Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

Experiments as Code and its application to VR studies in human-building interaction.

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

A new coumarin glucoside from Angelica pubescens.

A new coumarin glucoside named angelol A-3'-ß-D-glucoside and four known compounds (2-5) were isolated from the root of Angelica pubescens. Their chemical structures were elucidated by extensive spectroscopic methods involving HR-ESI-MS, 1D and 2D NMR. The absolute configuration of compound 1 was confirmed by the CD experiment. All compounds were tested for nitric oxide (NO) inhibitory activity in vitro. The results showed that all compounds exhibited weak to moderate inhibition activities of NO production except compound 5.

Calycosin Ameliorates Bleomycin-Induced Pulmonary Fibrosis via Suppressing Oxidative Stress, Apoptosis, and Enhancing Autophagy.

Calycosin (CA) is a flavonoid extracted from the root of Astragalus membranaceus and has antioxidant, anti-inflammation, and antiapoptosis properties. The objective of this study was to investigate the efficacy of CA in protecting against pulmonary fibrosis. CA (14 mg/kg) and SB216763 (20 mg/kg) were administrated to bleomycin-induced pulmonary fibrosis mice for 3 weeks. The results concluded that CA alleviated the inflammation and collagen deposition in pulmonary fibrosis. In addition, CA reduced MDA level, enhanced SOD and TAC activities, and increased the activity of the Nrf2/HO-1 pathway. CA also regulated the expressions of apoptosis-related proteins. Moreover, CA enhanced autophagy via upregulating LC3, beclin1, PINK1, and reducing p62. CA also increased expression of LAMP1 and TFEB, and inhibited the release of lysosome enzymes from ruptured lysosomes. These results provide new evidence that CA protects against pulmonary fibrosis through inhibiting oxidative stress and apoptosis. In addition, autophagy abnormality and lysosome dysfunction are restored by CA.

[Chemical constituents of cyclic peptides from fibrous roots of Pseudostellaria heterophylla].

Four cyclic peptides were isolated from the 75% ethanol extract of the fibrous roots of Pseudostellaria heterophylla by silica gel, Sephadex LH-20 column chromatography, and semi-preparative HPLC. Through mass spectrometry, NMR and other methods, they were identified as pseudostellarin L(1), heterophyllin B(2), pseudostellarin B(3), and pseudostellarin C(4). Among them, compound 1 was a new cyclic peptide, and compounds 2-4 were isolated from the fibrous roots of P. heterophylla for the first time. None of these compounds displayed cytotoxic activities against MCF-7, A549, HCT-116, and SGC-7901 cells.

A new cyclic peptide from the fibrous root of Pseudostellaria heterophylla.

A new cyclic peptide, Pseudostellarin K (1), together with thirteen known compounds, including two cyclic peptides (2 and 3), one ß-carboline alkaloid (4), two amides (5 and 6), three phenylpropanoids (7-9) and other compounds (10-14), were isolated from the fibrous root of Pseudostellaria heterophylla. Their structures were elucidated by extensive spectroscopic analysis. Compounds 1, 4-6, 10 were isolated from the genus pseudostellaria for the first time. All compounds were evaluated for cytotoxic activities against MCF-7, A549, HCT-116 and SGC-7901 cell lines by MTT assay. Unfortunately, all these compounds displayed weak cytotoxic activities.

ExteriorTag: Automatic Semantic Annotation of BIM Building Exterior Via Voxel Index Analysis.

The growing demand for building information modeling (BIM) data and ubiquitous applications make it increasingly necessary to establish a reliable way to share the models on lightweight devices. Building scenes have strong occlusion features and the building exterior plays an important role in digital devices with limited computational resources. This allows the possibility to reduce the resource consumption while roaming in outdoor scenes by culling away the interior building data. This article addresses the task of automatic annotation of BIM building exterior via voxel index analysis. We showcase the research of using industry foundation classes (IFC) and other mainstream formats as our input data and proposed an automatic algorithm for annotating the building exterior. Afterward, a practical and accurate voxel index analysis procedure is designed for frequently flawed models. The annotation can be added directly into the original data file under the same IFC standard, avoiding the complex procedure and information loss in semantics mapping between different standards. The final examinations show the robustness of our algorithm and the capability of handling large BIM building models.

Intelligent spectral algorithm for pigments visualization, classification and identification based on Raman spectra.

Raman spectroscopy is a molecular vibrational spectroscopic technique has developed rapidly in recent years, especially in rapid field detection. In this paper, we discuss the Raman spectral pretreatment method and classification algorithm by using nearly 300 pigments spectral data as an example. Here, more than 5 kinds of classification algorithms such as SVM, KNN, ANN and et al are used to sovle the problem of pigments visualization, classification and identification via Raman spectral, and the results show that most of the algorithms fit well, with an accuracy of 90%. Moreover, SNR (Signal to noise ratio) is introduced to evaluate the stability of our algorithm. When the SNR is low, the accuracy of the algorithm decreases sharply. When the SNR was 1, the accuracy rate reached the highest value of 39.46%. In order to slove this problem, the flattopwin, hanning, blackman algorithm was introduced to denoise the signal with low SNR, even when SNR = 1, the signal is 80% accurate. It is proved that in the extreme case of this application, the algorithm still maintains good accuracy, and our research pave the way to use interlligent algorithms to solve the problems in the fields of Raman spectral detection.

Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality.

Dense crowds in public spaces have often caused serious security issues at large events. In this paper, we study the 2010 Love Parade disaster, for which a large amount of data (e.g. research papers, professional reports and video footage) exist. We reproduce the Love Parade disaster in a three-dimensional computer simulation calibrated with data from the actual event and using the social force model for pedestrian behaviour. Moreover, we simulate several crowd management strategies and investigate their ability to prevent the disaster. We evaluate these strategies in virtual reality (VR) by measuring the response and arousal of participants while experiencing the simulated event from a festival attendee's perspective. Overall, we find that opening an additional exit and removing the police cordons could have significantly reduced the number of casualties. We also find that this strategy affects the physiological responses of the participants in VR.

The interaction between map complexity and crowd movement on navigation decisions in virtual reality.

A carefully designed map can reduce pedestrians' cognitive load during wayfinding and may be an especially useful navigation aid in crowded public environments. In the present paper, we report three studies that investigated the effects of map complexity and crowd movement on wayfinding time, accuracy and hesitation using both online and laboratory-based networked virtual reality (VR) platforms. In the online study, we found that simple map designs led to shorter decision times and higher accuracy compared to complex map designs. In the networked VR set-up, we found that co-present participants made very few errors. In the final VR study, we replayed the traces of participants' avatars from the second study so that they indicated a different direction than the maps. In this scenario, we found an interaction between map design and crowd movement in terms of decision time and the distributions of locations at which participants hesitated. Together, these findings can help the designers of maps for public spaces account for the movements of real crowds.

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

Virtual Reality Experiments with Physiological Measures.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.