Discrete populations of isotype-switched memory B lymphocytes are maintained in murine spleen and bone marrow

At present, it is not clear how memory B lymphocytes are maintained over time, and whether only as circulating cells or also residing in particular tissues. Here we describe distinct populations of isotype-switched memory B lymphocytes (Bsm) of murine spleen and bone marrow, identified according to individual transcriptional signature and B cell receptor repertoire. A population of marginal zone-like cells is located exclusively in the spleen, while a population of quiescent Bsm is found only in the bone marrow. Three further resident populations, present in spleen and bone marrow, represent transitional and follicular B cells and B1 cells, respectively. A population representing 10-20% of spleen and bone marrow memory B cells is the only one qualifying as circulating. In the bone marrow, all cells individually dock onto VCAM1+ stromal cells and, reminiscent of resident memory T and plasma cells, are void of activation, proliferation and mobility

Extending k-means with the description comes first approach

This paper describes a technique for clustering large collections of short and medium length text documents such as press articles, news stories and the like. The technique called description comes first (DCF) consists of identification of related document clusters, selection of salient phrases relevant to these clusters and reallocation of documents matching the selected phrases to form final document groups. The advantages of this technique include more comprehensive cluster labels and clearer (more transparent) relationship between cluster labels and their content. We demonstrate the DCF by taking a standard k-means algorithm as a baseline and weaving DCF elements into it; the outcome is the descriptive k-means (DKM) algorithm. The paper goes through technical background explaining how to implement DKM efficiently and ends with the description of an experiment measuring clustering quality on a benchmark document collection 20-newsgroups. Short fragments of this paper appeared at the poster session of the RIAO 2007 conference, Pittsburgh, PA, USA (electronic proceedings only)

An experimental evaluation of two approaches to mining context based sequential patterns

The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem. In this extension, two kinds of context attributes are introduced for describing the source of a sequence and for each element inside this sequence. Such context based sequential patterns may be discovered by a new algorithm, called Context Mapping Improved, specific for handling attributes with similarity functions. For numerical attributes an alternative approach could include their pre-discretization, transforming discrete values into artificial items and, then, using an adaptation of an algorithm for mining sequential patterns from nominal items. The aim of this paper is to experimentally compare these two approaches to mine artificially generated sequence databases with numerical context attributes where several reference patterns are hidden. The results of experiments show that the Context Mapping Improved algorithm has led to better re-discovery of reference patterns. Moreover, a new measure for comparing two sets of context based patterns is introduced