Detoxification and Activating Blood Circulation Decoction Promotes Reendothelialization of Damaged Blood Vessels via VEGF Signaling Pathway Activation by miRNA-126.

The occurrence of in-stent restenosis (ISR) poses a significant challenge for percutaneous coronary intervention (PCI). Thus, the promotion of vascular reendothelialization is essential to inhibit endothelial proliferation. In this study, we clarified the mechanism by which Detoxification and Activating Blood Circulation Decoction (DABCD) promotes vascular reendothelialization to avoid ISR by miRNA-126-mediated modulation of the vascular endothelial growth factor (VEGF) signaling pathway. A rat model of post-PCI restenosis was established by balloon injury. The injured aortic segment was collected 14 and 28 d after model establishment. Our findings indicate that on the 14th and 28th days following balloon injury, DABCD reduced intimal hyperplasia and inflammation and promoted vascular reendothelialization. Additionally, DABCD markedly increased nitric oxide (NO) expression and significantly decreased ET-1 production in rat serum. DABCD also increased the mRNA level of endothelial nitric oxide synthase (eNOS) and the protein expression of VEGF, p-Akt, and p-extracellular signal-regulated kinase (ERK)1/2 in vascular tissue. Unexpectedly, the expression of miR-126a-5p mRNA was significantly lower in the aortic tissue of balloon-injured rats than in the aortic tissue of control rats, and higher miR-126a-5p levels were observed in the DABCD groups. The results of this study indicated that the vascular reendothelialization effect of DABCD on arterial intimal injury is associated with the inhibition of neointimal formation and the enhancement of vascular endothelial activity. More specifically, the effects of DABCD were mediated, at least in part, through miR-126-mediated VEGF signaling pathway activation.

Association Between Remnant Cholesterol and Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography and/or Percutaneous Coronary Intervention.

Contrast-induced nephropathy (CIN) is an acute renal complication that can occur after the use of iodinated contrast media. Remnant cholesterol (RC) is one of the markers of atherosclerotic cardiovascular disease risk. We evaluated the impact of RC on CIN and clinical outcomes after coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). Consecutive patients (n = 3332) undergoing CAG and/or PCI were assessed in this retrospective study. Patients were divided into four groups based on baseline RC levels. In the quartile analysis, RC were associated with a higher risk of CIN, especially when RC ≤0.20 or ≥0.38 mmol/L (P < .05). However, after adjustment, the association of RC with CIN was not significant. There was a significant correlation between RC and repeated revascularization in patients undergoing PCI (P < .001) and driven primarily by the highest quartile level. After adjustment, this remained statistically significant (adjusted odds ratio (aOR) 4.06; 95% CI 2.10-7.87; P < .001). This is the first large study to show a possible association between RC and the risk of CIN after CAG and/or PCI; however, this finding was not further confirmed after adjustment. The complex clinical risk profile of patients, rather than RC itself, may contribute to the risk of CIN in this high-risk subgroup.

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs.

OBJECTIVE: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. METHODS: This article proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. RESULTS: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. SIGNIFICANCE: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.

User Identity Protection in EEG-Based Brain-Computer Interfaces.

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction.

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs.

A bilevel data-driven method for sewer deposit prediction under uncertainty.

Deposit accumulation is one of the predominant causes of sewer blockage and overflow. Nevertheless, the traditional detection methods are costly and time-consuming, and the accuracy of the mathematical models for deposit prediction is usually affected by some uncertain factors (e.g., pipe properties and flow velocity of water). This paper proposes a framework of global sensitivity analysis (GSA) to identify the most sensitive indicators for sewer deposit prediction by (i) developing a data-driven bilevel (i.e., catchment level and segment level) model to map the relation between input and output indicators and (ii) employing three different GSA methods, namely, the Morris method, Sobol method, and Borgonovo index method to identify the indicators as important or unimportant (insensitive). The results show that the likelihood of combined sewer overflow occurrences (LCSOO), pipe age (PA), and pipe material (PM) are influential parameters for the thickness of deposits. Here, we pay close attention to the most influential parameters, which can help improve forecast prediction accuracy.

Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers.

An electroencephalogram (EEG)-based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China.

Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R2 = 86.87%) than the back-propagation neural network (R2 = 75.14%) and multiple linear regression (R2 = 61.93%).

Ultra-rapid delivery of specialty field hospitals to combat COVID-19: Lessons learned from the Leishenshan Hospital project in Wuhan.

With the outbreak of the 2019 novel coronavirus (COVID-19) epidemic in Wuhan, China, in January 2020, the escalating number of confirmed and suspected cases overwhelmed the admission capacity of the designated hospitals. Two specialty field hospitals-Huoshenshan and Leishenshan-were designed, built and commissioned in record time (9-12 days) to address the outbreak. This study documents the design and construction of Leishenshan Hospital. Based on data collected from various sources such as the semi-structured interviews of key stakeholders from Leishenshan Hospital, this study found that adhering to a product, organization, and process (POP) modeling approach combined with building information modeling (BIM) allowed for the ultra-rapid creation, management, and communication of project-related information, resulting in the successful development of this fully functional, state-of-the-art infectious disease specialty hospital. With the unfortunate ongoing international COVID-19 outbreak, many countries and regions face similar hospital capacity problems. It is thus expected that the lessons learned from the design, construction and commissioning of Leishenshan Hospital can provide a valuable reference to the development of specialty field hospitals in other countries and regions.

Understanding the Factors and the Corresponding Interactions That Influence Construction Worker Safety Performance from a Competency-Model-Based Perspective: Evidence from Scaffolders in China.

Purpose: Construction workers' reactions to safety-related issues during operation vary from person to person due to their different occupational levels, which can be attributed to various influencing factors and their correspondingly complicated interactions. This research aims to propose an integrated framework to combine the concepts of these factors and provide a holistic interpretation of the interrelationship among them. Methods: Based on items that were mainly extracted from competency theory, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to identify the critical factors from the data collected from 243 scaffolders on Wuhan Metro construction sites. The interactions among the identified factors were then analyzed, and the safety competency model was thus established with the use of structural equation modeling (SEM). Results: A total of 17 items were identified as critical to workers' safety competency, and these were further tested and attributed to four factors: (1) individual character and inclination; (2) self-adjustment and adaptability; (3) working attitudes; (4) safety-related operation qualification. Subsequent analysis showed that all the factors significantly contributed to one's safety competency, and individual character and inclination contributed most to the formation of one's ability, while the intermediating effects of self-adjustment and adaptability should not be neglected both in theoretical and practical terms. The resultant safety competency model consisting of these four factors was revealed to share a hierarchical structure with the classical competency model. Significance: This study provided an integrated theoretical framework and a set of modeling approaches to combine the related concepts and facilitate a greater understanding of construction safety in terms of workers' characteristics and behaviors. Practical implications: This study presented a tentative approach for assessing construction workers' safety competency, as well as emphasized to the managers and professionals the necessity of developing training systems to ensure workers are integrated into a crew in an appropriate and smooth manner. Limitations and Future Work: The volume and the scope of samples impeded the study from achieving a more generalized result and a more cost-efficient data collection approach is in need of development for a comprehensive and in-depth investigation.

Identification of Safety-Related Opinion Leaders among Construction Workers: Evidence from Scaffolders of Metro Construction in Wuhan, China.

This study aimed to reveal opinion leaders who could impact their coworkers' safety-related performance in Chinese construction teams. Questionnaires were distributed to 586 scaffolders in Wuhan to understand their opinions about influencing their coworkers, serving as the foundation for a social network analysis to identify the potential opinion leaders among workers. A further controlled trial with the identified workers was conducted to select real opinion leaders by comparing their influence on others' safety-related behavior, followed by an association analysis to profile these opinion leaders. Two main sources of opinion leaders were identified: foremen and seasoned workers. Implementing interventions through opinion leaders resulted in better safety-related behavior performance. Furthermore, compared with education level, the association analysis results indicated that one's practical skills and familiarity with respondents was more important in the formulation of opinion leaders. This research introduces the concept of opinion leaders into construction safety and proposes an approach to identify and validate opinion leaders within a crew, thus providing a tool to improve behavior promotion on sites, as well as a new perspective for viewing interactions among workers.