A pilot study comparing two weight loss maintenance interventions among low-income, mid-life women

BACKGROUND: Despite high obesity prevalence rates, few low-income midlife women participate in weight loss maintenance trials. This pilot study aims to assess the effectiveness of two weight loss maintenance interventions in this under-represented population. METHODS: Low-income midlife women who completed a 16-week weight loss intervention and lost ≥ 8 lbs (3.6 kg) were eligible to enroll in one of two 12-month maintenance programs. The programs were similar in content and had the same number of total contacts, but were different in the contact modality (Phone + Face-to-Face vs. Face-to-Face Only). Two criteria were used to assess successful weight loss maintenance at 12 months: (1) retaining a loss of ≥ 5% of body weight from the start of the weight loss phase and (2) a change in body weight of < 3%, from the start to the end of the maintenance program. Outcome measures of changes in physiologic and psychosocial factors, and evaluations of process measures and program acceptability (measured at 12 months) are also reported. For categorical variables, likelihood ratio or Fisher’s Exact (for small samples) tests were used to evaluate statistically significant relationships; for continuous variables, t-tests or their equivalents were used to assess differences between means and also to identify correlates of weight loss maintenance. RESULTS: Overall, during the 12-month maintenance period, 41% (24/58) of participants maintained a loss of ≥ 5% of initial weight and 43% (25/58) had a <3% change in weight. None of the comparisons between the two maintenance programs were statistically significant. However, improvements in blood pressure and dietary behaviors remained significant at the end of the 12-month maintenance period for participants in both programs. Participant attendance and acceptability were high for both programs. CONCLUSIONS: The effectiveness of two pilot 12-month maintenance interventions provides support for further research in weight loss maintenance among high-risk, low-income women. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT0028830

A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid detection of Bacillus spores and identification of Bacillus species

Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA

Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

Sensing the fuels: glucose and lipid signaling in the CNS controlling energy homeostasis

The central nervous system (CNS) is capable of gathering information on the body’s nutritional state and it implements appropriate behavioral and metabolic responses to changes in fuel availability. This feedback signaling of peripheral tissues ensures the maintenance of energy homeostasis. The hypothalamus is a primary site of convergence and integration for these nutrient-related feedback signals, which include central and peripheral neuronal inputs as well as hormonal signals. Increasing evidence indicates that glucose and lipids are detected by specialized fuel-sensing neurons that are integrated in these hypothalamic neuronal circuits. The purpose of this review is to outline the current understanding of fuel-sensing mechanisms in the hypothalamus, to integrate the recent findings in this field, and to address the potential role of dysregulation in these pathways in the development of obesity and type 2 diabetes mellitus

Teaching: Natural or Cultural?

In this chapter I argue that teaching, as we now understand the term, is historically and cross-culturally very rare. It appears to be unnecessary to transmit culture or to socialize children. Children are, on the other hand, primed by evolution to be avid observers, imitators, players and helpers—roles that reveal the profoundly autonomous and self-directed nature of culture acquisition (Lancy in press a). And yet, teaching is ubiquitous throughout the modern world—at least among the middle to upper class segment of the population. This ubiquity has led numerous scholars to argue for the universality and uniqueness of teaching as a characteristically human behavior. The theme of this chapter is that this proposition is unsustainable. Teaching is largely a result of recent cultural changes and the emergence of modern economies, not evolution