Computational Modeling of the Prefrontal-Cingulate Cortex to Investigate the Role of Coupling Relationships for Balancing Emotion and Cognition.

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.

Perovskite photodetector-based laser absorption spectroscopy for gas detection.

A gas detection method based on CH3NH3PbI3 (MAPbI3) and poly (3,4-ethylenedioxythiophene): poly (4-styrene sulfonate) (PEDOT:PSS) composite photodetectors (PDs) is proposed. The operation of the PD primarily relies on the photoelectric effect within the visible light band. Our study involves constructing a gas detection system based on tunable diode laser spectroscopy (TDLAS) and MAPbI3/PEDOT:PSS PD, and O2 was selected as the target analyte. The system has achieved a minimum detection limit (MDL) of 0.12% and a normalized noise equivalent absorption coefficient (NNEA) of 8.83 × 10-11 cm-1⋅W⋅Hz-1/2. Furthermore, the Allan deviation analysis results indicate that the system can obtain sensitivity levels as low as 0.058% over an averaging time of 328 seconds. This marks the first use of MAPbI3/PEDOT:PSS PD in gas detection based on TDLAS. Despite the detector's performance leaves much to be desired, this innovation offers a new approach to developing spectral based gas detection system.

A prognostic model for triple-negative breast cancer patients with liver metastasis: A population-based study.

However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course. Introduction/background: Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients. Materials and methods: Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed. Results: A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS). Conclusion: A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.

Theabrownins in dark tea form complexes with tea polysaccharide conjugates.

BACKGROUND: Theabrownins (TBs) are one of most important quality components in dark tea, but have not been produced industrially. In this study, the aqueous extract was obtained from Pu-erh ripe tea, one kind of dark tea. Caffeine, theaflavin, catechin and saponin were removed by trichloromethane, ethyl acetate and n-butanol in turn to obtain a TB isolate. The TB isolate was subjected to column chromatography using a macroporous resin HPD-750 and eluted with a gradient of 0-700 g kg-1 ethanol aqueous solution. Four fractions were obtained, and named as TBs-FC1, TBs-FC2, TBs-FC3 and TBs-FC4. RESULTS: These four fractions contained polysaccharides and no small molecules such as catechins, caffeine and theaflavins as well as average molecular weights of 123.000 kDa, 23.380 kDa, 89.870 kDa and 106.600 kDa. It was revealed that they were complexes of TBs and tea polysaccharide conjugates (TPCs). Ultraviolet-visible (UV-visible) and infrared (IR) spectra showed the properties of TBs and TPCs. Their zeta potentials ranged from -13.40 mV to -38.80 mV in aqueous solutions at pH 3.0-9.0. CONCLUSION: This study reveals that TBs do not exist in free state but in combined state in dark tea, which provide the theoretical basis for the industrialization of TBs. © 2024 Society of Chemical Industry.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

A highly efficient Mn4+ activated Nb-based oxyfluoride red fluorescent material with excellent water stability: preparation and performance analysis.

In this study, an efficient non-rare earth Mn4+-doped K3(NbOF5)(HF2) red fluorescent material was synthesized by using the coprecipitation method. Replacing KF with K2CO3 effectively solved the problem that KF was difficult to stir due to its strong water absorption. The sample was composed of rods. The excitation spectra consisted of two strong excitation peaks at 366 nm and 468 nm. The emission spectra consisted of a series of narrow-band emissions between 580 nm and 680 nm. Besides, the luminescence quantum efficiency (QE) reached 84.3% under the excitation of 468 nm. The fluorescent lifetime of K3(NbOF5)(HF2):Mn4+ was less than 4 ms, which can achieve fast response display in backlight display applications. The WLED was fabricated with K3(NbOF5)(HF2):Mn4+ and commercial YAG:Ce3+ and the commercial InGaN blue chip. At a 30 mA drive current, the WLED device exhibited excellent luminescence properties. The correlated color temperature (CCT) was 3853 K, the Ra was 90.1 and the luminous efficiency was 310.432 lm W-1. Therefore, K3(NbOF5)(HF2):Mn4+ has very broad prospects in WLED lighting and backlight display applications.

[Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal].

