When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles.

BACKGROUND: The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis. METHODS: A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach. RESULTS: This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles. CONCLUSIONS: This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions.

Improving Neural Machine Translation for Low Resource Algerian Dialect by Transductive Transfer Learning Strategy.

This study is the first work on a transductive transfer learning approach for low-resource neural machine translation applied to the Algerian Arabic dialect. The transductive approach is based on a fine-tuning transfer learning strategy that transfers knowledge from the parent model to the child model. This strategy helps to solve the learning problem using limited parallel corpora. We tested the approach on a sequence-to-sequence model with and without the Attention mechanism. We first trained the models on a parallel multi-dialects Arabic corpus and then switch them to a low-resource of the Algerian dialect. Transductive transfer learning raises the BLEU score for the Seq2Seq model from 0.3 to more than 34, and for the Attentional-Seq2Seq model from less than 17 to more than 35. The obtained results prove the validity of this approach.

Inflammation is increased with anxiety- and depression-like signs in a rat model of spinal cord injury.

Spinal cord injury (SCI) leads to increased anxiety and depression in as many as 60% of patients. Yet, despite extensive clinical research focused on understanding the variables influencing psychological well-being following SCI, risk factors that decrease it remain unclear. We hypothesized that excitation of the immune system, inherent to SCI, may contribute to the decrease in psychological well-being. To test this hypothesis, we used a battery of established behavioral tests to assess depression and anxiety in spinally contused rats. The behavioral tests, and subsequent statistical analyses, revealed three cohorts of subjects that displayed behavioral characteristics of (1) depression, (2) depression and anxiety, or (3) no signs of decreased psychological well-being. Subsequent molecular analyses demonstrated that the psychological cohorts differed not only in behavioral symptoms, but also in peripheral (serum) and central (hippocampi and spinal cord) levels of pro-inflammatory cytokines. Subjects exhibiting a purely depression-like profile showed higher levels of pro-inflammatory cytokines peripherally, whereas subjects exhibiting a depression- and anxiety-like profile showed higher levels of pro-inflammatory cytokines centrally (hippocampi and spinal cord). These changes in inflammation were not associated with injury severity; suggesting that the association between inflammation and the expression of behaviors characteristic of decreased psychological well-being was not confounded by differential impairments in motor ability. These data support the hypothesis that inflammatory changes are associated with decreased psychological well-being following SCI.