Structural Engineering of Co-Metal-Organic Frameworks via Ce Incorporation for Improved Oxygen Evolution.

The rational design of metal-organic framework (MOF)-based electrocatalysts plays a key role in achieving high-efficiency oxygen evolution reaction (OER). Herein, a synergetic morphology and electronic structure engineering strategy are proposed to design a Co-MOF nanoflower grown on carbon paper via rare-earth cerium doping (CoCe-MOF/CP). Compared with Co-MOF/CP, the developed CoCe-MOF/CP exhibited superior OER performance with a low overpotential of 267 mV at 10 mA cm-2 and outstanding long-term stability over 100 h. Theoretical calculations show that the unique 4f valence electron structure of Ce induced charge redistribution of the Co-MOF surface through the strong Co 3d-O 2p-Ce 4f orbital electronic coupling below the Fermi level. Ce-doped plays a key role in the engineering of the electronic states of the Co sites to endow them with the optimal free energy landscape for enhanced OER catalytic activity. This work provides new insights into comprehending the RE-enhanced mechanism of electrocatalysis and provides an effective strategy for the design of MOF-based electrocatalysts.

Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet.

Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network.

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology.

The clinical therapy of schizophrenia (SCZ) replies on the corresponding accurate and reliable recognition. Although efforts have been paid, the diagnosis of SCZ is still roughly subjective, it is thus urgent to search for related objective physiological parameters. Motivated by the great potential of resting-state networks in underling the brain deficits among different SCZ groups, in this study, we then developed a multi-class feature extraction approach that could effectively extract the spatial network topology and facilitate the recognition of the SCZ, by combining a network structure based supervised learning with an ensemble co-decision strategy. The results demonstrated that the multi-class spatial pattern of the network (MSPN) features outperformed the other conventional electrophysiological features, such as relative power spectrums and network properties, and achieved the highest classification accuracy of 71.58% in the alpha band. These findings did validate that the resting-state MSPN is a promising tool for the clinical assessment of the SCZ.

Combining Dual-Light Emissions to Achieve Efficient Broadband Yellowish-Green Luminescence in One-Dimensional Hybrid Lead Halides.

In recent years, low-dimensional lead halides have emerged as some of most attractive photoelectric materials due to their intrinsic broadband emissions with a potential application in white-light emitting diodes. To achieve the desired performance, tremendous research has emphasized the modulation of inorganic components as optical centers; however, less work has paid attention to the direct contribution of the organic components. Herein, we successfully assembled two new hybrid lead halides of [H2BPP]Pb2X6 (X = Br, 1, and Cl, 2) containing one-dimensional double [Pb2X6]2- chains using optically active 1,3-bis(4-pyridyl)-propane (BPP) as an organic cation. Under UV-light excitation, compounds 1 and 2 exhibit broadband yellowish-green emissions, which were verified by promising photoluminescence quantum efficiencies (PLQEs) of 8.10% and 4.84%, respectively. The broadband light emissions are derived from the combination of dual higher-energy blue and lower-energy yellow light spectra, which can be attributed to the individual contributions of the organic and inorganic components, respectively, according to the time-resolved and temperature-dependent emission spectra as well as theoretical calculations. This work proves the great contribution of organic components to the photophysical properties and provides a new design strategy to realize broadband light emission by rationally combining the dual-emitting properties of different assembly blocks.

Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting-State EEG Network.

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics; however, the current diagnosis of TS patients is subjective, as it is mainly assessed based on the parents' description alongside specific evaluations. The early and accurate diagnosis of TS based on its potential symptoms in children would be of benefit in their future therapy, but reliable diagnoses are difficult due to the lack of objective knowledge of the etiology and pathogenesis of TS. In this study, resting-state electroencephalograms were first collected from 36 patients and 21 healthy controls (HCs); the corresponding resting-state functional networks were then constructed, and the potential differences in network topology between the two groups were extracted by using the topology of the spatial pattern of the network (SPN). Compared to the HCs, the TS patients exhibited decreased frontotemporal/occipital/parietal connectivity. When classifying the two groups, compared to the network properties, the derived SPN features achieved a much higher accuracy of 92.31%. The intrinsic long-range connectivity between the frontal and the temporal/occipital/parietal lobes was damaged in the patient group, and this dysfunctional network pattern might serve as a reliable biomarker to differentiate TS patients from HCs as well as to assess the severity of tic symptoms.

miR-5188 augments glioma growth, migration and invasion through an SP1-modulated FOXO1-PI3K/AKT-c-JUN-positive feedback circuit.

