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Objective. The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone. Method. Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model. Results. The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set. Conclusion. The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone. [ABSTRACT FROM AUTHOR]

A miniaturized wideband duplex antenna is presented in this paper. The presented duplex antenna is developed based on a dipole antenna. By changing the structure of the dipole antenna, the antenna has dual‐band characteristic. The inductive type feed port was used to improve the impedance matching and isolation. Moreover, to further improve port isolation, all ports of the antenna are connected with miniature filters. The design has been verified by measurements, which show good agreements with simulations. The measured results show that the duplex antenna has good performance in two operating bands of 1.5–2.5 GHz and 5.6–5.95 GHz, the return loss of corresponding ports is greater than 14.5 dB, and the isolation between the two ports is greater than 29.5 dB. [ABSTRACT FROM AUTHOR]

Effective extraction and representation of action information are critical in action recognition. The majority of existing methods fail to recognize actions accurately because of interference of background changes when the proportion of high-activity action areas is not reinforced and by using RGB flow alone or combined with optical flow. A novel recognition method using action sequences optimization and two-stream fusion network with different modalities is proposed to solve these problems. The method is based on shot segmentation and dynamic weighted sampling, and it reconstructs the video by reinforcing the proportion of high-activity action areas, eliminating redundant intervals, and extracting long-range temporal information. A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field of feature extraction. Compared with existing approaches, the proposed method achieves superior or comparable classification accuracies on benchmark datasets UCF101 and HMDB51. [ABSTRACT FROM AUTHOR]

Summary: Coulomb Counting (CC) method plays an important role in the state of charge (SOC) estimation theory of lithium‐ion batteries, and a lot of improvement and optimization strategies are based on it. With the increasing demand for precise management of lithium‐ion battery systems, the performance of the traditional CC method is no longer suitable for more complex working conditions. First, the battery aging, extreme temperature, and high‐rate discharging were considered as the main influencing factors which limit the SOC estimation accuracy of the CC method, and the performance degradation mechanism of the traditional CC method under the influence of the above factors are experimentally analyzed, especially the change of battery total dischargeable capacity after aging, and the change rules of key parameters in the CC equation are analyzed. Then the initial SOC and the total dischargeable capacity of the CC method are modified and estimated respectively to realize the accurate estimation of SOC under complex working conditions, especially the accurate SOC estimation during the whole life cycle of lithium‐ion batteries. The experimental results show that the improved CC method can effectively deal with complex working conditions, and the comprehensive estimation accuracy of SOC is within 3.6%. [ABSTRACT FROM AUTHOR]

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model. [ABSTRACT FROM AUTHOR]

Plasma free metanephrines are widely used for the diagnosis of pheochromocytoma and paraganglioma (PPGL), yet quantifying metanephrines using a simple and cost-effective approach may be challenging due to preanalytical and analytical constraints. In this study, we established and validated a new method for quantitative measurement of plasma free metanephrines based on microextraction by packed sorbent (MEPS) with porous graphitic carbon (PGC) and liquid chromatography-tandem mass spectrometry (HILIC-MS/MS). The elution step was fully compatible with HILIC mode without evaporation and reconstitution. The analytes were well resolved, and potential interferences (54 substances) were investigated. This method was linear from 24.7–2717 pg/mL for metanephrine (MN) and 24.5–4010 pg/mL for normetanephrine (NMN) with a coefficient of determination (R2) higher than 0.994. The limit of MN and NMN detection were 12.4 pg/mL and 12.3 pg/mL, respectively. The intra- and interassay impressions were ≤12.8% for spiked quality controls and ≤13.6% for commercial quality controls; the method recoveries ranged within 88.0–109.0%, respectively. The area under the receiver operating characteristic (ROC) curve was 0.848 ± 0.047 for MN and 0.979 ± 0.021 for NMN. Validation that was performed by comparing clinical specimens with various biochemical results showed that plasma free metanephrines in a seated position had comparable sensitivity and lower specificity to urinary free metanephrines, which could be compensated by combining other biochemical tests. The newly developed MEPS method resulted as a time-saving, reliable, and cost-effective microextraction technique that can be applied for a successful screening of PPGL. [ABSTRACT FROM AUTHOR]

Summary: The research of the real‐time state of charge (SOC) estimation method for lithium‐ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere‐hour (Ah) method is still widely used. First, temperature, charge‐discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open‐circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge‐discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real‐time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective. Highlights: The factors affecting the accuracy of traditional ampere‐hour method are analyzed in detail.The necessary shelf time of battery at extreme temperature was explored.Based on the semi‐empirical model, the capacity attenuation function k is proposed.Compound correction factor F is proposed to synthetically characterize the influence of internal and external factors of battery.A novel ampere‐hour (Ah) integral formula is proposed.A novel practical state of charge (SOC) estimation method is proposed. [ABSTRACT FROM AUTHOR]

Summary: The research of the real‐time state of charge (SOC) estimation method for lithium‐ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere‐hour (Ah) method is still widely used. First, temperature, charge‐discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open‐circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge‐discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real‐time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective. Highlights: The factors affecting the accuracy of traditional ampere‐hour method are analyzed in detail.The necessary shelf time of battery at extreme temperature was explored.Based on the semi‐empirical model, the capacity attenuation function k is proposed.Compound correction factor F is proposed to synthetically characterize the influence of internal and external factors of battery.A novel ampere‐hour (Ah) integral formula is proposed.A novel practical state of charge (SOC) estimation method is proposed. [ABSTRACT FROM AUTHOR]

Background. Cognitive impairment is the leading cause of traumatic brain injury- (TBI-) related disability; however, the underlying pathogenesis of this dysfunction is not completely understood. Methods. Using an isobaric tagging for relative and absolute quantitation- (iTRAQ-) based quantitative proteomic approach, serum samples from healthy control subjects, TBI patients with cognitive impairment, and TBI patients without cognitive impairment were analysed to identify differentially expressed proteins (DEPs) related to post-TBI cognitive impairment. In addition, DEPs were further analysed using bioinformatic platforms and validated using enzyme-linked immunosorbent assays (ELISA). Results. A total of 56 DEPs were identified that were specifically related to TBI-induced cognitive impairment. Bioinformatic analysis revealed that a wide variety of cellular and metabolic processes and some signaling pathways were involved in the pathophysiology of cognitive deficits following TBI. Five randomly selected DEPs were validated using ELISA in an additional 105 cases, and the results also supported the experimental findings. Conclusions. Despite limitations, our findings will facilitate further studies of the pathological mechanisms underlying TBI-induced cognitive impairment and provide new methods for the research and development of neuroprotective agents. However, further investigation on a large cohort is warranted. [ABSTRACT FROM AUTHOR]

Insect radar is an important tool for studying the ecological behaviors of insects. Due to the harsh environment, radar is susceptible to interference from ground echoes, which makes it difficult to monitor insects in low altitude. To study the flying insects at low altitude in radar entomology, the research of ultra-wideband (UWB) insect radar based on log-periodic antenna array was proposed. This method uses a conical conformal array to receive signals and an improved multiple signal classification (MUSIC) algorithm to process the signals. This array is capable of monitoring insects of different sizes in the ultra-wideband range, adjusting the angle of the cone tip to change the position and polarization information, and obtaining information on various insects flying at low altitude. The problem of two-dimensional angle of arrival estimation of insect cone conformal array was analyzed by computer simulation, which are low-altitude flying targets with different cone apex angles. In addition, a modified multiple signal classification algorithm was implemented to make the radar search and catch multiple targets faster and more accurate. The algorithm greatly reduces computational complexity while maintaining the accuracy of estimation. There is a linear relationship between the time calculation and the accuracy with the modified algorithm, compared with the relationship between exponential growth and the generalized MUSIC algorithm. Finally, the experimental simulation has proved the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future. [ABSTRACT FROM AUTHOR]

Background. Adipokines are reported to participate in many common pathologic processes of glucose dysregulation, such as insulin resistance, β-cell dysfunction, and chronic inflammation. Objective. To detect the concentrations of plasma asprosin in subjects with impaired glucose regulation (IGR) and newly diagnosed type 2 diabetes (nT2DM) and its relationship to parameters of glucose and lipid metabolism, insulin resistance, and pancreatic β-cell function. Methods. 143 eligible participants were included and were divided into three groups including normal glucose regulation (NGR, n=52), IGR (n=40), and nT2DM group (n=51). The intravenous glucose tolerance test (IVGTT) and clinical and biochemical parameters were measured in all participants. Results. Plasma asprosin levels were higher in IGR (82.40 ± 91.06 ng/mL, P<0.001) and nT2DM (73.25 ± 91.69 ng/mL, P<0.001) groups compared with those in the NGR (16.22 ± 9.27 ng/mL) group, especially in IGR subjects. Correlation analysis showed that plasma asprosin levels were positively correlated with waist circumference (Wc), fasting plasma glucose (FPG), postchallenge plasma glucose (2hPG), HbA1c, triglyceride (TG), and homeostasis model assessment for insulin resistance (HOMA-IR) and negatively correlated with homeostasis model assessment for β-cell function (HOMA-β), area under the curve of the first-phase (0–10 min) insulin secretion (AUC), acute insulin response (AIR), and glucose disposition index (GDI) (all P<0.05). Multiple logistical regression analyses revealed that plasma asprosin concentrations were significantly correlated with IGR and nT2DM after controlling for age, sex, BMI, and WHR. Conclusions. Circulating asprosin might be a predictor of early diagnosis in DM and might be a potential therapeutic target for prediabetes and T2DM. [ABSTRACT FROM AUTHOR]

