Ecological Inference with Entropy Econometrics: using the Mexican Census as a benchmark

Most regional empirical analyses are limited by the lack of data. Researchers have to use information that is structured in administrative or political regions which are not always economically meaningful. The non-availability of geographically disaggregated information prevents to obtain empirical evidence in order to answer some relevant questions in the field of urban and regional economics. The objective of this paper is to suggest an estimation procedure, based on entropy econometrics, which allows for inferring disaggregated information on local income from more aggregated data. In addition to a description of the main characteristics of the proposed technique, the paper illustrates how the procedure works taking as an empirical application the estimation of income for different classes of Mexican municipalities. It would be desirable to apply the suggested technique to a study case where some observable data are available and confront the estimates with the actual observations. For this purpose, we have taken the information contained in the Mexican census as a benchmark for our estimation technique. Assuming that the only available data are the income aggregates per type of municipality and State, we make an exercise of ecological inference and disaggregate these margins to recover individual (local) data.

Genome-Wide Associations of Signaling Pathways in Glioblastoma Multiforme

Background: eQTL analysis is a powerful method that allows the identification of causal genomic alterations, providing an explanation of expression changes of single genes. However, genes mediate their biological roles in groups rather than in isolation, prompting us to extend the concept of eQTLs to whole gene pathways. Methods: We combined matched genomic alteration and gene expression data of glioblastoma patients and determined associations between the expression of signaling pathways and genomic copy number alterations with a non-linear machine learning approach. Results: Expectedly, over-expressed pathways were largely associated to tag-loci on chromosomes with signature alterations. Surprisingly, tag-loci that were associated to under-expressed pathways were largely placed on other chromosomes, an observation that held for composite effects between chromosomes as well. Indicating their biological relevance, identified genomic regions were highly enriched with genes having a reported driving role in gliomas. Furthermore, we found pathways that were significantly enriched with such driver genes. Conclusions: Driver genes and their associated pathways may represent a functional core that drive the tumor emergence and govern the signaling apparatus in GBMs. In addition, such associations may be indicative of drug combinations for the treatment of brain tumors that follow similar patterns of common and diverging alterations

Teenage Pregnancy in Mexico: Evolution and Consequences

We analyze the consequences of a teenage pregnancy event in the short- and long-run in Mexico. Using longitudinal and cross-section data, we match females who got pregnant and those that did not based on a propensity score. Several balancing tests and specifications indicate that the main assumptions to estimate the average treatment effect on the treated using a propensity score are satisfied. In the short-run, we find that a teenage pregnancy causes a decrease of 0.6-0.8 years of schooling, lower attendance to school, less hours of work and a higher marriage rate. At the household level, we do not find any effect in parental hours of work or income per capita. In the long-run, we find a loss in years of education of 1-1.2 and a higher probability of being married, but also higher probability of being separated or divorced. We also find that household income per capita is lower at least in the long-run.teenage, pregnancy, labor outcomes, propensity score, matching

Roles of transcriptional and translational control mechanisms in regulation of ribosomal protein synthesis in Escherichia coli

ABSTRACTBacterial ribosome biogenesis is tightly regulated to match nutritional conditions and to prevent formation of defective ribosomal particles. InEscherichia coli, most ribosomal protein (r-protein) synthesis is coordinated with rRNA synthesis by a translational feedback mechanism: when r-proteins exceed rRNAs, specific r-proteins bind to their own mRNAs and inhibit expression of the operon. It was recently discovered that the second messenger nucleotide guanosine tetra and pentaphosphate (ppGpp), which directly regulates rRNA promoters, is also capable of regulating many r-protein promoters. To examine the relative contributions of the translational and transcriptional control mechanisms to the regulation of r-protein synthesis, we devised a reporter system that enabled us to genetically separate thecis-acting sequences responsible for the two mechanisms and to quantify their relative contributions to regulation under the same conditions. We show that the synthesis of r-proteins from the S20 and S10 operons is regulated by ppGpp following shifts in nutritional conditions, but most of the effect of ppGpp required the 5′ region of the r-protein mRNA containing the target site for translational feedback regulation and not the promoter. These results suggest that most regulation of the S20 and S10 operons by ppGpp following nutritional shifts is indirect and occurs in response to changes in rRNA synthesis. In contrast, we found that the promoters for the S20 operon were regulated during outgrowth, likely in response to increasing nucleoside triphosphate (NTP) levels. Thus, r-protein synthesis is dynamic, with different mechanisms acting at different times.IMPORTANCEBacterial cells have evolved complex and seemingly redundant strategies to regulate many high-energy-consuming processes. InE. coli, synthesis of ribosomal components is tightly regulated with respect to nutritional conditions by mechanisms that act at both the transcription and translation steps. In this work, we conclude that NTP and ppGpp concentrations can regulate synthesis of ribosomal proteins, but most of the effect of ppGpp is indirect as a consequence of translational feedback in response to changes in rRNA levels. Our results illustrate how effects of seemingly redundant regulatory mechanisms can be separated in time and that even when multiple mechanisms act concurrently their contributions are not necessarily equivalent.</jats:p

Cosmological parameter inference with Bayesian statistics

Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found their place in the field of Cosmology. They have become important mathematical and numerical tools, especially in parameter estimation and model comparison. In this paper, we review some fundamental concepts to understand Bayesian statistics and then introduce MCMC algorithms and samplers that allow us to perform the parameter inference procedure. We also introduce a general description of the standard cosmological model, known as the $\Lambda$CDM model, along with several alternatives, and current datasets coming from astrophysical and cosmological observations. Finally, with the tools acquired, we use an MCMC algorithm implemented in python to test several cosmological models and find out the combination of parameters that best describes the Universe.Comment: 30 pages, 17 figures, 5 tables; accepted for publication in Universe; references adde