The biological effect and molecular mechanism of miR-5188 have not been thoroughly investigated. The study aims at elucidating the role of miR-5188 in glioma progression. Human glioma cell lines and tissues were used for functional and expression analysis. Cellular and molecular techniques were performed to explore the functions and mechanisms of miR-5188 in glioma. In our investigation, we demonstrated that miR-5188 promoted cell proliferation, the G1/S transition of the cell cycle, migration and invasion in glioma and reduced the lifespan of glioma-bearing mice. miR-5188 directly targeted FOXO1 and activated PI3K/AKT-c-JUN signalling, which enhanced miR-5188 expression. Moreover, the c-JUN transcription factor functionally bound to the miR-5188 promoter region, forming the positive feedback loop. The feedback loop promoted glioma progression through activating the PI3K/AKT signalling, and this loop is augmented by the interaction between SP1 and c-JUN. Moreover, it was also found that the miR-5188/FOXO1 axis is facilitated by SP1-activated PI3K/AKT/c-JUN signalling. In glioma samples, miR-5188 expression was found to be an unfavourable factor and was positively associated with the mRNA levels of SP1 and c-JUN, whereas negatively associated with the mRNA levels of FOXO1. Our investigation demonstrates that miR-5188 could function as a tumour promoter by directly targeting FOXO1 and participating in SP1-mediated promotion of cell growth and tumorigenesis in glioma.

Variation of Electrocardiogram Features Across Sleep Stages in Healthy Controls and in Patients with Sleep Apnea Hypopnea Syndrome.

Sleep apnea hypopnea syndrome (SAHS) is an independent risk factor for various cardiovascular diseases. Electrocardiogram (ECG) features such as the RR, PR, QT, QTc, Tpe intervals and the Tpe/QT, Tpe/QTc ratios are used to predict and study cardiovascular diseases. It is not clear whether regular patterns of PR and Tpe-related features across sleep stages exist in SAHSs or healthy controls nor whether sleep stages affect the short- and long-range influences of respiratory events on ECG indices. We enrolled 36 healthy controls and 35 patients with SAHS in our study and analyzed the abovementioned ECG features. In the healthy controls, a significant regularity existed in these indices across sleep stages, which were weakened or disturbed in the patient group, especially the Tpe-related features. The differences between the patients and healthy controls were generally consistent across all sleep stages: patients had smaller RR, PR, QT and Tpe/QTc values, but larger QTc, Tpe and Tpe/QT values. After filtering the short-range influence of respiratory events, the differences in most features remained highly significant, except the QT interval. In the patient group, respiratory events decreased RR and PR intervals in most sleep stages and increased the Tpe-related features' values in deep sleep stages. These results may aid in the study of the relationships among SAHS, sleep disorders and cardiovascular diseases.

Polyanionic Polydentate Europium Complexes as Ultrabright One- or Two-photon Bioprobes.

A family of europium (III) complexes based on a polydentate ligand functionalized by charge-transfer antennae presents remarkable one- and two-photon photophysical proper-ties in water or buffer. A detailed analysis of their emission properties suggests that the wrapping of the ligand around the central rare-earth ion results in an overall Cs symmetry in agreement with the theoretical simulation and that about 65-70 % of the emission intensity is concentrated in the hypersensitive 5 D0 →7 F2 transition at 615 nm. Their brightness is excellent, in the range of the best lanthanide bioprobes making them very attractive for bio-imaging experiments.

Exploring Hybrid Imogolite Nanotube Formation via Si/Al Stoichiometry Control.

Hybrid imogolite aluminosilicate nanotubes with methylated internal surface can be obtained by introduction of the corresponding organosilane during their synthesis. However, similarly to pristine imogolite, a number of side products, including proto-imogolite (open-imoLS), allophanes, and aluminum hydroxides, are formed, which ultimately impact on the properties of the dispersions. In order to minimize the proportion of these side products, we have here systematically explored the impact of the initial Si/Al ratio on the content of hybrid imogolite dispersions before and after dialysis. By combining cryo-transmission electron microscopy, inductively coupled plasma mass spectrometry, infrared spectroscopy, and small-angle X-ray scattering, we evidenced that the Si/Al ratio has a large impact on the formation of aluminum hydroxides that can be minimized with a slight excess of Si precursor. However, a large excess of Si is detrimental to the reaction yield leading to an important proportion of proto-imogolite. We propose that the optimal Si/Al ratio of ca. 0.6 can both minimize the proportion of aluminum hydroxides and proto-imogolite. These results suggest that the dynamic and therefore reactive character of imogolite dispersions may have been so far underlooked.

