Molecular cloning, sequence, expression, and processing of the interleukin 16 precursor

Interleukin 16 (IL-16) has been shown to function as chemoattractant factor, as a modulator of T-cell activation, and as an inhibitor of immunodeficiency virus replication. The recent identification of inconsistencies in published IL-16 cDNA nucleotide sequences led to the proposal that IL-16 is synthesized in the form of a large precursor protein (pro-IL-16). To identify the true transcriptional start of the IL-16 mRNA rapid amplification of cDNA ends methods were applied. The complete pro-IL-16 cDNA was subsequently molecularly cloned, sequenced, and expressed in COS-7 cells. We report here that pro-IL-16 is most likely synthesized as a 67-kDa protein and is encoded from a major 2.6-kb transcript. Recombinant pro-IL-16 polypeptides are specifically cleaved in lysates of CD8(+) cells, suggesting that the naturally secreted bioactive form of IL-16 is smaller than the originally published 130 amino acids fragment. Moreover, in contrast to other interleukins such as IL-15, IL-16 mRNA expression is almost exclusively limited to lymphatic tissues underlining the potential of IL-16 as an immune regulatory molecule.

Information processing in the human brain: magnetoencephalographic approach.

Rapid progress in effective methods to image brain functions has revolutionized neuroscience. It is now possible to study noninvasively in humans neural processes that were previously only accessible in experimental animals and in brain-injured patients. In this endeavor, positron emission tomography has been the leader, but the superconducting quantum interference device-based magnetoencephalography (MEG) is gaining a firm role, too. With the advent of instruments covering the whole scalp, MEG, typically with 5-mm spatial and 1-ms temporal resolution, allows neuroscientists to track cortical functions accurately in time and space. We present five representative examples of recent MEG studies in our laboratory that demonstrate the usefulness of whole-head magnetoencephalography in investigations of spatiotemporal dynamics of cortical signal processing.

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.