Theories of the Distribution of Labor Earnings

Several empirical regularities motivate most theories of the distribution of labor earnings. Earnings distributions tend to be skewed to the right and display a long right tail. The are leptokurtic (positive fourth cumulant) and have a fat tail. Mean earnings always exceed median earnings and the top percentiles of earners account for a disproportionate share of total earnings. Mean earnings also differ greatly across groups defined by occupation, education, experience, and other observed traits. With respect to the evolution of the distribution of earnings for a given cohort, initial earnings dispersion is smaller than the dispersion observed in prime working years. We explore several classes of models that address these stylized facts. Stochastic theories begin with distributional assumptions about worker endowments and then examine the stochastic structures that might generate observed features of the aggregate distribution of earnings. Selection models describe how workers allocate their skills to tasks. Because workers choose their best option from a menu of careers, these allocation decisions generate earnings distributions which tend to be skewed. Sorting models provide dynamic versions of selection models and illustrate how gradual learning about endowments leads to sorting patterns that amplify dispersiion and generate a skewed distribution of earnings within a cohort of experienced workers. Human capital theory demonstrates that earnings dispersion is a prerequisite for significant skill investments. Without earnings dispersion, workers would not willingly make the investments necessary for high-skill jobs. Human capital model illustrate how endowments of wealth and talent influence the investment decisions that generate observed distributions of earnings.

The BAD protein integrates survival signaling by EGFR/MAPK and PI3K/Akt kinase pathways in PTEN-deficient tumor cells

SummaryTumor cells with mutated PTEN proliferate in an EGFR-independent manner. Induction of PTEN sensitizes cells to EGFR inhibition, and the combination causes synergistic apoptosis. Synergy is due to inhibition of two parallel pathways that phosphorylate the proapoptotic protein BAD at distinct sites. Serine 112 phosphorylation is EGFR/MEK/MAPK dependent, whereas serine 136 phosphorylation is PI3K/Akt dependent. Either phosphorylation is sufficient to sequester BAD to 14-3-3. BAD is released and apoptosis is induced only if both serines are dephosphorylated in response to inhibition of both pathways. Reduction of BAD expression by RNA interference prevents apoptosis in response to pathway inhibition. Thus, BAD integrates the antiapoptotic effects of both pathways. Combined inhibition of EGFR and PI3K signaling may be a useful therapeutic strategy

Bayesian nonparametric models for name disambiguation and supervised learning

This thesis presents new Bayesian nonparametric models and approaches for their development, for the problems of name disambiguation and supervised learning. Bayesian nonparametric methods form an increasingly popular approach for solving problems that demand a high amount of model flexibility. However, this field is relatively new, and there are many areas that need further investigation. Previous work on Bayesian nonparametrics has neither fully explored the problems of entity disambiguation and supervised learning nor the advantages of nested hierarchical models. Entity disambiguation is a widely encountered problem where different references need to be linked to a real underlying entity. This problem is often unsupervised as there is no previously known information about the entities. Further to this, effective use of Bayesian nonparametrics offer a new approach to tackling supervised problems, which are frequently encountered. The main original contribution of this thesis is a set of new structured Dirichlet process mixture models for name disambiguation and supervised learning that can also have a wide range of applications. These models use techniques from Bayesian statistics, including hierarchical and nested Dirichlet processes, generalised linear models, Markov chain Monte Carlo methods and optimisation techniques such as BFGS. The new models have tangible advantages over existing methods in the field as shown with experiments on real-world datasets including citation databases and classification and regression datasets. I develop the unsupervised author-topic space model for author disambiguation that uses free-text to perform disambiguation unlike traditional author disambiguation approaches. The model incorporates a name variant model that is based on a nonparametric Dirichlet language model. The model handles both novel unseen name variants and can model the unknown authors of the text of the documents. Through this, the model can disambiguate authors with no prior knowledge of the number of true authors in the dataset. In addition, it can do this when the authors have identical names. I use a model for nesting Dirichlet processes named the hybrid NDP-HDP. This model allows Dirichlet processes to be clustered together and adds an additional level of structure to the hierarchical Dirichlet process. I also develop a new hierarchical extension to the hybrid NDP-HDP. I develop this model into the grouped author-topic model for the entity disambiguation task. The grouped author-topic model uses clusters to model the co-occurrence of entities in documents, which can be interpreted as research groups. Since this model does not require entities to be linked to specific words in a document, it overcomes the problems of some existing author-topic models. The model incorporates a new method for modelling name variants, so that domain-specific name variant models can be used. Lastly, I develop extensions to supervised latent Dirichlet allocation, a type of supervised topic model. The keyword-supervised LDA model predicts document responses more accurately by modelling the effect of individual words and their contexts directly. The supervised HDP model has more model flexibility by using Bayesian nonparametrics for supervised learning. These models are evaluated on a number of classification and regression problems, and the results show that they outperform existing supervised topic modelling approaches. The models can also be extended to use similar information to the previous models, incorporating additional information such as entities and document titles to improve prediction

Changes in Bone Turnover Marker Levels and Clinical Outcomes in Patients With Advanced Cancer and Bone Metastases Treated With Bone Antiresorptive Agents

Purpose: Bone antiresorptive agents can significantly reduce bone turnover markers (BTMs) in patients with advanced cancer. We evaluated association of changes in BTMs with overall survival (OS), disease progression (DP), and disease progression in bone (DPB) in patients with advanced cancer and bone metastases following denosumab or zoledronic acid treatment. Experimental Design: This is an integrated analysis of patient-level data from three identically designed, blinded, phase III trials with patients randomized to subcutaneous denosumab or intravenous zoledronic acid. Levels of the BTMs urinary N-telopeptide (uNTx) and serum bone-specific alkaline phosphatase (sBSAP) measured at study entry and month 3 were analyzed. OS, DP, and DPB were compared in patients with BTMs {greater than or equal to} median vs < median based on month 3 assessments. Results: uNTx levels {greater than or equal to} the median of 10.0 nmol/mmol at month 3 were associated with significantly reduced OS compared with levels < median (HR for death 1.85, P<0.0001). sBSAP levels {greater than or equal to} median of 12.6 ng/mL were associated with significantly reduced OS compared with levels < median (HR 2.44, P<0.0001). uNTx and sBSAP levels {greater than or equal to} median at month 3 were associated with significantly greater risk of DP (HR 1.31, P<0.0001 and HR 1.71, P<0.0001, respectively) and DPB (HR 1.11, P=0.0407 and HR 1.27, P<0.0001, respectively). Conclusions: BTM levels {greater than or equal to} median after 3 months of bone antiresorptive treatment were associated with reduced OS and increased risk of DP and DPB. Assessment of uNTx and sBSAP levels after bone antiresorptive therapy may add to identification of patients at risk for worse clinical outcomes

Coordinated transcriptional regulation of bone homeostasis by Ebf1 and Zfp521 in both mesenchymal and hematopoietic lineages

Bone homeostasis is maintained by the coupled actions of hematopoietic bone-resorbing osteoclasts (OCs) and mesenchymal bone-forming osteoblasts (OBs). Here we identify early B cell factor 1 (Ebf1) and the transcriptional coregulator Zfp521 as components of the machinery that regulates bone homeostasis through coordinated effects in both lineages. Deletion of Zfp521 in OBs led to impaired bone formation and increased OB-dependent osteoclastogenesis (OC-genesis), and deletion in hematopoietic cells revealed a strong cell-autonomous role for Zfp521 in OC progenitors. In adult mice, the effects of Zfp521 were largely caused by repression of Ebf1, and the bone phenotype of Zfp521+/− mice was rescued in Zfp521+/−:Ebf1+/− mice. Zfp521 interacted with Ebf1 and repressed its transcriptional activity. Accordingly, deletion of Zfp521 led to increased Ebf1 activity in OBs and OCs. In vivo, Ebf1 overexpression in OBs resulted in suppressed bone formation, similar to the phenotype seen after OB-targeted deletion of Zfp521. Conversely, Ebf1 deletion led to cell-autonomous defects in both OB-dependent and cell-intrinsic OC-genesis, a phenotype opposite to that of the Zfp521 knockout. Thus, we have identified the interplay between Zfp521 and Ebf1 as a novel rheostat for bone homeostasis

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers