we have used the "in vivo flow cytometer" to study if there is any relationship between metastatic potential and depletion kinetics of circulating tumor cells. The in vivo flow cytometer has the capability to detect and quantify continuously the number and flow characteristics of fluorescently labelled cells in vivo. We have improved the counting algorithm and measured the depletion kinetics of cancer cells with different metastatic potential. Interestingly, more invasive PC-3 prostate cancer cells are depleted faster from the circulation than LNCaP cells. In addition, we have measured the depletion kinetics of two related human hepatocellular carcinoma (liver cancer) cell lines, high-metastatic HCCLM3 cells, and low-metastatic HepG2 cells. More than 60% HCCLM3 cells are depleted within the first hour. Interestingly, the low-metastatic HepG2 cells possess noticeably slower depletion kinetics. In comparison, <40% HepG2 cells are depleted within the first hour. The differences in depletion kinetics might provide insights into early metastasis processes.

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals.

Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people's emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject's emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016-2021.

Numerical Simulations and the Design of Magnetic Field-Enhanced Electron Impact Ion Source with Hollow Cylinder Structure.

An electron impact ion source-adopted magnetic field-enhanced technology has been designed for enhancing the electron intensity and the ionization efficiency. Based on the ion optic focus mechanism, an electron impact ionization source was designed, and the electron entrance into the ionization chamber was designed with a hollow cylinder structure to improve the ion extraction efficiency. Numerical simulation and optimal geometry were optimized by SIMION 8.0 to provide higher electron intensity and ion transmission efficiency. To improve the electron intensity, the influence of the filament potential and magnetic intensity was investigated, and the values of 70 eV and 150 Gs were chosen in our apparatus. Based on the optimal parameters, the air in the lab and oxygen gas was detected by the homemade apparatus, and the ion intensity was detected in the positive and negative ion modes, respectively. The homemade electron impact ion source apparatus has the potential to enhance ionization efficiency applied in the mass spectrometer ionization source.

A neurodevelopmental origin of behavioral individuality in the Drosophila visual system.

The genome versus experience dichotomy has dominated understanding of behavioral individuality. By contrast, the role of nonheritable noise during brain development in behavioral variation is understudied. Using Drosophila melanogaster, we demonstrate a link between stochastic variation in brain wiring and behavioral individuality. A visual system circuit called the dorsal cluster neurons (DCN) shows nonheritable, interindividual variation in right/left wiring asymmetry and controls object orientation in freely walking flies. We show that DCN wiring asymmetry instructs an individual's object responses: The greater the asymmetry, the better the individual orients toward a visual object. Silencing DCNs abolishes correlations between anatomy and behavior, whereas inducing DCN asymmetry suffices to improve object responses.

Multiclass heterogeneous domain adaptation via bidirectional ECOC projection.

Heterogeneous domain adaptation aims to exploit the source domain data to train a prediction model for the target domain with different input feature space. Current methods either map the data points from different domains with different feature space to a common latent subspace or use asymmetric projections for learning the classifier. However, these learning methods separate common space learning and shared classifier training. This may lead complex model structure and more parameters to be determined. To appropriately address this problem, we propose a novel bidirectional ECOC projection method, named HDA-ECOC, for heterogeneous domain adaptation. The proposed method projects the inputs and outputs (labels) of two domains into a common ECOC coding space, such that, the common space learning and the shared classifier training can be performed simultaneously. Then, classification of the target testing sample can be directly addressed by an ECOC decoding. Moreover, the unlabeled target data is exploited by estimating the two domains projected instances consistency through a maximum mean discrepancy (MMD) criterion. We formulate this method as a dual convex minimization problem and propose an alternating optimization algorithm for solving it. For performance evaluation, experiments are performed on cross-lingual text classification and cross-domain digital image classification with heterogeneous feature space. The experimental results demonstrate that the proposed method is effective and efficient in solving the heterogeneous domain adaptation problems.

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising.

Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the "mode mixing" problem in EMD effectively, but it can do nothing about the "end effect," another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the "end effect" efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.