High Sensitivity and Wide Linear Range Flexible Piezoresistive Pressure Sensor with Microspheres as Spacers for Pronunciation Recognition.

Flexible piezoresistive pressure sensors have received great popularity in flexible electronics due to their simple structure and promising applications in health monitoring and artificial intelligence. However, the contradiction between sensitivity and detection range limits the application of the sensors in the medium-pressure regime. Here, a flexible piezoresistive pressure sensor is fabricated by combining a hierarchical spinous microstructure sensitive layer and a periodic microsphere array spacer. The sensor achieves high sensitivity (39.1 kPa-1) and outstanding linearity (0.99, R2 coefficient) in a medium-pressure regime, as well as a wide range of detection (100 Pa-160.0 kPa), high detection precision (<0.63‰ full scale), and excellent durability (>5000 cycles). The mechanism of the microsphere array spacer in improving sensitivity and detection range was revealed through finite element analysis. Furthermore, the sensors have been utilized to detect muscle and joint movements, spatial pressure distributions, and throat movements during pronouncing words. By means of a full-connect artificial neural network for machine learning, the sensor's output of different pronounced words can be precisely distinguished and classified with an overall accuracy of 96.0%. Overall, the high-performance flexible pressure sensor based on a microsphere array spacer has great potential in health monitoring, human-machine interface, and artificial intelligence of medium-pressure regime.

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition.

Background: Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition. Method: Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model. Results: Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%. Conclusion: Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.

EEGformer: A transformer-based brain activity classification method using EEG signal.

Background: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain. Method: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG). Results: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance. Conclusion: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.

Inherent regional brain activity changes in male obstructive sleep apnea with mild cognitive impairment: A resting-state magnetic resonance study.

Obstructive sleep apnea (OSA) is the most common sleep disorder worldwide. Previous studies have shown that OSA patients are often accompanied by cognitive function loss, and the underlying neurophysiological mechanism is still unclear. This study aimed to determine whether there are differences in regional homogeneity (Reho) and functional connectivity (FC) across the brain between OSA patients with MCI (OSA-MCI) and those without MCI (OSA-nMCI) and whether such differences can be used to distinguish the two groups. Resting state magnetic resonance data were collected from 48 OSA-MCI patients and 47 OSA-nMCI patients. The brain regions with significant differences in Reho and FC between the two groups were identified, and the Reho and FC features were combined with machine learning methods for classification. Compared with OSA-nMCI patients, OSA-MCI patients showed significantly lower Reho in bilateral lingual gyrus and left superior temporal gyrus. OSA-MCI patients also showed significantly lower FC between the bilateral lingual gyrus and bilateral cuneus, left superior temporal gyrus and left middle temporal gyrus, middle frontal gyrus, and bilateral posterior cingulate/calcarine/cerebellar anterior lobe. Based on Reho and FC features, logistic regression classification accuracy was 0.87; sensitivity, 0.70; specificity, 0.89; and area under the curve, 0.85. Correlation analysis showed that MoCA scale score in OSA patients was significant positive correlation sleep efficiency and negatively correlation with neck circumference. In conclusion, our results showed that the OSA-MCI group showed decreased Reho and FC in specific brain regions compared with the OSA-nMCI group, which may help to understand the underlying neuroimaging mechanism of OSA leading to cognitive dysfunction and may serve as a potential biomarker to distinguish whether OSA is accompanied by cognitive impairment.

Effects of 3-month CPAP therapy on brain structure in obstructive sleep apnea: A diffusion tensor imaging study.

