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We have various interesting time series data in our daily life, such as weather data (e.g., temperature and air pressure)and stock prices. Polyline chart is one of the most common ways to represent such time series data. We often draw multiple polylines in one space to compare the time variation of multiple values. However, it is often difficult to read the(More)
We have various interesting time-varying data in our daily life, such as weather data (e.g., temperature and air pressure) and stock prices. Such time-varying data is often associated with other information: for example, temperatures can be associated with weather, and stock prices can be associated with social or economic incidents. Meanwhile, we often(More)
For thrombotic microangiopathies (TMAs), the diagnosis of atypical hemolytic uremic syndrome (aHUS) is made by ruling out Shiga toxin-producing Escherichia coli (STEC)-associated HUS and ADAMTS13 activity-deficient thrombotic thrombocytopenic purpura (TTP), often using the exclusion criteria for secondary TMAs. Nowadays, assays for ADAMTS13 activity and(More)
The human pathogen Legionella pneumophila delivers a large array of the effector proteins into host cells using the Dot/Icm type IVB secretion system. Among the proteins composing the Dot/Icm system, an inner membrane protein DotI is known to be crucial for the secretion function but its structure and role in type IV secretion had not been elucidated. We(More)
This paper presents a technique for visualizing large scale time series data. This technique represents variation over time in data as line charts. We cannot figure out feature of time series data, when we draw the data which contain a large amount of elements as line charts in one display. To solve this problem, this technique uses a level-of-detail(More)
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