Native human autoantibodies targeting GIPC1 identify differential expression in malignant tumors of the breast and ovary

<p>Abstract</p> <p>Background</p> <p>We have been studying the native humoral immune response to cancer and have isolated a library of fully human autoantibodies to a variety of malignancies. We previously described the isolation and characterization of two fully human monoclonal antibodies, 27.F7 and 27.B1, from breast cancer patients that target the protein known as GIPC1, an accessory PDZ-domain binding protein involved in regulation of G-protein signaling. Human monoclonal antibodies, 27.F7 and 27.B1, to GIPC1 demonstrate specific binding to malignant breast cancer tissue with no reactivity with normal breast tissue.</p> <p>Methods</p> <p>The current study employs cELISA, flow cytometry, Western blot analysis as well as immunocytochemistry, and immunohistochemistry. Data is analyzed statistically with the Fisher one-tail and two-tail tests for two independent samples.</p> <p>Results</p> <p>By screening several other cancer cell lines with 27.F7 and 27.B1 we found consistently strong staining of other human cancer cell lines including SKOV-3 (an ovarian cancer cell line). To further clarify the association of GIPC1 with breast and ovarian cancer we carefully studied 27.F7 and 27.B1 using immunocytochemical and immunohistochemical techniques. An immunohistochemical study of normal ovarian tissue, benign, borderline and malignant ovarian serous tumors, and different types of breast cancer revealed high expression of GIPC1 protein in neoplastic cells. Interestingly, antibodies 27.F7 and 27.B1 demonstrate differential staining of borderline ovarian tumors. Examination of different types of breast cancer demonstrates that the level of GIPC1 expression depends on tumor invasiveness and displays a higher expression than in benign tumors.</p> <p>Conclusion</p> <p>The present pilot study demonstrates that the GIPC1 protein is overexpressed in ovarian and breast cancer, which may provide an important diagnostic and prognostic marker and will constitute the basis for further study of the role that this protein plays in malignant diseases. In addition, this study suggests that human monoclonal antibodies 27.F7 and 27.B1 should be further evaluated as potential diagnostic tools.</p

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

DRIVING PERFORMANCE: A GROWTH THEORY OF NON-COMPETE LAW

Traditional law and economic analysis views post-employment restrictions, ranging from non-compete agreements to intellectual property controls over an ex-employee’s knowledge and skill, as necessary for economic investment and market growth. The orthodox economic analysis theorizes that without such contractual and regulatory protections, businesses would under-invest in R&D and human capital. This Article challenges the orthodox analysis by introducing both behavioral dimensions and endogenous growth effects of job mobility over time. The article empirically tests the behavioral dimension with original experimental research demonstrating that contractual backgrounds in market relations impact motivation and performance. The behavioral study, simulating a job market, finds that participants constrained by post-employment restrictions significantly under-performed in the assigned experimental tasks. The article integrates these experimental findings with new empirical evidence about positive spillovers, network effects, and economic growth in jurisdictions with lesser legal constraints on job mobility and information flows. The behavioral and dynamic growth effects elaborated in the article help explain regional advantage in patenting rates, entrepreneurship, and market growth of jurisdictions that employ weaker human capital controls. Combining the behavioral and network perspectives, the article develops a new lens through which to analyze the costs and benefits of human capital restrictions

Long Term Follow Up of Children with Downgaze Nystagmus at Infancy (.pdf)

Paroxysmal downgaze nystagmus of infant is an unsettling phenomenon that leads to extensive work-up, although benign outcomes have been reported in sporadic cases. However, long term outcome of these children was not published. Our aim is to report a case series of 7 healthy infants with long neurological and ophthalmological follow up. They all presented with acute onset of episodic paroxysmal tonic downgaze at the ages of 2-12 weeks of age. We describe their spontaneous resolution without neurologic or ophthalmological sequelae