White matter (WM) fiber alterations in patients with obstructive sleep apnea (OSA) is associated with cognitive impairment, which can be alleviated by continuous positive airway pressure (CPAP). In this study, we aimed to investigate the changes in WM in patients with OSA at baseline (pre-CPAP) and 3 months after CPAP adherence treatment (post-CPAP), and to provide a basis for understanding the reversible changes after WM alteration in this disease. Magnetic resonance imaging (MRI) was performed on 20 severely untreated patients with OSA and 20 good sleepers. Tract-based spatial statistics was used to evaluate the fractional anisotropy (FA), mean diffusion coefficient, axial diffusion coefficient, and radial diffusion coefficient (RD) of WM. To assess the efficacy of treatment, 20 patients with pre-CPAP OSA underwent MRI again 3 months later. A correlation analysis was conducted to evaluate the relationship between WM injury and clinical evaluation. Compared with good sleepers, patients with OSA had decreased FA and increased RD in the anterior thalamic radiation, forceps major, inferior fronto-occipital tract, inferior longitudinal tract, and superior longitudinal tract, and decreased FA in the uncinate fasciculus, corticospinal tract, and cingulate gyrus (P < 0.05). No significant change in WM in patients with post-CPAP OSA compared with those with pre-CPAP OSA. Abnormal changes in WM in untreated patients with OSA were associated with oxygen saturation, Montreal cognitive score, and the apnea hypoventilation index. WM fiber was extensively alteration in patients with severe OSA, which is associated with cognitive impairment. Meanwhile, cognitive recovery was not accompanied by reversible changes in WM microstructure after short-term CPAP therapy.

Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis.

In this study, we aimed to use voxel-level degree centrality (DC) features in combination with machine learning methods to distinguish obstructive sleep apnea (OSA) patients with and without mild cognitive impairment (MCI). Ninety-nine OSA patients were recruited for rs-MRI scanning, including 51 MCI patients and 48 participants with no mild cognitive impairment. Based on the Automated Anatomical Labeling (AAL) brain atlas, the DC features of all participants were calculated and extracted. Ten DC features were screened out by deleting variables with high pin-correlation and minimum absolute contraction and performing selective operator lasso regression. Finally, three machine learning methods were used to establish classification models. The support vector machine method had the best classification efficiency (AUC = 0.78), followed by random forest (AUC = 0.71) and logistic regression (AUC = 0.77). These findings demonstrate an effective machine learning approach for differentiating OSA patients with and without MCI and provide potential neuroimaging evidence for cognitive impairment caused by OSA.

Abnormal Functional Connectivity of Hippocampal Subdivisions in Obstructive Sleep Apnea: A Resting-State Functional Magnetic Resonance Imaging Study.

The hippocampus is involved in various cognitive function, including memory. Hippocampal structural and functional abnormalities have been observed in patients with obstructive sleep apnoea (OSA), but the functional connectivity (FC) patterns among hippocampal subdivisions in OSA patients remain unclear. The purpose of this study was to investigate the changes in FC between hippocampal subdivisions and their relationship with neurocognitive function in male patients with OSA. Resting-state fMRI were obtained from 46 male patients with untreated severe OSA and 46 male good sleepers. The hippocampus was divided into anterior, middle, and posterior parts, and the differences in FC between hippocampal subdivisions and other brain regions were determined. Correlation analysis was used to explore the relationships between abnormal FC of hippocampal subdivisions and clinical characteristics in patients with OSA. Our results revealed increased FC in the OSA group between the left anterior hippocampus and left middle temporal gyrus; between the left middle hippocampus and the left inferior frontal gyrus, right anterior central gyrus, and left anterior central gyrus; between the left posterior hippocampus and right middle frontal gyrus; between the right middle hippocampus and left inferior frontal gyrus; and between the right posterior hippocampus and left middle frontal gyrus. These FC abnormalities predominantly manifested in the sensorimotor network, fronto-parietal network, and semantic/default mode network, which are closely related to the neurocognitive impairment observed in OSA patients. This study advances our understanding of the potential pathophysiological mechanism of neurocognitive dysfunction in OSA.

Functional Connectivity Density with Frequency-Dependent Changes in Patients with Diffuse Axonal Injury: A Resting-State Functional Magnetic Resonance Imaging Study.

