Reduction in Dietary Acrylamide Exposure—Impact of Potatoes with Low Acrylamide Potential

Acrylamide forms primarily from a reaction between reducing sugars (e.g., glucose and fructose) and an amino acid (asparagine, Asn) formed naturally in foods, including potatoes. This reaction occurs when carbohydrate‐rich foods are heated at temperatures above 120 °C. Multiple potato varieties were transformed with potato genomic DNA that results in down‐regulation of the expression of the asparagine synthetase‐1 gene (Asn1), significantly reducing synthesis of free Asn, and consequently lowering the potential to form acrylamide during cooking. These potatoes with low acrylamide potential (LAP) were tested in agronomic trials, and processed into French fries and potato chips. Decreased levels of acrylamide were measured in these cooked food products when derived from LAP potatoes compared with those derived from conventional potatoes. These reductions can be directly attributed to reduction in Asn levels in the LAP potatoes. The corresponding average reduction in exposure to acrylamide from French fry and potato chip consumption is estimated to be 65%, which would amount to approximately a 25% reduction in overall dietary exposure. Considering that children consume nearly three times more acrylamide than adults on a per kg body weight basis, they would experience the most impact from the reduced acrylamide associated with LAP potatoes. The potential public health impacts, in context of dietary acrylamide exposure reduction, are discussed in this study.     

The competing risks analysis for parallel and series systems using Type-II progressive censoring

Abstract

Recently, the study of the lifetime of systems in reliability and survival analysis in the presence of several causes of failure (competing risks) has attracted attention in the literature. In this paper, series and parallel systems with exponential lifetime for each item of the system are considered. Several causes of failure independently affect lifetime distributions and observations of failure times of the systems are considered under progressive Type-II censored scheme. For series systems, the maximum likelihood estimates of parameters are computed and confidence intervals for parameters of the model are obtained using Fisher information matrix. For parallel systems, the generalized EM algorithm which uses the Newton-Raphson algorithm inside the EM algorithm is used to compute the maximum likelihood estimates of parameters. Also, the standard errors of the maximum likelihood estimates are computed by using the supplemented EM algorithm. The simulation study confirms the good performance of the introduced approach.     

Estimation for seasonal fractional ARIMA with stable innovations via the empirical characteristic function method

Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

Do Work Demands and Resources Predict Work-to-Family Conflict and Facilitation? A Study of Iranian Male Employees

This study examined the effects of some work demands-resources on two dimensions of work-to-family interface among Iranian employees. The results of canonical correlation and multiple-regression showed that among work resources (i.e., social support and autonomy), social support was associated negatively with work-to-family conflict and autonomy was associated positively with work-to-family facilitation. Also, among work demands (i.e., job demands and working hours), job demands were associated with both work-to-family conflict and work-to-family facilitation. Unlike previous studies, working hours shows a positive association with work-to-family facilitation, which may be explained by the specific cultural context in Iran. The findings of the study will be of importance to employees and organizations.

Doin’ Meth or Doin’ Math: What Client Constructions of Social Class Mean for Social Work Practice

The voices of people experiencing poverty are underrepresented in social work research, public policy development, and practice interventions. This study explored the social class attributions of clients receiving poverty-related services through qualitative interviews. Findings reveal dynamic contributions of individual, environmental, and structural factors of social class positioning and significant stress and stigmatization associated with experiencing economic hardship. Participants indicate a sense of lived contradiction, viewing social class to be the result of fate while simultaneously endorsing individualistic attributions of poverty. Results have implications for social work research and practice, as well as poverty-related policy and program development.     

Marginal models for the association structure of hierarchical binary responses

Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.     

Conceptualising and Measuring Social Cohesion in Africa: Towards a Perceptions-Based Index

Social cohesion is a key concept in development studies. Weak social cohesion is often related to slow economic growth and (violent) conflict. So far few attempts have been made to measure this complex concept in a systematic manner. This paper introduces an innovative method to measure national-level social cohesion based on survey data from 19 African countries. We distinguish three dimensions of social cohesion; i.e. the extent of perceived inequalities, the level of societal trust, and the strength of people’s adherence to their national identity. Importantly, our Social Cohesion Index (SCI) is based on individuals’ perceptions vis-à-vis these three different dimensions of social cohesion rather than certain macro-level ‘objective’ indicators such as GDP/capita or Gini-coefficients. We develop two social cohesion indices: a national average SCI and a Social Cohesion Index Variance-Adjusted (SCIVA); the latter one takes into account the level of variation across different ethnic groups within countries. The SCI and SCIVA are computed for and compared across nineteen African countries for the period 2005–2012 on the basis of Afrobarometer survey rounds 3, 4 and 5. We also investigate quantitatively the relationship between countries’ levels of social cohesion and the occurrence of a range of conflict events. As expected, we find that countries with low levels of social cohesion in a particular year according to our SCI are more likely to experience a range of different violent conflict events in the subsequent year.     

Usability in product design--the importance and need for systematic assessment models in product development--Usa-Design Model (U-D) ©

In product design, human factors are considered as an element of differentiation given that today's consumer demands are increasing. Safety, wellbeing, satisfaction, health, effectiveness, efficiency, and other aspects must be effectively incorporated into the product development process. This work proposes a usability assessment model that can be incorporated as an assessment tool. The methodological approach is settled in two stages. First a literature review focus specifically on usability and developing user-centred products. After this, a model of usability named Usa-Design (U-D?) is presented. Consisted of four phases: understanding the use context, pre-preliminary usability assessment (efficiency/effectiveness/satisfaction); assessment of usability principles and results, U-D? features are modular and flexible, allowing principles used in Phase 3 to be changed according to the needs and scenario of each situation. With qualitative/quantitative measurement scales of easy understanding and application, the model results are viable and applicable throughout all the product development process.