PolyphyllinVI alleviates the spared nerve injury-induced neuropathic pain based on P2X3 receptor-mediated the release of inflammatory mediators.

ETHNOPHARMACOLOGICAL RELEVANCE: PolyphyllinVI (PPⅥ) is the main bioactive component of Chonglou which is a traditional Chinese herbal with various effects, including antitumor, anti-inflammatory, and analgesia. AIM OF THE STUDY: This study aimed to investigate the properties and mechanisms of the analgesia of PPⅥ by using neuropathic pain (NPP) mice. MATERIALS AND METHODS: The potential targets and mechanisms of PPⅥ in alleviating NPP were excavated based on the network pharmacology. Subsequently, the construction of a spared nerve injury (SNI) mice model was used to evaluate the effect of PPⅥ on NPP and the expression of the P2X3 receptor. We identified the signaling pathways of PPⅥ analgesia by RNA sequencing. RESULTS: The results of network pharmacology showed that BCL2, CASP3, JUN, STAT3, and TNF were the key targets of the analgesic effect of PPⅥ. PPⅥ increased the MWT and TWL of SNI mice and decreased the level of P2X3 receptors in the dorsal root ganglion (DRG) and spinal cord (SC). Additionally, PPⅥ reduced the release of pro-inflammatory mediators (TNF-α, IL-1ß, and IL-6) in the DRG, SC, and serum. Based on the KEGG enrichment of differentially expressed genes (DEGs) identified by RNA-Seq, PPVI may relieve NPP by regulating the AMPK/NF-κB signaling pathway. Western blotting results showed that the AMPK signaling pathway was activated, followed by inhibition of the NF-κB signaling pathway. CONCLUSION: PPⅥ increased the MWT and TWL of SNI mice maybe by inhibiting the expression of the P2X3 receptor and the release of inflammatory mediators. The properties of the analgesia of PPⅥ may be based on the AMPK/NF-κB pathway.

Coupled Ferroelectric-Photonic Memory in a Retinomorphic Hardware for In-Sensor Computing.

The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Indoor air quality improvement with filtration and UV-C on mitigation of particulate matter and airborne bacteria: Monitoring and modeling.

Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts people's daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 µg/m3) and ON days (49 µg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.

Anti-inflammatory and analgesic properties of Polyphyllin VI revealed by network pharmacology and RNA sequencing.

Inflammatory pain, sustained by a complex network of inflammatory mediators, is a severe and persistent illness affecting many of the general population. We explore possible anti-inflammatory pathways of Polyphyllin VI (PPVI) based on our prior study, which showed that PPVI reduces inflammation in mice to reduce pain. Network pharmacology and RNA-Seq identified the contribution of the MAPK signaling pathway to inflammatory pain. In the LPS/ATP-induced RAW264.7 cell model, pretreatment with PPVI for 1 h inhibited the release of IL-6 and IL-8, down-regulated expression of the P2X7 receptor(P2X7R), and decreased phosphorylation of p38 and ERK1/2 components of the MAPK pathway. Moreover, PPVI decreased expression of IL-6 and IL-8 was observed in the serum of the inflammatory pain mice model and reduced phosphorylation of p38 and ERK1/2 in the dorsal root ganglia while the reductions of expression of IL-6 and phosphorylation of ERK1/2 were not observed after the pre-treatment with A740003 (an antagonist of the P2X7R). These results suggest that PPVI may inhibit the release of IL-8 by regulating P2X7R to reduce the phosphorylation of p38. However, the modulation of PPVI on the release of IL-6 and phosphorylation of ERK1/2 may mediated by other P2X7R-independent signals.

Flaxseed Gum/Arabic Gum/Tween 80-Based Oleogel as a Fat Substitute Applied in Emulsified Sausage: Physicochemical Properties, Sensory Attributes and Nutritional Quality.