PURPOSE: We explored changes in spontaneous brain connectivity in patients with diffuse axonal injury (DAI), assessed via functional connectivity density (FCD) tests using different frequency bands. PATIENTS AND METHODS: In all, 23 patients with DAI (17 males and 6 females) and 23 healthy controls (HCs; 17 males and 6 females) were included. Functional magnetic resonance imaging scans were performed when the participants were in a resting state and the FCD levels in three frequency bands (slow-4: 0.027-0.073 Hz, slow-5: 0.01-0.027 Hz, and typical: 0.01-0.08 Hz) were measured. In addition, Pearson's correlation coefficient was used to explore the relationship between clinical indices and brain regions with abnormal FCD values. RESULTS: Compared to HCs, DAI patients had significantly greater FCD values in the right extranuclear/limbic lobe/cingulate gyrus and left limbic lobe/hippocampus/parahippocampal gyrus, and significantly lower FCD values in the left precuneus/posterior cingulate gyrus, in the slow-4 band. In the slow-5 band, the DAI patients had higher FCD values in the left inferior temporal gyrus/superior temporal gyrus, left parahippocampal gyrus/limbic lobe, left extranuclear/cingulate gyrus, and right medial frontal gyrus, and lower values in the right inferior frontal gyrus, right inferior parietal lobule, and left cingulate gyrus/limbic lobe. Moreover, compared to HCs, the values in the typical band were higher in the right extranuclear/limbic lobe/hippocampus/parahippocampal gyrus, but were significantly lower in the right precuneus/posterior cingulate gyrus and right inferior parietal lobule/supramarginal gyrus. The abnormal FCD values of these brain regions were linearly correlated with different clinical scale scores. CONCLUSION: DAI patients had abnormal FCD values in various brain regions, indicating disruption to the brain functional network. Moreover, the values were frequency dependent. Our results provide new evidence for the pathogenesis of functional impairment and may explain the neuropathological or compensatory mechanism of the disease.

Performance of bitumen coating sheet using biomass pyrolysis oil.

The "green" production of bitumen has raised increasing interest in recent years to reduce the environmental, energy-related and petro-based concerns. Bio-oil, prepared by biomass pyrolysis, can be used as a substitute for petro-based bitumen in bitumen or bitumen-based coatings, for its similar properties of good adhesion and anti-corrosion characteristics as bitumen. Although biomass is a renewable and widespread chemical resource, its high-valued utilization is still difficult. Several studies have qualitatively demonstrated the use of bio-bitumen in practical applications. The present study investigates that adding some bio-oil to traditional bitumen to form a bio-bitumen could help improve the properties of traditional bitumen. Bio-bitumen was prepared from biomass pyrolysis oil and applied to self-adhesive and doped hot-melt sheets. Results of physical properties demonstrate that bio-bitumen is a potential substitute in bitumen coating sheet.Implications: This paper is intended to verify the effect of pyrolyzed bio-oil from wheat straw on the performance of bitumen, as well as the feasibility of application in the coating sheet. Up to now, the research on bio-bitumen is mainly in pavement bitumen. In the present research, bio-bitumen was applied to the coating sheet in different proportions. Interestingly, the prepared coating sheet exhibited higher adhesion. Other performances, such as temperature stability, mechanical strength and temperature flexibility of coating sheet showed improvement in the presence of bio-oil, which indicated the suitability of bio-oil in coating sheet bitumen.

ICAR ATRP in PEG with Low Concentration of Cu(II) Catalyst: A Versatile Method for Synthesis of Block Copolymer Nanoassemblies under Dispersion Polymerization.