An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8-13 Hz), beta frequency band (14-30 Hz) and gamma frequency band (31-60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.

3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.

Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.

Phosphorescent-Dye-Sensitized Quantum-Dot Light-Emitting Diodes with 37% External Quantum Efficiency.

Exciton harvesting is of paramount importance for quantum-dot light-emitting diodes (QLEDs). Direct exciton harvesting by the quantum dots (QDs) emitting layer suffers from poor hole injection due to the low conduction bands and valence bands of QDs, leading to unbalanced electron-hole injection and recombination. To address this issue, here, an exciton sensitizing approach is reported, where excitons form on a phosphorescent-dye-doped layer, which then transfer their energies to adjacent QDs layer for photon emission. Due to the very efficient exciton formation and energy-transfer processes, higher device performance can be achieved. To demonstrate the above strategy, red QLEDs with a phosphorescent dye, iridium (III) bis(2-methyldibenzo-[f,h]quinoxaline) (acetylacetonate), Ir(MDQ)2 (acac), doped hole-transporting layer are fabricated and studied. At a doping concentration of 10 wt%, the best device achieves record high current efficiency, power efficiency, and external quantum efficiency (EQE) of 37.3 cd A-1 , 41 lm W-1 , and 37%, respectively. Simultaneously, the efficiency roll-off characteristic is greatly improved, in that 35% EQE can be well retained at a high luminance level of 450 000 cd m-2 . Moreover, the devices also exhibit good stability and reproducibility.

EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.

Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.

A novel m6A/m5C/m1A score signature to evaluate prognosis and its immunotherapy value in colon cancer patients.

BACKGROUND: Colon cancer features strong heterogeneity and invasiveness, with high incidence and mortality rates. Recently, RNA modifications involving m6A, m5C, and m1A play a vital part in tumorigenesis and immune cell infiltration. However, integrated analysis among various RNA modifications in colon cancer has not been performed. METHODS: RNA-seq profiling, clinical data and mutation data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We first explored the mutation status and expression levels of m6A/m5C/m1A regulators in colon cancer. Then, different m6A/m5C/m1A clusters and gene clusters were identified by consensus clustering analysis. We further constructed and validated a scoring system, which could be utilized to accurately assess the risk of individuals and guide personalized immunotherapy. Finally, m6A/m5C/m1A regulators were validated by immunohistochemical staining and RT-qPCR. RESULTS: In our study, three m6A/m5C/m1A clusters and gene clusters were identified. Most importantly, we constructed a m6A/m5C/m1A scoring system to assess the clinical risk of the individuals. Besides, the prognostic value of the score was validated with three independent cohorts. Moreover, the level of the immunophenoscore of the low m6A/m5C/m1A score group increased significantly with CTLA-4/PD-1 immunotherapy. Finally, we validated that the mRNA and protein expression of VIRMA and DNMT3B increased in colon cancer tissues. CONCLUSIONS: We constructed and validated a stable and powerful m6A/m5C/m1A score signature to assess the survival outcomes and immune infiltration characteristics of colon cancer patients, which further guides optimization of personalized treatment, making it valuable for clinical translation and implementation.

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.

Introduction: The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. Methods: For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. Results: The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. Conclusion: The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions.

BACKGROUND: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. METHODS: To solve the aforementioned problems, nonlinear feature Lempel-Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. RESULTS: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. CONCLUSIONS: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.

Gesture recognition of continuous wavelet transform and deep convolution attention network.

To solve the problem of missing data features using a deep convolutional neural network (DCNN), this paper proposes an improved gesture recognition method. The method first extracts the time-frequency spectrogram of surface electromyography (sEMG) using the continuous wavelet transform. Then, the Spatial Attention Module (SAM) is introduced to construct the DCNN-SAM model. The residual module is embedded to improve the feature representation of relevant regions, and reduces the problem of missing features. Finally, experiments with 10 different gestures are done for verification. The results validate that the recognition accuracy of the improved method is 96.1%. Compared with the DCNN, the accuracy is improved by about 6 percentage points.

Highly sensitive light-induced thermoelastic spectroscopy oxygen sensor with co-coupling photoelectric and thermoelastic effect of quartz tuning fork.