Cationic Two-Photon Lanthanide Bioprobes Able to Accumulate in Live Cells.

An original cationic water-soluble cyclen-based Eu(III) complex [EuL(1)](+) featuring a chromophore-functionalized antenna to increase the two-photon (2P) absorption properties was synthesized. The photophysical properties were thoroughly studied in various solvents and rationalized with the help of theoretical calculations. The complex exhibits an optimized 2P absorption cross section. Finally, 2P microscopy imaging experiments on living T24 human cancer cells highlighted the spontaneous internalization and the biological stability of this 2P bioprobe in vitro. Macrocyclic-based antennas open new perspectives for future optimization of the photophysical properties and allows envisaging the design of Eu, Tb, Yb, and Sm bioprobes. This result also opens the way for the design of functional two-photon Ln complexes able to monitor intracellular physicochemical parameters.

Spectroscopy of BODIPY in solid phase: crystal and nanoparticles.

We compare the absorption and fluorescence spectra of single crystals and suspensions of nanoparticles with the prediction of the Frenkel theory. The single crystals of a novel synthesized fluorescent BODIPY derivative dye, adamantyl mesityl BODIPY (4,4-difluoro-3,5-di-(adamantyl)-8-mesityl-4-bora-3a,4a-diaza-s-indacene), have been prepared. Their birefringence and dichroism have been studied. The X-ray crystallography shows a monoclinic crystal with all transition moments parallel to one common plane. The refractive indices along the two neutral axes have been measured for a wavelength from 530 to 700 nm, with a difference, Δn equal to 0.11. The Frenkel exciton theory was used here to describe the coupling of the electronic excited states in the crystals. The coupling estimated by the dipolar approximation was compared with the excited state splitting calculated by TDDFT in dimers. A perfect crystal absorption spectrum is predicted. The spectral broadening that occurs at room temperature is also taken into account. The absorption spectrum of the monocrystal is reproduced without adjustable parameters. But we had to take into account the presence of optical leaks in our microspectrophotometer before comparing experiment and theory. The controlled size nanoparticles (NPs) produced by our 3D hydrodynamic focusing microfluidic system exhibit molecule like absorption. We could reproduce their absorption and fluorescence spectra assuming a strong disorder in the Frenkel model. We conclude that the nanoparticles are amorphous.

Research on the Effect of Evidence-Based Intervention on Improving Students' Mental Health Literacy Led by Ordinary Teachers: A Meta-Analysis.

BACKGROUND: the purpose of this study was to systematically review the effects of intervention experiments led by ordinary teachers to improve students' mental health literacy and to provide evidence-based research and new ideas for improving students' mental health literacy. METHODS: A systematic search using 5 English (Web of Science, PubMed, ProQuest, EBSCO, Springer Link) and 3 Chinese (CNKI, WanFang, and VIP) databases was initiated to identify controlled trials assessing the immediate effect and delay effect of the intervention experiment led by ordinary teachers on improving students' mental health knowledge, anti-stigma, willingness, or behavior to seek-help. RESULTS: a total of 14 experiments with 7873 subjects were included. The results showed that the immediate effect of the intervention on promoting students' mental health knowledge [g = 0.622, 95% CI (0.395, 0.849)] and anti-stigma [g = 0.262, 95% CI (0.170, 0.354)] was significant, but the amount of delay effect is not significant. CONCLUSIONS: the results of this review show that ordinary classroom teachers can effectively participate in projects to improve students' mental health literacy, significantly improve students' mental health knowledge and attitudes towards psychological problems, and make up for the shortage of full-time mental health teachers in schools. In future, more attention should be paid to students' mental health literacy, and evidence-based intervention research should be strengthened. Furthermore, we can improve students' mental health literacy and avoid poor mental health by addressing delays in early intervention, as well as improve experimental design, prolong the intervention time, and improve the effectiveness of the intervention.

Synthesis of Hierarchical Layered Quasi-Triangular Ce(OH)CO3 and Its Thermal Conversion to Ceria with High Polishing Performance.

