Why does the Engel method work? Food demand, economies of size and household survey methods

Estimates of household size economies are needed for the analysis of poverty and inequality. This paper shows that Engel estimates of size economies are large when household expenditures are obtained by respondent recall but small when expenditures are obtained by daily recording in diaries. Expenditure estimates from recall surveys appear to have measurement errors correlated with household size. As well as demonstrating the fragility of Engel estimates of size economies, these results help resolve a puzzle raised by Deaton and Paxson (1998) about differences between rich and poor countries in the effect of household size on food demand

Low-field H-1 NMR spectroscopy for distinguishing between arabica and robusta ground roast coffees

This work reports a new screening protocol for addressing issues of coffee authenticity using low-field (60 MHz) bench-top H-1 NMR spectroscopy. Using a simple chloroform-based extraction, useful spectra were obtained from the lipophilic fraction of ground roast coffees. It was found that 16-O-methylcafestol (16-OMC, a recognized marker compound for robusta beans) gives rise to an isolated peak in the 60 MHz spectrum, which can be used as an indicator of the presence of robusta beans in the sample. A total of 81 extracts from authenticated coffees and mixtures were analysed, from which the detection limit of robusta in arabica was estimated to be between 10% and 20% w/w. Using the established protocol, a surveillance exercise was conducted of 27 retail samples of ground roast coffees which were labelled as "100% arabica". None were found to contain undeclared robusta content above the estimated detection limit. (C) 2016 Published by Elsevier Ltd

Pattern scaling the parameters of a Markov-Chain gamma-distribution daily precipitation generator

General circulation models (GCMs) are the most sophisticated tools at our disposal for studying future climates, but there are limitations to overcome. These include resolutions that may be too coarse for impact assessments, limited or zero availability of some policy-relevant scenarios, and limited time-series length for assessing the risk of extreme events. We illustrate how these limitations can be addressed by combining a stochastic precipitation generator (SPG) with pattern scaling (PS) of its key parameters. Computationally inexpensive, SPG parameters can be perturbed to generate time-series representative of weather under a future climate with high spatial and temporal resolution. If the SPG parameter perturbations are derived directly from GCM simulations projections can only be made for scenarios already simulated by the GCM. Instead, we obtain the parameter perturbations using PS, facilitating emulation of scenarios not necessarily explicitly simulated by the GCM, and where we scale perturbations approximately linearly with global temperature change. PS is commonly applied to estimate perturbations in the mean of climate variables, but rarely to higher-order parameters as we demonstrate here. We apply PS for the first time, globally, to the parameters of a daily, first-order Markov-chain gamma-distribution SPG using output from the IPSL-CM6A-LR GCM to perturb an SPG fitted to observed data from two stations in diverse climates (Santarém, Brazil and Reykjavik, Iceland) to illustrate this novel approach. We produce time series corresponding to a range of GWLs and demonstrate the capability of the combined SPG-PS approach to study local-scale, future daily precipitation characteristics, climate and subsequent risk of extreme weather events

The measurement of household consumption expenditures

Household-level data on consumer expenditures underpin a wide range of empirical research in modern economics, spanning micro-and macroeconomics. This research includes work on consumption and saving, on poverty and inequality, and on risk sharing and insurance. We review different ways in which such data can be collected or captured: traditional detailed budget surveys, less onerous survey procedures that might be included in more general surveys, and administrative or process data. We discuss the advantages and difficulties of each approach and suggest directions for future investigation. © 2014 by Annual Reviews. All rights reserved

Acute Consumption of Flavan-3-ol-Enriched Dark Chocolate Affects Human Endogenous Metabolism

Flavan-3-ols and methylxanthines have potential beneficial effects on human health including reducing cardiovascular risk. We performed a randomized controlled crossover intervention trial to assess the acute effects of consumption of flavan-3-ol-enriched dark chocolate, compared with standard dark chocolate and white chocolate, on the human metabolome. We assessed the metabolome in urine and blood plasma samples collected before and at 2 and 6 h after consumption of chocolates in 42 healthy volunteers using a nontargeted metabolomics approach. Plasma samples were assessed and showed differentiation between time points with no further separation among the three chocolate treatments. Multivariate statistics applied to urine samples could readily separate the postprandial time points and distinguish between the treatments. Most of the markers responsible for the multivariate discrimination between the chocolates were of dietary origin. Interestingly, small but significant level changes were also observed for a subset of endogenous metabolites. H-1 NMR revealed that flavan-3-ol-enriched dark chocolate and standard dark chocolate reduced urinary levels of creatinine, lactate, some amino acids, and related degradation products and increased the levels of pyruvate and 4-hydroxyphenylacetate, a phenolic compound of bacterial origin. This study demonstrates that an acute chocolate intervention can significantly affect human metabolism

16-O-methylcafestol is present in ground roast Arabica coffees: Implications for authenticity testing

High-field and low-field proton NMR spectroscopy were used to analyse lipophilic extracts from ground roast coffees. Using a sample preparation method that produced concentrated extracts, a small marker peak at 3.16 ppm was observed in 30 Arabica coffees of assured origin. This signal has previously been believed absent from Arabicas, and has been used as a marker for detecting adulteration with robusta. Via 2D 600 MHz NMR and LC-MS, 16-O-methylcafestol and 16-O-methylkahweol were detected for the first time in Arabica roast coffee and shown to be responsible for the marker peak. Using low-field NMR, robusta in Arabica could be detected at levels of the order of 1-2% w/w. A surveillance study of retail purchased "100% Arabica" coffees found that 6 out of 60 samples displayed the 3.16 ppm marker signal to a degree commensurate with adulteration at levels of 3-30% w/w

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods

Goal Commitment And Competition As Drivers For Group Productivity In Business Process Modeling

Many studies have looked at the factors that control the productivity of collaborative work. We claim that goal commitment and competition have a strong impact on group productivity in collaborative modelling. To substantiate this claim we first take a look at existing factor models to identify the factors that potentially mediate the effect on group productivity. We then investigate the relation between the factors with the help of controlled field experiments in five different organisations. We confirm the theoretical results with the help of structured equation modelling