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- 저자 : Weimin He (검색결과 2 건)
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Yue He,Xu Xu,Jianfeng Qiu,Weimin Yin,Yang-Hu Sima,Shi-Qing Xu 한국응용곤충학회 2020 Journal of Asia-Pacific Entomology Vol.23 No.1
Acetaminophen (APAP) is an effective and widely used analgesic. However, APAP overdose is the principal cause of acute liver failure (ALF) in many countries. Here, we report the phenomenon of liver melanization which occurred before APAP-induced ALF in mice. A melanic surface induced by APAP which was time- and dosedependent in the silkworm invertebrate model was observed. In addition, an APAP-induced acute tissue failure model (ATF) was established using a metabolic detoxification tissue fat body which simulated mouse liver. An investigation of the anabolic mechanism of melanin in experimental animals showed that dopaquinone and dopamine which were synthesized from tyrosine via dopa in silkworms were further metabolized to melanin, while in mice, epinephrine was synthesized via the dopamine branch and melanin was only synthesized via the dopaquinone branch. On this basis, it is proposed that melanin-metabolic levels in plasma could be used as an early diagnostic marker of APAP overdose and the black spots on insect epidermis could be used as a fast detection model of toxicity.
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On Uncertain Probabilistic Data Modeling
Teng Lv,Ping Yan,Weimin He 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.12
Uncertainty in data is caused by various reasons including data itself, data mapping, and data policy. For data itself, data are uncertain because of various reasons. For example, data from a sensor network, Internet of Things or Radio Frequency Identification is often inaccurate and uncertain because of devices or environmental factors. For data mapping, integrated data from various heterogonous data sources is commonly uncertain because of uncertain data mapping, data inconsistency, missing data, and dirty data. For data policy, data is modified or hided for policies of data privacy and data confidentiality in an organization. But traditional deterministic data management mainly deals with deterministic data which is precise and certain, and cannot process uncertain data. Modeling uncertain data is a foundation of other technologies for further processing data, such as indexing, querying, searching, mapping, integrating, and mining data, etc. Probabilistic data models of relational databases, XML data and graph data are widely used in many applications and areas today, such as World Wide Web, semantic web, sensor networks, Internet of Things, mobile ad-hoc networks, social networks, traffic networks, biological networks, genome databases, and medical records, etc. This paper presents a survey study of different probabilistic models of uncertain data in relational databases, XML data, and graph data, respectively. The advantages and disadvantages of each kind of probabilistic modes are analyzed and compared. Further open topics of modeling uncertain probabilistic data such as semantic and computation aspects are discussed in the paper. Criteria for modeling uncertain data, such as expressive power, complexity, efficiency, extension are also proposed in the paper.
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