A light-induced thermoelastic spectroscopy (LITES) gas detection method based on CH3NH3PbI3 perovskite-coated quartz tuning fork (QTF) was proposed. By coating CH3NH3PbI3 thin film on the surface of ordinary QTF, a Schottky junction with silver electrodes was formed. The co-coupling of photoelectric effect and thermoelastic effect of CH3NH3PbI3-QTF results in a significant improvement in detection performance. The oxygen (O2) was select as the target analyte for measurement, and experimental results show that compared with the commercial standard QTF, the introduction of CH3NH3PbI3 perovskite Schottky junction increases the 2f signal amplitude and signal-to-noise ratio (SNR) by ∼106 times and ∼114 times, respectively. The minimum detection limit (MDL) of this LITES system is 260 ppm, and the corresponding normalized noise equivalent absorption coefficient (NNEA) is 9.21 × 10-13 cm-1·W·Hz-1/2. The Allan analysis of variance results indicate that when the average time is 564 s, the detection sensitivity can reach 83 ppm. This is the first time that QTF resonance detection has been combined with perovskite Schottky junctions for highly sensitive optical gas detection.

A double-channel multiscale depthwise separable convolutional neural network for abnormal gait recognition.

Abnormal gait recognition is important for detecting body part weakness and diagnosing diseases. The abnormal gait hides a considerable amount of information. In order to extract the fine, spatial feature information in the abnormal gait and reduce the computational cost arising from excessive network parameters, this paper proposes a double-channel multiscale depthwise separable convolutional neural network (DCMSDSCNN) for abnormal gait recognition. The method designs a multiscale depthwise feature extraction block (MDB), uses depthwise separable convolution (DSC) instead of standard convolution in the module and introduces the Bottleneck (BK) structure to optimize the MDB. The module achieves the extraction of effective features of abnormal gaits at different scales, and reduces the computational cost of the network. Experimental results show that the gait recognition accuracy is up to 99.60%, while the memory size of the model is reduced 4.21 times than before optimization.

Quartz tuning fork-based high sensitive photodetector by co-coupling photoelectric and the thermoelastic effect of perovskite.

This paper reports a new strategy for enhancing the photoresponse of a quartz tuning fork (QTF). A deposited light absorbing layer on the surface of QTF could improve the performance only to a certain extent. Herein, a novel strategy is proposed to construct a Schottky junction on the QTF. The Schottky junction presented here consists of a silver-perovskite, which has extremely high light absorption coefficient and dramatically high power conversion efficiency. The co-coupling of the perovskite's photoelectric effect and its related QTF thermoelastic effect leads to a dramatic improvement in the radiation detection performance. Experimental results indicate that the CH3NH3PbI3-QTF obtains two orders of magnitude enhancement in sensitivity and SNR, and the 1σ detection limit was calculated to be 1.9 µW. It was the first time that the QTF resonance detection and perovskite Schottky junction was combined for optical detection. The presented design could be used in photoacoustic spectroscopy and thermoelastic spectroscopy for trace gas sensing.

Surface electromyography-based analysis of the lower limb muscle network and muscle synergies at various gait speeds.

Gait movement is an important activity in daily human life. The coordination of gait movement is directly affected by the cooperation and functional connectivity between muscles. However, the mechanisms of muscle operation at different gait speeds remain unclear. Therefore, this study addressed the gait speed effect on the changes in cooperative modules and functional connectivity between muscles. To this end, surface electromyography (sEMG) signals were collected from eight key lower extremity muscles of twelve healthy subjects walking on a treadmill at high, middle, and low motion speeds. Nonnegative matrix factorization (NNMF) was applied to the sEMG envelope and intermuscular coherence matrix, yielding five muscle synergies. Muscle functional networks were constructed by decomposing the intermuscular coherence matrix, revealing different layers of functional muscle networks across frequencies. In addition, the coupling strength between cooperative muscles grew with gait speed. Different coordination patterns among muscles with changes in gait speed related to the neuromuscular system regulation were identified.

Cross-subject classification of depression by using multiparadigm EEG feature fusion.

BACKGROUND AND OBJECTIVE: The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification. METHODS: To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm. RESULTS: The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%. CONCLUSION: The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.