The Impact of Takeovers on the Fundamental Value of Acquirers

This paper develops a new methodology to examine the financial impact of acquisitions, designed to address whether takeovers yield a positive net present value for the acquiring company. Specifically, we employ the residual income valuation method to compare the fundamental value of the acquiring company before acquisition with the fundamental value after acquisition.We apply this methodology to 303 UK acquisitions completed during 1985–1996, and compare the results with the effects of takeover on profitability and short- and long-run share returns. We find that the impact of acquisition on fundamental value is slightly negative but statistically insignificant. This result differs from the effect of takeover on profitability, which is significantly positive, and the effect of takeover on share returns, which is significantly negative

Apolipoprotein L1 gene variants associate with prevalent kidney but not prevalent cardiovascular disease in the Systolic Blood Pressure Intervention Trial.

Apolipoprotein L1 gene (APOL1) G1 and G2 coding variants are strongly associated with chronic kidney disease (CKD) in African Americans (AAs). Here APOL1 association was tested with baseline estimated glomerular filtration rate (eGFR), urine albumin:creatinine ratio (UACR), and prevalent cardiovascular disease (CVD) in 2571 AAs from the Systolic Blood Pressure Intervention Trial (SPRINT), a trial assessing effects of systolic blood pressure reduction on renal and CVD outcomes. Logistic regression models that adjusted for potentially important confounders tested for association between APOL1 risk variants and baseline clinical CVD (myocardial infarction, coronary, or carotid artery revascularization) and CKD (eGFR under 60 ml/min per 1.73 m(2) and/or UACR over 30 mg/g). AA SPRINT participants were 45.3% female with a mean (median) age of 64.3 (63) years, mean arterial pressure 100.7 (100) mm Hg, eGFR 76.3 (77.1) ml/min per 1.73 m(2), and UACR 49.9 (9.2) mg/g, and 8.2% had clinical CVD. APOL1 (recessive inheritance) was positively associated with CKD (odds ratio 1.37, 95% confidence interval 1.08-1.73) and log UACR estimated slope (β) 0.33) and negatively associated with eGFR (β -3.58), all significant. APOL1 risk variants were not significantly associated with prevalent CVD (1.02, 0.82-1.27). Thus, SPRINT data show that APOL1 risk variants are associated with mild CKD but not with prevalent CVD in AAs with a UACR under 1000 mg/g

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

INTRODUCTION. Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation. METHODS. We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies. RESULTS. We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient. CONCLUSIONS. Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.V Foundation for Cancer Research (Partners in Excellence grant

Propuesta de virtualización de servidores con Hyper-V en el centro de datos de la Facultad de Ciencias Médicas de la UNAN-Managua

La importancia del crecimiento en la potencia de cómputo y la existencia de problemas relacionados con el uso del hardware, ha hecho de la virtualización la solución más idónea para resolver tales dificultades, dentro de sus propósitos se encuentran hacer uso eficiente de los recursos y disminuir el costo total asociado a los mismos. Este trabajo de investigación fue realizado con la finalidad de proponer una solución para la virtualización servidores. La virtualización es una tecnología que permite la creación de equipos, basados en software, que reproducen el ambiente de una máquina física en sus aspectos de CPU, memoria, almacenamiento y entrada y salida de dispositivos. Se limita a trabajar básicamente con Hyper-V con el fin de acotar y definir la solución de virtualización , debido a la numerosa cantidad de soluciones que existen actualmente, como lo son VMware, Cytrix, entre otros. El enfoque principal se encontrará relacionado principalmente a la virtualización de servidores, a la disposición de Hyper-V para trabajar en cluster y al tipo de cluster que se puede implementar. El objetivo general de este trabajo es entonces, proponer una solución para efectuar la virtualización ya manera explicativa se describe como trabaja un cluster de alta disponibilidad con Hyper-V para efectuar tareas de migración de maquinas virtuales, empleando técnicas propias que vienen incorporadas en el software, como Live Migration ó Quick Migration que facilitan de gran forma la gestión y administración del entorno virtual. También se describirá brevemente los detalles técnicos para la implementación del centro de datos, la disposición de las áreas funcionales, el diagrama de distribución y otros parámetros importantes a tenerse en cuenta para disponer de un centro de datos confiable

Knowledge driven decomposition of tumor expression profiles

<p>Abstract</p> <p>Background</p> <p>Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.</p> <p>Results</p> <p>We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.</p> <p>Conclusion</p> <p>The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.</p