Cucurbitacin IIb mitigates concanavalin A-induced acute liver injury by suppressing M1 macrophage polarization.

Cucurbitacins are a class of triterpenoid compounds extracted from plants and possess various pharmacological applications. Cucurbitacin IIb (CuIIb), extracted from the medicinal plant Hemsleya amabilis (Cucurbitaceae), has served as a traditional Chinese medicine for the treatment of bacterial dysentery and intestinal inflammation. CuIIb has been shown to exhibit anti-inflammatory activity; however, the protective effect of CuIIb against concanavalin A (Con A)-induced acute liver injury (ALI) and the fundamental mechanism remain unelucidated.

Computing pulsatile blood flow of coronary artery under incomplete boundary conditions.

Background: Accurate measurement of pulsatile blood flow in the coronary arteries enables coronary wave intensity analysis, which can serve as an indicator for assessing coronary artery physiology and myocardial viability. Computational fluid dynamics (CFD) methods integrating coronary angiography images and fractional flow reserve (FFR) offer a novel approach for computing mean coronary blood flow. However, previous methods neglect the inertial effect of blood flow, which may have great impact on pulsatile blood flow calculation.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD GRL) framework to model EEG signals as graph data.

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function.

LDER: a classification framework based on ERP enhancement in RSVP task.

Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper.

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT).

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis.

Computing intracoronary blood flow rate under incomplete boundary conditions: Combing coronary anatomy and fractional flow reserve.

Background: Accurate measurement of intracoronary blood flow rate is of great significance for the diagnosis of ischemic heart disease (IHD). Computational fluid dynamic (CFD) method, combining coronary angiography images and fractional flow reserve (FFR), provides a new way to calculate the mean flow rate. However, due to the incomplete boundary conditions obtained by FFR, side branches were ignored which was likely to have a significant impact on the accuracy.

A transfer learning-based feedback training motivates the performance of SMR-BCI.

. Feedback training is a practical approach to brain-computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMRs). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation.

Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.

Background: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions.

Why do people resist algorithms? From the perspective of short video usage motivations.

Algorithms embedded in media applications increasingly influence individuals' media practice and behavioral decisions. However, it is also important to consider how the influence of such algorithms can be resisted. Few studies have explored the resistant outcomes of the interactions with algorithms.

[A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals].

Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary.

Research on online advertising attention evaluation decision based on the stability of delay differential equations and Hopf bifurcation analysis.

This paper uses the stability of the delay differential equation to study its impact on online advertising, helps analyze Hopf branch characteristics in a big data environment, helps companies make online advertising decisions, and maximizes the benefits of product sales. The thesis fully considers various factors such as advertising volume, advertising schedule, and advertising investment level, discusses the singularity types of the advertising delay differential equation, and gives the best decision for advertising investment.The stability of the time-lag differential equation studied in this paper is to study its impact on online advertising, to help analyze the Hopf branch characteristics in the big data environment, and to help companies make online advertising decisions.

An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training.

Brain-computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time.

Validation and Diagnostic Performance of a CFD-Based Non-invasive Method for the Diagnosis of Aortic Coarctation.

The clinical diagnosis of aorta coarctation (CoA) constitutes a challenge, which is usually tackled by applying the peak systolic pressure gradient (PSPG) method. Recent advances in computational fluid dynamics (CFD) have suggested that multi-detector computed tomography angiography (MDCTA)-based CFD can serve as a non-invasive PSPG measurement. The aim of this study was to validate a new CFD method that does not require any medical examination data other than MDCTA images for the diagnosis of CoA.

Pressure-flow curve derived from coronary CT angiography for detection of significant hemodynamic stenosis.

Objectives: Coronary CT angiography (cCTA) has been used to non-invasively assess both the anatomical and hemodynamic significance of coronary stenosis. The current study investigated a new CFD-based method of evaluating pressure-flow curves across a stenosis to further enhance the diagnostic value of cCTA imaging.

Methods: Fifty-eight patients who underwent both cCTA imaging and invasive coronary angiography (ICA) with fractional flow reserve (FFR) within 2 weeks were enrolled.

[Analysis of imagery motor effective networks based on dynamic partial directed coherence].

The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time-frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed.

Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface.

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall.

Computation of hemodynamics in eccentric coronary stenosis: A morphological parametric study.

Background: Flow recirculation occurs in eccentric coronary stenosis, which can lead to adverse outcome. The complex local geodesic patterns of eccentric stenosis are critical factors in determining the flow characteristics in post-stenotic flow.

Objective: The main objective of this study is to relate the relationship between the detailed morphological parameters in eccentric coronary stenosis and the post-stenotic flow characteristics.

A new CFD based non-invasive method for functional diagnosis of coronary stenosis.

Background: Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFR) for functional measurement of stenosis. However, the accuracy of FFR is limited due to the approximate modeling approach of maximal hyperemia conditions.

Stimulator Selection in SSVEP-Based Spatial Selective Attention Study.

Steady-State Visual Evoked Potentials (SSVEPs) are widely used in spatial selective attention. In this process the two kinds of visual simulators, Light Emitting Diode (LED) and Liquid Crystal Display (LCD), are commonly used to evoke SSVEP. In this paper, the differences of SSVEP caused by these two stimulators in the study of spatial selective attention were investigated.

Theoretical Model of Coronary Blood Flow Regulation: Role of Myocardium Compressive Forces.

Objective: Auto-regulatory reserve of coronary blood flow is nonuniformly distributed across the ventricular wall. MCF are thought to play an important role in determining the transmural distribution of myocardium blood flow. Here, impacts of MCF on coronary flow regulation are analyzed using a theoretical model.

Flow regulation in coronary vascular tree: a model study.

Background: Coronary blood flow can always be matched to the metabolic demand of the myocardium due to the regulation of vasoactive segments. Myocardial compressive forces play an important role in determining coronary blood flow but its impact on flow regulation is still unknown. The purpose of this study was to develop a coronary specified flow regulation model, which can integrate myocardial compressive forces and other identified regulation factors, to further investigate the coronary blood flow regulation behavior.

Functional assessment of coronary artery disease by intravascular ultrasound and computational fluid dynamics simulation.

Clinical trials have shown that functional assessment of coronary stenosis by fractional flow reserve (FFR) improves clinical outcomes. Intravascular ultrasound (IVUS) complements conventional angiography, and is a powerful tool to assess atherosclerotic plaques and to guide percutaneous coronary intervention (PCI). Computational fluid dynamics (CFD) simulation represents a novel method for the functional assessment of coronary flow.

Computation of hemodynamics in tortuous left coronary artery: a morphological parametric study.

Coronary tortuosity (CT) would alter the local wall shear stress (WSS) and may become a risk factor for atherosclerosis. Here we performed a systematic computational study to relate CT morphological parameters to abnormal WSS, which is a predisposing factor to the formation of atherosclerotic lesions. Several idealized left coronary artery (LCA) models were created to conduct a series of morphological parametric studies, in which we concentrate on three specific morphological parameters, the center line radius (CLR), the bend angle (BA), and the length between two adjust bends (LBB).