A versatile method for synthesis of block copolymer nanoassemblies via initiators for continuous activator regeneration (ICAR) atom transfer radical polymerization (ATRP) dispersion polymerization in a low molecular weight poly(ethylene glycol) (PEG) is discussed. This ICAR ATRP dispersion polymerization uses a low concentration of CuBr2 catalyst, which is stable under atmospheric conditions and is soluble in most polar solvents and employs a polymerization medium of viscous and nonvolatile PEG. Through this ICAR ATRP dispersion polymerization, various block copolymer nanoassemblies, including poly(ethylene glycol)-block-polystyrene, poly(ethylene glycol)-block-poly(methyl methacrylate), and poly(2-hydroxypropyl methacrylate)-block-poly(methyl methacrylate), have been synthesized. The parameters affecting the size and morphology of the block copolymer nanoassemblies are briefly discussed.

Non-covalently Functionalized Graphene Oxide-Based Coating to Enhance Thermal Stability and Flame Retardancy of PVA Film.

The synergistic effect of conventional flame-retardant elements and graphene has received extensive attention in the development of a new class of flame retardants. Compared to covalent modification, the non-covalent strategy is simpler and expeditious and entirely preserves the original quality of graphene. Thus, non-covalently functionalized graphene oxide (FGO) with a phosphorus-nitrogen compound was successfully prepared via a one-pot process in this study. Polyethyleneimine and FGO were alternatively deposited on the surface of a poly(vinyl alcohol) (PVA) film via layer-by-layer assembly driven by electrostatic interaction, imparting excellent flame retardancy to the coated PVA film. The multilayer FGO-based coating formed a protective shield encapsulating the PVA matrix, effectively blocking the transfer of heat and mass during combustion. The coated PVA has a higher initial decomposition temperature of about 260 °C and a nearly 60% reduction in total heat release than neat PVA does. Our results may have a promising prospect for flame-retardant polymers.

A novel water-soluble fluorescent polymer based on perylene bisimides dyes: one-pot preparation and its bio-imaging.

Perylene bisimides dye-based water-soluble fluorescent polymer P3, N,N'-bis(3-amyl)-1-bromo-7-{4'-[3''-(S-poly(N-acryloyl ethylene diamine hydrochloride)-2'''-methyl propionic acid)propionyloxy hexyloxy]phenyl} perylene-3,4:9,10-tetracarboxylic bisimides, was synthesized with polyelectrolyte modification via one-pot reaction (the reduction reaction of trithioester and click reaction between the thiol group and carbon-carbon double bond were simultaneously conducted in one pot with high conversion). One-pot method can overcome the limitation that usual click reaction between thiol and other groups has low conversion because thiol group is subject to rapid oxidation during purification and storage. Chemical, structural, and optical properties of P3 and intermediate products were fully characterized by nuclear magnetic resonance spectroscopy, Fourier transform infrared, gel permeation chromatograph, UV-vis spectra, and fluorescence spectra, respectively. The results revealed that P3 displayed excellent water solubility and not only exhibited red strong fluorescence emission band in water but also had the similar photoluminescent spectra to those of intermediate products (M4 and P2) in chloroform. Allowing for the potential application in biological detection field, cell viability and live cell imaging with the presence of P3 were further investigated with Hela cells. The results showed that P3 had low cytotoxicity with strong intracellular fluorescence entry. Meanwhile, with the augment of concentration of P3 (0-0.500 mg mL(-1)), the cell uptake and accumulation of P3 increased and thereby result in enhancement of the intracellular fluorescence. These experiment results suggested that P3 had enormous potential as a fluorescence probe to be an important component in biological detection field.

A membrane permeable fluorescent Ca2+ probe based on bis-BODIPY with branched PEG.

A new class of Ca2+ probes is developed based on PEG-BODIPY-BAPTA conjugates. These newly formed probes with BAPTA acid exhibit high sensitivity and selectivity for Ca2+ over other metal ions, can pass through the cell membranes of living cells without special procedures, and can monitor changes in the intracellular Ca2+ signal.

Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality.

A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of 'acceptable' versus 'unacceptable' quality recordings using the 'leave M out' approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of 'acceptable' and 'unacceptable' recordings, (ii) unbalanced, in which the ratio of 'acceptable' to 'unacceptable' recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.