Salvianolic acid A is a water-soluble component from Danshen, which is frequently used in traditional Chinese medicine. High performance liquid chromatography was often used to analyze content of ...salvianolic acid A. The yield of salvianolic acid A increased by the technological improvement of extraction and separation. Salvianolic acid A possessed multiple pharmacological activities, including antioxidants, myocardial ischemic protection, antithrombatic, neuroprotection, anti fibrosis, prevention of diabetes and complications. Recently, preliminary pharmacokinetics characteristics of salvianolic acid A were clarified. Based on the research literature and study work from author's laboratory, this review will focus on recent developments concerning the chemistry, pharmacology and pharmacokinetic of salvianolic acid A, and prospect further research.
Multiple extended target tracking (ETT) has gained increasing attention due to the development of high-precision LiDAR and radar sensors in automotive applications. For LiDAR point cloud-based ...vehicle tracking, this paper presents a probabilistic measurement-region association (PMRA) ETT model, which can describe the complex measurement distribution by partitioning the target extent into different regions. The PMRA model overcomes the drawbacks of previous data-region association (DRA) models by eliminating the approximation error of constrained estimation and using continuous integrals to more reliably calculate the association probabilities. Furthermore, the PMRA model is integrated with the Poisson multi-Bernoulli mixture (PMBM) filter for tracking multiple vehicles. Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both positions and extents of the vehicles comparing with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.
The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and ...efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by an ill-posed evaluation metric, known as point adjustment (PA), which results in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three aspects - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the DL potential in TSAD, utilizing both rigorously designed datasets and evaluation metrics. Experimental results demonstrate that TriAD achieves a consistent and significant performance increase over both DL and non-DL SOTA baselines. Moreover, in comparison to SOTA discord discovery algorithms, TriAD improves anomaly detection accuracy by 50 % while cutting the inference time down to just one-tenth. Illuminating the significance of rigorous datasets and evaluation metrics, this paper offers a new direction for addressing the multifaceted challenges of TSAD. The source code is publicly available at https://github.com/pseudo-Skye/TriAD.
Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous ...driving (AD). Among the existing 3D MOT frameworks for ADAS and AD, conventional point object tracking (POT) framework using the tracking-by-detection (TBD) strategy has been well studied and accepted for LiDAR and 4D imaging radar point clouds. In contrast, extended object tracking (EOT), another important framework which accepts the joint-detection-and-tracking (JDT) strategy, has rarely been explored for online 3D MOT applications. This paper provides the first systematical investigation of the EOT framework for online 3D MOT in real-world ADAS and AD scenarios. Specifically, the widely accepted TBD-POT framework, the recently investigated JDT-EOT framework, and our proposed TBD-EOT framework are compared via extensive evaluations on two open source 4D imaging radar datasets: View-of-Delft and TJ4DRadSet. Experiment results demonstrate that the conventional TBD-POT framework remains preferable for online 3D MOT with high tracking performance and low computational complexity, while the proposed TBD-EOT framework has the potential to outperform it in certain situations. However, the results also show that the JDT-EOT framework encounters multiple problems and performs inadequately in evaluation scenarios. After analyzing the causes of these phenomena based on various evaluation metrics and visualizations, we provide possible guidelines to improve the performance of these MOT frameworks on real-world data. These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.
Aberrant functionality of the cell cycle has been implicated in the pathology of traumatic SCI. Although it has been reported that the expressions of various cell cycle related proteins were altered ...significantly following SCI, detailed information on the subject remains largely unclear. The embryonic pyruvate kinase M2 (PKM2) is an important metabolic kinase in aerobic glycolysis or the warburg effect, however, its functions in central nervous system (CNS) injury remains elusive. Here we demonstrate that PKM2 was not only significantly upregulated by western blot and immunohistochemistry but certain traumatic stimuli also induced translocation of PKM2 into the nucleus in astrocytes following spinal cord injury (SCI). Furthermore, the expression levels and localization of p-β-catenin, p27, cyclin D1 and PCNA were correlated with PKM2 after SCI. In vitro, we also found that PKM2 co-immunoprecipitation with p-β-catenin and p27 respectively. Knockdown of PKM2 apparently decreased the level of PCNA, cyclinD1, p27 in primary astrocyte cells. Taken together, our findings indicate that nuclear translocation of PKM2 promotes astrocytes proliferation after SCI through modulating cell cycle signaling. These discoveries firstly uncovered the role of PKM2 in spinal cord injury and provided a potential therapeutic target for CNS injury and repair.
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Worms have become an emergent threat towards information confidentiality, integrity, and service availability. While playing an important role for people to interact with surrounding environments, ...wireless sensor networks suffer from growing security concerns posed by worms because of sensor networks' low physical security, lack of resilience and robustness of underlying operating systems, and the ever increased complexity of deployed applications. In this paper, we study worm propagation in 802.15.4 based wireless sensor networks. First we present a baseline worm model in the context of wireless sensor networks. Then we describe a preliminary study of the impact of various protocol parameters and network scenarios on worm propagation dynamics. Our simulation study can provide insight into deriving a suitable model to characterize worm propagation in sensor networks.
After Gross, Hacking, Keel, Kontsevich GHKK18 introduced the theta basis which is shown to be indexed by its highest term exponent in cluster variables of any given seed, we are interested in all the ...non-vanishing exponents in these cluster variables. We prove that the coefficients of the exponents of any cluster variable of type \(A_n\) are log-concave. We show that the cluster monomials of \(A_2\) type are log-concave. As for larger generality, we conjecture that the log-concavity of cluster monomials is also true.
The environmental changes arising from nitrogen (N) deposition and precipitation influence soil ecological processes in forest ecosystems. However, the corresponding effects of environmental changes ...on soil biota are poorly known. Soil nematodes are the important bioindicator of soil environmental change, and their responses play a key role in the feedbacks of terrestrial ecosystems to climate change. Therefore, to explore the responsive mechanisms of soil biota to N deposition and precipitation, soil nematode communities were studied after 3 years of environmental changes by water and/or N addition in a temperate forest of Changbai Mountain, Northeast China. The results showed that water combined with N addition treatment decreased the total nematode abundance in the organic horizon (O), while the opposite trend was found in the mineral horizon (A). Significant reductions in the abundances of fungivores, plant-parasites and omnivores-predators were also found in the water combined with N addition treatment. The significant effect of water interacted with N on the total nematode abundance and trophic groups indicated that the impacts of N on soil nematode communities were mediated by water availability. The synergistic effect of precipitation and N deposition on soil nematode communities was stronger than each effect alone. Structural equation modeling suggested water and N additions had direct effects on soil nematode communities. The feedback of soil nematodes to water and nitrogen addition was highly sensitive and our results indicate that minimal variations in soil properties such as those caused by climate changes can lead to severe changes in soil nematode communities.
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The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient ...solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.
Photolithography assisted chemo-mechanical etching (PLACE), a technique specifically developed for fabricating highquality large-scale photonic integrated circuits (PICs) on thin-film lithium niobate ...(TFLN), has enabled fabrication of a series of building blocks of PICs ranging from high-quality (high-Q) microresonators and low-loss waveguides to electrooptically (EO) tunable lasers and waveguide amplifiers. Aiming at high-throughput manufacturing of the PIC devices and systems, we have developed an ultra-high-speed high-resolution laser lithography fabrication system employing a high repetition rate femtosecond laser and a high-speed polygon laser scanner, by which a lithography fabrication efficiency of 4.8 cm2/h has been achieved at a spatial resolution of 200 nm. We demonstrate wafer-scale fabrication of TFLN-based photonic structures, optical phase masks as well as color printing
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