Objectives. In China, Oldenlandia diffusa (OD) is a natural herb that is widely used and has been proven to be effective in the treatment of rheumatoid arthritis (RA). This study aimed to preliminarily reveal the mechanism by which OD exerts its beneficial effect. Methods. Ultra-performance liquid chromatography photodiode array was applied to identify the absorbable compounds in the plasma of collagen-induced arthritis (CIA) model rats. After 2 weeks, an OD decoction or the identified absorbable compound was administered to CIA rats. Morphology, X-ray images of the joints, pathological images, arthritis index, and cytokine (TNF-α and IL-6) levels were evaluated. Results. p-Coumaric acid (p-CA) was identified as the absorbed compound in plasma. After administration of p-CA solution or the OD decoction, symptoms in the treated rats were alleviated as compared to the untreated model rats, and inflammatory cell infiltration was suppressed. The arthritis index and serum levels of TNF-α and IL-6 were decreased as compared to the control group. Conclusions. OD may exert its anti-inflammatory effect on RA via its active ingredient, p-CA. This information sheds light on the mechanism by which OD exerts its anti-inflammatory effort in RA and forms the basis for further development of therapeutic agents for RA. [ABSTRACT FROM AUTHOR]

Fuzi (Aconitum carmichaelii Debx) has been traditionally used for the treatment of ulcerative colitis (UC) in China for thousands of years. The total alkaloids of A. carmichaelii (AAC) have been considered as the main medicinal components of fuzi, whereas its underlying anti-UC mechanisms remain elusive. In the present study, the dextran sulfate sodium (DSS)-induced UC mice model, which was consistent with the symptoms and pathological features of human UC, was established to comprehensively evaluate the anti-UC effects of AAC. The results indicated that AAC effectively improved the weight loss, disease activity index (DAI), spleen hyperplasia, and colon shortening, and thus alleviated the symptoms of UC mice. Meanwhile, AAC not only inhibited the MPO enzyme and the abnormal secretion of inflammatory cytokines (TNF-α, IL-1β, IL-6, IFN-γ, and IL-17A) and suppressed the overexpression of inflammatory mediators (TNF-α, IL-1β, and IL-6) of mRNA but also reduced the phosphorylation of p38 MAPK, ERK, and JNK, and the protein expressions of NF-κB, IκB-α, STAT3, and JAK2 in the colon tissue. Furthermore, the LC-MS/MS quantitative determination suggested that the three low toxic monoester alkaloids were higher in both contents and proportion than that of the three high toxic diester alkaloids. Additionally, molecular docking was hired to investigate the interactions between alkaloid-receptor complexes, and it suggested the three monoester alkaloids exhibited higher binding affinities with the key target proteins of MAPK, NF-κB, and STAT3. Our finding showcased the noteworthy anti-UC effects of AAC based on the MAPK/NF-κB/STAT3 signaling pathway, which would provide practical and edge-cutting background information for the development and utilization of A. carmichaelii as a potential natural anti-UC remedy. [ABSTRACT FROM AUTHOR]