In the present study, flaxseed gum (FG), Arabic gum (GA) and Tween 80 were used to prepare oleogels through an emulsion-templated method, and the obtained oleogels were designed for the partial substitution of pork fat in emulsified sausage. An increment in FG concentrations enhanced the viscoelasticity of emulsions, which resulted in the improved stability of emulsion systems, with smaller droplet sizes. In addition, increased FG concentrations contributed to higher mechanical strength, denser network structure and lower oil loss of oleogels. As a fat substitute, the prepared oleogels improved the textural properties and nutritional quality of emulsified sausages. With the increase in the substitution level of oleogels, the hardness and chewiness of the emulsified sausage increased, and the cooking loss decreased. Meanwhile, the reformulation with oleogels decreased the saturated fat from 57.04 g/100 g lipid to 12.05 g/100 g lipid, while increasing the ratio of omega-6 to omega-3 essential fatty acids from 0.10 to 0.39. The obtained results demonstrated that the flaxseed gum/Arabic gum/Tween 80-based oleogels had huge potential to successfully replace pork fat in emulsified sausage products.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

The electrophysiology and structural difference between humans with distinct risky preference: a study based on EEG and MRI.

Risky decision-making is affected by past feedback, especially after encountering the beneficial loss in the past decision-making round, yet little is known about the mechanism accounting for the distinctive decision-making that different individuals may make under the past loss context. We extracted decision functional medial frontal negative (MFN) and the cortical thickness (CT) from multi-modality electroencephalography (EEG) and T1-weighted structural MRI (sMRI) datasets to assess the individual risky decision under the past loss context. First, concerning the MFN, the low-risk group (LRG) exhibits larger MFN amplitude and longer reaction time than the high-risk group (HRG) when making risky decisions under the loss context. Subsequently, the sMRI analysis reveals a greater CT in the left anterior insula (AI) for HRG compared with LRG, and a greater CT in AI is associated with a high level of impulsivity, driving individuals to make risky choices under the past loss context. Furthermore, for all participants, the corresponding risky decision behavior could be exactly predicted as a correlation coefficient of 0.523 was acquired, and the classification by combing the MFN amplitude and the CT of the left AI also achieves an accuracy of 90.48% to differentiate the two groups. This study may offer new insight into understanding the mechanism that accounts for the inter-individual variability of risky decisions under the loss context and denotes new indices for the prediction of the risky participants.

Robust Bayesian Estimation of EEG-Based Brain Causality Networks.

OBJECTIVE: The multivariate autoregression (MVAR) model is an effective model to construct brain causality networks. However, the accuracy of MVAR parameter estimation is considerably affected by outliers such as head movements and eye blinks contained in EEG signals, especially in short time windows. METHODS: We proposed a robust MVAR parameter estimation method based on a Bayesian probabilistic framework and Laplace fitting error known as Lap-SBL. With the Bayesian inference framework, we can accurately estimate the MVAR parameters under short time windows. Additionally, to alleviate the influence of outliers, we model the fitting error using the Laplace distribution instead of the typical Gaussian distribution. We employ convex analysis to model the inference task by approximating the Laplace noise prior with a maximum over Gaussian functions with varying scales. The variational inference approach was used to efficiently estimate the MVAR parameters. RESULTS: The numerical results suggest that the proposed method obtains less parameter estimation bias and more consistent linkages than existing benchmark methods, i.e., LS, LASSO, LAPPS and SBL. The motor imagery experimental data analysis shows that Lap-SBL can better describe the lateralization characteristics of brain network. This lateralization is less apparent in a subject with poor MI classification accuracy. CONCLUSION AND SIGNIFICANCE: Lap-SBL effectively suppresses the influence of outliers and recovers reliable networks in the presence of outliers and short time windows.

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

Polyphyllin VI screened from Chonglou by cell membrane immobilized chromatography relieves inflammatory pain by inhibiting inflammation and normalizing the expression of P2X3 purinoceptor.

Objective: Inflammatory pain is one of the most common diseases in daily life and clinic. In this work, we analysed bioactive components of the traditional Chinese medicine Chonglou and studied mechanisms of their analgesic effects. Material and methods: Molecular docking technology and U373 cells overexpressing P2X3 receptors combined with the cell membrane immobilized chromatography were used to screen possible CL bioactive molecules interacting with the P2X3 receptor. Moreover, we investigated the analgesic and anti-inflammatory effects of Polyphyllin VI (PPIV), in mice with chronic neuroinflammatory pain induced by CFA (complete Freund's adjuvant). Results: The results of cell membrane immobilized chromatography and molecular docking showed that PPVI was one of the effective compounds of Chonglou. In mice with CFA-induced chronic neuroinflammatory pain, PPVI decreased the thermal paw withdrawal latency and mechanical paw withdrawal threshold and diminished foot edema. Additionally, in mice with CFA-induced chronic neuroinflammatory pain, PPIV reduced the expression of the pro-inflammatory factors IL-1, IL-6, TNF-α, and downregulated the expression of P2X3 receptors in the dorsal root ganglion and spinal cord. Conclusion: Our work identifies PPVI as a potential analgesic component in the Chonglou extract. We demonstrated that PPVI reduces pain by inhibiting inflammation and normalizing P2X3 receptor expression in the dorsal root ganglion and spinal cord.

Cueing effect of attention among nurses with different anxiety levels: an EEG study.

The attention to cueing among nurses with anxiety affects their nursing quality seriously. Nevertheless, the neural mechanism of attention under anxiety among nurses has not been revealed. In this study, we utilized the event-related potential (ERP) and functional brain networks to investigate the neural mechanism of the cueing attention differences between anxiety and non-anxiety nurse groups (AG-20 nurses; NAG-20 nurses) in the spatial cueing task. The results revealed that in the invalid cues (144 trials), longer reaction times, larger P2 amplitudes, and more linkages between the right frontal and parietal areas were found in AG compared to NAG. In the valid cues (288 trials), there were no significant behavioral and neural differences between the two groups. The AG in the invalid cues showed slower response times, larger P2 and N5 amplitudes, and denser linkages originating from the occipital cortex than those in the valid cues. The convolutional neural network was trained for discriminating between the anxiety nurses and the normal ones, with the average accuracy being 0.76. The findings provided a potential physiological biomarker to predict the anxiety group who need to give more psychological attention. Nurse leaders maybe get more information for offering solutions to retain mental health among nurses.

An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification.

BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research. NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals. RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition. COMPARISON WITH EXISTING METHODS: Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition. CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.

Cell-inspired selective potassium removal towards hyperkalemia therapy by microphase-isolated core-shell microspheres.

Hyperkalemia is a common metabolic problem in patients with chronic kidney disease. Although oral medications and hemodialysis are clinically applied for lowering serum potassium, the intrinsic limitations encourage alternative therapy in the trend of adsorbent-based miniaturized blood purification devices. Cells serve as the biological K+ storage units that accumulate K+ through multiple mechanisms. Inspired by cells, our strategy aims at favorable permeation and enrichment of K+ in the microsphere. We incorporate cation-affinitive groups into core-shell structures with submicron-sized phase separation. These nano-spaced side-groups cooperate to form interlinked clusters, where crown ethers with Angstrom-scale ring for size-matched complexation, while ionic sulfonic acid groups for hydrophilicity and charge-buffering. The unique structure with such non-covalent interactions facilitates K+ for permeation across the shell and binding to the core while also ensuring mechanical strength and anti-swelling durability in biofluids. The microspheres exhibit high selectivity ratios of K+ (SK/Na, SK/Ca, SK/Mg up to 9.8, 21.6, and 17.7). As column adsorbents for hemoperfusion simulation, they effectively lower elevated K+ levels to the normal range (clearance rates up to 44.4%/45.3% for hyperkalemic human serum/blood). Blood compatibility tests show low protein adsorption, preferable hemocyte compatibility, and anticoagulation property in vitro. This promising strategy has clinical potential for hyperkalemia in high-risk patients. STATEMENT OF SIGNIFICANCE: Hyperkalemia (serum potassium >5 mmol/L) is a common complication in chronic renal failure patients. The limitations of existing treatments prompt a shift to wearable artificial kidney technology for clinical convenience and efficacy. Existing treatments have limitations, and we turn to adsorbent-based miniaturized blood purification devices in the prospect of wearable artificial kidney technology. There exists a lack of ion-specific adsorbents applied in extracorporeal circuits to redress electrolyte imbalances like hyperkalemia. Inspired by cells, we aim at the favorable permeation and enrichment of K+ by microspheres. The microspheres have a microphase-isolated core-shell structure, whose nano-spaced groups form cation-affinitive clusters. Selective K+ removal and blood compatibility are achieved. We expect this strategy to enlighten alternative hyperkalemia therapy for these high-risk patients.

