This study examines the effectiveness of two modes of exercise on inhibitory control in adults with Down Syndrome (DS). Thirteen participants attended four sessions: a baseline assessment, an Assisted Cycling Therapy (ACT) session, a Resistance Training (RT) session, and a session of No Training (NT). In the baseline assessment, 1-repetition max (1RM) measurements and voluntary pedal rate measurements were taken. In the resistance training session, the leg press, chest press, seated row, leg curl, shoulder press, and latissimus pulldown were performed. In the cycling intervention, the participant completed 30 minutes of cycling. The Erikson Flanker task was administered prior to each session (i.e., pretest) and after the intervention (i.e., post-test). The results were somewhat consistent with the hypothesis that inhibition time improved more following RT and ACT than NT. there was also a significant difference between ACT and NT. Additionally, it was hypothesized that all measures would improve following each acute exercise intervention, but the most significant improvements were seen following ACT. In conclusion, an acute session of ACT demonstrated a significant trend towards improvements in inhibitory control in adults with DS which we interpreted using a model of neural changes.
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.