Fndc5/irisin is regulated by myogenesis stage, irisin, muscle type and training.

OBJECTIVES: Irisin, a novel myokine that responds to exercise, was initially identified as a regulator of fat tissue metabolism. We aimed to investigate fibronectin type III domain-containing protein 5 (Fndc5)/irisin, auto/para-crine role in different muscle fibers, different activities, and muscle cell differentiation. METHODS: Using in-vitro, ex-vivo, and in-vivo muscle models, Fndc5 was studied at the physiological and molecular levels. RESULTS: Following training, C57BL/6 mice (n=10) were subject to fast and slow-twitch muscles dissection and molecular analysis. Isolated mice (C57BL/6, n=14) slow and fast-twitch muscles were subject to electrical aerobic and anaerobic pulses stimulation (EPS). L6 muscle cells differentiation was characterized by Fndc5 differentiation-depended expression pattern parallel with significant hypertrophy, Myogenin elevation, and overlapping Peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (Pgc-1α) expression pattern. Exogenous irisin significantly altered Fndc5 expression; augmented at early differentiation (3-4-fold, P<0.05) and decreased (2-fold, P<0.05) at late differentiation. Training induced a significant elevation in Fndc5/irisin and Pgc-1α expression levels in all muscle types compared to the sedentary state, where soleus muscle (slow) Fndc5 expression levels were significantly higher compared to levels in all other fast muscles (3-140-fold, P<0.001). Similarly, following EPS, Fndc5 expression levels were significantly augmented in the soleus slow muscle following both aerobic and anaerobic activity (3-3.5-fold, P<0.05) compared to extensor digitorum longus (fast) muscle. CONCLUSIONS: Muscle cell's Fndc5 expression has a differentiation-depended pattern paralleling Pgc-1α expression and hypertrophy. Irisin autocrinally and significantly regulate Fndc5 and Pgc-1α in a differentiation-depended manner. Muscle Fndc5 expression levels are dependent on fiber type and activity type.

A prediction-focused approach to personality modeling.

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people's personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model's predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the "Predictive Five" (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.