Taxonomy of the order Bunyavirales : second update 2018

In October 2018, the order Bunyavirales was amended by inclusion of the family Arenaviridae, abolishment of three families, creation of three new families, 19 new genera, and 14 new species, and renaming of three genera and 22 species. This article presents the updated taxonomy of the order Bunyavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV).Non peer reviewe

Taxonomy of the family Arenaviridae and the order Bunyavirales : update 2018

In 2018, the family Arenaviridae was expanded by inclusion of 1 new genus and 5 novel species. At the same time, the recently established order Bunyavirales was expanded by 3 species. This article presents the updated taxonomy of the family Arenaviridae and the order Bunyavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV) and summarizes additional taxonomic proposals that may affect the order in the near future.Peer reviewe

Herstellung und massenspektrometrische Untersuchung metallischer und ionischer Mikrocluster

SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Identifying regulated genes through the correlation structure of time dependent microarray data

Since microarray technology has become widely available, it is possible to study the transcription of thousands of genes simultaneously. Experiments can be conducted in which measurements of transcription levels on the same set of genes are taken repeatedly over time. Often these time course gene transcription experiments aim to understand the behavior of genes in a certain process, such as the cell cycle, or the organism\u27s reaction to injury or disease. The transcription levels of genes are influenced by many factors: genes may be regulated by other genes, as well as by enzyme or protein levels in the cell, or by processes such as DNA methylation. Understanding and describing an organism\u27s entire gene regulatory network is an ambitious goal that is considered here in the context of time dependent microarray data. A method is proposed that uses a state space model to represent a gene regulatory network. An algorithm is developed that estimates the optimal model parameters, as well as the behavior of hidden regulators. Based on the model parameter estimates, a criterion is proposed that describes the degree of regulation of every observed gene. Biological assumptions are incorporated to place restrictions on the model parameters, while mathematical restrictions assure statistical validity of the model. The power of the proposed method to identify regulated genes in time dependent microarray data is investigated via simulations and the algorithm is applied to several real microarray time series data sets. Recommendations are made for a minimum number of time point observations that a microarray experiment should include in order to achieve a desired degree of statistical separation between regulated and unregulated genes

Newton's method for interpolation by generalized polynomials of one or several variables

SIGLETIB Hannover: RN 3109(218) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman