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Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies. •The relationship between LFP and BOLD is dominated by the high-amplitude events.•FMRI frames that co-occur with high LFP events can resemble LFP-BOLD correlation map.•Such fMRI frames can be divided into a few groups showing distinct spatial patterns.•Multimodal methods might provide insights into dynamic functional connectivity.

PurposeThe main purpose of this paper is to explore the influence mechanism of corporate social responsibility (CSR) for smart cities on consumers' purchase intention. The authors aim to identify the key components of CSR for smart cities based on the perspective of consumers, namely responsibility toward consumers, environment and community and validate their relationship.Design/methodology/approachThe authors exploit data collected by questionnaire surveys to estimate the effects of CSR for smart cities on consumers' purchase intentions and to investigate the statistical causality between them. The multilinear regression model is used to figure out the different impact levels of the three dimensions of CSR for smart cities on consumers' purchase intention.FindingsThe results illustrate that CSR for smart cities and its three dimensions all have significant positive impacts on consumers' purchase intentions. Besides, consumer–corporate identity (CCI) exerts a partial mediation effect on this influence mechanism.Research limitations/implicationsThis research is based on a rather small sample size. Besides, due to the time limitation and other factors, some other control variables are neglected in the regression model. Therefore, the impact level could be distorted.Practical implicationsThe authors put forward management implications according to research conclusions. Corporates should actively fulfill the CSR in the field of consumer responsibility to boost consumers' purchase intention. Corporate should strengthen the interaction with consumers to improve their corporate identity.Originality/valueThe main contribution of this paper is to provide convincing evidence of the impacts of CSR for smart cities on consumer purchase intention (CPI), thus proposing effective measures for corporates to win more consumers by taking on social responsibility for smart cities. This paper takes CCI as mediating variable to deepen the understanding of the impacts of CSR for smart cities on CPI, which is innovative and beneficial to enriching literature in related fields.

Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the L2 norm of the component-wise effects, and develop a Monte-Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR-223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR-223 on GBM survival.

Background Epilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient’s work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals. Methodology Firstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted. Results The model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively. Conclusion The findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

A bstract We show that there exists a huge amount of multipartite entanglement in holography by studying the upper bound for holographic n -partite information I n that n − 1 fixed boundary subregions participate. We develop methods to find the n -th region E that makes I n reach the upper bound. Through the explicit evaluation, it is shown that I n , an IR term without UV divergence, could diverge when the number of intervals or strips in region E approaches infinity. At this upper bound configuration, we could argue that I n fully comes from the n -partite global quantum entanglement. Our results indicate: fewer-partite entanglement in holography emerges from more-partite entanglement; n − 1 distant local subregions are highly n -partite entangling. Moreover, the relationship between the convexity of a boundary subregion and the multipartite entanglement it participates, and the difference between multipartite entanglement structure in different dimensions are revealed as well.

A bstract Pole-skipping refers to the special phenomenon that the pole and the zero of a retarded two-point Green’s function coincide at certain points in momentum space. We study the pole-skipping phenomenon in holographic Green’s functions of boundary operators that are dual to massive p -form fields and the dRGT massive gravitational fields in the AdS black hole background. Pole-skipping points for these systems are computed using the near horizon method. The relation between the pole-skipping points of massive fields and their massless counterparts is revealed. In particular, as the field mass m is varied from zero to non-zero, the pole-skipping phenomenon undergoes an abrupt change with doubled pole-skipping points found in the massive case. This arises from the breaking of gauge invariance due to the mass term and the consequent appearance of more degrees of freedom. We recover the gauge invariance using the Stueckelberg formalism by introducing auxiliary dynamical fields. The extra pole-skipping points are identified to be associated with the Stueckelberg fields. We also observe that, as the mass varies, some pole-skipping points of the wave number q may move from a non-physical region with complex q to a physical region with real q .