Layered quasi-triangular Ce(OH)CO3 assembled from primary nanoparticles was synthesized via a solvothermal method and converted into CeO2 abrasive particles by calcination at 800-1000 °C. With the increase of calcination temperature, the primary particle size increased and the microstructure, mechanical hardness, and chemical activity of the CeO2 particles changed, thus affecting the polishing performance. The calcined products obtained at 800, 850, and 900 °C maintained the layered edge structure of the Ce(OH)CO3 precursor and had a relatively high specific surface area and surface Ce3+ concentration. The samples calcined at 950 and 1000 °C lost the layered structure due to the large-scale melting of the primary particles, and their surface chemical activity decreased. The polishing experiments on K9 glass showed that, with the calcination temperature rising from 800 to 1000 °C, the material removal rate (MRR) first increased and then decreased sharply. The initial increase of MRR was attributed to the increase of mechanical hardness of the layered quasi-triangular CeO2, and the subsequent decrease of MRR was related to the decrease in surface chemical activity and disappearance of the layered edge structure. The product calcined at 900 °C had the highest MRR and best surface quality after polishing due to the layered edge structure and optimal match of chemical activity and mechanical hardness.

High-Throughput Fabrication of Size-Controlled Pickering Emulsions, Colloidosomes, and Air-Coated Particles via Clog-Free Jetting of Suspensions.

Microcapsules with a liquid core and a solid shell composed of hydrophobic nanoparticles are broadly applied in food, pharmaceutics, and biotechnologies. For example, Pickering emulsions, colloidosomes, or antibubbles (droplets surrounded by air layers in water) enable controlled release of active agents, biocompatibility, and contact-less liquid transportation. However, producing controlled nanoparticle- or polymer-laden hydrophobic shells at scale is highly challenging, since bulk methods are polydisperse and microfluidic chips are prone to clogging and slow. Here, clog-free coating of an aqueous jet with silica nanoparticle suspensions with concentrations up to 10% (w/v), as well as high concentrations of polymers (30% (w/v) poly(lactic acid) (PLA)), is demonstrated, enabling continuous generation of microcapsules at flow rates up to 4 mL min-1 . Pickering emulsions are converted into capsules, providing hydrophobic shells consisting of nanoparticles for controlled release. As a highlight, the scalable fabrication of air-coated capsules (antibubbles) in the sub-millimeter range is demonstrated. The shell contains an air film that protects the liquid core for days yet enables ultrasound-induced release within 3 min. By enabling rapid fabrication of controlled Pickering emulsions, colloidosomes, antibubbles, and biodegradable capsules, jetting through a liquid layer (JetALL) provides a versatile platform for advanced applications in food, pharmacy, and life science.

Effects of chronic heat stress on the immunophenotyping of lymphocytes in immune organs of growing pigs.

This study aimed to investigate the effects of chronic heat stress on the immunophenotyping of lymphocytes in immune organs of growing pigs. A single-factor randomized block design was used, and 15 healthy growing large white barrows (5 litters, 3 pigs/litter) with similar body weight (40.8 kg) were assigned into 3 groups (5 pigs in each group). Groups were: control group (Con, in 23 °C environmental control chamber, fed ad libitum), heat stress group (HS, in 33 °C environmental control chamber, fed ad libitum), and pair-fed group (PF, in 23 °C environmental control chamber, fed diets according to the feed intake of HS group). After a 7-d adaption, the experiment lasted for 21 d. The results showed as follows: (1) activated T cells in the thymus of HS pigs were higher than those in PF pigs (P < 0.05). Monocytes and dendritic cells in the thymus of HS pigs were significantly higher than that in Con and PF pigs (P < 0.05), while the proportions of these 2 lymphocytes in the thymus of Con pigs did not differ from PF pigs (P > 0.05). Compared with Con pigs, the proportion of CD4+ (P < 0.05) and CD8+ T cells (P < 0.10) in the thymus was increased in HS pigs, while the proportion of CD4+ and CD8+ T cells in PF pigs did not differ from Con pigs (P > 0.05). (2) Compared with Con pigs, significantly decreased T cells, increased B cells and monocytes were found in the spleen of pigs exposed to heat stress (P < 0.05); the proportions of these 3 types of lymphocytes were not significantly different between Con and PF pigs (P > 0.05). The proportions of CD4+ T cells and Treg cells in the spleen of pigs exposed to heat stress tended to be lower than those in the Con pigs (P < 0.10). (3) The proportion of lymphocytes in the tonsils of pigs exposed to heat stress did not differ from Con pigs (P > 0.05); compared with PF pigs, the proportion of Treg cells was significantly decreased in HS pigs (P < 0.05). In conclusion, chronic heat stress stimulates the development and maturation of T cells in the pig thymus toward CD4+ and CD8+ T cells and increases the proportion of monocytes and dendritic cells; under the condition of chronic heat stress, the immune response process in the spleen of pigs is enhanced, but chronic heat stress impairs the survival of CD4+ T cells in the spleen.

Chronic heat stress (HS) has become a common hazard to livestock and poultry as global warming intensifies and breeding densities increase, which undoubtedly causes enormous economic losses to animal husbandry annually. Furthermore, it could also negatively impact the immune function of poultry and vaccines, resulting in various animal diseases. Until now, very few studies have focused on how HS affects the immune system of growing pigs, especially the immunophenotyping of lymphocytes in their immune organs (thymus, spleen and tonsils). In this study, the spleen and thymus are more severely affected by chronic HS than tonsils in growing pigs. Chronic HS stimulates the development and maturation of CD4+ and CD8+ T lymphocytes in the thymus. Under chronic HS, the immune response process in the spleen is enhanced, that is, the proportion of monocytes and B lymphocytes supporting immune responses increased, while the proportion of Treg cells decreased; yet long-term HS damaged the survival of CD4+ T lymphocytes in spleen.

The complete mitochondrial genome of a marine triclad Miroplana shenzhensis (Platyhelminthes, Tricladida, Maricola).

The complete mitochondrial genome (mitogenome) of Miroplana shenzhensis Yu & Wang, 2013 is reported in the present study, representing the second mitogenome recorded in the suborder Maricola. The circular mitogenome is 14,344 bp in length, containing 12 protein-coding genes, 2 ribosomal RNAs and 22 transfer RNAs. Comparative analysis on mitochondrial gene order reveals a rearrangement in the suborder Maricola, indicating that mitochondrial gene order is conserved only in Continenticola, and is divergent across Tricladida. Phylogenetic analysis shows M. shenzhensis is clustered with an another marine triclad, forming a well-supported monophyletic group of Maricloan.

Altered temporal variability in brain functional connectivity identified by fuzzy entropy underlines schizophrenia deficits.

Investigation of the temporal variability of resting-state brain networks informs our understanding of how neural connectivity aggregates and disassociates over time, further shedding light on the aberrant neural interactions that underlie symptomatology and psychosis development. In the current work, an electroencephalogram-based sliding window analysis was utilized for the first time to measure the nonlinear complexity of dynamic resting-state brain networks of schizophrenia (SZ) patients by applying fuzzy entropy. The results of this study demonstrated the attenuated temporal variability among multiple electrodes that were distributed in the frontal and right parietal lobes for SZ patients when compared with healthy controls (HCs). Meanwhile, a concomitant strengthening of the posterior and peripheral flexible connections that may be attributed to the excessive alertness or sensitivity of SZ patients to the external environment was also revealed. These temporal fluctuation distortions combined reflect an abnormality in the coordination of functional network switching in SZ, which is further the source of worse task performance (i.e., P300 amplitude) and the negative relationship between individual complexity metrics and P300 amplitude. Notably, when using the network metrics as features, multiple linear regressions of P300 amplitudes were also exactly achieved for both the SZ and HC groups. These findings shed light on the pathophysiological mechanisms of SZ from a temporal variability perspective and provide potential biomarkers for quantifying SZ's progressive neurophysiological deterioration.

A millisecond passive micromixer with low flow rate, low sample consumption and easy fabrication.

Fast mixing of small volumes of solutions in microfluidic devices is essential for an accurate control and observation of the dynamics of a reaction in biological or chemical studies. It is often, however, a challenging task, as the Reynolds number (Re) in microscopic devices is typically < 100. In this report, we detail a novel mixer based on the "staggered herring bone" (SHB) pattern and "split-recombination" strategies with an optimized geometry, the periodic rotation of the flow structure can be controlled and recombined in a way that the vortices and phase shifts of the flow induce intertwined lamellar structures, thus increasing the contact surface and enhancing mixing. The optimization improves the mixing while using a low flow rate, hence a small volume for mixing and moderate pressure drops. The performances of the patterns were first simulated using COMSOL Multiphysics under different operating conditions. The simulation indicates that at very low flow rate (1-12 µL·min-1) and Re (3.3-40), as well as a very small working volume (~ 3 nL), a very good mixing (~ 98%) can be achieved in the ms time range (4.5-78 ms). The most promising design was then visualized experimentally, showing results that are consistent with the outcomes of the simulations. Importantly, the devices were fabricated using a classical soft-lithography method, as opposed to additive manufacturing often used to generate complex mixing structures. This new device minimizes the sample consumption and could therefore be applied for studies using precious samples.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study.

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.