Regulation and Capacity Competition in Health Care: Evidence from U.S. Dialysis Markets

This paper studies entry and capacity decisions by dialysis providers in the United States. We estimate a structural model where providers make continuous strategic choices of capacity based on their private information about own costs and knowledge of the distribution of competitors’ private information. We evaluate the impact on the market structure and providers’ profits under counterfactual regulatory policies that increase the costs or reduce the payment per unit of capacity. We find that these policies reduce the market capacity as measured by the number of dialysis stations. However, the downward-sloping reaction curve shields some providers from negative profit shocks in certain markets. The paper also has a methodological contribution in that it proposes new estimators for Bayesian games with continuous actions

Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such microstructure-graph-based GNN model therefore enables an accurate and interpretable prediction of the properties of polycrystalline materials.Comment: 28 pages, 6 figures

Graph Neural Network for Predicting the Effective Properties of Polycrystalline Materials: A Comprehensive Analysis

We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A large-scale dataset with >5000 different three-dimensional polycrystalline microstructures of finite-width grain boundary is generated by Voronoi tessellation and processing of the electron backscatter diffraction images. The effective ion conductivities and elastic stiffness coefficients of these microstructures are calculated by high-throughput physics-based simulations. The optimized PGNN model achieves a low error of <1.4% in predicting all three diagonal components of the effective Li-ion conductivity matrix, outperforming a linear regression model and two baseline convolutional neural network models. Sequential forward selection method is used to quantify the relative importance of selecting individual grain (boundary) features to improving the property prediction accuracy, through which both the critical and unwanted node (edge) feature can be determined. The extrapolation performance of the trained PGNN model is also investigated. The transfer learning performance is evaluated by using the PGNN model pretrained for predicting conductivities to predict the elastic properties of the same set of microstructures.Comment: 23 pages, 6 figures; added testing results on a new dataset and sequential feature selectio

Design and Implementation of Service-Oriented Expert System

In recent years, the Internet technologies are well developed and the Internet is filled with all kinds of information. Since the data storage is increasingly distributed and data formats are more diverged, data collection and integration for providing value- added services have gradually become important topics. In this study, we propose the Service-Oriented Expert System (SOES) based on Service Component Architecture (SCA) which can make the services on different platforms turn into a common service component on the Internet, concatenate all the service components by combining with the Enterprise Service Bus (ESB), and use both expert rules and data mining techniques to perform the data classification. The SOES is applied to analyze the annual financial information derived from electronic industry in the Taiwan Economic Journal (TEJ) during 2006 to 2008 for discovering the financial crisis enterprises. The experiment results show that using expert rules and decision tree to find the financial crisis enterprise is higher performance

Mammalian DNA2 helicase/nuclease cleaves G-quadruplex DNA and is required for telomere integrity

Efficient and faithful replication of telomeric DNA is critical for maintaining genome integrity. The G-quadruplex (G4) structure arising in the repetitive TTAGGG sequence is thought to stall replication forks, impairing efficient telomere replication and leading to telomere instabilities. However, pathways modulating telomeric G4 are poorly understood, and it is unclear whether defects in these pathways contribute to genome instabilities in vivo. Here, we report that mammalian DNA2 helicase/nuclease recognizes and cleaves telomeric G4 in vitro. Consistent with DNA2’s role in removing G4, DNA2 deficiency in mouse cells leads to telomere replication defects, elevating the levels of fragile telomeres (FTs) and sister telomere associations (STAs). Such telomere defects are enhanced by stabilizers of G4. Moreover, DNA2 deficiency induces telomere DNA damage and chromosome segregation errors, resulting in tetraploidy and aneuploidy. Consequently, DNA2-deficient mice develop aneuploidy-associated cancers containing dysfunctional telomeres. Collectively, our genetic, cytological, and biochemical results suggest that mammalian DNA2 reduces replication stress at telomeres, thereby preserving genome stability and suppressing cancer development, and that this may involve, at least in part, nucleolytic processing of telomeric G4

A Selective Small Molecule DNA2 Inhibitor for Sensitization of Human Cancer Cells to Chemotherapy

Cancer cells frequently up-regulate DNA replication and repair proteins such as the multifunctional DNA2 nuclease/helicase, counteracting DNA damage due to replication stress and promoting survival. Therefore, we hypothesized that blocking both DNA replication and repair by inhibiting the bifunctional DNA2 could be a potent strategy to sensitize cancer cells to stresses from radiation or chemotherapeutic agents. We show that homozygous deletion of DNA2 sensitizes cells to ionizing radiation and camptothecin (CPT). Using a virtual high throughput screen, we identify 4-hydroxy-8-nitroquinoline-3-carboxylic acid (C5) as an effective and selective inhibitor of DNA2. Mutagenesis and biochemical analysis define the C5 binding pocket at a DNA-binding motif that is shared by the nuclease and helicase activities, consistent with structural studies that suggest that DNA binding to the helicase domain is necessary for nuclease activity. C5 targets the known functions of DNA2 in vivo: C5 inhibits resection at stalled forks as well as reducing recombination. C5 is an even more potent inhibitor of restart of stalled DNA replication forks and over-resection of nascent DNA in cells defective in replication fork protection, including BRCA2 and BOD1L. C5 sensitizes cells to CPT and synergizes with PARP inhibitors

Analysis of tall fescue ESTs representing different abiotic stresses, tissue types and developmental stages

<p>Abstract</p> <p>Background</p> <p>Tall fescue (<it>Festuca arundinacea </it>Schreb) is a major cool season forage and turf grass species grown in the temperate regions of the world. In this paper we report the generation of a tall fescue expressed sequence tag (EST) database developed from nine cDNA libraries representing tissues from different plant organs, developmental stages, and abiotic stress factors. The results of inter-library and library-specific <it>in silico </it>expression analyses of these ESTs are also reported.</p> <p>Results</p> <p>A total of 41,516 ESTs were generated from nine cDNA libraries of tall fescue representing tissues from different plant organs, developmental stages, and abiotic stress conditions. The <it>Festuca </it>Gene Index (FaGI) has been established. To date, this represents the first publicly available tall fescue EST database. <it>In silico </it>gene expression studies using these ESTs were performed to understand stress responses in tall fescue. A large number of ESTs of known stress response gene were identified from stressed tissue libraries. These ESTs represent gene homologues of heat-shock and oxidative stress proteins, and various transcription factor protein families. Highly expressed ESTs representing genes of unknown functions were also identified in the stressed tissue libraries.</p> <p>Conclusion</p> <p>FaGI provides a useful resource for genomics studies of tall fescue and other closely related forage and turf grass species. Comparative genomic analyses between tall fescue and other grass species, including ryegrasses (<it>Lolium </it>sp.), meadow fescue (<it>F. pratensis</it>) and tetraploid fescue (<it>F. arundinacea var glaucescens</it>) will benefit from this database. These ESTs are an excellent resource for the development of simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) PCR-based molecular markers.</p