A Typology of Interorganizational Relationships: Implications for IS Design

We are currently witnessing an explosion in the number and variety of interorganizational relationships reported in the business press that are often described using buzzwords such as \u27partnership\u27 and strategic alliance\u27. Unfortunately, theory lags practice in the examination of this phenomenon that is increasingly becoming the model for success in many industries. From the perspective of Transaction Cost Economics, a dominant theoretical anchor, these interorganizational relationships are considered to fall between the well described extremes of market exchange and hierarchically controlled exchanges and belong to a less understood type termed the \u27hybrid\u27 (Clemons, Reddi, Row 1993, Hennart 1994). Information Technology (IT) is often the fundamental enabler of these non traditional forms of organizing (Quinn 1992) and a theoretical understanding of the phenomenon is indispensable to enable the effective exploitation of IT capabilities in such relationships. In an exploratory study to derive a process based understanding of interorganizational relationships in the distribution channel, we find evidence that interorganizational relationships can be classified into four distinct types. The four types differ significantly in the processes of operational control and boundary management as well as in the nature of information exchange and the role of information technologies. The results provide a greater understanding of action in interorganizational relationships and have implications for the design of interorganizational information systems (IOS)

Gaps That Matter: The Influence of Perspectives on IS Service Quality

It is now well established that firms need to make significant changes to organizational processes to derive advantages from the deployment of Information Technologies (IT). The strength of the interface between Information Systems providers (the IS group) and their users in organizations is a critical determinant of the firm\u27s ability to visualize, design and deploy appropriate IT solutions and make the necessary organizational design changes to utilize the investments in IT (Davenport 1992). While the creation of partnerships between IS groups and their users has often been highlighted as important to ensure effective IT implementation (Lasher, Ives, Jarvenpaa 1991), the critical dimensions along which the two groups need to be convergent and the impact of convergence on outcomes for users has received little attention. Using the theoretical lens of role theory, we examine the impact of convergence in perspectives on six key issues between IS groups and the users that they serve in three large organizations. Our results provide empirical support for the view that convergence in the perspectives of IS and user groups is associated with increased levels of Service Quality. One contribution of this study is the explication of key issues on which convergence of perspectives between IS and User groups is central to the improvement of the quality of services provided by the IS group

Posterior Association Networks and Functional Modules Inferred from Rich Phenotypes of Gene Perturbations

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the ‘search space’ for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html

Atomic-resolution spectroscopic imaging of ensembles of nanocatalyst particles across the life of a fuel cell

The thousandfold increase in data-collection speed enabled by aberration-corrected optics allows us to overcome an electron microscopy paradox - how to obtain atomic-resolution chemical structure in individual nanoparticles, yet record a statistically significant sample from an inhomogeneous population. This allowed us to map hundreds of Pt-Co nanoparticles to show atomic-scale elemental distributions across different stages of the catalyst aging in a proton-exchange-membrane fuel cell, and relate Pt-shell thickness to treatment, particle size, surface orientation, and ordering.Comment: 28 pages, 5 figures, accepted, nano letter

Long-term clinical outcomes in cirrhotic chronic hepatitis B patients treated with tenofovir disoproxil fumarate for up to 5 years

Background: Phase 3 clinical studies have shown that long-term treatment with tenofovir disoproxil fumarate (TDF) can suppress hepatitis B viral load and promote significant fibrosis regression and cirrhosis reversal in a majority of treated chronic hepatitis B (CHB) patients. This retrospective analysis investigated the impact of baseline cirrhosis status on virologic, serologic, and histologic outcomes in patients treated with TDF. Methods: Patients enrolled in studies GS-US-174-0102 and GS-US-174-0103 who had baseline liver biopsy–diagnosed cirrhosis and entered the open-label phase of the studies were included in the virologic and serologic analyses. Patients (both HBeAg positive and negative) with paired liver biopsies at baseline and 5 years (N = 348) were included in a histologic analysis. Results: After 5 years on study, comparing patients with and without baseline cirrhosis, respectively: 99.2 and 98.0 % achieved virologic response (hepatitis B viral load < 69 IU/ml) (p = 0.686); 79.7 and 81.9 % had normal serum levels of alanine aminotransferase (p = 0.586); 4.0 and 1.2 % developed hepatocellular carcinoma (p = 0.044). In HBeAg-positive patients with and without baseline cirrhosis, HBsAg loss occurred in 14.4 and 8.3 % of patients, respectively (p = 0.188). One HBeAg-negative patient had HBsAg loss. Conclusions: This represents the largest analyses to date of CHB patients with sequential liver biopsies demonstrating that treatment with TDF for up to 5 years is associated with favorable virologic, serologic, and histologic outcomes, regardless of baseline cirrhosis status. Notably, histologic improvement was observed in the majority of cirrhotic and noncirrhotic patients

Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data

<p>Abstract</p> <p>Background</p> <p>Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context.</p> <p>Results</p> <p>We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'.</p> <p>Conclusions</p> <p>Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.</p

Inferring Pathway Activity toward Precise Disease Classification

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease

Lysophosphatidic acid and sphingosine-1-phosphate promote morphogenesis and block invasion of prostate cancer cells in three-dimensional organotypic models

Normal prostate and some malignant prostate cancer (PrCa) cell lines undergo acinar differentiation and form spheroids in three-dimensional (3-D) organotypic culture. Acini formed by PC-3 and PC-3M, less pronounced also in other PrCa cell lines, spontaneously undergo an invasive switch, leading to the disintegration of epithelial structures and the basal lamina, and formation of invadopodia. This demonstrates the highly dynamic nature of epithelial plasticity, balancing epithelial-to-mesenchymal transition against metastable acinar differentiation. This study assessed the role of lipid metabolites on epithelial maturation. PC-3 cells completely failed to form acinar structures in delipidated serum. Adding back lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P) rescued acinar morphogenesis and repressed invasion effectively. Blocking LPA receptor 1 (LPAR1) functions by siRNA (small interference RNA) or the specific LPAR1 inhibitor Ki16425 promoted invasion, while silencing of other G-protein-coupled receptors responsive to LPA or S1P mainly caused growth arrest or had no effects. The G-proteins Gα12/13 and Gαi were identified as key mediators of LPA signalling via stimulation of RhoA and Rho kinases ROCK1 and 2, activating Rac1, while inhibition of adenylate cyclase and accumulation of cAMP may be secondary. Interfering with these pathways specifically impeded epithelial polarization in transformed cells. In contrast, blocking the same pathways in non-transformed, normal cells promoted differentiation. We conclude that LPA and LPAR1 effectively promote epithelial maturation and block invasion of PrCa cells in 3-D culture. The analysis of clinical transcriptome data confirmed reduced expression of LPAR1 in a subset of PrCa's. Our study demonstrates a metastasis-suppressor function for LPAR1 and Gα12/13 signalling, regulating cell motility and invasion versus epithelial maturation