The Benefit Function Approach to Modeling Price-Dependent Demand Systems: An Application of Duality Theory

In this article we advocate more extensive use of the benefit function in specifying price-dependent or inverse demand models. In particular, we demonstrate how duality theory may be used to establish the inter-relationships between the Marshallian (or Hicksian) inverse demands and Luenberger's adjusted price functions, allowing estimable inverse demands to be derived directly from a benefit function. We also make use of a numerical inversion estimation method to rectify the "unobservability of utility problem" encountered in the empirical analysis of these inverse demands. To illustrate the usefulness of the proposed methods, we estimate two systems of inverse demands for Japanese quarterly fish consumption. Results generally indicate that the proposed methods are promising and operationally feasible so that we have opened up a wider range of empirical inverse demand specifications that can be subjected to tight theoretical restrictions.Benefit Functions; Duality Theory; Numerical Inversion Estimation Method

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Introduction Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes. Methods This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications. Result The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p <  0.05). Significant association was found between hypoglycemic frequency (p <  0.0001), HbA1c (p <  0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR). Conclusion Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies

Inhibition of microbial sulfate reduction in a flow-through column system by (per)chlorate treatment.

Microbial sulfate reduction is a primary cause of oil reservoir souring. Here we show that amendment with chlorate or perchlorate [collectively (per)chlorate] potentially resolves this issue. Triplicate packed columns inoculated with marine sediment were flushed with coastal water amended with yeast extract and one of nitrate, chlorate, or perchlorate. Results showed that although sulfide production was dramatically reduced by all treatments, effluent sulfide was observed in the nitrate (10 mM) treatment after an initial inhibition period. In contrast, no effluent sulfide was observed with (per)chlorate (10 mM). Microbial community analyses indicated temporal community shifts and phylogenetic clustering by treatment. Nitrate addition stimulated Xanthomonadaceae and Rhizobiaceae growth, supporting their role in nitrate metabolism. (Per)chlorate showed distinct effects on microbial community structure compared with nitrate and resulted in a general suppression of the community relative to the untreated control combined with a significant decrease in sulfate reducing species abundance indicating specific toxicity. Furthermore, chlorate stimulated Pseudomonadaceae and Pseudoalteromonadaceae, members of which are known chlorate respirers, suggesting that chlorate may also control sulfidogenesis by biocompetitive exclusion of sulfate-reduction. Perchlorate addition stimulated Desulfobulbaceae and Desulfomonadaceae, which contain sulfide oxidizing and elemental sulfur-reducing species respectively, suggesting that effluent sulfide concentrations may be controlled through sulfur redox cycling in addition to toxicity and biocompetitive exclusion. Sulfur isotope analyses further support sulfur cycling in the columns, even when sulfide is not detected. This study indicates that (per)chlorate show great promise as inhibitors of sulfidogenesis in natural communities and provides insight into which organisms and respiratory processes are involved

Exploring the structure of digital literacy competence assessed using authentic software applications

Digital literacy competence (DL) is an important capacity for students' learning in a rapidly changing world. However, little is known about the empirical structure of DL. In this paper, we review major DL assessment frameworks and explore the dimensionality of DL from an empirical perspective using assessment data collected using authentic software applications, rather than simulated assessment environments. Secondary analysis on representative data collected from primary and secondary school students in Hong Kong using unidimensional and multidimensional item response theory reveals a general dimension of digital literacy performance and four specific, tool-dependent dimensions. These specific DL dimensions are defined by the software applications that students use and capture commonality among students' performance that is due to their familiarity with the assessment tools and contexts. The design of DL assessment is discussed in light of these findings, with particular emphasis on the influence of the nature of digital applications and environments used in assessment on the DL achievement scores measured

Early life exposure to farm animals and symptoms of asthma, rhinoconjunctivitis and eczema : an ISAAC Phase Three study

We are grateful to the children and parents who willingly cooperated and participated in ISAAC Phase Three and the coordination and assistance by the school staff is sincerely appreciated. The authors also acknowledge and thank the many funding bodies throughout the world that supported the individual ISAAC centres and collaborators and their meetings. The funders of the study had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.Background: Associations between early life exposure to farm animals and respiratory symptoms and allergy in children have been reported in developed countries, but little is known about such associations in developing countries. Objective: To study the association between early life exposure to farm animals and symptoms of asthma, rhinoconjunctivitis and eczema in a worldwide study. Methods: Phase Three of the International Study of Asthma and Allergies in Childhood (ISAAC) was carried out in 6- to 7-year-old children in urban populations across the world. Questions about early life exposure to farm animals (at least once/week) were included in an additional questionnaire. The association between such exposures and symptoms of asthma, rhinoconjunctivitis and eczema was investigated with logistic regression. Adjustments were made for gender, region of the world, language, gross national income and 10 other subject-specific covariates. Results: A positive association was found between early exposure to farm animals and the prevalence of symptoms of asthma, rhinoconjunctivitis and eczema, especially in non-affluent countries. In these countries, odds ratios (ORs) for 'current wheeze', 'farm animal exposure in the first year of life' and 'farm animal exposure in pregnancy' were 1.27 [95% confidence interval (CI) 1.12-1.44] and 1.38 (95% CI 1.21-1.58), respectively. The corresponding ORs for affluent countries were 0.96 (95% CI 0.86-1.08) and 0.95 (95% CI 0.84-1.08), respectively. Conclusion: Exposure to farm animals during pregnancy and in the first year of life was associated with increased symptoms of asthma, rhinoconjunctivitis and eczema in 6- to 7-year-old children living in non-affluent but not in affluent countries.peer-reviewe

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Introduction: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes. Methods: This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications. Result: The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p <  0.05). Significant association was found between hypoglycemic frequency (p <  0.0001), HbA1c (p <  0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR). Conclusion: Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R(2 )= 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies