Estimating Variable Returns to Scale Production Frontiers with Alternative Stochastic Assumptions

A stochastic production frontier model is formulated within the generalized production function framework popularized by Zellner and Revankar (1969) and Zellner and Ryu (1998). This framework is convenient for parsimonious modeling of a production function with variable returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered, one where they are appended to the existing equation for the production relationship and one where the existing equation is solved for the log of output before the stochastic terms are added. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, firm efficiencies and the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined.

The oral microbiome of denture wearers is influenced by natural dentition

Objectives: The composition of dental plaque has been well defined, whereas currently there is limited understanding of the composition of denture plaque and how it directly influences denture related stomatitis (DS). The aims of this study were to compare the microbiomes of denture wearers, and to understand the implications of these towards inter-kingdom and host-pathogen interactions within the oral cavity. Methods: Swab samples were obtained from 123 participants wearing either a complete or partial denture; the bacterial composition of each sample was determined using bar-coded illumina MiSeq sequencing of the bacterial hypervariable V4 region of 16S rDNA. Sequencing data processing was undertaken using QIIME, clustered in Operational Taxonomic Units (OTUs) and assigned to taxonomy. The dentures were sonicated to remove the microbial flora residing on the prosthesis, sonicate was then cultured using diagnostic colorex Candida media. Samples of unstimulated saliva were obtained and antimicrobial peptides (AMP) levels were measured by ELISA. Results: We have shown that dental and denture plaques are significantly distinct both in composition and diversity and that the oral microbiome composition of a denture wearer is variable and is influenced by the location within the mouth. Dentures and mucosa were predominantly made up of Bacilli and Actinobacteria. Moreover, the presence of natural teeth has a significant impact on the overall microbial composition, when compared to the fully edentulous. Furthermore, increasing levels of Candida spp. positively correlate with Lactobacillus spp. AMPs were quantified, though showed no specific correlations. Conclusions: This is the first study to provide a detailed understanding of the oral microbiome of denture wearers and has provided evidence that DS development is more complex than simply a candidal infection. Both fungal and bacterial kingdoms clearly play a role in defining the progression of DS, though we were unable to show a defined role for AMPs

Candida albicans biofilm heterogeneity does not influence denture stomatitis but strongly influences denture cleansing capacity

Approximately 20  % of the UK population wear some form of denture prosthesis, resulting in denture stomatitis in half of these individuals. Candida albicans is primarily attributed as the causative agent, due to its biofilm -forming ability. Recently, there has been increasing evidence of C. albicans biofilm heterogeneity and the negative impact it can have clinically; however, this phenomenon has yet to be studied in relation to denture isolates. The aims of this study were to evaluate C. albicans biofilm formation of clinical denture isolates in a denture environment and to assess antimicrobial activity of common denture cleansers against these tenacious communities. C. albicans isolated from dentures of healthy and diseased individuals was quantified using real-time PCR and biofilm biomass assessed using crystal violet. Biofilm development on the denture substratum poly(methyl methacrylate), Molloplast B and Ufi-gel was determined. Biofilm formation was assessed using metabolic and biomass stains, following treatment with denture hygiene products. Although C. albicans was detected in greater quantities in diseased individuals, it was not associated with increased biofilm biomass. Denture substrata were shown to influence biofilm biomass, with poly(methyl methacrylate) providing the most suitable environment for C. albicans to reside. Of all denture hygiene products tested, Milton had the most effective antimicrobial activity, reducing biofilm biomass and viability the greatest. Overall, our results highlight the complex nature of denture- related disease, and disease development cannot always be attributed to a sole cause. It is the distinct combination of various factors that ultimately determines the pathogenic outcome

