A Budgeting Model for Analysis of Subsidized Programs

John J. Bernardo is an Associate Professor of Management in the College of Business and Economics at the University of Kentucky Alan Reinstein is an Associate Professor of Accounting at Oakland University in Rochester, Michigan

Ambient Air Pollution and Atherosclerosis in Los Angeles

Associations have been found between long-term exposure to ambient air pollution and cardiovascular morbidity and mortality. The contribution of air pollution to atherosclerosis that underlies many cardiovascular diseases has not been investigated. Animal data suggest that ambient particulate matter (PM) may contribute to atherogenesis. We used data on 798 participants from two clinical trials to investigate the association between atherosclerosis and long-term exposure to ambient PM up to 2.5 μm in aerodynamic diameter (PM(2.5)). Baseline data included assessment of the carotid intima-media thickness (CIMT), a measure of subclinical atherosclerosis. We geocoded subjects’ residential areas to assign annual mean concentrations of ambient PM(2.5). Exposure values were assigned from a PM(2.5) surface derived from a geostatistical model. Individually assigned annual mean PM(2.5) concentrations ranged from 5.2 to 26.9 μg/m3 (mean, 20.3). For a cross-sectional exposure contrast of 10 μg/m3 PM(2.5), CIMT increased by 5.9% (95% confidence interval, 1–11%). Adjustment for age reduced the coefficients, but further adjustment for covariates indicated robust estimates in the range of 3.9–4.3% (p-values, 0.05–0.1). Among older subjects (≥60 years of age), women, never smokers, and those reporting lipid-lowering treatment at baseline, the associations of PM(2.5) and CIMT were larger with the strongest associations in women ≥60 years of age (15.7%, 5.7–26.6%). These results represent the first epidemiologic evidence of an association between atherosclerosis and ambient air pollution. Given the leading role of cardiovascular disease as a cause of death and the large populations exposed to ambient PM(2.5), these findings may be important and need further confirmation

The Determining Risk of Vascular Events by Apnea Monitoring (DREAM) Study: Design, Rationale and Methods

Purpose The goal of the Determining Risk of Vascular Events by Apnea Monitoring (DREAM) study is to develop a prognostic model for cardiovascular outcomes, based on physiologic variables—related to breathing, sleep architecture, and oxygenation—measured during polysomnography in US veterans. Methods The DREAM study is a multi-site, retrospective observational cohort study conducted at three Veterans Affairs (VA) centers (West Haven, CT; Indianapolis, IN; Cleveland, OH). Veterans undergoing polysomnography between January 1, 2000 and December 31, 2004 were included based on referral for evaluation of sleep-disordered breathing, documented history and physical prior to sleep testing, and ≥2-h sleep monitoring. Demographic, anthropomorphic, medical, medication, and social history factors were recorded. Measures to determine sleep apnea, sleep architecture, and oxygenation were recorded from polysomnography. VA Patient Treatment File, VA–Medicare Data, Vista Computerized Patient Record System, and VA Vital Status File were reviewed on dates subsequent to polysomnography, ranging from 0.06 to 8.8 years (5.5 ± 1.3 years; mean ± SD). Results The study population includes 1840 predominantly male, middle-aged veterans. As designed, the main primary outcome is the composite endpoint of acute coronary syndrome, stroke, transient ischemic attack, or death. Secondary outcomes include incidents of neoplasm, congestive heart failure, cardiac arrhythmia, diabetes, depression, and post-traumatic stress disorder. Laboratory outcomes include measures of glycemic control, cholesterol, and kidney function. (Actual results are pending.) Conclusions This manuscript provides the rationale for the inclusion of veterans in a study to determine the association between physiologic sleep measures and cardiovascular outcomes and specifically the development of a corresponding outcome-based prognostic model

Photonic Sorting of Aligned, Crystalline Carbon Nanotube Textiles

Floating catalyst chemical vapor deposition uniquely generates aligned carbon nanotube (CNT) textiles with individual CNT lengths magnitudes longer than competing processes, though hindered by impurities and intrinsic/extrinsic defects. We present a photonic-based post-process, particularly suited for these textiles, that selectively removes defective CNTs and other carbons not forming a threshold thermal pathway. In this method, a large diameter laser beam rasters across the surface of a partly aligned CNT textile in air, suspended from its ends. This results in brilliant, localized oxidation, where remaining material is an optically transparent film comprised of few-walled CNTs with profound and unique improvement in microstructure alignment and crystallinity. Raman spectroscopy shows substantial D peak suppression while preserving radial breathing modes. This increases the undoped, specific electrical conductivity at least an order of magnitude to beyond that of single-crystal graphite. Cryogenic conductivity measurements indicate intrinsic transport enhancement, opposed to simply removing nonconductive carbons/residual catalyst

Contribution of shape and charge to the inhibition of a family GH99 endo-α-1,2-mannanase

[Image: see text] Inhibitor design incorporating features of the reaction coordinate and transition-state structure has emerged as a powerful approach for the development of enzyme inhibitors. Such inhibitors find use as mechanistic probes, chemical biology tools, and therapeutics. Endo-α-1,2-mannosidases and endo-α-1,2-mannanases, members of glycoside hydrolase family 99 (GH99), are interesting targets for inhibitor development as they play key roles in N-glycan maturation and microbiotal yeast mannan degradation, respectively. These enzymes are proposed to act via a 1,2-anhydrosugar “epoxide” mechanism that proceeds through an unusual conformational itinerary. Here, we explore how shape and charge contribute to binding of diverse inhibitors of these enzymes. We report the synthesis of neutral dideoxy, glucal and cyclohexenyl disaccharide inhibitors, their binding to GH99 endo-α-1,2-mannanases, and their structural analysis by X-ray crystallography. Quantum mechanical calculations of the free energy landscapes reveal how the neutral inhibitors provide shape but not charge mimicry of the proposed intermediate and transition state structures. Building upon the knowledge of shape and charge contributions to inhibition of family GH99 enzymes, we design and synthesize α-Man-1,3-noeuromycin, which is revealed to be the most potent inhibitor (K(D) 13 nM for Bacteroides xylanisolvens GH99 enzyme) of these enzymes yet reported. This work reveals how shape and charge mimicry of transition state features can enable the rational design of potent inhibitors

Bayesian Inference in Processing Experimental Data: Principles and Basic Applications

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as: model comparison (including the automatic Ockham's Razor filter provided by the Bayesian approach); parametric inference; quantification of the uncertainty about the value of physical quantities, also taking into account systematic effects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chi-square fits under well defined conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; Monte Carlo estimates of expectation, including a short introduction to Markov Chain Monte Carlo methods.Comment: 40 pages, 2 figures, invited paper for Reports on Progress in Physic

Development of a modified Cambridge Multimorbidity Score for use with SNOMED CT:an observational English primary care sentinel network study

Background People with multiple health conditions are more likely to have poorer health outcomes and greater care and service needs; a reliable measure of multimorbidity would inform management strategies and resource allocation. Aim To develop and validate a modified version of the Cambridge Multimorbidity Score in an extended age range, using clinical terms that are routinely used in electronic health records across the world (Systematized Nomenclature of Medicine — Clinical Terms, SNOMED CT). Design and setting Observational study using diagnosis and prescriptions data from an English primary care sentinel surveillance network between 2014 and 2019. Method In this study new variables describing 37 health conditions were curated and the associations modelled between these and 1-year mortality risk using the Cox proportional hazard model in a development dataset (n = 300 000). Two simplified models were then developed — a 20-condition model as per the original Cambridge Multimorbidity Score and a variable reduction model using backward elimination with Akaike information criterion as the stopping criterion. The results were compared and validated for 1-year mortality in a synchronous validation dataset (n = 150 000), and for 1-year and 5-year mortality in an asynchronous validation dataset (n = 150 000). Results The final variable reduction model retained 21 conditions, and the conditions mostly overlapped with those in the 20-condition model. The model performed similarly to the 37- and 20-condition models, showing high discrimination and good calibration following recalibration. Conclusion This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms that can be applied internationally across multiple healthcare settings

Genetic Variation and Breeding Potential of Phytate and Inorganic Phosphorus in a Maize Population

Seed P is predominantly bound in the organic compound phytate, which makes the bioavailability of P low for monogastric animals fed maize (Zea mays L.)-based diets. Decreasing phytate and increasing inorganic P (Pi, an available form of P) concentrations in maize grain would be desirable to help ameliorate environmental problems associated with high P in feces. Our objective was to investigate the potential of improving the P profile of maize grain through breeding and selection. Ninety S1 families from the BS31 population were evaluated at two locations for phytate, Pi, and other grain quality and agronomic traits. Phytate concentrations ranged from 1.98 to 2.46 g kg−1, and the broad-sense heritability (H) was relatively low (0.60). Both genetic variance and H (0.84) were much greater for Pi Few unfavorable genetic correlations were observed between either Pi or phytate and other key economic traits. Also, selection differentials of multiple trait indices indicated that the P profile of maize grain and grain yield and moisture could be improved simultaneously. Many cycles of selection will be needed, however, to reach desirable phytate and Pi concentrations, especially when selecting for multiple traits. Regardless, our results are encouraging given that the families evaluated were related S1 families and the number of families was relatively small

Predicting pharmaceutical powder flow from microscopy images using deep learning

The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al