The principles of allelic exclusion state that each B cell expresses a single light and heavy chain pair. Here, we show that B cells with both kappa and lambda light chains (Igκ and Igλ) are enriched in some patients with the systemic autoimmune disease systemic lupus erythematosus (SLE), but not in the systemic autoimmune disease control granulomatosis with polyangiitis. Detection of dual Igκ ...

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (K...

The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflic...

Após o fim da caça das baleias-francas, a interação com as atividades antrópicas, como atividade pesqueira, é uma principais ameaças à conservação desses animais no Sul do Brasil. De modo identificar potenciais áreas de interação, foram realizadas entrevistas pescadores artesanais ao longo Área Proteção Ambiental (APA Baleia-Franca) identificando pesqueira na unidade uso sustentável. Utilizando...

Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on (accidents roads, leakages pipes, species along rivers, etc.). In the last decade, they required extension spatial KDE. Several versions Network KDE (NKDE) have been propos...

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Densitybased Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of...

With the adoption of Nepomuk as an organic part of KDE the semantic desktop became a reality to a great number of users and is employed by a growing number of applications. Thus, the amount of semantic data is constantly growing on the desktop. Therefore users need a way to access this data outside of the limiting use cases of the applications employing Nepomuk-KDE. We aim to assist users in bu...

We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE) to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided...

Modal regression estimates the local modes of the distribution of Y given X = x, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of the joint distribution of Y and X. We derive asymptotic error bounds for this m...