Influence of mutation in the regulatory domain of α-isopropylmalate synthase from Saccharomyces cerevisiae on its activity and feedback inhibition.

Isoamyl alcohol (i-AmOH) is produced from α-ketoisocaproate in the l-leucine biosynthetic pathway in yeast and controlled by the negative feedback regulation of α-isopropylmalate synthase (IPMS), which senses the accumulation of l-leucine. It is known that i-AmOH production increases when mutations in the regulatory domain reduce the susceptibility to feedback inhibition. However, the impact of mutations in this domain on the IPMS activity has not been examined. In this study, we obtained 5 IPMS mutants, encoding the LEU4 gene, N515D/S520P/S542F/A551D/A551V, that are tolerant to 5,5,5-trifluoro-dl-leucine. All mutant proteins were purified and examined for both IPMS activity and negative feedback activity by in vitro experiments. The results showed that not only the negative-feedback regulation by l-leucine was almost lost in all mutants, but also the IPMS activity was greatly decreased and the difference in IPMS activity among Leu4 mutants in the presence of l-leucine was significantly correlated with i-AmOH production.

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study.

BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data-neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)-from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.