Purpose. To explore the relationship between craniomaxillofacial features and psychological distress among adult pretreatment orthodontic patients. Methods. A group of 190 patients (95 males and 95 females) was included. Questionnaires including the Kessler psychological distress scale (K10) were sent to patients, and cephalograms were collected. Patients were divided into two groups according to K10 score: psychological distress group (score ≥ 20) and no psychological distress group (score < 20). Nineteen hard tissue and thirteen soft tissue parameters were traced on cephalograms to characterize the craniomaxillofacial features. Results. There was no significant difference in gender or age distribution between the two groups. Male patients with psychological distress showed statistically significantly larger anterior facial height (AFH) (126.62 mm vs. 120.97 mm), upper lip length (25.11 mm vs. 23.26 mm), and smaller overbite (1.21 mm vs. 2.75 mm) than patients without psychological distress. Male patients with hyperdivergent pattern and open bite were more likely to have psychological distress. None of the parameters showed statistical differences across groups in females. Frankfort-mandibular plane angle (r = 0.235), Bjork's sum (r = 0.311), AFH (r = 0.322), overbite (r = − 0.238), AFH/posterior facial height (r = 0.251), and upper anterior facial height (UAFH)/lower anterior facial height (LAFH) (r = − 0.230) were correlated with K10 score in males. After adjusting gender and age, the AFH (B = 0.147) and UAFH/LAFH (B = − 14.923) were significantly related with the K10 score. Conclusion. Psychological distress was mainly correlated with hyperdivergent pattern, open bite, and larger lower anterior facial height proportion in pretreatment orthodontic patients. Orthodontists should be aware of the possible underlying psychological distress in patients with specific craniomaxillofacial features. Clinical assessment of psychological distress may need to take into account gender differences in patients. [ABSTRACT FROM AUTHOR]

Summary Accurate estimation of the state of charge (SOC) of Li‐ion battery can ensure the reliability of the storage system. A combined estimator of online full‐parameter identification and adaptive unscented particle filter for Li‐ion battery SOC based on an improved fractional‐order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li‐ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence. [ABSTRACT FROM AUTHOR]

It has been widely shown that biomaterial surface topography can modulate host immune response, but a fundamental understanding of how different topographies contribute to pro-inflammatory or anti-inflammatory responses is still lacking. To investigate the impact of surface topography on immune response, we undertook a systematic approach by analyzing immune response to eight grades of medical grade polyurethane of increasing surface roughness in three in vitro models of the human immune system. Polyurethane specimens were produced with defined roughness values by injection molding according to the VDI 3400 industrial standard. Specimens ranged from 0.1 μm to 18 μm in average roughness (Ra), which was confirmed by confocal scanning microscopy. Immunological responses were assessed with THP-1-derived macrophages, human peripheral blood mononuclear cells (PBMCs), and whole blood following culture on polyurethane specimens. As shown by the release of pro-inflammatory and anti-inflammatory cytokines in all three models, a mild immune response to polyurethane was observed, however, this was not associated with the degree of surface roughness. Likewise, the cell morphology (cell spreading, circularity, and elongation) in THP-1-derived macrophages and the expression of CD molecules in the PBMC model on T cells (HLA-DR and CD16), NK cells (HLA-DR), and monocytes (HLA-DR, CD16, CD86, and CD163) showed no influence of surface roughness. In summary, this study shows that modifying surface roughness in the micrometer range on polyurethane has no impact on the pro-inflammatory immune response. Therefore, we propose that such modifications do not affect the immunocompatibility of polyurethane, thereby supporting the notion of polyurethane as a biocompatible material. [ABSTRACT FROM AUTHOR]

BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children. [ABSTRACT FROM AUTHOR]

Objective. Several clinical studies have reported the application of dipeptidyl peptidase-4 (DPP-4) inhibitors as treatments for type 1 diabetes mellitus (T1DM). This study aims to review the outcomes of these existing studies and to discuss the therapeutic effects of DPP-4 inhibitors on T1DM. Methods. We thoroughly searched the Medline, Embase, PubMed, and Cochrane Library databases and ClinicalTrials.gov for studies concerning the use of DPP-4 inhibitors in patients with T1DM. Results. In preclinical trials, DPP-4 inhibitors improved the pathogenesis of T1DM. However, only a portion of the studies showed potential efficacy regarding clinical glycemic control and other clinical parameters. From this meta-analysis, pooled data from 5 randomized controlled trials revealed that the additional use of DPP-4 inhibitors resulted in a greater decrease in glycated hemoglobin A1c (HbA1c) levels (0.07%, 95% CI (−0.37%–0.23%)) than insulin monotherapy, although the decrease was not significant. A small decrease in postprandial glucose or insulin consumption was confirmed. Conclusion. Although DPP-4 inhibitors may be beneficial for T1DM, existing studies do not strongly support these positive effects in clinical practice. Further optimized clinical trials are needed. [ABSTRACT FROM AUTHOR]

As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies. [ABSTRACT FROM AUTHOR]