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

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

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

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

Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

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

Phenotyping renal leukocyte subsets by four-color flow cytometry: Characterization of chemokine receptor expression

To investigate mechanisms of cell-mediated injury in renal inflammatory disease it is critical to determine the surface phenotype of infiltrating renal leukocyte subsets. However, the cell-specific expression of many leukocyte receptors is difficult to characterize in vivo. Here, we report a protocol based on flow cytometry that allows simultaneous characterization of surface receptor expression on different subsets of infiltrating renal leukocytes. The described technique combines an adapted method to prepare single cell suspensions from whole kidneys with subsequent four-color flow cytometry. We recently applied this technique to determine the differential expression of murine chemokine receptors CCR2 and CCR5 on infiltrating renal leukocyte subsets. In this article, we summarize our current findings on the validity of the method as compared with immunohistology and in situ hybridization in two murine models of nonimmune ( obstructive nephropathy) and immune-mediated ( lupus nephritis) inflammatory renal disease. Flow cytometry analysis revealed an accumulation of CCR5-, but not CCR2-positive lymphocytes in inflamed kidneys, compared to the peripheral blood. Particularly renal CD8(+) cells expressed CCR5 (79% in obstructed kidneys, 90% in lupus nephritis). In both models, infiltrating renal macrophages were positive for CCR2 and CCR5. These data corresponded to immunohistological and in situ hybridization results. They demonstrate that flow cytometric analysis of single cell suspensions prepared from inflamed kidneys is a rapid and reliable technique to characterize and quantify surface receptor expression on infiltrating renal leukocyte subsets