Mechanotransduction couples mechanical stimulation with ion flux, which is critical for normal biological processes involved in neuronal cell development, pain sensation, and red blood cell volume regulation. Although they are key mechanotransducers, mechanosensitive ion channels in mammals have remained difficult to identify. In 2010, Coste and colleagues revealed a novel family of mechanically activated cation channels in eukaryotes, consisting of Piezo1 and Piezo2 channels. These have been proposed as the long-sought-after mechanosensitive cation channels in mammals. Piezo1 and Piezo2 exhibit a unique propeller-shaped architecture and have been implicated in mechanotransduction in various critical processes, including touch sensation, balance, and cardiovascular regulation. Furthermore, several mutations in Piezo channels have been shown to cause multiple hereditary human disorders, such as autosomal recessive congenital lymphatic dysplasia. Notably, mutations that cause dehydrated hereditary xerocytosis alter the rate of Piezo channel inactivation, indicating the critical role of their kinetics in normal physiology. Given the importance of Piezo channels in understanding the mechanotransduction process, this review focuses on their structural details, kinetic properties and potential function as mechanosensors. We also briefly review the hereditary diseases caused by mutations in Piezo genes, which is key for understanding the function of these proteins.

A bstract We investigate second-order gravitational perturbations in asymptotically AdS black branes, developing a gauge-invariant framework to compute the amplitude ratio between quadratic and linear quasi-normal modes. Our analysis reveals resonant divergences of this ratio when the summed frequencies of two source modes coincide with the frequency of a third mode. These divergences are shown to manifest as poles in three-point fully retarded correlators of the energy-momentum tensor in the holographically dual quantum field theory, establishing a concrete connection between bulk gravitational nonlinearities and observables in the dual boundary theory. Our findings contribute to the understanding of nonlinearity in quantum many-body systems while deepening the holographic dictionary between spacetime dynamics and quantum correlations.

The programmable one-pot oligosaccharide synthesis method was designed to enable the rapid synthesis of a large number of oligosaccharides, using the software Optimer to search Building BLocks (BBLs) with defined relative reactivity values (RRVs) to be used sequentially in the one-pot reaction. However, there were only about 50 BBLs with measured RRVs in the original library and the method could only synthesize small oligosaccharides due to the RRV ordering requirement. Here, we increase the library to include 154 validated BBLs and more than 50,000 virtual BBLs with predicted RRVs by machine learning. We also develop the software Auto-CHO to accommodate more data handling and support hierarchical one-pot synthesis using fragments as BBLs generated by the one-pot synthesis. This advanced programmable one-pot method provides potential synthetic solutions for complex glycans with four successful examples demonstrated in this work. The software Optimer has aided the programmable one-pot oligosaccharide synthesis with a library of 50 Building BLocks (BBLs). Here, the authors expanded Optimer's validated and virtual libraries of BBLs and developed Auto-CHO, a software which allows the one-pot programmable synthesis of more complex glycans.

To better understand the biomechanics of para-table tennis players, this study compared the shoulder, elbow, and wrist joint kinematics among able-bodied (AB) and wheelchair players in different classifications. Nineteen participants (AB, n = 9; classification 1 (C1), n = 3; C2, n = 3; C3, n = 4) executed 10 forehand and backhand topspin drives. Shoulder abduction/adduction, elbow flexion/extension, wrist extension/flexion, respective range of motion (ROM), and joint patterns were obtained using inertial measurement unit (IMU) sensors. The results showed clear differences in upper limb kinematics between the able-bodied and wheelchair players, especially in the elbow and wrist. For the para-players, noticeable variations in techniques were also observed among the different disability classes. In conclusion, wheelchair players likely adopted distinct movement strategies compared to AB to compensate for their physical impairments and functional limitations. Hence, traditional table tennis programs targeting skills and techniques for able-bodied players are unsuitable for para-players. Future work can investigate how best to customize training programs and to optimize movement strategies for para-players with varied types and degrees of impairment.