A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

Anaerobic Fungal Mevalonate Pathway Genomic Biases Lead to Heterologous Toxicity Underpredicted by Codon Adaptation Indices.

Anaerobic fungi are emerging biotechnology platforms with genomes rich in biosynthetic potential. Yet, the heterologous expression of their biosynthetic pathways has had limited success in model hosts like E. coli. We find one reason for this is that the genome composition of anaerobic fungi like P. indianae are extremely AT-biased with a particular preference for rare and semi-rare AT-rich tRNAs in E coli, which are not explicitly predicted by standard codon adaptation indices (CAI). Native P. indianae genes with these extreme biases create drastic growth defects in E. coli (up to 69% reduction in growth), which is not seen in genes from other organisms with similar CAIs. However, codon optimization rescues growth, allowing for gene evaluation. In this manner, we demonstrate that anaerobic fungal homologs such as PI.atoB are more active than S. cerevisiae homologs in a hybrid pathway, increasing the production of mevalonate up to 2.5 g/L (more than two-fold) and reducing waste carbon to acetate by ~90% under the conditions tested. This work demonstrates the bioproduction potential of anaerobic fungal enzyme homologs and how the analysis of codon utilization enables the study of otherwise difficult to express genes that have applications in biocatalysis and natural product discovery.