Sp1 and KLF15 regulate basal transcription of the human LRP5 gene

<p>Abstract</p> <p>Background</p> <p>LRP5, a member of the low density lipoprotein receptor superfamily, regulates diverse developmental processes in embryogenesis and maintains physiological homeostasis in adult organisms. However, how the expression of human <it>LRP5 </it>gene is regulated remains unclear.</p> <p>Results</p> <p>In order to characterize the transcriptional regulation of human <it>LRP5 </it>gene, we cloned the 5' flanking region and evaluated its transcriptional activity in a luciferase reporter system. We demonstrated that both KLF15 and Sp1 binding sites between -72 bp and -53 bp contribute to the transcriptional activation of human <it>LRP5 </it>promoter. Chromatin immunoprecipitation assay demonstrated that the ubiquitous transcription factors KLF15 and Sp1 bind to this region. Using <it>Drosophila </it>SL2 cells, we showed that KLF15 and Sp1 trans-activated the <it>LRP5 </it>promoter in a manner dependent on the presence of Sp1-binding and KLF15-binding motifs.</p> <p>Conclusions</p> <p>Both KLF15 and Sp1 binding sites contribute to the basal activity of human <it>LRP5 </it>promoter. This study provides the first insight into the mechanisms by which transcription of human <it>LRP5 </it>gene is regulated.</p

Two Pricing Mechanisms in Sponsored Search Advertising

Sponsored search advertising has grown rapidly since the last decade and is now a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers’ bidding. This paper studies and compares two pricing mechanisms: the generalized second-price auction (GSP) where the winner at the last ad position pays the larger value between the highest losing bid and reserve price, and the GSP with a posted reserve price (APR) where the winner at the last position pays the reserve price. We show that if advertisers’ per-click value has an increasing generalized failure rate, the search engine’s revenue rate is quasi-concave and hence there exists an optimal reserve price under both mechanisms. While the number of advertisers and the number of ad positions have no effect on the selection of reserve price in GSP, the optimal reserve price is affected by both factors in APR and it should be set higher than GSP

Two Pricing Mechanisms in Sponsored Search Advertising

Sponsored search advertising has grown rapidly since the last decade and is now a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers’ bidding. This paper studies and compares two pricing mechanisms: the generalized second-price auction (GSP) where the winner at the last ad position pays the larger value between the highest losing bid and reserve price, and the GSP with a posted reserve price (APR) where the winner at the last position pays the reserve price. We show that if advertisers’ per-click value has an increasing generalized failure rate, the search engine’s revenue rate is quasi-concave and hence there exists an optimal reserve price under both mechanisms. While the number of advertisers and the number of ad positions have no effect on the selection of reserve price in GSP, the optimal reserve price is affected by both factors in APR and it should be set higher than GSP

A revenue management model for products with two capacity dimensions

Many perishable products and services have multiple capacity attributes. Shipping capacity of container liners, for example, is measured by both volume and weight. Containers with different size consume various capacities in the two dimensions. Restaurant revenue management aims to maximize the revenue per available seat-hour that captures both the number of dining tables and service manpower. Similar issues arise in the air cargo, trucking and health care industries. We study the revenue management problem with two capacity features and formulate the problem as a continuous-time stochastic control model. Unlike heuristic approaches, we derive the optimal solution in an analytical form. Computation of the optimal solution is fairly efficient. With certain conditions we explore the structural properties of the optimal solution. We show that if the revenue rate is concave in the capacity usage, the expected value of marginal capacity is monotone. As a result, the control policy is featured by a sequence of thresholds which displays a significant difference when the remaining capacity-mix varies. Numerical examples are provided.Network revenue management Multi-dimensional capacity Dynamic pricing Monotonicity