Effects of Different Types of Human Disturbance on Total and Nitrogen-Transforming Bacteria in Haihe River.

Haihe River is the largest water system in North China and is injected into the Bohai Sea in Tianjin City. In this study, different types of human disturbance (urban sewage, industrial pollution, ship disturbance) were selected from the upper reaches of Haihe river Tianjin section down to the estuary that connected with Bohai Sea for evaluation. By metagenomic sequencing, the effects of different types of disturbances on bacteria communities in Haihe sediments were studied, with a special focus on the function of nitrogen-cycling bacteria that were further analyzed through KEGG comparison. By analyzing the physical and chemical characteristics of sediments, results showed that human disturbance caused a large amount of nitrogen input into Haihe River, and different types of human disturbance led to distinct spatial heterogeneity in different sections of Haihe River. The bacteria community was dominated by Proteobacteria, followed by Chloroflexi, Bacteroidetes, Actinobacteria and Acidobacteria. The relative abundance of each phylum varied at different sites as a response to different types of human disturbances. In nitrogen cycling, microorganisms including nitrogen fixation and removal were detected at each site, which indicated the active potential for nitrogen transformation in Haihe River. In addition, a large number of metabolic pathways relating to human diseases were also revealed in urban and pollution sites by function potential, which provided an important basis for the indicative role of urban river ecosystem for public health security. In summary, by evaluating both the ecological role and function potential of bacteria in Haihe River under different types of human disturbance, the knowledge of microorganisms for healthy and disturbed river ecosystems has been broadened, which is also informative for further river management and bioremediation.

Evaluation of an Air Cleaning Device Equipped with Filtration and UV: Comparison of Removal Efficiency on Particulate Matter and Viable Airborne Bacteria in the Inlet and Treated Air.

Since the COVID-19 pandemic, improving indoor air quality (IAQ) has become vital for the public as COVID-19 and other infectious diseases can transmit via inhalable aerosols. Air cleaning devices with filtration and targeted pollutant treatment capabilities can help improve IAQ. However, only a few filtration/UV devices have been formally tested for their effectiveness, and little data is publicly available and UV doses comparable. In this research, we upgraded a particulate matter (PM) air filtration prototype by adding UV-C (germicidal) light. We developed realistic UV dose metrics for fast-moving air and selected performance scenarios to quantify the mitigation effect on viable airborne bacteria and PM. The targeted PM included total suspended particulate (TSP) and a coarse-to-fine range sized at PM10, PM4, PM2.5, and PM1. The PM and viable airborne bacteria concentrations were compared between the inlet and outlet of the prototype at 0.5 and 1.0 m3/s (low and high) air flow modes. The upgraded prototype inactivated nearly 100% of viable airborne bacteria and removed up to 97% of TSP, 91% of PM10, 87% of PM4, 87% of PM2.5, and 88% of PM1. The performance in the low flow rate mode was generally better than in the high flow rate mode. The combination of filtration and UV-C treatment provided 'double-barrier' assurance for air purification and lowered the risk of spreading infectious micro-organisms.

Awareness and uptake of non-occupational post-exposure prophylaxis (nPEP) among gay, bisexual and other men who have sex with men in China: a cross-sectional study.

Non-occupational post-exposure prophylaxis (nPEP) is recommended to reduce the likelihood of HIV infection after potential exposure. However, little is known about this practice among Chinese gay, bisexual and other men who have sex with men (GBM). GBM were enrolled from both centers for disease prevention and control (CDCs) and community health centers in six cities in China. Multivariable logistics regression was used to assess factors associated with awareness of and willingness to use nPEP. A total of 516 eligible GBM were included, 67.2% of whom were aware of nPEP, 76.0% were willing to use nPEP, and 2.3% had ever used nPEP. GBM who had college or higher education, had disclosed sexual orientation, had increased number of male sex partners in the last 6 months, and had ever tested for HIV were more likely to be aware of nPEP. Willingness to use nPEP was significantly associated with college or higher education, STI history, gay app use, and awareness of nPEP prior to study. Uptake of nPEP is still low among MSM in China. Efforts are needed to improve awareness of and access to nPEP among GBM, especially those at higher risk of HIV infection.

Global Pattern and Trends in Penile Cancer Incidence: Population-Based Study.

BACKGROUND: Penile cancer is a relatively rare genital malignancy whose incidence and mortality are rising in many countries. OBJECTIVE: This study aims to assess the recent incidence and mortality patterns and incidence trends of penile cancer. METHODS: The age-standardized incidence and mortality rates (ASIR and ASMR, respectively) of penile cancer in 2020 were estimated from the Global Cancer Registries (GLOBOCAN) database. Incidence trends of penile cancer from 1973 to 2012 were assessed in 44 populations from 43 countries using the Cancer Incidence in Five Continents plus (CI5plus) and the Nordic Cancer Registries (NORDCAN) databases. Average annual percentage change was calculated to quantify trends in ASIR using joinpoint regression. RESULTS: Globally, the estimated ASIR and ASMR of penile cancer were 0.80 (per 100,000) and 0.29 (per 100,000) in 2020, equating to 36,068 new cases and 13,211 deaths in 2020, respectively. There was no significant correlation between the ASIR (P=.05) or ASMR (P=.90) and Human Development Index. In addition, 15 countries saw increasing ASIR for penile cancer, 13 of which were from Europe (United Kingdom, Lithuania, Norway, Estonia, Finland, Sweden, Cyprus, Netherlands, Italy, Croatia, Slovakia, Russia, and the Czech), and 2 from Asia (China and Israel). CONCLUSIONS: Although the developing countries still bear the higher incidence and mortality of penile cancer, the incidence is on the rise in most European countries. To mitigate the disease burden resulting from penile cancer, measures to lower the risk for penile cancers, including improving penile hygiene and male human papillomavirus vaccination, may be warranted.

The impact of the opening of high-speed rail on corporate financing constraints.

In this paper, the data of A-share non-financial listed companies from 2008 to 2019 are used to study the impact of the opening of high-speed railway on corporate financing constraints with the difference-in-differences model. The research results show that the opening of high-speed rail can effectively alleviate the financing constraints faced by enterprises. Through the analysis of its influence mechanism, it is found that the opening of high-speed rail can reduce the degree of information asymmetry and agency conflicts between enterprises and their stakeholders, and thereby ease corporate financing constraints. At the same time, the opening of the high-speed rail has a more significant effect on alleviating the financing constraints of non-state-controlled enterprises, technology-intensive enterprises, and enterprises in inland areas. This research is not only a supplement to the economic consequences related to the opening of the high-speed rail, but also a further expansion of the research on the factors affecting corporate financing constraints.

Outness and its correlates among gay and bisexual men attending university in China: a cross-sectional study.

BACKGROUND: Disclosure of sexual orientation to others (outness) might be associated with sexual and mental health among gay and bisexual men (GBM) attending university. We aimed to characterise outness and investigate factors correlated with outness among GBM attending university in China. METHODS: Between September 2018 and March 2019, GBM attending university were recruited in six cities in China. Information on sociodemographic characteristics, outness and sexual behaviours were collected using a self-administered questionnaire. Each participant was tested for HIV/STIs. Correlates of outness were assessed using multivariable logistic regression. RESULTS: A total of 400 GBM attending university were recruited, of whom 251 (62.8%) had disclosed their sexual orientation. Men who served as student leaders (adjusted odds ratio [AOR]=2.28, 95% CI: 1.46-3.54) and donated blood (AOR 1.85, 95% CI: 1.05-3.24) were more likely to disclose their sexual orientation, whereas men who had sex with a female (AOR 0.19, 95% CI: 0.05-0.74) and had group sex (AOR 0.52, 95% CI: 0.30-0.89) were less likely to disclose their sexual orientation. Mental health status, HIV/STI infections were not associated with outness. CONCLUSIONS: GBM attending university who disclosed their sexual orientation were more likely to be involved with student work and less likely to engage in high-risk sexual behaviours. More attention and education could focus on non-disclosing GBM men attending university through peer education or other ways.

Constructing Time-Varying Directed EEG Network by Multivariate Nonparametric Dynamical Granger Causality.

Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.