1,620 research outputs found
Cell-type specificity of regulatory elements identified by linker scanning mutagenesis in the promoter of the chicken lysozyme gene
The chicken lysozyme gene is constitutively expressed in macrophages, in oviduct cells its expression is controlled by steroid hormones, and in fibroblasts the gene is not expressed. A fusion gene consisting of promoter sequences of the lysozyme gene from –208 to +15 in front of the chloramphenicol acetyltransferase (CAT) coding region was more than 50 times less active in non-expressing cells as compared to expressing cells. In order to identify the element(s) responsible for this cell-type specificity 31 different linker scanning mutations were generated within this promoter fragment and analyzed by transient transfections in the three types of chicken cells mentioned above. Three mutation sensitive regions located around position –25, –100 and between –158 and –208 were detected in each cell type, however, several LS mutations displayed clear cell-type specific differences in their phenotypic effects. Interestingly, a few LS mutations led to an increase in promoter activity in fibroblasts suggesting that the corresponding wildtype sequences represent binding sites for negatively acting transcription factors
Reporter constructs with low background activity utilizing the cat gene
Reporter plasmids utilizing the cat gene for the analysis of promoter and enhancer sequences in vertebrate cells, were constructed. These plasmids minimize the background of transcription derived from cryptic promoters or cryptic regulatory elements within the vecto
A new method for constructing linker scanning mutants
A new procedure for the construction of linker scanning mutants is described. A plasmid containing the target DNA is randomly linearized and slightly shortened by a novel combination of established methods. After partial apurination with formic acid a specific nick or small gap is introduced at the apurinic site by exonuclease III, followed by nuclease S1 cleavage of the strand opposite the nick/gap. Synthetic linkers are ligated to the ends and plasmids having the linker inserted in the target DNA are enriched. Putative linker scanning mutants are identified by their topoisomer patterns after relaxation with topoisomerase I. This technique allows the distinction of plasmids differing in length by a single basepair. We have used this rapid and efficient strategy to generate a set of 32 linker scanning mutants covering the chicken lysozyme promoter from –208 to +1
Victory over Ignorance and Fear: The U.S. Minelaying Attack on North Vietnam
The war in Vietnam may now be described as typical of a pattern that limited wars might follow in the future: an ill-defined beginning, an intense hot phase, and an inconclusive ending. The United States made no formal declaration of war; there was no all-out mobilization of the country\u27s resources to support war, or no national political consensus to defeat the enemy decisively and force his surrender. The hot phase seems to have happened as a consequence of many individual decisions by at least three, and some will argue, four successive presidents of the United States
Gene Structure, cDNA Sequence, and mRNA Distribution
The rat HNF-3 (hepatocyte nuclear factor 3) gene family encodes three transcription factors known to be important in the regulation of gene expression in liver and lung. We have cloned and characterized the mouse genes and cDNAs for HNF-3α, β, and γ and analyzed their expression patterns in various adult tissues and mouse embryonic stages. The HNF-3 proteins are highly conserved between mouse and rat, with the exception of the amino terminus of HNF-3γ, which in mouse is more similar to those of HNF-3α and β than to the amino termini of the rat HNF-3γ protein. The mouse HNF-3 genes are small and contain only two or three (HNF-3β) exons with conserved intron-exon boundaries. The proximal promoter of the mouse HNF3β gene is remarkably similar to that of the previously cloned rat HNF-3β gene, but is different from the promoters of the HNF-3α and γ genes. The mRNA distribution of the mouse HNF-3 genes was analyzed by quantitative RNase protection with gene-specific probes. While HNF-3α and β are restricted mainly to endoderm-derived tissues (lung, liver, stomach, and small intestine), HNF-3γ is more extensively expressed, being present additionally in ovary, testis, heart, and adipose tissue, but missing from lung. Transcripts for HNF-3β and α are detected most abundantly in midgestation embryos (Day 9.5), while HNF-3γ expression peaks around Day 15.5 of gestation
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
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