Application of the Panzar-Rosse Model: An Analysis of the Brewery Industry in the U.S.

Waves of mergers and acquisitions has left the brewery industry in the United States considerably concentrated. The top two firms, Anheuser-Busch InBev and MillerCoors control more than 60% of the market share. It has become very important to assess the level of competition within the industry. The Panzar-Rosse model is an assessment of competitive conduct that has been widely used to study the competitiveness of the banking industry. The associated measure of competition, called the H-statistic, is obtained as the sum of elasticities of gross revenue with respect to input prices. For this study, the Panzar-Rosse model will be applied to the United States brewery industry and finds that the H-statistic has a negative value, meaning the industry operates under a neoclassical monopolist style or a collusive oligopoly

Extending the Latent Multinomial Model with Complex Error Processes and Dynamic Markov Bases

The latent multinomial model (LMM) model of Link et al. (2010) provided a general framework for modelling mark-recapture data with potential errors in identification. Key to this approach was a Markov chain Monte Carlo (MCMC) scheme for sampling possible configurations of the counts true capture histories that could have generated the observed data. This MCMC algorithm used vectors from a basis for the kernel of the linear map between the true and observed counts to move between the possible configurations of the true data. Schofield and Bonner (2015) showed that a strict basis was sufficient for some models of the errors, including the model presented by Link et al. (2010), but a larger set called a Markov basis may be required for more complex models. We address two further challenges with this approach: 1) that models with more complex error mechanisms do not fit easily within the LMM and 2) that the Markov basis can be difficult or impossible to compute for even moderate sized studies. We address these issues by extending the LMM to separately model the capture/demographic process and the error process and by developing a new MCMC sampling scheme using dynamic Markov bases. Our work is motivated by a study of Queen snakes (Regina septemvittata) in Kentucky, USA, and we use simulation to compare the use of PIT tags, with perfect identification, and brands, which are prone to error, when estimating survival rates

PON1 status does not influence cholinesterase activity in Egyptian agricultural workers exposed to chlorpyrifos.

Animal studies have shown that paraoxonase 1 (PON1) genotype can influence susceptibility to the organophosphorus pesticide chlorpyrifos (CPF). However, Monte Carlo analysis suggests that PON1 genotype may not affect CPF-related toxicity at low exposure conditions in humans. The current study sought to determine the influence of PON1 genotype on the activity of blood cholinesterase as well as the effect of CPF exposure on serum PON1 in workers occupationally exposed to CPF. Saliva, blood and urine were collected from agricultural workers (n=120) from Egypt's Menoufia Governorate to determine PON1 genotype, blood cholinesterase activity, serum PON1 activity towards chlorpyrifos-oxon (CPOase) and paraoxon (POase), and urinary levels of the CPF metabolite 3,5,6-trichloro-2-pyridinol (TCPy). The PON1 55 (P≤0.05) but not the PON1 192 genotype had a significant effect on CPOase activity. However, both the PON1 55 (P≤0.05) and PON1 192 (P≤0.001) genotypes had a significant effect on POase activity. Workers had significantly inhibited AChE and BuChE after CPF application; however, neither CPOase activity nor POase activity was associated with ChE depression when adjusted for CPF exposure (as determined by urinary TCPy levels) and stratified by PON1 genotype. CPOase and POase activity were also generally unaffected by CPF exposure although there were alterations in activity within specific genotype groups. Together, these results suggest that workers retained the capacity to detoxify chlorpyrifos-oxon under the exposure conditions experienced by this study population regardless of PON1 genotype and activity and that effects of CPF exposure on PON1 activity are minimal

Positive Relationship between Total Antioxidant Status and Chemokines Observed in Adults

Objective. Human evidence is limited regarding the interaction between oxidative stress biomarkers and chemokines, especially in a population of adults without overt clinical disease. The current study aims to examine the possible relationships of antioxidant and lipid peroxidation markers with several chemokines in adults. Methods. We assessed cross-sectional associations of total antioxidant status (TAS) and two lipid peroxidation markers malondialdehyde (MDA) and thiobarbituric acid reactive substances (TBARS) with a suite of serum chemokines, including CXCL-1 (GRO-α), CXCL-8 (IL-8), CXCL-10 (IP-10), CCL-2 (MCP-1), CCL-5 (RANTES), CCL-8 (MCP-2), CCL-11 (Eotaxin-1), and CCL-17 (TARC), among 104 Chinese adults without serious preexisting clinical conditions in Beijing before 2008 Olympics. Results. TAS showed significantly positive correlations with MCP-1 (r=0.15751, P=0.0014), MCP-2 (r=0.3721, P=0.0001), Eotaxin-1 (r=0.39598, P<0.0001), and TARC (r=0.27149, P=0.0053). The positive correlations remained unchanged after controlling for age, sex, body mass index, smoking, and alcohol drinking status. No associations were found between any of the chemokines measured in this study and MDA or TBARS. Similar patterns were observed when the analyses were limited to nonsmokers. Conclusion. Total antioxidant status is positively associated with several chemokines in this adult population

Non-steroidal anti-inflammatory drugs (NSAIDs) and breast cancer risk: differences by molecular subtype.

Use of non-steroidal anti-inflammatory drugs (NSAIDs) has been associated with reduced risk of breast cancer, though findings have been inconsistent. This inconsistency may result from differences in etiology for breast tumors of different subtypes. We examined the association between NSAID use and breast cancer characterized by molecular subtypes in a population-based case-control study in Western New York. Cases (n = 1,170) were women with incident, primary, histologically confirmed breast cancer. Controls (n = 2,115) were randomly selected from NY Department of Motor Vehicles records (<65 years) or Medicare rolls (≥ 65 years). Participants answered questions regarding their use of aspirin and ibuprofen in the year prior to interview and their use of aspirin throughout their adult life. Logistic regression models estimated odds ratios (OR) and 95% confidence intervals (95% CI). Recent and lifetime aspirin use was associated with reduced risk, with no differences by subtype. Recent use of ibuprofen was significantly associated with increased risk of ER+/PR+(OR 1.33, 95% CI: 1.09-1.62), HER2- (OR 1.27, 95% CI: 1.05-1.53), and p53- breast cancers (OR 1.28, 95% CI: 1.04-1.57), as well as luminal A or B breast cancers. These findings support the hypothesis of heterogeneous etiologies of breast cancer subtypes and that aspirin and ibuprofen vary in their effects

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution