On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

The State of Research in Latino Academic Attainment

In this essay, I present an interdisciplinary examination of the state of research in Latino education, addressing not only the cultural forces influencing Latinos’ educational attainment, but also the structural conditions faced by them—as a racialized minority group—throughout their schooling and that have a cumulative effect on their academic attainment. Overall, I suggest that by engaging in a critical review of the literature on Latinos’ educational attainment, the reader will acknowledge that the racially driven hierarchical structural arrangements are highly responsible for the patterns observed.     

Beyond Contact? Policy approaches to work with families of looked after children in four European countries

In England, placement within the looked after system is not viewed as a desirable long‐term solution for most children, and policy has prioritised continued contact with parents, and swift return home, wherever possible. This review examines policy approaches to work with families of looked after children in England and in three other European countries: Denmark, France and the Netherlands, aiming to identify areas for shared learning in relation to this challenging area of policy and practice. The research highlights relationships between care populations and policy understandings of the purpose of work with families, including understandings of children's and/or parents’ rights.     

Toddlers Help Anonymously

Young children are extremely motivated to help others, but it is not clear whether they do so in anonymous situations without social recognition. In two studies, we found that 18‐month‐old toddlers provided help equally in situations where an adult recipient was present and in situations where an adult recipient was not present. We included several control conditions to rule out that toddlers were simply unaware of their anonymity or were merely motivated to restore the physical order of things. Together, these findings suggest that early in ontogeny children are motivated to help others in need regardless of whether they can immediately be recognized for their prosocial intentions.     

Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Bayesian and Robust Bayesian analysis under a general class of balanced loss functions

For estimating an unknown parameter θ, we introduce and motivate the use of balanced loss functions of the form L_{r, w, d0}(q, d)=wr(d₀, d)+ (1-w) r(q, d){L_{\rho, \omega, \delta_0}(\theta, \delta)=\omega \rho(\delta_0, \delta)+ (1-\omega) \rho(\theta, \delta)}, as well as the weighted version q(q) L_{r, w, d0}(q, d){q(\theta) L_{\rho, \omega, \delta_0}(\theta, \delta)}, where ρ(θ, δ) is an arbitrary loss function, δ ₀ is a chosen a priori “target” estimator of q, w ? [0,1){\theta, \omega \in[0,1)}, and q(·) is a positive weight function. we develop Bayesian estimators under L_{r, w, d0}{L_{\rho, \omega, \delta_0}} with ω > 0 by relating such estimators to Bayesian solutions under L_{r, w, d0}{L_{\rho, \omega, \delta_0}} with ω = 0. Illustrations are given for various choices of ρ, such as absolute value, entropy, linex, and squared error type losses. Finally, under various robust Bayesian analysis criteria including posterior regret gamma-minimaxity, conditional gamma-minimaxity, and most stable, we establish explicit connections between optimal actions derived under balanced and unbalanced losses.     

Community Wise: The Development of an Anti-Oppression Model to Promote Individual and Community Health

Communities with histories of oppression have shown great resilience, yet few health interventions focus on structural oppression as a contributor to health problems in these communities. This article describes the development and active ingredients of Community Wise, a unique behavioral health intervention designed to reduce substance use frequency, related health risk behaviors, and recidivism among individuals with a history of incarceration and substance abuse residing in distressed and predominantly African American communities. Community Wise, developed through the collaborative efforts of a board of service providers, researchers, consumers, and government officials, is a 12-week group intervention that aims to address behavioral health problems by raising critical consciousness in distressed communities.