Trends in the Association of Parental History of Obesity over 60 Years

Objective: The association of familial as compared to genetic factors in the current obesogenic environment, compared to earlier, leaner time periods, is uncertain. Design and Methods Participants from the Framingham Heart Study were classified according to parental obesity status in the Original, Offspring, and Third Generation cohorts; mean BMI levels were estimated and we compared the association of parental history across generations. Finally, a genetic risk score comprised of 32 well-replicated single nucleotide polymorphisms for BMI was examined in association with BMI levels in 1948, 1971, and 2002. Results: BMI was 1.49 kg/m2 higher per each affected parent among the Offspring, and increased to 2.09 kg/m2 higher among the Third Generation participants (p-value for the cohort comparison=0.007). Parental history of obesity was associated with increased weight gain (p<0.0001) and incident obesity (p=0.009). Despite a stronger association of parental obesity with offspring BMI in more contemporary time periods, we observed no change in the effect size of a BMI genetic risk score from 1948 to 2002 (p=0.11 for test of trend across the time periods). Conclusions: The association of parental obesity has become stronger in more contemporary time period, whereas the association of a BMI genetic risk score has not changed

Mapping the human platelet lipidome reveals cytosolic phospholipase A2 as a regulator of mitochondrial bioenergetics during activation

Human platelets acutely increase mitochondrial energy generation following stimulation. Herein, a lipidomic circuit was uncovered whereby the substrates for this are exclusively provided by cPLA2, including multiple fatty acids and oxidized species that support energy generation via β-oxidation. This indicates that acute lipid membrane remodeling is required to support energetic demands during platelet activation. Phospholipase activity is linked to energy metabolism, revealing cPLA2 as a central regulator of both lipidomics and energy flux. Using a lipidomic approach (LipidArrays), we also estimated the total number of lipids in resting, thrombin-activated, and aspirinized platelets. Significant diversity between genetically unrelated individuals and a wealth of species was revealed. Resting platelets demonstrated ∼5,600 unique species, with only ∼50% being putatively identified. Thrombin elevated ∼900 lipids >2-fold with 86% newly appearing and 45% inhibited by aspirin supplementation, indicating COX-1 is required for major activation-dependent lipidomic fluxes. Many lipids were structurally identified. With ∼50% of the lipids being absent from databases, a major opportunity for mining lipids relevant to human health and disease is presente

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts

Background: One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). Methods: Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89th-99th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC). Results: RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and “vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs. Conclusions: We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders, hold promise for deriving predictive disease models and networks. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0474-5) contains supplementary material, which is available to authorized users

Impact of Scotland’s comprehensive, smoke-free legislation on stroke

&lt;p&gt;Background: Previous studies have reported a reduction in acute coronary events following smoke-free legislation. Evidence is lacking on whether stroke is also reduced. The aim was to determine whether the incidence of stroke, overalland by sub-type, fell following introduction of smoke-free legislation across Scotland on 26 March 2006.&lt;/p&gt; &lt;p&gt;Methods and Findings: A negative binomial regression model was used to determine whether the introduction of smokefree legislation resulted in a step and/or slope change in stroke incidence. The model was adjusted for age-group, sex, socioeconomic deprivation quintile, urban/rural residence and month. Interaction tests were also performed. Routine hospital administrative data and death certificates were used to identify all hospital admissions and pre-hospital deaths due to stroke (ICD10 codes I61, I63 and I64) in Scotland between 2000 and 2010 inclusive. Prior to the legislation, rates of all stroke, intracerebral haemorrhage and unspecified stroke were decreasing, whilst cerebral infarction was increasing at 0.97% per annum. Following the legislation, there was a dramatic fall in cerebral infarctions that persisted for around 20 months. No visible effect was observed for other types of stroke. The model confirmed an 8.90% (95% CI 4.85, 12.77, p,0.001) stepwise reduction in cerebral infarction at the time the legislation was implemented, after adjustment for potential cofounders.&lt;/p&gt; &lt;p&gt;Conclusions: Following introduction of national, comprehensive smoke-free legislation there was a selective reduction in cerebral infarction that was not apparent in other types of stroke.&lt;/p&gt

Estimation and inference under economic restrictions

Estimation of economic relationships often requires imposition of constraints such as positivity or monotonicity on each observation. Methods to impose such constraints, however, vary depending upon the estimation technique employed. We describe a general methodology to impose (observation-specific) constraints for the class of linear regression estimators using a method known as constraint weighted bootstrapping. While this method has received attention in the nonparametric regression literature, we show how it can be applied for both parametric and nonparametric estimators. A benefit of this method is that imposing numerous constraints simultaneously can be performed seamlessly. We apply this method to Norwegian dairy farm data to estimate both unconstrained and constrained parametric and nonparametric models